a framework for dynamic traffic monitoring using vehicular ad-hoc networks

A FRAMEWORK FOR DYNAMIC TRAFFIC MONITORING USING VEHICULAR AD-HOC NETWORKS

Hadi ArbabiPhD in Computer ScienceDepartment Of Computer ScienceOld Dominion UniversityAdvisor: Dr. Michele C. Weigle

M.S. in Computer ScienceOld Dominion University, May 2007 Advisor: Dr. Stephan Olariu

B.S. in Computer Engineering Shiraz University , June 2001

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Content INTRODUCTION

Traffic Monitoring and Technologies in Use Motivations and Our Approach

DTMon: Dynamic Traffic Monitoring Components Deployment Investigation Analysis

EVALUATION Free-Flow Traffic Transient Flow Traffic Traffic with Congestion

CONCLUSION CONTRIBUTIONS

Hadi Arbabi [email protected]

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Introduction

Traffic Monitoring Vehicle classification Count information

Flow rate Volume Density

Traffic speed Time mean speed (TMS) Space mean speed (SMS)

Travel time (TT)


Traffic Management Center (TMC)

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Monitoring Techniques

Spatial Probing (Sensing) Fixed Point Sensors and Detectors

Inductive loop detectors (ILDs) Acoustic sensors Microwave radar sensors Video cameras


Adv.: Speed (TMS), flow rate, volume, density

Disadv.: Static, locations must be carefully chosen in advance, no travel times

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Monitoring Techniques Temporal Probing

Probe vehicle-based system Automatic vehicle location (AVL) Wireless location technology (WLT)


Adv.: Real-time monitoring, travel times, speed (SMS)

Disadv.: Affected by market penetration rate,hard to extrapolate some stats, must interpolate to estimate stats at a particular location

e.g., probing vehicles every 5, 10, 15, 30, or 60 seconds

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Motivatio

n

Real-time monitoring of traffic TMCs need high quality data Fixed point sensors and detectors cannot estimate travel time

and space mean speed and they are not flexible High demand for accurate estimation of travel time and speed

Trend toward probe vehicle-based systems

How can vehicular ad-hoc networks (VANETs) be used? Requires investigations Augment current technologies?


Investigation

Hadi Arbabi [email protected] 7

Related Work NOTICE (Abuelela et. al, IEEE (VTC), 2008)

VANETs + Belts

CarTel (Hull et. al, SenSys, 2006) Uses cell phones and cars as nodes in a dynamic sensor network

TrafficView (Nadeem, IEEE (MDM), 2004) Scalable traffic monitoring system for inter-vehicle communication

considering road conditions

GEMS project (http://www.path.berkeley.edu) Based on AVL and WLT technologies

Mobile Millennium project (http://traffic.berkeley.edu) Cell phones

Nirecell (ACM SenSys 2008) Smart phones

Traffic.com, Inrix, etc. Deployed microwave radar sensors and acoustic sensors in combination

with data collected by DOT sensors

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OUR APPROACHDynamic Traffic Monitoring (DTMon) DTMon - A probe vehicle-based system

using VANET and dynamically defined points of interest on the road Task Organizers (TOs) Vehicles Virtual Strips (VS)

Imaginary lines or points


*A dynamic spatial probing without disadvantages of temporal probing

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Task Organizer and Virtual Strips


TO

Virtual

Strip

Virtual

Strip

Virtual Segment

TMC

Med

iu

m

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Task Organizer (TO) Communicates with passing

vehicles Assigns measurement tasks Collects reports from the vehicles Organizes received measurements Informs upcoming traffic conditions

Multiple TOs (also can be moveable) Centralized

Aggregate information about the whole region


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Vehicles Equipped

GPS and DSRC communications device CPU and Required Applications

Record Speed GPS Position Travel Direction Timestamp Classification, Route Number, and …

Receive tasks from a TO Triggered at a specific time, speed, or location

Report (or Message) Forwarded to the listed TOs Stored and carried to the next available TO

Hadi Arbabi [email protected] Sample Header of A Message or A Report

Type: Volume-Speed-Travel-TimeDelivery Method: Forwarding (RF)Source TO: TOA (xa, ya, za)

Target TO: TOA (xa, ya, za)

Target Strips: VS1(X1, Y1, Z1),VS2, VS3, ...A Sample Task from A TO

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Deployment

Multiple VS and Segments Dynamically Defined

Multiple TOs


A Sample Task From TO to Vehicles

Type: Volume-SpeedDelivery Method: Store-and-Carry (SAC)Source TO: TOA (xa, ya, za)

Target TO: TOB (xb, yb, zb)

Target Strips: VS1, VS2, VS3, ...

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Investigation

Amount of Information Delivered to TO Market Penetration Rate (PR) Message Reception Rate (MRR) Information Reception Rate (IRR)

IRR ≈ MRR x PR

Various Traffic Characteristics Traffic conditions (speed, flow, density)

Inter-Vehicle Spacing Distance to TO Transmission Range Message Delay (and Latency)

Quality of Traffic data Delivery Methods, Type of Data, etc.


MRR for a VS = #MSG Recv. / #MSG Generated IRR for a VS = #MSG Recv. / #Vehicles Passed

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Message Reception


B = inter-vehicle spacingp = penetration rateS = mean speedv = flow rateEp = inter-vehicle spacing of equipped vehiclesR0 = transmission range d = distance to TOE[C] = expected inter-vehicle spacing

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What Message Delivery Method?


180036005400veh/h

Flow Rate

Transm

ission R

ange

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Methods of Message Delivery Regular Forwarding (RF) Dynamic Transmission Range (DTR) Store-and-Carry (SAC)

If Multiple TOs

Hybrid RF+SAC DTR+SAC


Note: • Using traffic in opposite direction• Hybrid adds some redundancy• Message Delay?

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Message Delay


nf = total number of distinct received forwarded messages received by forwardingnc = total number of distinct received carried messagesn = total number of distinct received messagestf = forwarding delay ≈ 0.0tc = carrying delay ≈ average travel timewf = nf /nwc = nc/n

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Performance Evaluation of DTMon Traffic Conditions

Free Flow Traffic Transient Flow Traffic

Transient Congestion Extended Congestion

Compare Delivery Methods Message Reception Rate Message Delay and Latency Quality of Data (estimated measurements)

Compare with Probe Vehicle-Based Systems (e.g., AVL) Fixed Point Sensors and Detectors (e.g., ILD)


Methods that can collect more information from vehicles with less latency are preferred in up-to-date traffic monitoring

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Using Our Contributed Integrated VANET Simulator


Several experiments using VANET modules that we developed for the ns-3 simulator

•H. Arbabi, M. C. Weigle, "Highway Mobility and Vehicular Ad-Hoc Networks in ns-3," In Proc. of the Winter Simulation Conference. Baltimore, MD, December 2010•Highway Mobility for Vehicular Networks (Project and Google Code)• http://code.google.com/p/ns-3-highway-mobility/

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Free Flow Traffic (Eval.) Bi-directional six-lane highway

TO1 is located at 1 km away

TO5 is located at 5 km away (optional secondary TO) Vehicles enter the highway with

Medium flow rate (average 1800 veh/h) Free flow traffic with poor connectivity

Desired speed 110±18 km/h (30±5 m/s)


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Free Flow Traffic (Eval.) 10 runs, 30 min each, PR {5%, 25%,

50%, 100%} Major defined strips by TOs {VS1 , VS2 ,

VS5 , VS9} Compute avg., variance, significance,

etc. Comparison

Each delivery method with the others Actual simulation (ground truth) data


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Freception


Higher Penetration = Higher RFFarther Distance = Lower RF

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Message Reception Rate (MRR)


VS250%

Hybrid = Forwarding + Carrying = Full MRR

Higher Penetration = More Forwarding = Less Carrying

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MRR and Traffic In Opposite Direction

PRRF, w/o

oppRF,

w/oppDTR w/o

oppDTR, w/opp

5% 0% 0% 1.1% 2.4%

50% 59% 72% 78% 96.7%


20-25% 20-25%

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Message Delay


RF Delay Very Low

Hybrid Delay 1. Amount of Carried Messages2. TTMore ForwardingLess DelayMore SAC More Delay

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Transient Flow Traffic (Eval.) Bi-directional four-lane highway

TO1 is located at 1 km away

TO5 is located at 5 km away (optional secondary TO) Vehicles enter the highway with

Medium flow rate (average 1800 veh/h) Desired speed 65±5 mph (29±2.2 m/s)

Normal Distribution 20% of vehicles are Truck (for comparison with AVL)

Uniform Distribution


A vehicle breaks down for 5 min

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Transient Flow Traffic (Eval.) The performance of DTMon compared with

Actual simulation status (ground truth) Fixed point sensors and detectors

Actual simulation data sampled from VS1 and VS2

AVL Equipped Trucks

10 runs of the simulation (20 min each) for each experiment

Test with penetration rates of 5, 10, 25, 50, and 100%

Compute avg., variance, significance, etc.


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Estimated Travel Time (ILDs vs. Actual)


Fixed Point Sensor and Detector’s Poor Estimation of TT and SMS

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Travel Time


VS2

VS2

Quality of DataRF+SAC >= RF > AVL

Time

(min)

Actual AVL25

t-Stat p-Value Sig.?Mean Var Mean Var

0-5 38.55 0.40 43.12 0.03 -1.5326 0.0393 Yes

5-10 119.51 0.46 138.01 0.02 -7.0277 0.0055 Yes

10-15 99.59 0.32 127.86 0.60 -1.8161 0.0018 Yes

15-20 40.62 0.28 42.97 1.10 -2.1121 0.0400 Yes

0-20 74.57 1456.39 87.99 1163.09 -0.8172 0.0360 Yes

SMS 13.41 - 11.36 - - - Yes

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Space Mean Speed (SMS)


VS2

VS2

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Flow Rate


VS2

Count Information (e.g., Flow Rate and Volume)

Only in High PR

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Message Delay


TO1VS2TO5

RF Delay Very Low

RF+SAC Delay 1. Amount of Carried Messages2. TTMore RFLess Delay

More SAC More Delay

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Quality of Data

Good Estimate?

Sensors and Detectors AVL DTMon

Flow Rate and Density Yes No

See Next Table

TMS YesUnderestimat

e Yes

Travel Time Not Available Overestimate Yes

SMS Not AvailableUnderestimat

e Yes

Vehicle Classification Not Accurate Limited Yes


t-test Alpha = 0.05 (Confidence

> 95%)

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Quality of Data


High Quality Estimation Conf. ≥ 95%

Traffic Density

orPenetration

Rate

Message Delivery Method

Flow Rate and Density High Any

Classification,TMS

Travel Time,or

SMS

LowSAC, RF+SAC,

or DTR+SAC

Medium or High Any

t-test Alpha = 0.05 Confidence

> 95%

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Free Flow and Transient Flow (Summary)

DTMon can estimate good quality Travel Time and Speed

DTMon can detect transition in traffic flow using estimated Travel Time and Speed

DTMon can estimate good quality flow rate and density in higher penetration rates

Hybrid message delivery improves information reception rate with cost of latency as an option for low penetration rates

DTMon can augment current technologies and monitoring systems


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Traffic With Congestion (Eval.) Goal

Use our findings about DTMon in detecting transitions in traffic flow using travel time and speed

Show advantage of DTMon’s dynamically defined virtual strips by TOs For example, show DTMon’s ability in

detecting congestion and the end of the queue No delay when RF is used


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Example: End-of-Queue Detection During Congestion Using DTMon Create congestion near by VS4 (long period 30 min)

Let TO1 dynamically define two additional new VS (VS2.5 and VS3.5 ) after the vehicle breaks down

Observe transitions in travel times and speeds for each virtual strip, segments, and new sub-segments


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Time Mean Speed (TMS)


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Time/Space/Speed


VS4VS3.5VS3VS2.5VS2

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Travel Time


Congestion Must Have Reached VS2VS3Upper Section Or Lower Section?VS2.5VS3 Or V2V2.5?

VS3VS2.5VS2

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Congestion (Summary)

Benefits of Dynamically Defined Virtual Strips in DTMon

Spatial probing from traffic Ability to monitor various points with only one TO Ability to monitor various segments with only one

TO Ability to create virtual sub-segments No need for extrapolation/interpolation

Detection of the end of the queue No flow rate information is required Speeds and travel times are sufficient No delay (using RF)


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Contributions A method for using probe vehicles to

perform spatial sampling of traffic conditions To provide real-time measurements of

speed and travel time To allow for the measurements to be

made at specific and dynamic locations of interest on the roadway

To avoid the need for interpolation and estimation that is required when temporal sampling of probe vehicles is performed


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Contributions An analysis of the factors that can

impact the quality of monitored traffic data when using vehicular networks Market penetration rate Traffic conditions Communication range Distance between communicating entities Methods of message delivery Information and message reception rate Message delay


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Contributions An evaluation of the impact of different

methods of message delivery on the quality of traffic data that can be gathered by vehicular networks Regular forwarding Dynamic transmission range Store-and-carry Hybrid

Comparisons Information and message reception rates Message delay (and latency) In-use technologies


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Contributions A demonstration of the usefulness of

DTMon’s monitoring approach for monitoring congested traffic conditions To allow a TMC to dynamically place

additional monitoring points (virtual strips) in locations where congestion is building up

To detect transitions in traffic flow using travel times and speeds, without having to rely on flow rate information

To detect and track the end-of-the-queue in traffic with congestion


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Contributions Highway mobility modules for the ns-

3 network simulator The first highway mobility modules

designed to produce realistic vehicle mobility and communications in ns-3

Validated modules have been released to the ns-3 community and are now being used by other researchers around the world


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Avg. visit 150/mon [code + paper]Avg. new user 10/mon [our simulator]in past 9 months!


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Expansion of its Academic Use


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Future Work Investigate the usage of the most recent security/routing

techniques and algorithms in VANETs suitable for DTMon

Adapt DTMon and the same framework toward mobile nodes (e.g., cell phones) TOs are service providers (or TMCs) and … Vehicles are smart-phones (and with installed DTMon apps) Apps are updated with most recent defined virtual strips for the region

Extend our implementation of VANET simulation modules for urban areas (e.g., intersections) Add the ability to read in and use detailed maps instead of a single

straight highway Investigate the use of dynamically-defined virtual strips and TOs in

DTMon to evaluate the performance of our proposed framework in urban area

Methods to estimate the market penetration rate


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Questions?

Hadi Arbabi Department of Computer Science at

Old Dominion University Vehicular Networks, Sensor Networks, and

Internet Traffic Research http://oducs-networking.blogspot.com/

Source Code Wiki: Installation and Documentation

http://code.google.com/p/ns-3-highway-mobility/

[email protected]


This work was supported in part by the National Science Foundation under grants CNS-0721586 and CNS-0709058.

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Publications Hadi Arbabi and Michele C. Weigle, "Monitoring Free-Flow Traffic using

Vehicular Networks," In Proceedings of the IEEE Intelligent Vehicular Communications System Workshop (IVCS). Las Vegas, NV, January 2011.

Hadi Arbabi and Michele C. Weigle, “Using DTMon to Monitor Transient Flow Traffic”, In Proceedings of the IEEE Vehicular Networking Conference (VNC). Jersey City, NJ, December 2010.

Hadi Arbabi and Michele C. Weigle, “Highway Mobility and Vehicular Ad-Hoc Networks in ns-3,” In Proceedings of the Winter Simulation Conference. Baltimore, MD, December 2010.

Hadi Arbabi and Michele C. Weigle, "Using Vehicular Networks to Collect Common Traffic Data," In Proceedings of the ACM International Workshop on Vehicular Internetworking (VANET). Beijing, September 2009.

Hadi Arbabi, "Channel Management in Heterogeneous Cellular Networks", Master's Thesis, June 2007.

Hadi Arbabi, "PCI Interface to Control Parallel Stepper Motors Simultaneously: Design, Implementation, Driver, and GUI", Bachelor's Thesis and Technical Report, June 2001.


http://www.cs.odu.edu/~mweigle/papers/arbabi-wsc10.pdf

http://www.cs.odu.edu/~mweigle/papers/arbabi-vanet09.pdf

52Hadi Arbabi [email protected]

a framework for dynamic traffic monitoring using vehicular ad-hoc networks

Education

pointshadi arbabi

regionhadi arbabi

message receptionhadi

virtual stripshadi arbabi

dot sensorshadi arbabi

available tohadi arbabi

speed tmsspace

speed smsdisadv