Traffic Management and Networking for Autonomous Vehicular Highway Systems

Izhak Rubina, Andrea Baiocchib, Yulia Sunyotoa, Ion Turcanub,c

aElectrical and Computer Engineering Department,University of California Los Angeles (UCLA), Los Angeles, CA, USA

bDept. of Information Engineering, Electronics and Telecommunications,University of Rome Sapienza, Italy

cInterdisciplinary Centre for Security, Reliability and Trust (SnT),University of Luxembourg, Luxembourg

Abstract

We develop traffic management and data networking mechanisms and study their integrated design for an autonomoustransportation system. The traffic management model involves a multi-lane multi-segment highway. Ramp managersregulate admission of vehicles into the highway and their routing to designated lanes. Vehicles moving across each laneare organized into platoons. A Platoon Leader (PL) is elected in each platoon and is used to manage its members andtheir communications with the infrastructure and with vehicles in other platoons. We develop new methods that areemployed to determine the structural formations of platoons and their mobility processes in each lane, aiming to maximizethe realized flow rate under vehicular end-to-end delay constraints. We set a limit on the vehicular on-ramp queueingdelay and on the (per unit distance) transit time incurred along the highway. We make use of the platoon formations todevelop new Vehicle-to-Vehicle (V2V) wireless networking cross-layer schemes that are used to disseminate messagesamong vehicles traveling within a specified neighborhood. For this purpose, we develop algorithms that configure ahierarchical networking architecture for the autonomous system. Certain platoon leaders are dynamically assigned toact as Backbone Nodes (BNs). The latter are interconnected by communications links to form a Backbone Network(Bnet). Each BN serves as an access point for its Access Network (Anet), which consists of its mobile clients. We studythe delay-throughput performance behavior of the network system and determine the optimal setting of its parameters,assuming both TDMA and IEEE802.11p oriented wireless channel sharing (MAC) schemes. Integrating these trafficmanagement and data networking mechanisms, we demonstrate the performance tradeoffs available to the system designerand manager when aiming to assure an autonomous transportation system operation that achieves targeted vehicular flowrates and transit delays while also setting the data communications network system to meet targeted message throughputand delay objectives.

1. Introduction

Autonomous driving is taking momentum at a vastlygrowing pace. Early implementations for various applica-tions, such as taxi services [1], are being introduced. Afull understanding of large scale deployment of systems ofautonomous vehicles is a key component of a safe growth ofthe Intelligent Transportation Systems (ITS) as a criticalcomponent in the development of a paradigm for the archi-tecture of the next generation smart city. A wide range ofmechanisms, technologies and regulation procedures needto be developed and enacted under this perspective, includ-ing technical, legal, economic and social impact factors.

Among the multitude of technical aspects that dom-inate the design, management and control of high per-formance autonomous driving systems, the following two

Email addresses: rubin@ee.ucla.edu (Izhak Rubin),andrea.baiocchi@uniroma1.it (Andrea Baiocchi),yulia.sunyoto@ucla.edu (Yulia Sunyoto), ion.turcanu@uni.lu(Ion Turcanu)

elements are of paramount importance: vehicular trafficmanagement and data networking – the latter involvingcommunications and data dissemination among vehicles, i.e.Vehicle-to-Vehicle (V2V), and between vehicles and fixedinfrastructure, i.e. Vehicle-to-Infrastructure (V2I). A com-mon characteristic of these traffic management and datacommunications networking elements is that they addresscoordination and reliable interactions among distributedentities, moving in space and time, at a potentially largescale. For the specific case of autonomous driving, theinter-dependence of those two components are strongerthan ever. High performance reliable communications net-working of data messages among moving vehicles is a keyingredient in enabling the learning by autonomous vehiclesof their surrounding environment, the rapid reaction to crit-ical events assure the safety of the operation, coordinationamong vehicles to synchronize mobility and maneuvering.They also aid in the execution of processes that serve tooptimize travel safety, reduce transit delays, disseminatesensed data reports to nearby vehicles that include envi-

Article published in Elsevier Ad Hoc Networks 83 (2019) 125-148. (10.1016/j.adhoc.2018.08.018)

ronmental and congestion conditions, uploading data toremote cloud servers, and enabling remote monitoring, su-pervision, management and control of the autonomousvehicular system.

As for vehicular traffic management, the formation of adynamically controlled intelligent autonomous transporta-tion system that properly manages autonomous vehiclesleads to a host of applications that will induce a paradigmshift in transportation systems.

While human controlled vehicles are often guided tomake mobility decisions based on traffic advisories, thefinal decision as to the route, the driving style and thespeed to use is in human hands, leading to wide variability.In turn, an autonomous driving system can be managedto produce a controlled high performing mobility opera-tion for each vehicle and for the overall transportationsystem. Such an intelligent regulation can be used to meetthe travel objectives of individual drivers, yield a muchimproved transportation safety and efficiency, enhancedvehicular throughput rates, reduced travel latencies andlower polluting emissions and/or improved utilization ofenergy resources.

In this paper, we consider a highway populated withautonomous vehicles [2]. We study the optimized designof vehicular traffic flows along a highway by regulatingvehicles that move in each lane into platoon formations.Such formations are advantageous in that an elected vehiclewithin the platoon, identified as the Platoon Leader (PL),can be used to rapidly and efficiently regulate mobility andmaneuvering of its platoon vehicular members. Further-more, we make use of these formations in synthesizing adynamically adaptive V2V networking architecture. We de-velop models that provide system design guidelines and thatdemonstrate the performance tradeoffs characterizing thesystem when integrating the embedded traffic managementand data communications networking operations.

The major innovative contributions of this work aresummarized as follows:

• We state and solve optimization problems that aimto maximize the achievable vehicular throughput ratecarried by the highway, under a vehicular delay con-straint. We account for on-ramp queueing delays aswell as for transit delay across highway lanes. Wedetermine the best settings to be used in formingplatoons along each lane, including parameters thataccount for vehicular speeds and inter-vehicular spac-ing levels. We also determine the proper scheme tobe used in routing admitted vehicles into faster orslower lanes.

• We investigate the design of a V2V data communica-tion networking system that is aided by the formationof autonomous vehicular platoons along the highway.For this purpose, we introduce a two layer hierarchicalnetwork architecture that consists of a dynamicallyformed Backbone Network (Bnet) and Access Net-works (Anets). The Bnet is dynamically synthesized

through the election of PLs that are set to serve asBackbone Nodes (BNs). Each BN manages its Anet,which consists of its mobile clients. We examine theuse of mixes of candidate Medium Access Control(MAC) schemes across the Bnet and the Anets, inconsidering Time-Division Multiple Access (TDMA)and IEEE802.11p based protocols. We carry out ex-tensive analyses to determine the optimal cross-layerparameter settings for each candidate scheme, deter-mining the highest data communication throughputlevels that are attainable in each case, under specifiedpacket delay limits. To demonstrate the underlyingdesign options, we assume data applications that re-quire the packets generated by a vehicle that is activein producing a data flow to be disseminated across aspecified distance span.

• We study the tradeoffs involved in integrating themechanisms developed by us to induce an effectivetraffic management operation that strives to producehigh vehicular flows along the highway under vehic-ular delay constraints, and those developed by usto attain a high capacity V2V data communicationsnetwork system.

The remainder of the paper is organized as follows. InSection 2, we present an overview of related works anddiscuss the motivation for platoon-based traffic manage-ment and for the employed networking schemes. The trafficmanagement system model is described in Section 3. Theassociated system optimization problems and solutions forthe traffic management system are presented in Section 4.The architecture, schemes and protocols used for dissemi-nation of data flows across the V2V data communicationsnetwork system are presented in Section 5. In Section 6, wepresent the parameters employed by the network systemsas well as define the metrics used to characterize the per-formance behavior of the networked system. Performancebehavior characteristics for the data communications net-work systems under consideration are presented in Section 7.In Section 8, we discuss and illustrate the design tradeoffsavailable to the system manager when traffic managementand data networking performance objectives are combined.Conclusions are drawn in Section 9.

2. Related Work

The impact of vehicular platoon applications on trafficflows and road safety have attracted significant interest.Published works have shown that organizing vehicles inplatoons can improve traffic flows and reduce fuel consump-tion [3, 4, 5], while increasing safety and enhancing drivingexperience [6, 7, 8]. Generally, platoon-related publishedworks can be divided in two main categories: (i) studiesthat focus on traffic management and flow optimizationby considering such problems as platoon lane assignment,speed control and on-ramp admission, and (ii) works that

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study the impact of the network communication technologyon platoon stability and vice versa. A summary of relatedworks is presented in Table 1.

2.1. Network communication schemesWith respect to the synthesis of the data communica-

tions network, a common approach found in the literatureis to dynamically select a subset of vehicles to act as re-lay nodes in forwarding data messages, serving to reducethe network data traffic load [9, 10, 11, 12, 13, 14]. In[9], the authors describe a high throughput disseminationscheme for Vehicular Ad Hoc Networks (VANETs) basedon dynamic formation of a multi-hop backbone network.Analysis and design is performed of such a networkingprotocol when used to broadcast message flows that aregenerated by a Roadside Unit (RSU) along a linear road,forming a VANET structure that is identified as a Vehic-ular Backbone Network (VBN). Please see this paper forreferences to other studies that develop clustering basednetworking schemes for non-autonomous vehicular systems.

The VBN scheme, originally developed by ProfessorIzhak Rubin based on his development of the Mobile Back-bone Networks (MBNs) scheme for use by mobile ad hocwireless networks, is based on using vehicles that resideat preferred locations to act as backbone relay nodes.While an MBN involves the mobility of vehicles over atwo-dimensional plane, or over a three-dimensional spacewhen Unmanned Aerial Vehicle (UAV)-aided, a VBN in-volves mobility of vehicles across a multi-lane linear single-dimensional road. Consequently, a more powerful net-working scheme has been developed for the latter. Thenetworking scheme developed in this paper inherits keyfeatures of the VBN scheme, yet it modifies it by incorporat-ing new mechanisms that are enabled by the autonomousmode of operation, leading to simplified implementationand enhanced performance behavior of its data networkingoperation.

As noted above, under the VBN approach, vehicles thatreside at preferred locations are elected to act as backbonerelay nodes. These relay nodes are optimally configured tooperate at designated link data rates, modulation/codingschemes and spatial reuse factors. Significant performanceupgrade is achieved under a VBN architecture in attain-ing enhanced throughput capacity rate and in providingfor a wider dissemination coverage span. Admitted pack-ets are assured low end-to-end packet delays through theemployment of a flow control scheme. Furthermore, dis-tinct savings are attained in energy consumption, as onlyselected relay nodes that act as backbone nodes need toengage in data packet forwarding processes.

In [10], a topology synthesis algorithm is presentedfor the synthesis of the Bnet for an MBN based mobilead hoc network. The algorithm is shown to be highlyscalable and fast converging to a solution of an effectiveconnected backbone network that consists of elected mobilenodes. The use of multicast protocols over a mobile ad hocnetwork is discussed and studied in [11]. The advantage of

multicasting packet flows through their dissemination over aMBN-oriented synthesized backbone is well demonstrated.

In [12], the VBN concept is introduced, presentingour approach to a GPS-aided synthesis of a cross-layerbackbone. The study in [13] provides performance analysisand design of the VBN system, as it is optimally configuredto broadcast message flows from a source RSU to highwayvehicles using flow pacing mechanisms. It also shows thatunder such flow regulation, an IEEE802.11p MAC schemecan achieve throughput rate performance that is similarto that attained under a TDMA scheme. A VBN-basedscheme is also employed in [14], where the authors exploitthe created backbone network to periodically disseminateand collect floating car data from vehicles roaming insidean area of interest.

For platoon-based communications networking studies,whereby a TDMA wireless access scheme is used, we notethe following references. In [15], a TDMA scheme isemployed to coordinate the access of vehicles that travel inplatoon formations. Time slots are arranged in accordancewith the relative distance of the message source to thelocation of an accident. Vehicles that are located closerto an accident location are granted earlier slots, aimingto reduce data message transport latency. An interferencereduction mechanism is also illustrated. The employedprotocol uses hop-by-hop routing within a platoon. Nouse is made of platoon structures to execute inter-platooncommunications.

In [3], highway vehicles traveling across a single lanehighway are arranged in platoon formations. Platoon for-mation parameters include inter-platoon and intra-platoondistances, vehicular speeds and the number of vehicles in-cluded in a platoon. The desired values of these parametersare determined in relation to performance considerationsthat are based on the trade-offs between two categories ofperformance metrics: realized vehicular throughput rateand underlying V2V wireless network performance. A ve-hicle that produces a packet flow aims to disseminate itspackets across a span of the highway whose scope is speci-fied in accordance with the engaged application type. Forcritical event-driven (Class I) packet flows, the key perfor-mance metric is the packet end-to-end dissemination delay.For periodic status-update type (or other stream typesof) packet flows (Class II), the attained message through-put rate serves as the key metric. The authors employa spatial-reuse TDMA protocol, using hop-by-hop com-munications among vehicles inside and outside a platoon.Multiple information sources can be simultaneously active.Optimal platoon formations are determined, aiming to pre-serve Class I message delays prescriptions while attaininghigh Class II message throughput rates and high vehicularthroughput rates. Only vehicles that are members of thesame platoon are assumed to be synchronized.

In contrast, we assume in this paper, that when TDMA-based transmissions between platoons are executed, timesynchronization information is made available to platoonleaders (e.g., by using a Control Channel (CCH) or a

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Focus Approach References Features

Networking& communica-tions

VBN-based [9, 10, 11, 12,13, 14]

• Focus on dynamic formation of multi-hop vehicular backbone network.• Vehicles residing at preffered locations act as backbone relay nodes

and are in charge of handling the network data flows.• The vehicular traffic flow optimization problem is not tackled.

TDMA-based [15, 3, 16] • Use TDMA wireless access scheme to coordinate the access of vehiclesthat travel in platoon formations.

• Targeted performance metrics include message transport latency andthroughput rate.

• Only vehicles within the same platoon are assumed to be synchronized.

IEEE802.11p-based

[17, 18, 6] • Employ IEEE802.11p schemes to study the impact of different bea-coning frequencies, platoon sizes, intra-platoon distances, and variousvehicular highway loading levels on the communication channels.

• Typical measured metrics are probability of successful transmissionand platoon stability.

• Communication problems due to inter-platoon coordination are notconsidered.

Hybrid TDMAand IEEE802.11p-based

[19, 8] • Exploit TDMA-like schemes for platoon stability preservation andCSMA/CA for disseminating event-based safety messages.

• Vehicular traffic management problems are not considered.

Traffic flow op-timization

[4, 20, 5] • Exploit platoon formation techniques to optimize road network capac-ities and increase vehicular traffic throughput.

• Do not consider the impact of the platoon management on the com-munication channels.

Table 1: Summary of related works

road side backbone). Also we note that in this paper,we do not impose hop-by-hop transmission of messagesamong platoon mobiles. In turn, a message transmittedby a platoon leader is broadcasted over a specified range,reaching also its platoon members.

The model mentioned above is extended in [16], usingboth data networking and vehicular throughput metrics,to include modeling of waiting times incurred by vehiclesthat form queues across their entrance ramps, prior totheir admission to the highway. The paper carries outqueuing delay analyses. The employed admission controlmechanism is based on having on-ramp waiting vehiclesadmitted to the highway as they join platoons of vehiclesthat pass-by the ramp access points, when the latter haveplatoon membership vacancies.

Relating to papers that employ IEEE802.11p-basedschemes, we note the following studies. The study in [17]presents analytical and simulation models that are usedto calculate the probability of successful transmission ofbeacon messages within a platoon. The study explores theeffect on system performance of using different beaconingfrequencies, fixed platoon sizes and various vehicular high-way loading levels. The minimum beacon rate frequencyis determined. This study does not consider the impactof different platoon configurations on data throughput. Italso does not involve inter-platoon communications.

The study presented in [18] presents analytical andsimulation models for capturing the impact of Coopera-

tive Awareness Messages (CAMs) exchange process, whenapplied for platooning control. The authors assume bothplatoon-organized and regular vehicles traveling on a high-way and periodically exchanging CAM messages. From anetwork communication perspective, the probability of asuccessful transmission is computed under varying trafficdensity levels. The impact of the communication process onplatoon stability is also analyzed. The parameters takeninto account include the highway density, intra-platoondistance values, and beacon frequency. The employed ana-lytical model neglects the hidden node terminal problemand considers synchronous communications. This workdoes not study routing of message flows and optimal pla-toon formations.

In [6], the authors propose a distributed consensus-based control algorithm for performing vehicular platoon-ing with Inter-Vehicle Communication (IVC). Generally,the coordination of a platoon of vehicles involves the useof a control algorithm that adjusts the relative distanceamong adjacent vehicles in the platoon, and a communica-tion network that allows vehicles to exchange information.This paper considers the IEEE802.11p protocol as themain scheme used for executing V2V communications net-working. The proposed control algorithm is proved tobe robust with respect to variations in logical communi-cations topologies (e.g., predecessor-following, leader andpredecessor-following bidirectional), as well as with respectto communication delays and losses. The authors provide

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a theoretical analysis of the stability of their proposed al-gorithm and validate their solution through simulationsthat consider detailed physical characteristics of vehiculardynamics. In contrast to our paper the inter-platoon coor-dination and communications problem is not considered.

In noting papers that include the use of TDMA, as wellIEEE802.11p protocols for V2V networking, we observethe following ones. In [19], just two types of communicationscopes are involved: broadcasting from a platoon leader toall platoon vehicles, and transmission of a message froma vehicle to its neighboring vehicles. Protocols includeslotted beaconing within a platoon (intra-platoon TDMA)and beaconing using Carrier-Sense Multiple Access withCollision Avoidance (CSMA/CA). The number of vehicleson the highway serves as a parameter, under fixed platoonsize and intra-platoon distance values. This study alsoexplores the effect of varying the beacon frequency onthe minimum intra-platoon distance levels, under targeteddata communication delay constraints. The paper does notperform a study on communication between platoons andit does not account for message routing between differentplatoons. It also does not investigate the setting of platoonconfigurations for the aim of optimizing communicationsand/or vehicular throughput metrics, as performed by usin this paper.

The study in [8] presents a consensus-based controlalgorithm for vehicular platooning and an adaptive mes-sage dissemination scheme to guarantee a platoon stabilityrequirement. The study considers a scenario that involvesboth platoon-organized and individual vehicles travelingalong a highway. The theoretical analysis of the proposedcontrol algorithm shows that the state error (position andspeed) function incurred between platoon members and pla-toon leader can converge within a prescribed time bound inpresence of beacon packet loss. This bound depends on theplatoon size, beacon delivery ratio, and magnitude of the ac-celeration perturbation component. The proposed messagedissemination scheme uses the current channel quality andthe leader’s dynamics to adaptively select beacon sendingtime slots for each platoon member. A TDMA-like MACmechanism is used for intra-platoon beaconing, while aCSMA/CA-based approach is considered for disseminatingevent-based safety messages. An inter-platoon commu-nication management mechanism is also proposed. Theauthors perform analytical and simulation-based evalua-tions to validate the effectiveness of their proposed solution.The traffic management functions studied in our paper andthe associated optimization problems are not considered.

2.2. Traffic flow optimization schemesWhen considering traffic management and flow control

schemes that employ platoons, we note the following works.The study in [4] addresses the traffic management problemby exploiting the idea of organizing vehicles into platoons.They propose a vehicular platoon assignment strategy withthe objective of maximizing the distance across which pla-toon formations remain unchanged. Also, they introduce

a lane assignment solution according to which platoonshaving longer origin/destination distances tend to be as-signed to faster lanes. These problems are formulated asoptimization problems and are solved by using linear pro-gramming based algorithms. Observing simulation results,they note that organizing vehicles into platoons leads to asignificant increase in lane capacities and vehicular trafficthroughput. Platoon and lane reassignment proceduresare repeated every time vehicles approach the start of anew highway segment, which can be challenging when im-plemented in real-time. The authors assume the presenceof inter-vehicular communications component, but do notperform corresponding analysis to determine its impact onthe communication channels.

In [20], the authors propose an integrated traffic man-agement and hierarchical traffic control approach for Au-tomated Highway Systems (AHS). They describe a model-based predictive control scheme that determines at whichlane and at which speed a platoon should travel within theAHS. They also consider the problem of properly choosingthe release times of vehicles that are intending to enter theAHS in order to optimize the overall performance of thetraffic system. Their solution is evaluated in terms of totaltime spent in a traffic network, while considering dynamicspeed limits, lane allocation, and on-ramp metering as con-trol measures. This study does not however perform anevaluation of the impact of the system on the performancebehavior of the communications network.

The study in [5] explores the idea of using connectedvehicle technologies to increase the capacity of an intersec-tion in an urban road system. Three queuing models areconsidered and analyzed, serving to predict the mobilitybenefits of organizing vehicles in platoons at intersections.To prove their claims, the authors perform a simulationstudy that involves a road network near Los Angeles, wherevehicles are assumed to have Cooperative Adaptive CruiseControl (CACC) capability and to be organized into pla-toons. Evaluation results show that the capacity of the roadnetwork can be increased by a factor of 3, with no increasein queuing delay or travel time. However, no analysis ofthe communication network is carried out.

In contrast with the above mentioned studies, we char-acterize in this paper the optimal setting of vehicular pla-toon configurations by combining traffic management basedvehicular traffic flow rate objectives with data network com-munications performance behavior aims.

3. Traffic Management System Model

We consider a highway system with access/exit rampsand multiple lanes. The highway system is made up ofhighway span, ranging from one access ramp to the nextone, in a given direction. Typical examples are linearhighways or ring highways (beltways). Figure 1 illustratesa highway made up of three spans as an example.

To start with, we focus on a unidirectional single-lanehighway span, equipped with a single input ramp and a

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l1

l2 l3

Figure 1: Example of highway span with three input/output ramps.

single output ramp. We refer to the highway span, com-prised between the entry point at the input ramp and theexit point at the output ramp, as highway link. Later onwe will generalize the considered highway to the multiplelink multiple lane scenario and introduce further notationaccordingly.

Assume a vehicular flow under which vehicles enterat the access ramp and move across the link to the exitramp, exiting the highway link or continuing to anotherlink thereafter. The vehicular flow entering the highwaylink is modeled as a Poisson process with mean rate λ.We assume that vehicles are organized in platoons whentraveling on the highway. The vehicle that travels at thehead of a platoon is identified as the PL.

We introduce the following notation:

vmax maximum allowable vehicular speed across the high-way span.

l length of the highway link.

λ mean vehicular arrival rate at the entry point.

µ mean effective serving rate provided by the highwaylink; it is defined as the mean rate at which vehiclesfrom the input ramp are admitted into the highway,in joining existing platoons or forming new ones.

dPL distance between two successive platoon leaders.

dV minimum distance between vehicles that are membersof the same platoon.

dP minimum distance between platoons, i.e, the mini-mum distance required between the ending locationof a platoon and the starting location of a subsequentplatoon; it is set as dP = dV + g, where g representan excess gap range, beyond dV.

g excess minimum gap distance between two subsequentplatoons; its value is a function of the rate of vehiclesthat need to merge into and/or out of the lane and/ortransit through the lane for the purpose of joiningthe lane flow or for the purpose of moving across thelane as they maneuver to enter to or exit from otherhighway lanes; higher such gap margins are typicallyrequired for setting platoon formations across slowerlanes, as they need to accommodate vehicles thatcross the lane as they enter to join flows across a

faster lane, or vehicles that enter the lane from fasterlanes as they are in the process of exiting the highway,or switching to another highway.

n maximum number of vehicles that are allowed to forma platoon.

v mean velocity of vehicles on the considered highwaylink.

vtg target velocity of vehicles on the considered highwaylink/

s mean length of a vehicle.

u nominal deceleration of a vehicle.

∆u deviation of the deceleration level from its nominalvalue.

When considering multiple link and/or multiple-lanehighways, we add subscripts. A single subscript is usedin case of single-lane highway to denote the highway link.When a double subscript is used, the first index refers tothe lane, while the second one to the highway link. Asa matter of example, vtg,k is the target velocity on thehighway link k single lane; vtg,ik is the target velocity ofvehicles traveling on lane i of link k.

The platoon leader of platoon k, denoted as PLk, isthe leading vehicle in platoon k. The platoon trailer ofplatoon k, denoted as PTk, is the trailing vehicle in platoonk. The distance spanned between PLk−1 and PLk, definedas the distance between consecutive platoon leaders, dPL,assuming each platoon to have full occupancy of n vehicles,is given by

dPL = (n− 1)dV + dP = ndV + g = ns+ nζv2

2u + g (1)

where the second term in the rightmost side of the equationis calculated by accounting for a safety distance that mustbe maintained between adjacent vehicles, and using thefactor ζ ≤ 1 to account for the range of dispersion ofdeceleration factor values realized by different vehicles1.For notation convenience, we define b ≡ ζ/(2u).

1The headway distance used for safety reasons here is set as equalto the braking range, assuming a maximum deceleration of u. Thisuse is somewhat pessimistic. We could account for the fact that, whena vehicle starts decelerating, it still moves forward and this givessome margin to the subsequent vehicle to brake in turn and avoida collision. If the decelerations were rigidly constant and reliable,we could theoretically use zero safety gaps, apart from reactiontimes. However, we have to account for mechanical and performancedifferences among vehicles. Let [u − ∆u, u + ∆u] be the realizedrange of decelerations of vehicles. In the worst case, the leadingvehicle has the maximum deceleration and the following one has theminimum deceleration. If we aim at guaranteeing the maintenanceof a minimum separation dV,min between the two vehicles after theyhave stopped, the initial separation they need is

dV = dV,min +v2

2u2∆u/u

1− (∆u/u)2

By letting dV,min = 0 and ζ ≡ 2∆u/u/[1 − (∆u/u)2] we recover

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The time elapsed between the instants of two consecu-tive platoons passing by a fixed highway location is equal todPL/v. We assume that vehicles are admitted into the high-way link at the times at which platoons pass by the accessramp, joining (or forming) platoons. Hence, the maximum“serving rate” of the highway link (realized when everyplatoon is available to admit into the highway n vehicles),is given by

µ = n

dPL/v= nv

ns+ nbv2 + g= v

bv2 + a.

where a ≡ s+ g/n. This equation is a consequence of thebasic transportation equation, stating that, under equi-librium conditions, the mean flow equals the product ofthe mean density and the mean velocity across the roadsegment. Under our model, the mean density of vehiclesalong the highway is determined by the configured platoonformations and structures.

The serving rate µ is 0 for v = 0. It grows with vinitially, and then it gradually decreases as v increases, dueto the requirement of maintaining safety spacing rangesbetween adjacent vehicles.

The key structural parameters of the platoon formationcan be further elaborated, by assuming that the parameterg dictating the excess distance between a platoon trailerand the following platoon leader be a multiple of the inter-vehicle spacing within a platoon (hence, dependent on v).We then set g = ψdV = ψ(s+ bv2). With this setting, themean serving rate can be written as

µ(v) = v

bv2 + a= v

bv2 + (1 + ψ/n)s+ ψ/n bv2 =

11 + ψ/n

v

bv2 + s

(2)

For fixed n and s values, the maximum serving rateis achieved when the velocity dependent component ofdPL equals the constant component, i.e., when s = bv2.Therefore, the maximal value of the serving rate and thevelocity at which it is attained are given by

µ∗ = 11 + ψ/n

12√bs, v∗ =

√s

b.

The parameter b depends only on the mechanical func-tionality of the vehicles. In our model, b is a function ofthe maximum deceleration parameter u and the allowanceparameter ∆u representing an average deviation with re-spect to the nominal deceleration. Assuming u = 3 m/s2

and ∆u/u = 0.1, we find b = 0.0337 s2/m. To illustrate,the parameter s is set to s = 6 m. The resting value of v∗is v∗ = 13.3 m/s.

the expression of dV used in Equation (1). This formula merelyaccounts for the braking distance. Most importantly, we note that thedependence on the square of the vehicular velocity remains unaffectedby using the deviation factor (though the numerical values of thesafety distance for a given velocity are definitely affected).

Thus, platoon formations are characterized by threekey parameters: the maximum number n of vehicles thatare members of a given platoon, the inter-platoon distancefactor ψ, which accounts for the inter-platoon gap requiredto handle add/drop mergers and transit movements, andthe velocity parameter v.

4. Traffic Management System Optimization

Our first aim is to determine the system parametervalues that maximize the vehicular throughput rate λ at-tainable under prescribed vehicular delay constraints. Thedelay experienced by a vehicle as it arrives to the roadacross an input ramp and waits to access the road con-sists of this waiting time component and of the subsequenttransit time delay elapsed as the vehicle travels along thehighway to its destination. We assume the correspond-ing system state process to have reached equilibrium. Wemodel the queuing process of vehicles waiting at the inputramp as an M/M/1 queue. Then, the mean access delaytime experienced by a vehicle at the input ramp is given as1/(µ−λ). The transit time taken by the vehicle in travelingalong the highway link is equal to l/v. In designing thesystem, and in particular in synthesizing the optimal vehic-ular speeds and platoon formations, it is often of interest toimpose delay constraints on each of these two components,or on their sum.

4.1. Single lane single link caseIn the following we determine the optimal synthesis

of the system, aiming to maximize the road’s vehicularthroughput rate λ under the following composite vehiculardelay constraint, accounting for both on ramp queuingdelay and highway transit time:

1µ(v)− λ + l

v≤ D0 = D0q + l

vtg(3)

We have shaped the delay constraint D0 by setting it equalto D0q + l/vtg, where vtg is a target velocity level and D0qis the on-ramp waiting time bound. Note that only thesum of the two components acts as a constraint, not theindividual components. As a consequence of this specifieddelay bound, we obtain the following upper bound on thesystem’s vehicular throughput rate:

λ ≤ µ(v)− 1D0q + l/vtg − l/v

= 11 + ψ/n

v

bv2 + s−

1D0q + l/vtg − l/v

(4)

We then proceed to determine the vehicular velocitylevel v that maximizes the latter bound on λ. For futurereference we define the function

f(v, l; θ) = µ(v)− 1D0q + l(1/vtg − 1/v) (5)

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Figure 2: Optimal velocity levels with respect to the highway linklength under the mean delay constraint D0q + l/vtg and differentvalues of n.

where θ is a vector of parameters of the considered linkand lane (including vtg, D0q, ψ). It is noted that f(v, l; θ)is defined for v ∈ [vmin, vmax], where vmin is the minimumlevel of velocity v such that f(v, l; θ) is non-negative. Inthis range f has a global maximum as a function of v.

We note that the constraint in the form D0q + l/vtgcaptures the fact that the end-to-end travel time of avehicle, from when it joins an access ramp queue, untilwhen it arrives at its destination exit and leaves the highway,depends on two distinct effects: (i) waiting time at theaccess ramp, until when the vehicle is permitted to enterthe highway and join a platoon; (ii) travel time alongthe highway from the access point to the exit point. It issensible to allow the overall delay requirement to depend onthe distance traveled (l in this case). The delay requirementis surely met if a vehicle spends a time D0q on the averagein the access ramp line and then performs its highway tripat a constant velocity vtg. However, setting the overalldelay requirement as the sum of the two delay components,broadens the feasible solution search space of the systemoptimization, thus giving more flexibility to the design ofthe highway traffic management system, while at the sametime still providing the desired end-to-end average delay.

To illustrate, consider a system characterized by thefollowing parameters: u = 3 m/s2, ∆u/u = 0.1, s = 6 m,ψ = 2, D0q = 3 min, vtg = 30 m/s. We let l vary on thex-axis of the graphs and n act as a parameter, rangingfrom 2 to 10. We solve the optimization problem andanalytically derive the performance results. We plot thefollowing three functions:

1. v, the velocity level at which the vehicular through-put is maximized under the specified mean delayconstraint (Figure 2).

2. λ, the maximum achievable vehicular throughput,under the specified mean delay constraint (Figure 3).

3. dPL, the value of the inter-platoon distance, whereby

Figure 3: Optimal vehicular throughput with respect to the highwaylink length under the mean delay constraint D0q + l/vtg and differentvalues of n.

Figure 4: Platoon length with respect to the highway link lengthunder the mean delay constraint D0q + l/vtg and different values ofn.

each platoon consists of the maximum allowed num-ber of vehicles, at the optimal velocity level (Fig-ure 4).

As for v, it appears that the optimal level of velocityvaries widely as the length of the highway link grows. Onthe contrary, the size of the platoon, i.e., how many vehiclesit is composed of, has a very minor impact on the settingof the optimal velocity.

As expected, the platoon size impacts more significantlythe average realized vehicular throughput under the opti-mal setting of the average velocity. However, as n increases,a saturation effect is noted. The curves in Figure 3 are ob-served to get closer and closer as n increases, thus yieldingvanishing returns in terms of the attained average vehicularthroughput rate.

We also observe that the length of a platoon under opti-mal parameter setting ranges between 50–250m when l andn are varied over the entire considered range. Those values

8

of platoon lengths are well suited to accommodate highperformance networking protocols, such as those describedand evaluated in later sections.

4.2. Multiple lane single link caseConsider a single highway link comprising ` lanes. Lane

1 is the fastest, while lane ` the slowest. Let vtg,i be the tar-get velocity on lane i and µi be the serving rate provided bylane i. Platoon formation structures can change from laneto lane. We expect slower lanes to have to accommodate ahigher rate of mobile maneuvers, sustaining a higher rateof lane changing and merging. For example, slower lanestend to experience more transits by vehicles that aim tomerge into faster lanes or temporarily switch to slower lanesin the process of leaving the highway. To accommodatesuch higher maneuvering rates, the inter-platoon distancegap parameter g for slower lanes should be set to a highervalue than the gap configured for faster lanes. This settingimpacts the parameter ψi for lane i.

Let ri be the fraction of the input vehicular flow enteringlink k that is directed to lane i. The mean delay experiencedby a vehicle routed through the i-th lane is given by

1µi(vi)− riλ

+ l

vi≤ D0q + l

vtg,i, i = 1, . . . , r, (6)

where the left-hand part of the inequality represents thedelay per lane. By following the same approach as thatpresented for the single lane case, we express an upperbound on the vehicular throughput flowing along the laneas: riλ ≤ µi(vi)− 1

D0q+l(1/vtg,i−1/vi) = f(vi, l; θi). The ve-locity level per lane, vi, is set to maximize the latter boundon riλ. Consequently, incorporating the derived maximumvelocity levels, vi, the overall vehicular throughput rateattained along the multi-lane road is upper bounded asfollows: λ ≤

∑`i=1 f(vi, l; θi). Assume the design objective

is to maximize the overall vehicular throughput rate λ,under the above prescribed vehicular delay constraints. Wereadily conclude that this optimization objective is attainedwhen the latter upper bounds on the throughput rate perlane are set to assume the same value. This is achieved bysetting

ri = f(vi, l; θi)∑`j=1 f(vj , l; θj)

, i = 1, . . . , `. (7)

In conclusion, assuming a road link that employs ` lanesand permits the formation of separate on-ramp queues, thevehicular throughput of the single link is maximized byperforming the following:

1. Find the per-lane vehicular velocity vi that maximizesthe function

f(v, l; θi) ≡v

(1 + ψi/ni)(bv2 + s)−

1D0q + l(1/vtg,i − 1/v)

(8)

where the i-th lane parameters are θi = (ψi, vtg,i).

2. Set the probability of routing a vehicle to lane i tori, whereby the latter is calculated by using eq. (7).Then, the maximum vehicular throughput rate thatcan be accommodated under the delay constraint isλ =

∑`i=1 f(vi, l; θi)

4.3. Single lane multiple link caseWe now move on to consider a highway system com-

posed of a number of links. To that end, we introduce somemore notation.

F set of vehicular end-to-end flows accessing the high-way system.

L set of highway links making up the highway system.

λs mean vehicle arrival rate of external vehicle flow s ∈F ; the overall offered vehicular rate is denoted withλ =

∑s∈F λs.

ask it is 1 if vehicles forming the external flow s includelink k in their route through the highway system.

νk mean rate of vehicles entering the highway systemthrough ramp of link k: note that

∑k∈L νk = λ.

αk fraction of the overall offered vehicular flow rate thatenters the highway system through the access ramplocated on the highway link k; it αk = νk/λ.

φk mean vehicle flow in the k-th highway link, also re-ferred to as internal flows: it is φk =

∑s∈F λsask.

phk probability that a vehicle, having completed its tran-sit thorough the highway link h, will move forward tothe highway link k, h, k ∈ L. Note that

∑k∈L phk ≤

1, being equal to 1 only if no vehicles leaves thehighway system after having traveled on link h.

Based on the definitions above, at equilibrium we canwrite the following balance equations:

φk = νk +∑h∈L

φhphk , k ∈ L, (9)

This is but a flow balance equation made at the pointwhere the access ramp of the k-th highway link is located(at the beginning of the k-th highway link). To state thebalance equation as in Equation (9), using flow independentrouting probabilities, we model the routing of vehicles asa memoryless process. Each vehicle, having traveled upto and including link h, can move forward to link k withprobability phk, independent of previous routing choicesand of all other vehicles. This kind of routing is akin to theone hypothesized in Jackson queuing networks [21]. It is areasonable approximation of reality when each single end-to-end flow contributes a little part of the overall flow travelingon a generic highway link. Obviously, the probabilities phkdepend on the topology of the highway system, i.e., phk = 0if link h termination is not connected to link k startingpoint.

9

Consider the following highway synthesis problem. Theaim is to maximize a weighted function of the vehicularflows through the highway system under a mean vehiculardelay constraint. At equilibrium, the mean vehicular delaythrough the highway system is proportional to the meannumber of vehicles in the highway system, according toLittle’s law [22].

The delay constraint is imposed separately for eachhighway link. The mean number of vehicles associatedwith link k (including those waiting on ramp and thosetraveling across the link) is written as

νkµ(vk)− φk

+ φklkvk

(10)

where νk is the mean vehicular arrival rate at ramp k thatis feeding vehicles to highway link k and φk is the overallvehicular flow rate on link k (the latter being the sum ofthe rate of the flow arriving from ingress ramp k plus therate of the flow arriving from upstream links connected tolink k).

We set the delay constraint as the sum of a term propor-tional to the access ramp delayD0q and a term proportionalto the travel time on link k. By converting mean delay intothe mean number of vehicles in the k-th link, according toLittle’s law, we have

νkµ(vk)− φk

+ φklkvk≤ νkD0q + φk

lkvtg

, k ∈ L, (11)

Dividing both sides by νk, we re-state the constraintas:

1µ(vk)− φk

+ xklkvk≤ D0q + xklk

vtg, k ∈ L, (12)

where xk ≡ φk/νk. This inequality is observed to havethe same format as the one presented for the single linkcase, except that: (i) the link flow rate φk replaces thearrival rate λ; (ii) the link length is modified to xklk. Thexk parameters depend only on the routing matrix and onthe load distribution across the links. By re-arranging thelinear equation system in (9), we write:

xk = φkνk

= 1 +∑h∈L

φhνh

νhνkphk = 1 +

∑h∈L

xhαhαkphk,

k ∈ L,(13)

hence

αkxk = αk +∑h∈L

αhxhphk , k ∈ L (14)

Let α be a row vector of the coefficients αk and Dα adiagonal matrix whose diagonal is composed of the elementsof α. The vector x = [xk, k ∈ L] can be expressed asx = α(I−P)−1D−1

α , where P is the square matrix collectingthe probabilities phk and I is the identity matrix with thesame size as P. The inverse of I−P exists since P is sub

stochastic. Moreover, the inverse is also a non-negativematrix entry-wise.

Once the fractions αk, k ∈ L, and the routing proba-bilities phk are given, the coefficients xk are computed bysolving the linear equation system in Equation (14). Thesecoefficients do not depend on the intensity of the vehiculartraffic, rather they depend only on the distribution of thevehicular traffic across the inputs of the highway systemand on the routing of vehicles through the highway system.

Assume our aim is to design the system in a mannerthat optimizes an objective function that is expressed asthe weighted sum of the end-to-end vehicular flow ratescarried along the highway, whereby each end-to-end flowrate is weighted by a route-dependent payoff L(s) (fromthe point of view of the highway system manager). In turn,the payoff of a given route is assume to be the sums of thepayoffs induced by a vehicle traveling on each link formingthe route, i.e., L(s) =

∑s∈F askqk, where qk is the payoff

of link k. An example of payoff is obtained by consideringtoll fees that are proportional to the distance traveled: thenqk = c · lk, where lk is the length of the highway link k.

This objective function is expressed as follows:

F ≡∑s∈F

λsL(s) =∑s∈F

λs∑k∈L

askqk =∑k∈L

qk∑s∈F

λsask =∑k∈L

qkφk(15)

Summing up, the optimization problem is

maxφk

F (φ) =∑k∈L

qkφk (16)

s.t. 1µ(vk)− φk

≤ D0q + xklk

(1vtg− 1vk

)k ∈ L

(17)The following upper bound is induced by the delay con-straint:

φk ≤ µ(vk)− 1D0q + xklk(1/vtg − 1/vk) = f(vk, xklk; θ)

(18)where f(·, ·; ·) is defined in Equation (5). The objectivefunction F is maximized by setting the velocity levels ineach link to a value that maximize the right-hand side ofEquation (18). This is achieved by finding the value of vkthat maximizes the function lkf(vk, xklk; θ).

Numerical examples are presented in the following forthe general multi-lane multi-link case.

4.4. Multiple lane multiple link caseIn this section, we generalize the analysis to the case

of a highway system which is composed of multiple inter-connected multi-lane links. The model assumptions aresimilar to those described above:

• per-sector, per-lane delay constraint is prescribed;

10

• memoryless assignment of vehicles to the differentlanes, i.e., a vehicle admitted to the highway link k isassigned to lane i with probability rik independentlyof all other assignments, either past or future.

• memoryless routing process through the highway sys-tem, described by the flow termination probabilitiesphk;

• the system state process has reached equilibrium.

In the following, we use a double subscript notation toidentify certain variables as associated with a specific lane(the first subscript) and across a specific link (the secondsubscript). To satisfy the delay constraint for vehicles thattravel in the i-th lane of the k-th link we require that:

νikµi(vik)− φik

+ φiklkvik≤ νikD0q + φik

lkvtg,i

(19)

Let the amount of vehicular flow entering the accessramp on link k and sent to the i-th lane be a fraction rik ofthe overall input vehicular flow rate νk, i.e., νik = rikνk. Weassume that the same fraction rik of the overall link carriedflow rate φk pertains to the i-th lane, i.e., φik = rikφk.Substituting into Equation (19) we find

rikνkµi(vik)− rikφk

+ rikφklkvik≤ rikνkD0q + rikφk

lkvtg,i

(20)

Note that, under our assumptions, we need to find therations xk = φk/νk. We have pointed out above that thoserations could be found by knowing the routing probabilitiesand the external vehicular traffic load profile at the accessramps, the αk. From practical deployment point of view,determining the xk can be obtained by monitoring thecurrent link flow φk and the overall rate of vehicles enteringthrough the ramp k, νk. Both monitoring operations canbe carried out by using simple technologies, given that onlyaggregated overall flows are required.

Canceling rik on both sides and re-arranging terms, wededuce the following upper bound on the vehicular flowrate:

φik = rikφk ≤ µi(vik)− 1D0q + xklk(1/vtg,i − 1/vik)

= f(vik, xklk; θi)(21)

where θi = (vtg,i, ψi), and xk = φk/νk is the same coef-ficient as found in the case of multiple links, single lane.Summing over i, we find φk ≤

∑`i=1 f(vik, xklk; θi). This

upper limit can be achieved by setting

rik = f(vik, xklk; θi)∑`j=1 f(vjk, xklk; θj)

(22)

We assume the same target function as that employedin the single lane case, that is to say, a weighted sum of

Figure 5: Picture of the GRA layout. Access/exit points are high-lighted.

the end-to-end vehicular flows. The weights are interpretedas payoffs for the highway system manager and they aredeemed to be the sum of the payoffs of the link traveled by avehicle. In the end, the target function of the optimizationproblem is

F (φ) =∑i=1

∑k∈L

qikφik (23)

where qik is the payoff for a vehicle traveling on lane i oflink k.

The maximum value achieved by this objective functionis obtained by performing the following steps.

1. Compute the fraction xk given the routing probabili-ties phk and the loading profile αk.

2. Set the lane and link velocity levels to values thatmaximize the function f(vik, xklk; θi). Let vik denotethe resulting velocity levels.

3. Set ri = f(vik, xklk; θ)/∑`j=1 f(vik, xklk; θj).

4.5. Performance behaviorTo illustrate the performance behavior of the system

under the traffic management models presented above, weapply in the following the optimization procedure to amulti-link multi-lane highway. We consider the ring high-way surrounding the city of Rome, Italy – the so calledGreat Ring Connection Highway (Grande Raccordo Anu-lare, GRA) (see Figure 5). It is a 69 km closed-ring, threelane highway with 29 main access/exit ramps. The highwaylinks delimited by two successive ramps are numbered from1 to 29. We associate the access/exit ramp at the beginningof the highway link k with link k.

In the following we focus on the inner ring direction,i.e., the ring highway where vehicles travel clockwise. Byexploiting sample field measurements, we can extrapolatea 29× 29 Origin/Destination (OD) vehicular flow matrix[23]. The generic element of the OD matrix λij gives the

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k lk(km) Exit name k lk(km) Exit name

1 3.4 Via Aurelia 16 3 Tor Bella Monaca2 9.2 Via Boccea 17 1.2 Casilina3 3.4 Via Cassia (+ Trionfale) 18 2.1 A1 Napoli4 1.6 Via Cassia Bis 19 2.9 Tuscolana/Anagnina5 1.7 Via Flaminia 20 3.6 Appia6 0.85 Castel Giubileo 21 4.5 Ardeatina7 0.6 Salaria 22 2.3 Laurentina8 1.1 Settebagni 23 1.3 Pontina9 4.9 A1 Firenze 24 1.5 Cristoforo Colombo10 1.8 Nomentana 25 2.7 Via del mare11 1.5 Centrale del Latte 26 1 Autostrada Fiumicino12 1.6 Via Tiburtina 27 2.9 Magliana13 1.6 A 24 Teramo 28 2.2 Pisana14 1.4 La Rustica 29 1.8 Casal Lumbroso15 0.6 Prenestina

Table 2: Name of GRA exits and link lengths.

mean vehicular flow rate entering the access ramp of linki and leaving through the exit ramp of link j. Given theunidirectional ring topology, routing has no choice butgoing through all links between the i-th one (included), upto the j-th one (excluded, since the exit ramp is locatedjust at the beginning of link j). The OD matrix thusgives enough information to estimate routing probabilitiesphk and load factors αk. We have νk =

∑29j=1 λkj for

k = 1, . . . , 29. The link flow rates φk can be calculated byusing the routing of the vehicular flows. This is simple,given the unidirectional ring topology. The flow rate onlink k is the sum of all OD flows λij such that i ≤ k < j,if i < j, or such that i ≤ k ≤ 29 and 1 ≤ k < j, for i > j.Since the topology of the considered highway system is aring, we have pk,k+1 = (φk+1 − νk+1)/φk, for k = 1, . . . , 28and p29,1 = (φ1 − ν1)/φ29. All other entries of the routingprobability matrix are equal to 0.

For the GRA beltway we have ` = 3 lanes. We labellanes with integers from 1 to 3 according to the maximumallowed traveling speed (1 being the slowest lane, 3 thefastest). We assume that vtg,1 = 20 m/s, vtg,2 = 30 m/sand vtg,3 = vmax = 40 m/s. As for the platoon structure,we assume it is the same throughout the GRA in eachlane, but it has different parameters for each distinct lane.Specifically, we let ψ1 = 3, ψ2 = 2, and ψ3 = 1. Thissetting is consistent with the fact that lane 1 is the closestto access/exit ramps, hence it is involved in most vehiclemaneuvers, whereas lane 3 is only affected by platoonadd/drop of vehicles that need to travel on that lane. Asfor the platoon size n, we let it vary between 1 and 10. Thelengths of the 29 highway links are listed in Table 2.

To illustrate, we use measurements performed over GRAto configure the above-mentioned parameter settings inorder to demonstrate the system’s performance behavior,as obtained by applying the above derived technique forthroughput optimization under the mean delay constraints(imposed per lane and per link). Here we assume D0q =3 min. Figure 6 plots the mean vehicular link flows as afunction of the distance from exit k = 1 on GRA. Thelink flow rates are obtained according to the optimization

Figure 6: Mean link vehicular flow rates φik for lane i = 1, 2, 3as a function of the distance from exit k = 1 on GRA (n = 10,D0q = 3 min).

Figure 7: Mean vehicle velocity vik for lane i = 1, 2, 3 as a function ofthe distance on GRA starting from exit k = 1 (n = 10, D0q = 3 min).

12

Figure 8: Overall vehicular throughput per lane as a function of theplatoon size n. The corresponding realized end-to-end mean delay isshown in the legend.

procedure. A mean flow rate value is obtained for eachlink. The flow rate values are depicted as function of thecoordinate along GRA to account for different lengths ofthe GRA links. The flow rates are higher along the slowestlane and lower across the fastest lane. This is consistentwith the results highlighted in Figure 7, which shows themean vehicle velocity levels assigned to platoons per lane asa function of the coordinate along the GRA, starting fromexit k = 1. It is apparent that lane 3 is actually the fastestand lane 1 is the slowest, though the assigned velocity levelsvary significantly from one link to the other. This is inducedby the outcome of the optimization process that aims toaccommodate the maximum allowable vehicular flow, underthe mean delay constraint, given the routing matrix andthe load profile used by the numerical evaluation process,which is consistent with real traffic measurements collectedon the GRA.

The overall mean throughput realized on each lane asa function of the platoon size is shown in Figure 8. Asexpected, it grows with n. We observed that the meandelay corresponding to the optimized offered flow and linkvehicular rates has negligible variations with n, indepen-dently of the considered lane. For lane 1, the mean delayvaries from 20.16min to 20.03min when the platoon size nranges between 1 and 10. Similarly, for lane 2 and lane 3,we have negligible variations of the mean delay: namely, itdecreases monotonically from 13.81min down to 13.73min(lane 2) and from 10.65min down to 10.62min (lane 3) forn ranging from 1 to 10. For this reason, we report justthe value of the mean delay averaged over all outcomesobtained as n varies from 1 to 10.

The target function of the optimization for the GRAis the weighted sum of end-to-end vehicular flows, withweights equal to the distance travelled by each vehicle. Inother words, we have chosen payoffs proportional to thedistance traveled by a vehicle on the highway. We havealready noted that the target function is then proportional

Figure 9: Platoon length averaged over all highway links as a functionof the platoon size n.

to∑`i=1∑k∈L lkφik =

∑k∈L lkφk. To avoid having unit-

measure dependent values, we normalize the target functionby the overall length of the GRA beltway. Consequently,the target function for the GRA optimization process isgiven as

F (φ) =∑k∈L

lk∑h∈L lh

φk (24)

It is found that F (φ) grows monotonically from 0.91 to2.23 as n varies between 1 and 10.

In Figure 9, we show the variation of the platoon length,averaged over all highway links in each lane, as a functionof n. It is important to note that by using platoon-orientedformations, and thus empowering the platoon leader tomanage its platoon members and coordinate operationswith leaders of neighboring platoons, the ensuing mecha-nism achieves enhanced energy consumption rates. Vehiclesthat are not elected to serve as platoon leaders realize re-duced processing rates and thus experience an improvedenergy aware operation. Also, by using the underlyingplatoon architecture to effectively regulate the access of ve-hicles to the highway, routing them to designated lanes andorganizing them into preferred vehicular formations in eachlane, end-to-end travel delays are much enhanced, furthercontributing to an improved energy efficient operation. Wenote that platoon leaders use periodically broadcasted sys-tem status message flows to make use of current conditionsto best configure system parameters. Accordingly, when ad-vantageous, vehicles that are identified with higher energyresources can be given preferential weights for selection asplatoon leaders.

5. Networking Architecture and Protocols

In the following sections, we illustrate the configura-tion and performance behavior of a V2V wireless networksystem that we synthesize by taking advantage of the orga-nization of vehicles flowing along the highway lanes into

13

platoon formations. As often assumed, the wireless mediumassigned for communications among vehicles is divided intodistinct communication channels. Included in our modelsare thus a Control Channel (CCH) and a Service Channel(SCH). As performed by IEEE802.11p systems, vehiclesuse the CCH to periodically (such as every 100ms) trans-mit and disseminate status updates to other vehicles intheir neighborhood. The CCH under consideration can beemployed in a manner similar to that used by the WAVEsystem [24], as well as by other architectures. In thesesystems, CCH is used by vehicular and by infrastructureentities to broadcast in each area status, management andcontrol messages [25, 26, 27]. It is also used at times toannounce resource allocations, such as the assignment oftime slots for use by contention-free MAC schemes [28, 29].

To demonstrate the performance behavior of a platoon-based V2V communications network that we hereby in-troduce, we consider two classes of event-driven messagesproduced by highway vehicles that are targeted for dissem-ination to other highway vehicles over a SCH. To illustrate,we assume that each such message has a disseminationspan of a range of at least dspan = 300 m. We note thatfor protocol simplification, the dissemination span mayat times somewhat exceed this range. Also, to simplifywe assume in the following that messages produced by asource vehicle are disseminated to vehicles that travel inthe specified span behind the source vehicle (i.e., behindits direction of motion). Our protocols and basic modelsare noted to readily extend to apply to scenarios wherebythe dissemination span bi-directionally covers vehicles thatmove behind and ahead of the source vehicle. We assumeClass 1 messages to include critical (e.g., safety) messagesand to thus be granted higher priority. The throughputrate of such event-induced message flows is relatively low.However, such a message should be disseminated acrossits span aiming to achieve a Packet Delivery Ratio (PDR)across the complete span that is at least 0.95. Successfullydelivered messages should complete their disseminationwithin strict time latencies. Class 2 message flows are oflower priority. Yet, they tend to produce message flows thatimpose high throughput rate requirements on the wirelessnetwork, while also generally imposing packet time delayrequirements. For either class, we assume in this paper a90 to 95 percentile time delay requirement of 50ms.

The network architectures and protocols presented inthis paper, extend the concepts and techniques introducedby us in developing a class of mobile ad hoc networks thatmove in two or three dimensions, identified as MBNs anda class of V2V wireless networks identified by us as VBNs.Yet, in contrast, the network systems introduced in thispaper involve the autonomous mobility of highway vehiclesand are based on the formation of vehicular platoons acrosshighway lanes. Otherwise, the networking systems studiedin this paper assume a two layer hierarchical architecturesimilar to that used in our MBN and VBN-based systems:certain vehicles are elected to act as BNs forming an in-terconnected wireless Bnet; each BN manages the access

BNPL Bnet Anet client associates to a BNnon-PL

d(BN) d(BN)

Figure 10: Network Architecture: BNs and associated Anet clients.The solid arrows represent association of Anet clients to BN and thedotted arrows represent Bnet communications

of its client mobiles across its wireless Anet. Figure 10illustrates the network systems which we use in this paper.We assume the wireless communications channels formingthe Bnet and Anet systems to be shared in accordancewith one of the following joint MAC protocols:

1 Demand-assigned (DA) TDMA scheme across eachAnet and TDMA scheme across the Bnet (MAC 1);

2 IEEE802.11p oriented protocol across each Anet andTDMA scheme across the Bnet (MAC 2);

3 IEEE802.11p oriented protocol across each Anet andIEEE802.11p protocol across the Bnet (MAC 3).

5.1. MBN-based networking protocolAs noted above, the network architecture studied in

this paper consists of the Bnet and its BNs and the corre-sponding Anets. An Anet client (i.e., a vehicle) associatesitself with a BN which it determines to be the closest to it(or, generally, from which it receives signals at the highestpower or signal quality, as often done in a cellular system).The messages produced by a vehicle that is a client of a BNare transmitted by the source vehicle across its Anet to itsassociated BN (identified as the corresponding source BN).The later disseminates the messages it slates to relay overthe Bnet across a multi-hop path that consists of BN-to-BNlinks, covering each flow’s targeted dspan range. This pathconsists of nhops = ddspan/dBNe hops. The last BN acrossa path which receives a flow’s packets (but does not furtherrelay them), following their transmission across the lastBnet link over their path, is identified as the destinationBN for this flow. Messages transmitted across each Bnetlink are assumed to be also received by the mobiles thatoverhear these transmissions. For this purpose, each mobileis programmed to listen and prepare to receive messages(which are of interest to it) that are disseminated acrossthe Bnet. In particular, message transmissions that aresent across the last Bnet link in a flow’s disseminationpath, sent from the next-to-last BN to the last BN in thepath, are assumed to be received by all vehicles that hearthem. The latter vehicles are generally client members ofthe Anets that are associated with either the next-to-lastBN or the last BN.

By using BNs (among elected PLs) to forward messages,rather than performing hop-by-hop forwarding, we reducein a significant manner the effective number of hops that are

14

used for the V2V dissemination of data messages and thusincrease the energy efficiency of the networking operation.

Under a borderline case, such as when dBN > dspan,the destination Anet client is a member of the sourceAnet. The performance metrics are calculated in a similarmanner for this case under the setting of dBN = dspan. Theoverall system throughput is scaled by using a factor that isproportional to the number of system BNs, NBN, similarlyfor the case where the source and destination mobile nodesare members of the same Anet, sharing the same BN. Incase the distance from Anet client to its BN is greater thanits dspan, the Anet client can proceed to directly reach itsdestination through a sole transmission across its Anet. Tosimplify, we focus on dissemination span coverage rangesthat exceed the Anet diameter span.

5.2. Synthesis of the hierarchical network architecture5.2.1. Platoon management for network synthesis

We assume that vehicles traveling across each lane ofthe highway are organized into platoon formations. Eachplatoon is managed by its PL. Alternatively, another mem-ber of a platoon may also be elected to act as the PL.The corresponding management architecture includes thefollowing management entities (whose functionality may bephysically realized to reside in a single or separate nodes):the Highway System Manager (HSM) and the Ramp AccessManager (RAM).

HSM collects and stores status and capability informa-tion about vehicles under its supervision; for this purpose,it also monitors the periodic status messages disseminatedby highway vehicles. The information includes vehicles’location coordinates, speeds, destination, communicationschannel, radio, memory, processing and energy resourcecapabilities, environmental status, and management re-sponsibilities, including whether it currently serves as aPL. This allows the system manager to also regulate theadmission process of vehicles waiting on-ramp for beingadmitted into the system. It also computes optimum trafficmanagement parameters including: dV , dP , n, dPL. Theseparameters can be adapted to different vehicular trafficloads. The status of vehicle i (among N vehicles currentlyadmitted into the highway segment under consideration) isrepresented by a status vector that includes the followingcomponents: xi = (pli, ci, bni, vi), i = 1, ..., N. The pli statevariable indicates whether vehicle i is a PL, ci representsthe coordinates of the location of vehicle i, bni indicateswhether a vehicle currently serves as BN, vi represents thevehicle’s velocity.

In considering the mobility of platoons relative to anaccess point of a ramp (that is used for vehicles enteringthe highway as well as for vehicles departing the highway),we identify the following platoon status indicators. Adeparting platoon is identified as a platoon which is eitherin the process of passing through a ramp’s access pointwhile its tail has not yet passed this access point, or hascompletely passed this access point (so that its tail vehicle

passed it as well). An arriving platoon is identified as aplatoon whose leader has not yet arrived at the ramp’saccess point (which is located upstream relative to itsmobility).

The state of the system at ramp k at a time t thatcorresponds to the time that a platoon within a highwayreaches the ramp and thus may incur vehicular departures(identified therefore as a potentially departing platoon) isrepresented by the vector Xt = ((Pd, Pa)(k)). The com-ponents of the vector Pd(nd, cd, v, bnd) identify the statesof a departing platoon. The component nd represents thenumber of vehicles belonging to a departing platoon, cdrepresents the coordinates of the last vehicle of this platoon,bnd indicates whether the platoon leader serves as a BN.The vector Pa(na, ca, v, bna) stores the states of the arrivingplatoon, whereby na is the number of vehicles within thearriving platoon, ca are the coordinates of leading vehicleof the platoon, bna indicates whether the platoon leaderserves as a BN.

The HSM computes and disseminates the optimal valuesof the communications network parameters, such as dBN,in considering the system’s desired vehicular traffic andcommunications networking performance features, usingthe formulas and approaches described in this paper.

The RAM receives state information from the HSM, aswell as the outcome of its computations that convey thedesired system traffic management parameters, includingrecommended platoon formation parameters, the optimalnetworking parameters (including dBN), and the states ofvehicles which are subjected to its local management. TheRAM uses the local vehicular state updates that it peri-odically receives from the HSM to regulate the admissionof vehicles into the highway, to determine the structureof new platoons that it forms, to control the admission ofvehicles in joining existing platoons, merge platoons, andto elect PLs and BNs.

For purpose of forming platoons and managing vehicularadmissions, the RAM computes several time spans. Itcalculates the time elapsed between the tail of a departingplatoon and the time of arriving PL at its access point,denoting it as Td,a = cd−ca

v ). It also calculates the timeparameter Tp that represents the minimum time requiredto elapse between the traversal time at its access pointof the tail vehicle of a departing platoon and the timeof arrival of the subsequent leader of an arriving platoon;Tp = dPL+dP

v , corresponding to the gap required to be setbetween such instants of time. The later parameter is usedin its decision as to whether there exists a sufficient gapthat allows it to form a new platoon.

5.2.2. Synthesis of platoon formationsThe principles that we have used for platoon formation

include the following assumptions:

• The RAM considers only arriving platoons in deter-mining the admission of vehicles into the highway.It does not consider (though generally feasible) the

15

admission of a new vehicle with the purpose that itwould accelerate to catch up with a departing pla-toon.

• Vehicles within a platoon move forward to occupyvacant platoon spaces, if any. Vehicles which areadmitted from the ramp join a platoon in occupyingits tail positions.

• The computed optimal platoon parameters apply toall platoons and vehicles that travel in the same lane,but can be different for different lanes.

• When there are multiple lanes, an admitted vehiclewould be routed to a platoon traveling in a designatedlane, based on the routing schemes described in theTraffic Management sections of this paper. Yet, dueto space limitations, we describe in the networkingsections a single lane operation.

The following principles govern the process used by avehicle admission and platoon formation algorithm (clearly,other versions can be similarly composed):

1. If no platoon is currently passing at a ramp’s accesspoint, and if the time to elapse between the lastdeparting platoon and the next arriving platoon issufficiently long, such that Td,a ≥ Tp and at leastone vehicle is queueing in ramp, the ramp managerwould then admit queued on-ramp vehicle(s) to forma new platoon, electing the first one admitted underthis event as the platoon’s PL. The manager wouldproceed to subsequently admit other queued vehiclesto join this platoon, assuring that the new platooncontains no more than a total of n vehicles.

2. If no platoon is currently passing by the ramp, andTd,a < Tp, the ramp manager waits until the nextplatoon arrives at its access point prior to makingnew admission decisions. If the platoon that willsubsequently arrive is not full (accounting for currentdepartures as well), so that na < n and there is atleast one queueing vehicle, the ramp manager admitsnew vehicles to join this in-transit platoon, assuringits membership to not exceed its designated capacityn.

3. If an arriving platoon is currently passing by the rampaccess point, and if there is at least one queueingvehicle, the platoon manager admits new vehicles tojoin the platoon in accordance with the principlesstated above.

5.2.3. Backbone network synthesisIn this section, we illustrate a protocol that is used for

the formation of a Bnet. Depending upon the managersobjectives, other synthesis protocols can be readily applied.Furthermore, RSUs, including Base Station or Access Pointnodes, that are installed at road-side may also be employed

and embedded into our models and analyses. To simplifythe discussion, we hereby assume that BNs are electedamong PLs.

For multi-lane highway systems, BNs can be elected byconsidering PLs of platoon that travel across any lane orin preference of PLs that travel along certain lanes. Forexample, the advantage of electing BNs from PLs thatare members of platoons that travel along the slow laneis that a lower rate of re-elections of PLs and BNs maybe invoked. Slower lane vehicles will then also incur lowerrate of re-associations (of a mobile with an elected BN).Furthermore, the slower lane is usually denser, allowingmore flexibility when wishing to elect a higher densityBnet. On the other hand, the advantage of electing BNsfrom PL vehicles that travel along a fast lane is that suchvehicles tend to travel longer distances, and thus stayinglonger in the highway and serving an assigned BN role. Forillustrative and discussion purposes, we assume that theRAM assigns higher priority for electing BNs among PLsthat travel along a slower lane. When the manager cannotfind at an underlying period of time a PL that travels ina slow lane, it proceeds to choose one that travels along afaster lane.

Each RAM receives information from the HSM regard-ing the status of vehicles that it manages, including theresidual lifetime (time to destination) and entry and depar-ture locations of these vehicles. As noted above, BNs areelected from the group of underlying PLs, including PLswhich have already entered the highway or vehicles queuedon the ramp which will be admitted into the highway. Therules for BN election for association of mobiles with a BNare as follows:

1 If a RAM observes that there is no vehicle that as-sumes a BN role traveling across its highway linkwithin its communication range, in upstream or down-stream direction, it will elect a PL whose location isclosest to its access point to serve as a BN, preferringone that travels along the slowest lane.

2 If the RAM detects at least one vehicle that acts as aBN to transit its location but determines that thereis no other vehicle that serve as a BN which is locateddBN away from the latter BN, in either upstream ordownstream direction, the RAM proceeds to elect aPL to serve as a BN, aiming this PL to be located arange of dBN away from the preceding detected BN.For this election, it can consider vehicles that travelin either upstream or downstream direction from theexisting BN, in its vicinity.

3 If the above process results in dBN ≤ dPL, the RAMattempts to elect a PL from another lane to serveas a BN, provided the latter is at a distance that iscloser to the targeted distance of dBN, to ensure thedesired Bnet connectivity throughout the highway. Itis noted that in periods during which the vehicularflow density levels along the lanes are low, it could

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help to elect BNs from PLs that belong to multiplelanes in aiming to synthesize a high capacity con-nected Bnet. If no available PL which satisfies thelatter dBN distance requirement can be detected, PLthat travel along the slow lane are configured to serveas BN.

4 A PL which is elected to serve as a BN advertisesthis role by indicating it within its periodic statusmessage. It is thus detected by all neighborhoodmobiles (including PLs, BNs and other mobiles).

5 The RAM initiates election of a new BN upon depar-ture of a PL which was elected as a BN.

6 A non-BN regularly listens to periodic status messageadvertisements on the CCH. It uses this informationto identify the identity of the BN which it receivesat the highest communications quality; often the onethat is closer to it. It then associates itself with theAnet managed by the latter BN.

6. Network Parameters and Performance Metrics

In the following, we study the performance behavior ofthe network under several Anet and Bnet MAC protocols,determining for each case the best configuration of theplatoons and the best inter-BN distances to be configured.Platoon compositions and velocity parameters that corre-spond to attaining high vehicular flow rates, as determinedby the traffic management models presented in this paper,are incorporated. For configuring the communications net-work to achieve high performance, we aim to synthesize anetwork system that yields a high message throughput rateunder message delay objectives. To characterize the highestachievable delay-aware data throughput capacity rate, wecarry out in the following performance analysis and designevaluations under the assumption that vehicular nodes arehighly loaded by messages that can belong to both Class 1and Class 2 application flows. Hence, we assume that pack-ets of either class are provided packet delay disseminationlimiting values. When analyzing multiple lane scenarios,a selective set of PLs are elected as BNs. As noted above,we select BNs from PLs that travel along the slow lane.Through our simulation and analysis based evaluations,we have found out that parametric results presented for asingle lane can point to performance attained in a multilane environment. However, due to space limitations, wefocus here on performance results for vehicles that travelalong a single lane. The total number of clients servedon the highway is set to N . Each BN serves across itsAnet nclients. We determine the best configuration of eachnetwork scenario by varying the inter-PL and inter-BNdistance levels, and by varying the employed Anet andBnet MAC schemes, and their corresponding transmissiondata rates and reuse (coloring) levels.

To illustrate the performance tradeoffs available to thedesigner under various network configuration options, our

simulations and analysis based performance evaluationsemploy the following parameter ranges. We consider Bnetlink ranges, dBN, to range from 100m to 500m. The num-ber of vehicles N admitted to the single lane system underconsideration is set to range from 100 to 500, representinghighway congestion levels that vary from light to heavyloading conditions. The inter-vehicular distances withina platoon, dV, is varied from dPL/20 to dPL/n, and thetransmit power level Ptx is varied between 23 dBm and33 dBm.

The simulations for IEEE802.11p-related MAC schemesare implemented by using the NS-3 simulator. For TDMA-related MAC schemes, we use analytical models and MAT-LAB based evaluations. For performance comparisons, wefocus our evaluations on setting dBN levels at 100, 150 and300m, noting that the assumed dspan for flow dissemina-tion is assumed to be equal to 300m, and under the laterdBN levels Bnet disseminations traverse an integral num-ber of inter-BN hops. The results however well illustratethe performance trends to be incurred under other settinglevels.

We assume throughout a gap factor of ψ = 2. Wefound out that when using a larger ψ value, for example,setting it to 3, yields generally better or similar networkperformance behavior, for all the MAC schemes that wehave studied. Note however that, as shown above in thetraffic management sections, higher ψ values, which mayhave to be imposed to accommodate trans-lane movements,lead to lower vehicular flow rates.

The signal power received at a node for a given trans-mission by a node that is located at a distance equal to d,denoted as Pr, is calculated by using the communicationspropagation loss model used in [30] :

P0 = Pr,dB(d0) = Pt,dB + 10log( λ2

(4π)2d20

)

Pr,dB(d) ={P0 − 10γ1log10

dd0, if d0 ≤ d ≤ dc

P0 − 10γ2log10ddc− 10γ1log10

dcd0, if d > dc

We aim to maximize the aggregate throughput rate ofdata packets which are transported across the V2V system,THAnet,Bnet, as realized under prescribed packet delay andPDR levels. We calculate this performance metric as fol-lows. A data packet is transmitted across the networkin traversing a path that consists of its source Anet linkfollowed by the Bnet communications links that belong toits dissemination path. Such a packet is said to be suc-cessfully transported across the system if it is successfullyreceived by its flow’s destination BN within the prescribeddelay limit, noting that it will then also be overheard byvehicles that travel along the corresponding path. A flowof such data packets (that are received in a timely fashion,in accordance with the packet delay prescription) is said tobe successfully delivered if the fraction of its successfullyreceived data packets is higher than the specified PDRlevel. We thus aim to maximize the system’s throughputrate of such successful flows.

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BN (3(i-1)+1) BN (3(i-1)+2) BN (3(i-1)+3)Anet (2i)Anet (2i-1)RSV RSV

Tp(Anet)/2 Tp(Anet)/2 Tp(Bnet)Tp(rsv)/2Tp(rsv)/2

Tf

Figure 11: Time Slot Allocation in a DA/TDMA Anet-TDMA BnetTime Frame for Anet coloring kc = 2 and TDMA Bnet reuse-M = 3and i ≥ 1

Under the MAC 1 and MAC 2 schemes, we obtain theTHAnet,Bnet metric by combining performance results thatare obtained by separately analyzing Anet and Bnet op-erations. Assuming that all allocated system bandwidthis allocated for Anet operations (thus isolating its perfor-mance from that induced by Bnet operations), the aggre-gate throughput rate of data packets supported across thesystem Anets is denoted as THAnet,I. Similarly, assumingall system bandwidth to be used for Bnet operations, thecorresponding Bnet throughput is denoted as THBnet,I.

The network parameters and performance metrics thatwe have used are summarized in Table 3.

6.1. DA/TDMA Anet - TDMA Bnet (MAC 1)Under this joint MAC configuration, allocated Anet

and Bnet bandwidth resources are shared on a TDMAbasis. Also, as shown in Figure 11, we assume the overallbandwidth allocated for data communications to be sharedby Anets and the Bnet on a TDMA basis (equivalently,one could use an FDMA configuration to separate Bnetand Anet transmissions). We assume BNs to acquire timesynchronization, as required for the implementation of theunderlying time division schemes. This can be attained byusing the periodic broadcasting of a master clock in beaconsthat are disseminated across the region by using a controlchannel, or by such message beacons that are periodicallytransmitted by road-side or other units. In each time frame,time slots are allocated for data transmissions made byAnet clients and by BN nodes. Also, during Anet periods,time slots are allocated for the transmission of reservationpackets (that are embedded as reservation indicators sentto the BN by a client that transmits the first messageof a new flow during the reservation slot). Thus, eachtime frame Tf consists of a time period allocated to Anetclients to make reservation requests, Tprsv, time allocatedfor Anet transmissions, TpAnet, and time allocated for Bnettransmissions, TpBnet. Hence:

Tf = Tprsv + TpAnet + TpBnet

To reduce interference caused by transmissions that areexecuted simultaneously across neighboring Anets, a kc-coloring MAC scheduling scheme is employed. Hence, dur-ing a TpAnet period, clients in a given Anet can transmitfor a fraction of time that is equal to 1

kcTpAnet, so that

during such a period only 1 out of kc consecutive Anetscan allow Anet transmissions to take place. We have de-termined the proper Anet reuse level kc to use to mitigateinter-Anet interference, in aiming to assure a high PDR

level, PDR ≥ 0.95 while attaining the highest feasiblethroughput level. The coloring value to be properly useddepends on the employed Anet data rate, RAnet. Undersuch higher data rate values, a higher reuse kc level istypically required induced by the higher SINR thresholdrequired at the intended BN receiver.

We allocate within each 1kcTpAnet period, a single Anet

time slot of duration TsAnet = PRAnet

to each one of thenserved actively served Anet clients, where nserved representsthe configured number of active Anet clients that can beserved by a BN during a time frame.

As noted, our throughput analysis has been performedby assuming heavily loaded BNs. The allocation of Anetslots to a newly active application flow is made on a first-come first-serve basis depending on the arrival time ofthe first packet of a stream flow. Hence, the index of thetransmitting Anet client in each Anet is effectively random.Some transmissions may fail due to interference betweenAnets (as they may then to not meet minimum SINR levelrequirement).

The Anet MAC scheme is based on the use of a Demand-assigned (DA) TDMA. The Anet data rate used by Anetclients is denoted as RAnet. Anet clients need to send reser-vation requests if the number of active clients associatedto a BN is higher than than the number of clients whichcan be served in a single time frame. When the number ofactive clients nactive is lower than the nserved, all time slotscan be allocated to these nactive mobiles on a dedicatedbasis. Otherwise, if nactive > nserved, Anet clients mustsend reservation packets to the associated BN. Reserva-tion transmissions use the allocated time periods using thebandwidth allocated to the SCH. Class 2 message flows canemploy a slotted ALOHA MAC to transmit reservationmessages during the reservation slots. In turn, Class 1messages, may use periods allocated within the CCH toassure a very high success rate in sending reservations. Forthis purpose, a high priority access mechanism employed bya priority-based IEEE802.11p protocol can be employed.To well accommodate real-time flows, the BN allocatesavailable data slots for the subsequent transmission of allpackets that are members of the flow for which reservationhas been successfully carried out. The BN may change theallocation of slots to its client mobiles if slots are requiredfor the support of a new high priority flow.

Across the Bnet links, each BN transmits its packetsto its BN neighbor at the RBnet data rate. To mitigateinterference among simultaneously transmitting BNs, andreuse-M level is used. We select the optimal M value,considering the most effective levels, which are establishedto be equal to the set of values M = 3,4,5. It is notedthat for a radio receiver to effectively receive messages ata prescribed bit error rate, for the underlying employedmodulation/coding scheme, one needs to assure the receiverto satisfy minimum SINR and minimum receiver sensitivitylevel requirements.

The throughput rate achieved along the Bnet is calcu-

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Acronym Definition Values Reference

N total number of vehicles served by the BNs along the highway 100–500NPL number of PLs on the highway bL/dPLcdBN distance between BNs along and parallel to the highway 100–500mNBN number of BNs on the highway bL/dBNcR transmission rate used by the Anet RAnet or Bnet RBnet 6, 12 and 24Mbit/s [31]SINRt minimum SINR to receive packets correctly for different R 7, 11 and 20 dB [32, 33]rvt minimum receiver sensitivity for 10MHz channel for different R −85, −82 and −77 dBm [31]P packet length 3024 bit [34]dspan targeted message dissemination span opposite to travel direction 300m [16, 35]Ptx transmission power used by all communicating nodes 23 and 33 dBm [36]Pn noise power −104 dBmM reuse-M factor used for the Bnet using TDMA 3, 4 and 5kc coloring applied on the Anet using TDMA 1, 2, 3 and 4CSt carrier sensing threshold −85 dBm [31, 30]dCS carrier sensing radius assuming single farthest transmission sensed with CSt 346m (Ptx = 23 dBm)γ1, γ2 path gain coefficients used in the used propagation loss model 1.9 and 3.8 [30]d0, dc reference distance and cut-off distance in the used propagation model 10 and 80m [30]THAnet,I aggregate data throughput achieved by Anet clients that transmit their

packets to their associated source BNs when the Anets occupy the completesystem bandwidth (isolated from the Bnet)

THBnet,I aggregate data throughput attained by transmissions across the Bnet fromsource BNs to destination BNs when the Bnet is allocated the completebandwidth of the system (isolated from the Anets)

THAnet,Bnet aggregate data throughput attained by vehicle-to-vehicle packet transmis-sions when the Anets and Bnet operate jointly in sharing the allocatedcommunications bandwidth resources

DP end-to-end communication delay from source node to destination nodeDP,max maximum end-to-end packet delay 50ms [16]PDR packet delivery ratio: fraction of flow packets successfully received at a flow’s

destination BN≥ 0.95

Table 3: Network parameters and performance metrics.

lated as follows:

THBnet,I ={RBnet

NBNME[nhops] , if (1) and (2)

0 , otherwise

The aggregate Bnet-only throughput THBnet,I is computedbased on the assumption that the following conditionsare met under the selected reuse-M and dBN values: (1)SINR > SINRt setting to meet minimum SINR value re-quirements. For example, to meet this requirement underRBnet = 6 Mbit/s, a minimum reuse M = 3 level is re-quired; under RBnet = 12 Mbit/s, a minimum M = 4 valueis required; under RBnet = 24 Mbit/s, a minimum M = 5value must be configured; (2)Pr > rvt is required to satisfyminimum receiver sensitivity value. For example, underRBnet = 6 Mbit/s the latter requirement induces a max-imum transmission range (under the employed transmitpower level of Ptx = 23 dBm) of about 340m, while underRBnet = 24 Mbit/s, the maximum transmission range isreduced to about 220m.

For design purposes, we consider a conservative casewhereby all BNs are assumed to be continuously residingin transmission mode during TpBnet. The SINR level mea-sured at each active BN is then readily calculated, undereach reuse-M level, noting neighboring BNs to be at fixeddistance dBN from each other. The lowest M value thatmeets the minimum SINR level requirement is then se-lected. This configuration assures Bnet transmissions to

be successfully executed under the prescribed packet errorrate.

We coordinate the joint allocation of bandwidth re-sources to the Anet and Bnet components, noting that theBnet must carry the traffic that is fed to it by Anet clients.Hence, the corresponding time periods TpAnet and TpBnetallocated for Anet and Bnet transmissions in each frame,are set to the proper values, such that one guarantees thatthe average rate of data messages successfully received by aBN from its Anet mobile clients, supplemented by the rateof disseminated messages that arrive from its neighboringBNs, is equal to the average rate at which data can beserved by a BN.

Consequently, to maximize the throughput rate of theshared communications media, yielding full utilization ofthe allocated per-frame time periods, the ratio of the cor-responding time period allocations is set to satisfy thefollowing:

TpBnetTpAnet

= E[nhops]RAnetPDRAnetM

RBnetkc

The time allocated for Bnet operation, as it applies fortransmissions carried out by each BN, has been calculatedto include the time taken to serve the traffic arriving fromits Anet and the multi-hop traffic arriving from other BNs.Based on the location of the associated BN relative to itsAnet clients, the data traffic disseminated across the Bnetis distributed by using a number of hops that vary between

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nhops and nhops + 1. In our simulation based analyses, theactual measured average value of the latter variable hasbeen used to allocate the corresponding TpAnet and TpBnetperiods. This average value is denoted as E[nhops]. It isnoted that we compute the time period durations based onaverage loading calculations (including the average numberof Bnet hops incurred by packet belonging to a specific flow).Consequently, the system should employ a flow controlmechanism that serves to block the admission of flows whichinduce overloading. Accordingly, in considering a BN, theabove noted ratio is determined by the time that it requiresto transmit messages across the Bnet (which is proportionalto ME[nhops]

RBnet) vs. the time needed to receive messages

transmitted by its Anet clients (which is proportional tokc

RAnetPDRAnet), where PDRAnet designates the probability

that a packet is successfully transmitted across its sourceAnet, which occurs when it is received at an acceptableSINR level at its targeted BN.

It is noted that active BNs are allocated Bnet slots ona reuse-M basis so that the corresponding data rate and Mvalues are set to assure the BN receiver’s SINR is higherthan the minimum level required to assure a specified errorrate level. In turn, active Anet clients may be located atvarying distances from their associated BN. By properlyselecting the number of colors kc, one can ensure that atleast 95% of the packets will meet the minimum SINR(and thus targeted error rate) requirement.

Assuming the system and access channel to be highlyloaded, so that admitted Anet clients are continuously busy,we deduce the following. The aggregate Anet throughput,when considering its operation to be isolated from that ofthe Bnet in that it is calculated by assuming it to occupythe full bandwidth of the communications system underconsideration, is equal to: THAnet,I = NBN

RAnetPDRAnetkc

.The aggregate Bnet throughput is given by: THBnet,I =NBN

RBnetME[nhops] . The aggregate joint Anet-Bnet throughput

rate is equal to the average data rate received at the desti-nation BNs. It is equal to: THAnet,Bnet = THAnet,I

TpAnetTf

,representing the effective data rate carried by the Anets.

To assess the packet delay components, we note thefollowing. The Frame Latency (FL) delay component repre-sents the time elapsed between the arrival (or production)time of a packet at the source Anet client and the timeinstant at which the mobile transmits its reservation packet.The average FL value is set equal to approximately half ofthe time frame. FL is incurred only at the start time of astream flow. We assume that the first packet piggybacksa reservation request. Subsequent flow packets will notexperience such FL delays, as we assume that the flow isallocated slots at a rate that matches its requested rate.To state an upper bound, we set a worst case frame latencyvalue of FL = Tf.

In calculating the end-to-end delay incurred by pack-ets across the system, we account for the delay incurredin traversing the source Anet, DAnet , and for the delayassociated with the dissemination of the packet across the

Bnet and reception by the destination Anet client, DBnet.For example, consider an Anet client that produces a

stream flow whose application produces packets at a rateof a single packet every rapp time frames. Such a flowrequires the allocation of a single Anet slot every rapp timeframes. The allocation will last for the duration of theflow; say, for nflow packet transmissions. The first packetwill incur reservation delay, including a FL component andfurther delay until its reservation packet is successfullyreceived at its BN (using, for example, a slotted ALOHAreservation access protocol). Once the reservation / firstpacket has been successfully sent, subsequent flow packetsare transmitted at periodically assigned time slots, so thatno further latencies are incurred. Other models, includingsuch that account for random reservation delays, could bereadily included as well. For example, when its Anet is nothighly loaded, the BN can proceed to announce also idleservice slots to be available for the transmission of reserva-tion packets, yielding a much reduced reservation latency.As the traffic loading increases, the BN can announce theassigned reservation slot(s) to be available for reservationsmade by only the highest priority client mobiles, assuringa targeted lower reservation delay level for such messages.Clearly, as a higher capacity is allocated for reservations,a lower residual capacity remains available to support thetransmissions of data packets with reservations. In theperformance analyses presented in the following sections,we however focus on determining an upper bound on thethroughput performance rate attainable under the demand-assigned scheme. Hence, we assume there that just a singlereservation slot per frame is allocated, when the system issubjected to high loading. Furthermore, by implementingthe above described dynamic scheme to allocate reserva-tion slots, by employing flow admission controls, and byassuming flow durations to be of the order that assuresotherwise high probability of success in reservation trans-missions across the allocated reservation slot in each frame,we further assume there that the targeted reservation laten-cies are met. Other analyses are readily carried out whenmaking other reservation process or delay assumptions.

By using the following flow control scheme, we provideadmitted packets with a bounded delay level. All packetsthat reach a BN during a period (of time frame duration)that precedes the Bnet service period (whose duration isequal to TpBnet) and are targeted for transmission acrossthe Bnet, are targeted for service during the current Bnetservice period. Such packets include all packets that arriveacross the Bnet from neighboring BNs and must be for-warded to other BNs, which can be provided higher priorityfor service by the BN. It also includes Anet packets thatare received by the BN from its Anet clients during thepreceding Anet period (whose duration has been set toTpAnet). By using flow admission control for regulatingthe admission of new Anet flows to a BN, using slidingwindow measurements of transit traffic and thus conclud-ing the rate available to support newly admitted flows,one can assure an end-to-end packet delay of the order of

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Dp ≤ FL + Tf ≤ 2Tf (plus the impact of reservation delaywhen applicable, normalized in relation to the average flowduration as only the first packet of the flow incurs suchdelay). In turn, if a higher end-to-end packet delay level isacceptable, a more relaxed access regulation scheme canbe employed.

6.2. TDMA Bnet - IEEE802.11p Anet (MAC 2)In this section, we discuss the performance features of

a system that employs an IEEE802.11p type CSMA/CAMAC scheme for Anet access and a TDMA scheme insharing the Bnet communications resources. Anet clientsthus use a contention based CSMA/CA access scheme. Weassume the carrier sensing range to be set at dCS. A mobilethat is currently in the process of transmitting its message,prevents other mobiles (which may be located in the sameAnet or in neighboring Anets) that are within carrier sens-ing range from it from initiating new transmissions. Toreduce interference signals caused by simultaneous transmis-sions occurring in neighboring Anets, we employ a reuse-kcAnet coloring scheme. Through the execution of systemsimulations, we have determined that the highest achiev-able Anet throughput rate, THAnet,I, is attained whenusing kc = 1 or kc = 2 scheme. Furthermore, the ensuingthroughput rate values were noted to be about the same.At low loading rates, the carrier sensing blocking effect isnoted to be equivalent to a coloring level of approximatelydCS/dBN. At high data traffic rate, the PDR value attainedby using kc = 2 is noted to be about twice higher but thenodes are noted to be able to initiate transmissions onlyhalf the time, resulting in comparable overall throughputrates.

The Bnet is configured to use a properly configuredTDMA based reuse-M and RBnet levels, consideringM = 3,4 and 5. As discussed above, the reuse-M level is configuredto attain sufficiently high SINR and received power, whileachieving high throughput rates.

Anet and Bnet periods share the SCH resources ona time division basis. Thus, each time frame Tf consistsof disjoint time slots and periods allocated for Bnet andAnet transmissions. The durations of Anet and Bnet timeperiods are denoted as TpAnet and TpBnet, respectively.

Corresponding time periods are allocated within eachframe for Anet and Bnet packet transmissions:

Tf = TpAnet + TpBnet

The corresponding periods are sized to accommodate thetransmission of admitted Anet and Bnet packets.

To simplify the implementation of the access scheme,TpAnet and TpBnet are allocated on a fixed ratio basis, whichis calculated based on THAnet,I,max, which represents thehighest attainable Anet throughput rate over the loadingrange under consideration when the Anet system is op-erated separately from the Bnet. The allocation is setto guarantee that the average data rate which is receivedby a source BN from its Anet clients, THAnet,I,max/NBN,

supplemented by the data rate for packets received fromneighboring BNs, is equal to the average rate at which datacan be served (i.e., transmitted) by a BN.

For a given RAnet, as the data load produced by Anetclients is increased, the aggregate Anet throughput rate (inisolation), THAnet,I, increases accordingly until it is mea-sured to reach its maximum value, denoted as THAnet,I,max.This throughput performance is impacted by the stochas-tic occurrence of collisions and re-transmissions across the802.11p-channel. It is further noted that packets that arere-transmitted four times are subsequently discarded andassumed to not be successfully delivered.

Accordingly, we set the ratio of durations of the corre-sponding periods to satisfy the following relationship:

TpBnetTpAnet

= THAnet,I,maxE[nhops]MNBNRBnet

where E[nhops] is defined as stated for the MAC 1 scheme.To evaluate the performance of the MAC 2 scheme, we carryout Monte Carlo simulations for the 802.11p-based Anetsystem in isolation. We set mobiles that belong to distinctAnets to be simultaneously active to account for the impactof inter-Anet interference signals. The performance of theTDMA based Bnet system in isolation is carried out byusing the mathematical expressions presented in SectionSection 6.1. The resulting performance metrics are thenused to calculate the Anet and Bnet per-time-frame periodsTpAnet and TpBnet by using the mathematical relationshipstated above. The realized combined system throughputrate is then given by

THAnet,Bnet = THAnet,ITpAnetTf

.

The data traffic loading each BN consists of data pack-ets received from the Anet mobiles associated with thisBN and of data packets that are received from neighboringBNs. Hence, the aggregate data packet arrival rate at a BNis equal to λBN = E[nhops] THAnet,I

NBN

TpAnetTf

1P (packets/sec).

It is noted that the packet arrival rate at each BN dependson the number of dissemination hops taken by a flow, onthe coloring factor kc and on the employed Anet data rateRAnet, through the realized THAnet,I, and the per-frame pe-riod allocated for Anet transmissions. The service rate pro-vided by a BN , µBN, is expressed as µBN = RBnetTpBnet

M(Tf)1P

(packets / sec). It is noted that µBN depends on the pro-portion of time allocated for Bnet transmissions withinthe time frame, on the reuse-M factor used by the Bnet’sTDMA mechanism, and on the employed Bnet data rateRBnet.

The time delay incurred by a packet across its sourceAnet, denoted as DAnet, is measured from the instant of itsgeneration to the instant that it is successfully transmittedto the associated BN (given that its flow has been admitted)is obtained through the execution of Monte Carlo simula-tions. The delay time incurred by a packet at each BN,denoted as dBN, has been approximated by using analyticalcalculations.

21

The waiting time performance incurred by a packetat each BN, denoted as WBN, has been evaluated by us-ing the following approximations. Within its Bnet per-frame service period, each BN forwards an average fraction(E[nhops]− 1)/E[nhops] of the packets that it receives fromanother BN. A fraction of the packets that it handles whichis equal to 1/E[nhops] is received from its Anet clients. Thesame fraction also represents the rate of packets that ittransmits to the destination mobiles which reside withinits Anet.

To reduce packet latency levels while being disseminatedacross the Bnet, we capitalize on the TDMA structure ofthe Bnet MAC, by employing a priority oriented servicepolicy at the BN. The BN queueing processor grants higherservice priority to transit packets (which arrive from otherBNs), while serving packets that it receives directly fromits Anet at a lower priority level. Assuming a fixed dis-semination span setting for all packet flows, packets that aBN receives from its neighboring nodes for relay to otherbackbone nodes are perfectly scheduled for transmissionby the BN in accordance with the setting of the reuse-MTDMA operation cross the backbone, and thus do not incurqueueing delays at intermediate backbone nodes. In turn,packets received by a BN from its Anet clients arrive to theBN in a stochastic manner, which is impacted by the ran-dom times at which successful transmissions occur acrossthe IEEE802.11p based channel sharing scheme, and henceincurred queueing delays at their source backbone node.To analyze this priority queueing system, we carry out anapproximate calculation of the queueing (i.e., waiting time)delays incurred by second priority Anet packets at a BN bymodeling the associated BN queueing system that is usedto serve the latter Anet packets as the following modifiedM/M/1 queueing system, whereby the service rate of lowerpriority packets is reduced by that dedicated for servicinghigher priority packets. Consequently, the cumulative dis-tribution function of the Anet packet waiting time (W) ata BN, denoted as Wx = P (W ≤ x), is given by:

Wx = 1− ρBN,se−µBN,s(1−ρBN,s)x

where µBN,s and ρBN,s denote the service rate grantedto and traffic intensity offered by lower priority packets,respectively, arriving from Anet sources at their source BNs.We have: µBN,s = 1

E[nhops]µBN and ρBN,s = λBN/E[nhops]µBN/E[nhops] .

In carrying out the system’s performance evaluations,we use the above noted expression for the packet waitingtime distribution at the source BN to determine the al-lowable corresponding queueing delay incurred at the BN,which in conjunction with the delay incurred by a packetin traversing the source Anet, and in conjunction with theinvolved fixed latencies involved in disseminating a packetacross its path, leads to the calculation of the end-to-endpacket delay value.

The targeted end-to-end packet delay, DP, is then cal-culated as the sum of the FL delay, the (DAnet) delay timeincurred across the source Anet, the waiting time incurred

at the queue of the source BN, and the total transmissiontime experienced in disseminating the packet across theBnet, which is equal to E[nhops] P

RBnet, yielding:

DP = FL +DAnet +DBnet

In our simulations runs, we examine varying THAnet,I lev-els. For each iteration, we calculate the 95-percentile delayincurred by a packet traversing its Bnet path, using thepriority queueing based analysis presented above. By sub-tracting this delay value from the specified end-to-end level,we obtain a value that is used as the targeted 95-percentiledelay to be incurred by packets transmitted across theirsource Anet. It is noted that, as an approximation, whenAnet and Bnet packet delays are assumed to behave asindependent random variables, the attained aggregate end-to-end packet delay value is then guaranteed to hold for atleast 90-percent of the packets.

For illustrative purpose, we assume to size one timeframe Tf to accommodate M Bnet slots. This serves toreduce the FL induced latency component.

As noted above for the previously considered MACscheme, a flow control scheme is enacted to ensure thattraffic with higher than average number of hops is admittedonly if under current conditions it can be served whileincurring acceptable packet delay levels.

6.3. IEEE 802.11p Anet and IEEE802.11p Bnet (MAC 3)The third MAC scheme examined assumes the use of

an IEEE802.11p protocol in sharing the Anet channels andthe Bnet links. We assume that the employed bandwidthis shared by all BNs and non-BNs (clients) on an IEEE802.11p contention access basis. An entity that wishes togain access to the channel for the successful transmissionof its packet must contend with currently active nodes thatreside within a range of dCS. Active nodes that are locatedfurther away may also cause interference at the intendedreceiver when the total interference energy detected at thereceiver is sufficiently high. To simplify our evaluation,we assume that the same transmission data rates are usedacross each Anet and the Bnet.

7. Performance Behavior of the Data Network Sys-tems

7.1. DA/TDMA Anet - TDMA Bnet (MAC 1)In this section, we discuss the performance behavior

of the DA/TDMA-Anet / TDMA-Bnet scheme. First, wedisplay the performance of a sole Anet system, showing thedependence of the attained throughput THAnet,I on thedata rate RAnet and kc reuse factor. In Figure 12, we showthe corresponding performance, assuming the followingparameter values: N = 250, l = 5 km, ψ = 2 with dBN =100, 150 and 300m, inducing the respective number ofclients served per BN to be nserved = 5, 7 and 15.

The performance evaluations carried out to obtainTHAnet,I are based on running a Matlab-based Monte Carlo

22

100 120 140 160 180 200 220 240 260 280 300

d(BN) (meter)

0

0.5

1

1.5

2

2.5

3

3.5

4

TH

(An

et)

(B

ps)

108 TH(Anet)(Bps) vs d(BN) (m) using TDMA

R(Anet)=6 Mbps,kc= 2

R(Anet)=12 Mbps,kc= 2

R(Anet)=24 Mbps,kc=3

Figure 12: Aggregate Anet Throughput THAnet,I (bit/s) vs dBN (m)using DA/TDMA, l = 5 km

simulation. The simulation has been used to determine thepower of the interference signal induced by simultaneousuplink transmissions executed by multiple Anet clients. Itis noted that Anet and Bnet transmission operations arecarried out over separate time periods. Simultaneous trans-missions by multiple vehicles are performed in accordancewith the employed reuse-kc scheme. The kc reuse level isselected to assure a PDR ≥ 0.95 level.

The higher the RAnet value, the higher one must set thenumber of Anet kc, as a higher RAnet value requires thereceiver to detect a higher minimum SINR value, notingthat a higher Anet coloring value leads to better mitiga-tion of inter-Anet interference. For example, specifying aPDR ≥ 0.95, and setting RAnet = 24 Mbit/s, induces anoptimum setting (yielding the highest Anet throughput) ofkc = 3 colors. The latter value is higher than that requiredwhen a lower data rate RAnet = 6 Mbit/s is used, whichrequires setting kc = 2 colors. As shown in Figure 12, thethroughput THAnet,I is reduced as the inter-BN range dBNincreases. This main cause for this drop is attributed tothe ensuing decrease in the number of elected BNs, whichin turn reduces the total Anet and Bnet communicationscapacity that is made available since each BN makes use ofits own associated transmission channel resources. Undera set dBN value, the attained THAnet,I increases as theemployed data rate RAnet increases. Yet, this increase ismoderated by the need to use a higher number of Anetcolors.

The behavior of the attained Bnet only throughput rateas a function of the inter-BN distance is depicted by Fig-ure 13. The corresponding performance results have beenobtained by using the mathematical formula presented insubsection 6.1. For inter-BN ranges of dBN = 100 m anddBN = 150 m, the highest throughput is obtained by set-ting RBnet = 24 Mbit/s, M = 5. In turn, when configuringdBN = 300 m, the highest throughput is obtained by set-ting RBnet = 6 Mbit/s, M = 3. We further note that thecorresponding curves for the throughput rate exhibit someperformance fluctuations as the inter-Bnet range increase.

100 150 200 250 300 350 400

d(BN)(meter)

0

1

2

3

4

5

6

7

8

TH

(Bn

et)

(Bp

s)

107 TH(Bnet) vs d(BN) (m), P(tx) = 23 dBm using TDMA

R(Bnet) = 6 Mbps, M = 3

R(Bnet) = 12 Mbps, M = 3

R(Bnet) = 24 Mbps, M = 3

R(Bnet) = 6 Mbps, M = 4

R(Bnet) = 12 Mbps, M = 4

R(Bnet) = 24 Mbps, M = 4

R(Bnet) = 6 Mbps, M = 5

R(Bnet) = 12 Mbps, M = 5

R(Bnet) = 24 Mbps, M = 5

Figure 13: THBnet,I (bit/s) vs dBN (m) for Ptx = 23 dBm

These variations are explained by noting that as the inter-BN distance is raised, the number of dissemination hopstraversed across the Bnet may decrease (showing quantiza-tion oriented fluctuations since the later number assumesintegral values), serving to reduce the internal traffic pro-duced across the Bnet links. In turn, the correspondingnumber of employed BNs also decreases causing reductionin the total bandwidth made available to support Bnettransmissions.

We also note that as the dBN range is further increased,say to 300m, RBnet must be decreased to 6Mbit/s. Thisis caused by the ensuing decrease in the received powerlevel, as the minimum received power level may then bereduced below the required receiver sensitivity value. Toresolve such an issue, we have studied a system that uses anincreased transmission power level, setting Ptx = 33 dBm.We observed the increased transmit power system to notlead to increased throughput performance when settingshorter inter-BN range distances, such as dBN = 100 mor 150m. However, an increase in the transmit powerwas noted by us to yield a significant throughput upgradeunder longer inter-BN distances such as dBN = 300 m,under RBnet = 24 Mbit/s, as it enabled the system tothen meet the required minimum receiver sensitivity level.Also, using higher transmit power levels could also beuseful when considering longer dissemination spans thanthose considered in our illustrative scenarios in this paper.It is also noted that by increasing the Bnet data ratefrom 6Mbit/s to 24Mbit/s, the resulting throughput rateincreases in a less than proportional manner due to theensuing increase in the reuse-M level.

In Figure 14, we show the variation of the optimum end-to-end combined Anet-Bnet throughput rate, THAnet,Bnet,as a function of the configured inter-BN distance value.The performance of this system was obtained by usinga hybrid simulation and analytical process, as expressedby the mathematical formula for THAnet,Bnet presented insubsection 6.1. We specify the targeted PDR to be higherthan 0.95, and the packet delay to be lower than 50ms for95% of the served packets. The highest throughput rate is

23

50 100 150 200 250 300 350

d(BN) (meter)

0

1

2

3

4

5

6

TH

(An

et,

Bn

et)

(Bp

s)

107 TH(Anet,Bnet)(Bps) vs d(BN)(m) using DA/TDMA Anet- TDMA Bnet

R(Anet)=6 Mbps,kc=2: d(BN)=100m,150m,R(Bnet)=24Mbps,M=5;d(BN)=300m,R(Bnet)=6Mbps,M=3

R(Anet)=12 Mbps,kc=2:d(BN)=100m,150m,R(Bnet)=24Mbps,M=5;d(BN)=300m,R(Bnet)=6Mbps,M=3

R(Anet)=24Mbps,kc=3:d(BN)=100m,150m,R(Bnet)=24Mbps,M=5;d(BN)=300m,R(Bnet)=6Mbps,M=3

Figure 14: THAnet,Bnet(bit/s) vs dBN (m) using DA/TDMA-TDMAwith P ≥ 0.95 (DP ≤ 50 ms) and PDR ≥ 0.95

50 100 150 200 250 300 350

d(BN) (meter)

1.5

2

2.5

3

3.5

4

4.5

5

5.5

TH

(An

et)

(B

ps)

107 TH(Anet)(Bps) vs d(BN)(m) using 802.11p

R(Anet)=6 Mbps,kc=1

R(Anet)=12 Mbps,kc=1

R(Anet)=24 Mbps,kc=1

Figure 15: Maximum delay-capped THAnet,I (bit/s) and PDR ≥ 0.95vs dBN (m) for IEEE802.11p Anet

noted to be attained when setting RAnet = 24 Mbit/s, kc =3, RBnet = 24 Mbit/s, M = 5 at dBN = 100 m. As dBNincreases from 100m to 150m, the system’s throughput rateTHAnet,Bnet is reduced due mainly to the lower number ofemployed BNs. As dBN is further increased to 300m, a moresignificant throughput drop is observed, as now a lowerRBnet = 6 Mbit/s must be used to meet the minimum SINRrequirement at the targeted receiver. We have examinedsetting different Anet and Bnet data rates to determine thehighest achievable system throughput rate. We note thatfor dBN = 100 m, setting RAnet = 6 Mbit/s yields a lowerthroughput rate than that achieved when setting RAnet =24 Mbit/s; yet, the difference is low as the performancewhen using RAnet = 6 Mbit/s is then dominated by thesetting of the same Bnet parameters, RBnet = 24 Mbit/s,M = 5 and a lower Anet-coloring value kc = 2.

7.2. TDMA Bnet and IEEE802.11p Anet (MAC 2)The attained maximum THAnet,I as calculated without

imposing a packet delay limit is used to calculate theAnet and Bnet time periods configured within each frame.We have noted RAnet = 24 Mbit/s corresponding attained

optimum throughput values of 69.8Mbit/s at dBN = 100 m,66.7Mbit/s at dBN = 150 m, 53Mbit/s at dBN = 300 m.

The delay constrained throughput performance is shownin Figure 15. Results have been obtained through the exe-cution of a NS-3 based simulation coupled with analyticalevaluations. Under the Monte-Carlo simulation process, theAnet client packet generation rate is gradually increased,leading to variations in the resulting Anet throughput rate.The latter induces the packet arrival rate levels loading thesource BN, as generated by the BN’s Anet clients, fromwhich the traffic intensity parameter ρBN,s presented in Sec-tion 6.2 is analytically calculated. It is subsequently usedto compute the ensuing end-to-end packet delay DP level.The realized Anet throughput rate under which the pre-scribed delay DP level is satisfied represents the THAnet,Ivalue shown in Figure 15. As expected, for this case, alower than THAnet,I,max Anet throughput rate THAnet,I isrealized when aiming to satisfy delay constraint.

The Bnet throughput THBnet,I performance behaviorfor this scheme is the same as that exhibited above underthe DA/TDMA Anet - TDMA Bnet scheme, shown inFigure 13. We have studied the setting of possibly differentAnet and Bnet data rate values in aiming to achieve thehighest system throughput rate.

The maximum delay-capped THAnet,I value achiev-able for RAnet = 24 Mbit/s is equal to about 54Mbit/sat dBN = 150 m. As dBN increases from 100m to 150m,the attainable THAnet,I increases because the dissemina-tion span involves a lower number of Bnet hops, inducinglower backbone data loading rate. The lower number ofincurred Bnet hops used across the path of a flow furtherinduces lower per-BN packet delay requirement. This con-sequently allows Anets to carry higher traffic loads. AsdBN further increases to 300m, attainable THAnet,I valuesdecrease. At this distance range, a lower data rate acrossthe Bnet must be used by setting RBnet = 6 Mbit/s, thusreducing the effective service rate available at a BN for thetransmission of arriving packets, causing the waiting timeincurred at source BN to increase. Consequently, to meetpacket delay prescriptions, the rate of packet flows arrivingto a BN from its Anet mobiles must be lowered, result-ing in a lower THAnet,I level. Such mobile transmissionsalso tend then to become more sensitive to signal inter-ference caused by simultaneously occurring transmissionsthat take place outside the underlying carrier sensing rangedCS, which is equal to about 346m when Ptx = 23 dBm.As dBN increases, the average ratio of the power receivedfrom an intended transmitter to the power received fromsignal interference decreases.

The THAnet,Bnet performance behavior is shown in Fig-ure 16. These results have been obtained by using themathematical formula presented in Section 6.2, which in-cludes parameters evaluated by using the NS-3 based sim-ulation process for the Anet system coupled with the useof analytical calculations for the Bnet system.

As shown in Figure 16, the setting of dBN = 150 m,RAnet = 24 Mbit/s, RBnet = 24 Mbit/s, M = 5, yields

24

50 100 150 200 250 300 350

d(BN)(meter)

0

0.5

1

1.5

2

2.5

3

TH

(Anet,B

net)

(Bps)

107 TH(Anet,Bnet)(Bps) vs d(BN)(m) using 802.11p Anet-TDMA Bnet

R(Anet)=6 Mbps,kc=1: d(BN)=100m,150m,R(Bnet)=24Mbps,M=5;d(BN)=300m,R(Bnet)=6Mbps,M=3

R(Anet)=12 Mbps,kc=1:d(BN)=100m,150m,R(Bnet)=24Mbps,M=5;d(BN)=300m,R(Bnet)=6Mbps,M=3

R(Anet)=24Mbps,kc=1:d(BN)=100m,150m,R(Bnet)=24Mbps,M=5;d(BN)=300m,R(Bnet)=6Mbps,M=3

Figure 16: Delay-capped THAnet,Bnet (bit/s) vs dBN (m) for IEEE802.11p Anet-TDMA Bnet

the highest delay-capped system throughput THAnet,Bnetvalue. It is followed by the setting of dBN = 100 m andthen dBN = 300 m. The THAnet,Bnet value is not sensitiveto changes in dBN when dBN is small; however, a significantdrop is noticed at longer dBN ranges such as 300m. TheTHAnet,Bnet values are insensitive when increasing dBNslightly from dBN = 100 m to dBN = 150 m as inducing aslight THAnet,Bnet increase from 25Mbit/s to 25.3Mbit/s,although the number of hops is reduced by 1. To explain,we note that low dBN values such as 100m and 150m arewithin the dCS range, resulting in an overall lower numberof packet collisions. A key factor impacting the attainedend-to-end delay level is noted to be contributed by thewaiting time value incurred at the queueing module of thesource BN, rather than the delay level incurred across theAnet channel, DAnet, and the latency values associatedwith transmissions across Bnet links. When compared toa configuration that uses dBN = 100 m, as we increase thelatter to dBN = 150 m, the rate of transit packet flowsloading a BN queueing system is noted to be reduced.Hence, packets arriving to a BN from its Anet are servedat a higher rate. Consequently, the waiting time incurredby such packets at their source BN is improved. Thethroughput rate supported across each source Anet canconsequently be allowed to increase (as packets can thenbe allowed to incur higher Anet delays). However, sincethe number of Anets is reduced, the attained aggregatethroughput rate is not expected to change in a noticeablemanner, as confirmed by the depicted simulation results.

Hence, the net delay gain achieved by the reducednumber of hops is not significant, while just a slight increasein the THAnet,I level is attained. Furthermore, as dBN isincreased from 100m to 150m, the SINR level recordedat a BN receiver may be reduced as the receiver may belocated closer to sources of signal interference that arelocated outside dCS. We also note that at higher inter-BN

50 100 150 200 250 300 350

d(BN)(meter)

0

2

4

6

8

10

12

TH

(An

et,

Bn

et)

(Bp

s)

106 802.11p Anet - 802.11p Bnet TH(Anet,Bnet)(Bps) vs d(BN)(m)

R(Anet)=R(Bnet)=6 Mbps

R(Anet)=R(Bnet)=12 Mbps

R(Anet)=R(Bnet)=24Mbps

Figure 17: THAnet,Bnet (bit/s) vs dBN (m) at various R (Mbit/s)P ≥ 0.95 (DP ≤ 50 ms), PDR ≥ 0.95 for IEEE802.11p Anet -IEEE802.11p Bnet

ranges, each Anet would need to support a higher numberof client mobiles, which can lead to higher collision rates. Inaddition, since a lower number of BNs are then employed,an overall lower Anet bandwidth level is available. Theperformance results depicted in Figure 16 show that thesetting of RAnet = 24 Mbit/s leads to a higher delay-cappedsystem throughput THAnet,Bnet values.

A significant drop in the throughput rate is observedas dBN increases from 150m to 300m under RAnet =24 Mbit/s. This behavior is mainly due to the lower valueexhibited by the maximum throughput rate attainable bydata packets that flow from Anet vehicles to their sourceBNs THAnet,I, under the specified packet delay constraint,as shown in Figure 15, induced by the low Bnet data rate,RBnet, needed to satisfy the SINR value required underthe long dBN level. Furthermore, dBN = 300 m is notedto be close to the carrier sensing range, dCS. Coupledwith the observation that the SINR is then very low and isquite close to the minimum SINR required for operation atRAnet = 24 Mbit/s, which induces a low PDR value. TheTHAnet,Bnet value attained when no packet delay limits areimposed, has been determined by us (not shown) to be 15–35% higher than the corresponding allowed delay-cappedthroughput rate.

Our performance evaluation results show that the at-tained throughput values are insensitive to variations inthe number nclients of Anet clients. A higher number ofsuch clients does not lead to a significant increase in thepacket collision rate, due in a large extent to the impactof the carrier sensing range. We have also noted that themaximum delay-capped THAnet,I throughput level doesnot vary much as the number of Anet clients increases,provided the overall loading rate per Anet is controlled(so that the maximal allowed total offered loading rate isregulated).

7.3. IEEE 802.11p Bnet and IEEE802.11p Anet (MAC 3)In Figure 17, we show the variation of the system

throughput rate under IEEE802.11p Anet and Bnet MAC

25

schemes vs. the setting of the inter-BN range. These per-formance results have been obtained by performing MonteCarlo NS-3 based simulations. We focus on the performancerealized by Class 2 messages, assuming Class 1 messagedelay and PDR requirements to have been met, assuminga single lane loading of N = 250. The setting of a (Anetand Bnet) data rate R = 24 Mbit/s and dBN = 100 m isshown to yield the maximum throughput rate. Under thishigh data rate, the throughput rate experiences significantdegradation as the the inter-BN distance is increased. Inturn, we observe the attained throughput values underR = 12 Mbit/s to be less sensitive to changes in dBN due toits lower minimum SINR requirement. As dBN is increasedfrom 150m to 300m at RBnet = 24 Mbit/s, the steep dropin data throughput is caused by the increased sensitivityto signal interference measured at the BN receiver due to ahigher minimum SINR requirement, and by the low valueof the received power value of the intended signal used fordata transmissions between BNs.

The observed performance result is insensitive to varia-tion of dBN from 100m to 150m level, for RAnet = RBnet =6 and 12Mbit/s. Although the average number of hopsdecreases from 3.5 hops to 2.5 hops, longer dBN valuemeans that an intended transmission by some Anet clientswill become more sensitive to signal interference causedby simultaneous transmissions occurring due to sourcesoutside the dCS range while also a reduced overall Anetbandwidth is available due to the reduced number of BNsconfigured along the highway. Hence, the gain in through-put attributed to the employed smaller number of hopsis partially offset by the lower SINR levels that can beinduced.

7.4. Comparison between the MAC schemesIn Figure 18, we compare the delay-capped highest

aggregate system throughput rate performance attainedunder the three MAC schemes under consideration. Wenote that the TDMA Anet-TDMA Bnet scheme yieldsthe highest throughput rate (which is equal to about58.5Mbit/s). It is followed by the Bnet TDMA-Anet/IEEE802.11p scheme, achieving a corresponding throughputrate of about 25.6Mbit/s, and then by the joint-IEEE802.11p scheme, which produces a throughput rate of about12Mbit/s). The highest throughput rate is attained by theTDMA-Anet/TDMA-Bnet scheme when configured to op-erate at RAnet = 24 Mbit/s, RBnet = 24 Mbit/s, M = 5,dBN = 100 m, kc = 3. When examining the sensitivityof the highest attainable throughput rate to variation inthe setting of the inter-BN distance, we conclude that theTDMA/TDMA scheme generally yields the best perfor-mance when dBN is set to a value that is lower than 300m.

By examining the delay capped throughput perfor-mance results exhibited in Figures 14, 16 and 17, we notethe following. The highest throughput rate is attainedby setting dBN = 100 m, RAnet = 24 Mbit/s, RBnet =24 Mbit/s, M = 5, kc = 3, nactive ≤ 10, yielding a through-put rate that is equal to 58.5Mbit/s, under the Anet

Optimum TH(Anet,Bnet) with Delay Constraint and PDR >= 0.95

A1: R(Anet)= 6 Mbps A2: R(Anet)= 12 Mbps A3: R(Anet)= 24 Mbps0

1

2

3

4

5

6

7

TH

(Anet,B

net)

(Bps)

107

MAC 3: 802.11p Anet-802.11p Bnet

MAC 2: 802.11p Anet-TDMA Bnet

MAC 1: DA/TDMA Anet-TDMA Bnet

Figure 18: Comparison Delay-capped THAnet,Bnet (Mbit/s) vs RAnet(Mbit/s) for N = 250. Bar explanation: (A1/A2/A3,MAC3):RAnet =RBnet; (A1/A2/A3,MAC2): RBnet = 24 Mbit/s, M = 5, dBN =100 m; (A1/A2,MAC1): kc = 2, RBnet = 24 Mbit/s, M = 5, dBN =100 m; (A3,MAC1): kc = 3, RBnet = 24 Mbit/s, M = 5, dBN =100 m

TDMA - Bnet TDMA scheme. The next highest through-put when using the Anet TDMA - Bnet TDMA scheme,yielding THAnet,Bnet = 50.9 Mbit/s, is attained by set-ting dBN = 150 m, in using the following configuration:RAnet = 24 Mbit/s, RBnet = 24 Mbit/s, M = 5, kc = 3,nactive ≤ 13. The third highest attainable throughputrate is THAnet,Bnet = 25 Mbit/s, achieved by using the802.11p Anet/TDMA Bnet scheme and setting dBN =100 m, RAnet = 24 Mbit/s, RBnet = 24 Mbit/s, M = 5.

8. Integrated System Design when joining TrafficManagement and Networking Performance Ob-jectives

The results presented by us in the traffic managementsections point to the configuration options available to thedesigner when aiming to achieve high vehicular flow ratesunder vehicular delay limits. The results presented in thenetworking sections identify the dependence of the systemmessage communications throughput rate on the systemtraffic parameter settings. In this section, we demonstratehow these results are combined to deduce an integratedsystems design that jointly meet prescribed vehicular andmessage communications performance objectives. For il-lustrative purposes, we consider a single lane single linkhighway segment of length l = 5 km, as illustrated in Fig-ures 2 to 4. As shown by us in the traffic managementpart, for n = 2 to n = 10, one should set the optimal speedto about 60 km/h. The corresponding vehicular flow ratethat results varies from 1800 veh/h to 3250 veh/h, and thedPL ranges vary from 60m to 175m. The optimal speedshould be configured to about 60 km/h when n = 10 anddPL = 175 m, yielding a vehicular throughput (flow rate)of 3250 veh/h.

26

2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200

Vehicular Throughput (Veh/h)

30

35

40

45

50

55

60

65

70

75

80

Da

ta T

hro

ug

hp

ut

(Mb

ps)

n=3,d(PL)=75 m,v=60.8km/h,d(BN)=75 m

n=5,d(PL)=100 m,v=60 km/h,d(BN)=100 m

n=6,d(PL)=125 m,v=59.9 km/h,d(BN)=125 m

n=8,d(PL)=150 m,v=59.7km/h,d(BN)=150 m

n=10,d(PL)=175 m,v=59.5km/h,d(BN)=175 m

TDMA-TDMA R(Anet)=24 Mbps,kc=3,R(Bnet)=24 Mbps,M=5

TDMA-TDMA R(Anet)=12 Mbps,kc=2,R(Bnet)=24 Mbps,M=5

TDMA-TDMA R(Anet)=6 Mbps,kc=2,R(Bnet)=24 Mbps,M=5

Figure 19: Data Throughput (Mbit/s) vs Vehicular Throughput(veh/h)

To illustrate, assume the system manager aims to config-ure a system that achieves a vehicular throughput flow ratelevel that assumes values in a range which includes a high3000 veh/h level and a medium 2000 veh/h level. At thesame time, the system manager aims to attain a sufficientlyhigh delay-capped packet communications throughput per-formance. This throughput performance accounts for thedissemination of Class 2 messages, assuming that Class 1messages are guaranteed a high PDR, PDR ≥ 0.95, and anaverage delay that is not higher than 50ms for successfullydisseminated messages.

In Figure 19, we illustrate the tradeoffs available to thesystem designer as we observe that different design settingsyield different combined (vehicular throughput, communi-cations throughput) operational points. The performancepoints identified in the figure as we traverse each designcombination (involving specified system parameters) byvarying the dPL values over the set 75, 100, 125, 150 and175m. For each case, we use the parameter values attainedas the result of the optimization process carried out for theDA/TDMA Anet-TDMA Bnet scheme.

To illustrate the underlying performance tradeoff avail-able to the designer, assume first that it is of interestto achieve a high vehicular throughput level, equal to3000 veh/h. It is attained, as shown in Figure 3, by set-ting dPL = 150 m and n = 8. Under these parametersetting, we aim to maximize the data communicationsthroughput performance in disseminating Class 2 messageflows, while also meeting Class 1 message communica-tions requirement. Assuming that dBN is selected fromthe set 100, 150 and 300m, we determine that the val-ues dBN = 150 m and dBN = 300 m are feasible as theysatisfy dBN ≥ dPL. Accordingly, we determine the set-ting dBN = 150 m, RAnet = 24 Mbit/s, RBnet = 24 Mbit/s,M = 5, kc = 3 to yield a higher data throughput per-formance (when compared with the setting dBN = 300 m,RAnet = 24 Mbit/s, RBnet = 6 Mbit/s, M = 3, kc = 3). Inconsequently configuring dBN = dPL = 150 m, the commu-nication networking function requires a setting for which we

have nserved ≤ 13 in considering Anet clients which becomeactive. Hence, all the platoon members can be served by theBN. The joint traffic management and networking parame-ters are therefore set as follows. The traffic managementparameters are set to: dPL = 150 m (v = 60 km/h) andn = 8. The optimal networking configuration synthesizedto achieve a high vehicular throughput implements the fol-lowing settings: dBN = 150 m, RAnet = 24 Mbit/s, RBnet =24 Mbit/s, M = 5, yielding an aggregate system through-put rate that is equal to THAnet,Bnet = 50.9 Mbit/s.

In turn, when the system designer is willing to ac-commodate a medium vehicular throughput rate level ofabout 2750 veh/h, achievable by setting n = 5 at the op-timal velocity level of v = 60 km/h and a dPL range setto 100m. The dBN level should be selected such that thenetworking configuration satisfies dBN ≥ dPL = 100 m (e.g.dBN = 100 m, or 300m). Considering the latter values,we have determined that setting a TDMA/TDMA schemeat dBN = 100 m, RAnet = 24 Mbit/s, RBnet = 24 Mbit/s,M = 5, kc = 3, nclient ≤ 10, would achieve (among the op-tions considered in our analyses) the highest data through-put rate, yielding THAnet,Bnet = 58.5 Mbit/s. Hence, thejoint traffic management and data networking parametersare set as follows. The selected traffic management re-lated parameters are: dPL = 100 m (v = 60 km/h) andn = 5. The related data networking parameters, selectedto realize the networking configuration which achieves ahigh vehicular throughput rate, are given as: dBN = 100 m,RAnet = 24 Mbit/s, RBnet = 24 Mbit/s, M = 5. This set-ting yields an aggregate data communications throughputrate that is equal to THAnet,Bnet = 58.5 Mbit/s. As welldemonstrated by these design settings, as we lower the tar-geted vehicular throughput requirement from 3000 veh/hto 2750 veh/h, we are able to increase the data commu-nications network throughput rate from THAnet,Bnet =50.9 Mbit/s to THAnet,Bnet = 58.5 Mbit/s.

It is interesting to note that as dPL is increased from100m to 125m, the attained vehicular throughput is in-creased to 2850 veh/h, while the data communicationsthroughput rate is reduced to 35Mbit/s. However, if dPL isfurther increased to 150m, the system can sustain a highervehicular throughput rate, of about 3000 veh/h, while alsorealizing a higher data throughput rate, which is equal to42Mbit/s. The joint performance is thus uniformly bet-ter than that attained when setting dPL = 125 m. Theoccurrence of such a performance spike has been explainedwhen discussed in connection with the performance trendsexhibited in discussing the results shown in Figures 12and 13.

9. Conclusions

We develop and study in this paper traffic managementand data networking mechanisms for an autonomous trans-portation system. Access and exit ramps are attached toeach segment of the highway. A ramp manager is used toregulate the admission of vehicles into the highway and

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their routing to designated lanes. Vehicles moving acrossa lane are organized into platoons. A platoon leader iselected in each platoon and is used to manage, coordinateand synchronize platoon members. The time spent bya vehicle in the system consists of its queueing delay atthe ramp supplemented by, once admitted, its transit timeacross the highway until it reaches its destination exit ramp.We determine the structural formations of platoons in eachlane, including their size and vehicular speeds, jointly withthe setting of the parameters for the employed admissioncontrol and routing, in aiming to maximize the realizedflow rate, which represents the vehicular throughput rateacross the highway, under vehicular end-to-end delay con-straint. The latter sets a limit on the vehicular on-rampqueueing delay and on the (per unit distance) transit timeincurred along the highway. We also account for the theneed to keep sufficient gaps between platoons to accommo-date the transition and merging of vehicles moving acrosslanes, and entering and exiting the highway. Accordingly,wider gaps are provided across the slower lanes. Fasterlanes are employed for supporting the mobility of vehiclesthat travel longer distances towards their destinations. Weshow that the vehicular flow rate across the highway canbe maximized, under delay prescriptions, by the setting ofproper platoon formations, vehicular per-lane speeds andadmission regulations.

Using such platoon based formations, we develop a V2Vwireless data communications networking protocol that isused to disseminate messages produced by highway vehiclesand sent to other vehicles that travel within a specifiedrange from the source vehicle. The hierarchical networkingscheme that is presented and studied employs algorithmsthat are used to dynamically synthesize a mobile BackboneNetwork (Bnet). The latter consists of interconnected pla-toon leaders that are elected to serve as Backbone Nodes(BNs). Each BN serves as an access point for its AccessNetwork (Anet) mobile clients. We study the performancebehavior of the network system and determine the opti-mal cross-layer setting of its parameters, assuming bothTDMA and IEEE802.11p oriented wireless channel sharing(MAC) schemes for the Anet and Bnet subsystems. Packetdelay limits are also imposed. Integrating our mechanismsand schemes developed for the traffic management andfor the data networking planes, we demonstrate the per-formance tradeoffs available to the system designer andto the transportation manager when aiming to assure atransportation system operation that achieves targeted ve-hicular flow rates and transit delays while also configuringthe data communications network system to meet targetedmessage throughput and delay objectives. For example, wenote that often the use of relatively short distances betweenelected BNs, jointly with the employment of higher trans-mission data rates across the Bnet and Anet systems, leadsto higher delay-capped data throughput rates. In turn, toachieve a high vehicular flow rate, it is often necessary tostructure platoons to have wider spans (inducing longerinter-BN ranges). Consequently, the system manager must

select an operating point that is based on a compromisein terms of the ability to meet individual targeted trafficmanagement and data networking performance metrics.

The approaches and mechanisms developed in this papercan also be applied to the design of autonomous highwaytransportation systems whereby platoon formations are notimposed. For the hierarchical networking protocol, we notethat the election of BNs, when coordinated by a regionalmanager or by a fully distributed algorithm, can be per-formed in a manner that does not depend on the existenceof platoon formations and their identified and announcedleaders. BNs (and consequently Anets and the Bnet) canbe elected to meet advantageous access and coverage re-quirements, following analyses similar to those performedin this paper, when not aided by the existence of platoonformations. Also, BNs can be elected, fully or partially,from a set of stationary Roadside Units (RSUs), or accesspoints, when they are (or become) available. Stationary(such as fiber optic based) RSU backbone networks, arethen employed, and/or used to supplement the wirelessV2V network over certain highway segments. Similarly, itis noted that our traffic management models, methods andresults are also applicable when used for non-platoon basedvehicular mobility patterns, including when consideringsemi-autonomous transportation systems. Yet, the platoonformation model is highly advantageous in serving to ef-fectively offer tight coordination and rapid safety basedreactive adaptations, when performed among vehicles thatmove as a group.

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