cost effective real-time traffic signal control using the tuc...

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Digital Object Identifier 10.1109/MITS.2010.939916

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE • 6 • WINTER 2010

Cost Effective Real-Time Traffic Signal Control

Using the TUC Strategy

Werner Kraus Jr. and Felipe Augusto de Souza

Department of Automation and Systems Engineering, Federal University of Santa Catarina,

Florianópolis, SC, Brazil, E-mails: [email protected] and [email protected]

Rodrigo Castelan Carlson and Markos Papageorgiou

Dynamic Systems and Simulation Laboratory, Technical University of Crete, Chania, Greece,

E-mails: [email protected] and [email protected]

Luciano Dionisio Dantas Technische Universität Braunschweig, Germany,

E-mail: [email protected]

Elias B. Kosmatopoulos Department of Electrical & Computer Engineering,

Democritus University of Thrace, Xanthi, Greece,


Eduardo Camponogara Department of Automation and Systems Engineering,

Federal University of Santa Catarina, Florianópolis, SC, Brazil,


Konstantinos Aboudolas Informatics and Telematics Institute,

Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece,


Date of publication: 4 February 2011

I. Introduction

Many reasons have been mentioned for the less than expected deployment of adaptive urban traffic control systems [6], among which the high cost of installation and maintenance is one of the most cit-

ed. This is particularly true for mid-sized cities (with less than 500 000 inhabitants) in developing countries which cannot afford the systems provided by the major vendors of urban traffic control (UTC) software. Often these cities do not have fiber optic communication cabling laid down as part of the public infrastructure nor high quality pavement on roads. Moreover, qualified technical person-nel dealing with traffic management and operations may be a scarce resource, commonly in charge of many du-ties beyond the proper setting of time-of-day (TOD) traffic signal plans. The situa-tion, then, is character-ized by a combination of lack of resources with rapidly growing car numbers, lead-ing frequently to poor performance of the urban traffic network and wide-spread congestions that could otherwise be avoided or, at least, better managed by use of modern UTC systems.

Addressing this situ-ation requires low-cost automation for traffic signal operation. The two most relevant items in the cost of UTC systems are the setting up of an appropriate com-munication infrastructure and the installation of sensors to collect traffic measurements in real time. As a consequence, in order to keep the related cost low, a UTC strategy must have low data exchange requirements in terms of sampling rates and message size, be able to oper-ate with few sensors, and be robust to failures. Moreover, the strategy must be simple enough to be deployed without the need for lengthy fine-tuning, implying that few param-eters must be present, or else, a systematic approach for tuning must be available.

The TUC real-time signal control strategy [2], [3] has been developed with special attention to simplicity; it is

economical to implement and yet was shown in simulation and field implementations to perform at a level comparable to other established strategies [12] such as SCOOT [11] and BALANCE [17]. The cost savings brought by TUC are due to the following reasons. First, traffic data measurements are needed only once per cycle and communication latencies are tolerated. Second, only occupancy data is needed so that messages sent to the central room are short, although flow data is typically transmitted as well for performance assess-ment and archival. Third, the centralized calculations done

by TUC allow both savings in the number of sensors placed in the network and the interpolation

of missing sensor data due to break-ages and failures. Performance

deteriorates with less sensors, but simulation studies and

field observations [12] in-dicate that the benefits

of real-time signal con-trol are still retained when loss of sensor information is com-pensated by the in-terpolation of data from neighboring sensors. According to a recent survey

[14], other UTC sys-tems have to rely on

historical data to sub-stitute faulty detectors.

Also, in the particular case of SCOOT, [16] states

that performance is retained up to 15% of sensor failure, below

the perceived TUC capabilities. Given these features and capabilities of

TUC, it has been chosen for an installation in the Brazilian city of Macaé, Rio de Janeiro state. The work was a close collaboration between Greek and Brazilian univer-sities, local contractors and equipment vendors. Besides the municipality own budget resources, funding support for the project also came from federal agencies that fos-ter innovation through research & development partner-ships between universities and industry. The result of the effort is an overall improvement of the urban traffic in the downtown area of the city in the order of 15% to 25% as reflected in the corresponding increase in average traffic speed when compared to the recently adjusted TOD plans.


Abstract–Real-t ime urban traffic control systems fre-

quently require precise traffic measure-ments and fast communications in order to achieve

desired performance levels. Such requirements may hin-der the adoption of these beneficial control systems because

of the installation and maintenance costs involved. The recently developed TUC strategy has been conceived in a way that simpli-

fies measurement requirements and yet achieves performance levels comparable to other well-established commercial systems. This was a major motivation to select TUC for a traffic control center installation in a mid-sized Brazilian city aiming at improving the traffic conditions despite the lack of wired communication between roadside control-lers and the central control room. A description of the implemented system is presented, followed by field data comparing pre and post

installation traffic behavior. It is found that TUC leads to a 15–25% improvement in average network speed compared with

pre-existing time-of-day plans.

Keywords– Adaptive urban traffic control, Feedback control,

TUC strategy.


In order to highlight the most important aspects of the work, this paper continues with a presentation of the urban scenario and technological limitations for the installation in Section II. Then, Section III reviews the main aspects of the TUC strategy and how they relate to the control prob-lem at hand. Section IV describes the system installation with emphasis on the measurement and communication approaches employed. Performance of the system is as-sessed based on loop detector data that reflect the benefits of the control decisions taken by TUC, as shown by field results of Section V. Concluding remarks and future work are discussed in Section VI.

II. Description of the Control ScenarioThe city of Macaé, Rio de Janeiro state, is located 200 km northeast of the state capital. It is an important harbor providing logistics for oil operations in the deep water exploration off the Brazilian coast. With a population of 195 000 in 2010 and a growth rate of 3.9% a year for the past decade, the city has traffic f lows larger than other similarly sized urban regions in Brazil because of its economic significance.

Figure 1 depicts a map of the central business district (CBD) where most of the traffic signal control is concen-trated. Blue circles indicate the controlled intersections. Roads are mostly two-lane one-directional streets with side parking space, resulting in two-phase control for most intersections. Such traffic arrangement facilitates signal control and allows for relatively high flows of up to 9 000veh/day/lane in the CBD, with most of the traffic occurring between 6:30 am and 9:00 p.m. The short lengths between adjacent intersections are a negative factor, with the short-est ones being only 100 m apart.

In order to improve traffic flow in the CBD, the city de-cided to renovate the traffic controllers and to establish a partnership with universities to implement an adaptive real-time traffic control center. As soon as the new traffic controllers were installed, an updating of the TOD plans was conducted by a consultant with good field experience about Macaé traffic. The two main features of the resulting plans are:

■ cycle times are restricted to a maximum of 90 s in order to avoid queue spillbacks in the relatively short links of the CBD;

■ offsets are calculated based solely on travel times between junctions without consideration of possible residual queues at the start of green in a down-stream junction.Traffic management person-

nel in the city were generally satisfied with the performance of the adjusted plans, but expected

to achieve better results with a real-time traffic-respon-sive strategy.

For real-time traffic control deployment in the CBD, it is not possible to use fast communication links such as fiber optic cables. Wired telephone lines are expensive to hire while installation of private cabling is also dis-carded due to the costs involved. Hence, a possible solu-tion is cellular telephone technology and, in fact, GPRS over GSM was elected due to availability and low cost of monthly fees.

Before proceeding to the details of the installation, the next section briefly reviews the TUC strategy and how it was configured for the control problem at hand.

III. The TUC Signal Control StrategyTUC (Traffic-responsive Urban Control) [2], [3] was de-veloped to provide coordinated real-time traffic control for large urban networks even under saturated traffic conditions. Its main feature is the functionally central-ized computation of split and cycle control. This is in contrast to many other real-time control methods such as SCOOT [10,16], RHODES [9], [13], OPAC [7], [8] and PRO-DYN [5], where control values are functionally decentral-ized due to the exponential complexity of the involved algorithms in relation to the number of intersections. It is important to note that all these systems can be concen-trated in a single computer with enough computational power, but only TUC carries the calculations in a func-tionally centralized fashion.

In TUC, the control variables for splits, cycle and offsets are calculated by three corresponding independent mod-ules. The only required measurement is the number of ve-hicles in the link during the cycle, which is estimated from occupancy measurements during one cycle. Based on the estimates, the cycle and offset modules compute the corre-sponding values and send them to the split module which, in turn, reconciles all three control actions to obtain the network-wide signal plan to be applied in the next cycle. A brief review of the calculations involved in each module is presented next.

A. Split ControlSplit control in TUC has the objective of minimizing the risk of oversaturation and blocking of intersections due to

In order to improve traffic flow in the CBD, the city decided to renovate the traffic controllers and to establish a partnership with universities to implement an adaptive real-time traffic control center.


queue spillover. To this end, split control is devised based on a store-and-forward model [3] that describes the net-work traffic dynamics as a linear, time-invariant discrete-time system of the form:

x 1k1 1 2 5Ax 1k 2 1 BDg 1k 2 , (1)

where x is the state vector holding the numbers of vehicles xz in the links z [ Z (the set of all links under TUC control); Dg is the control vector holding Dgj,i5 gj,i2 gj,i

N , 4i [ Fj (the set of traffic light phases), 4j [ J (the set of controlled inter-

sections) where gj,i is the green time of phase i at junction j and gj,i

N is a corresponding (pre-specified) nominal value; and A5 I and B are the state and input matrices, the latter reflecting the specific network topology, fixed staging, cycle, saturation flows, and turning rates.

Based on the linear model (1) and an appropriate qua-dratic objective, the LQR methodology [4] for controller synthesis is used to derive an efficient gain matrix to be used in a feedback control law of the form:

g 1k 2 5 gN2 Lx 1k 2 , (2)

FIG 1 Map of the main control area in Macaé, RJ, Brazil. Blue circles indicate traffic-light controlled intersections (available at http://transitoonline.mactran.rj.gov.br.).


where g is the vector of green times gj, i, gN is the vector of nominal green times gj, i

N , and L is the state feedback matrix that minimizes the following cost function:

J512

a`

k501xk

T Qxk1DgkT RDgk 2 , (3)

where Q and R are non-negative definite, diagonal weighting matrices. The elements of the diagonal matrix Q are chosen as the inverse of the storage capacity of the respective link, and the elements of R are chosen by the designer. Then, L depends on system parameters given by matrices A, B and Q and essentially one single design parameter, the control weights specified by R in the qua-dratic cost function (3).

The design methodology of the gain matrix L takes into account the storage capacity of the links as weights for the state x in order to avoid the risk of spillback of queues. Thus, matrix L lends a gating feature to the con-trol law, that is, restricting the flow into the overloaded network links. In doing so, it protects the downstream links with high number of vehicles from oversaturation by decreasing the green times of upstream links. This feature can be accentuated by weighting the estimates of the number of vehicles on the links. Simulation studies [1] have shown that L has low sensitivity with respect to traffic parameter variations.

In order to apply the control (2), online measurements of the state variables are necessary. However, the number of vehicles xz cannot be directly measured, except if video cameras are available. For this reason, local occupan-cy measurements oz, collected in real time by inductive loops, are transformed in estimated number of vehicles xz through appropriate non-linear functions xz5 fz 1oz 1k 22 . In other words, instead of attempting to accurately esti-mate traffic queues, TUC uses a static nonlinear function fz 1 # 2 that maps occupancy into the number of vehicles ac-cumulated in one cycle. fz 1 # 2 is adjusted to reflect sensor placement on the link; the nearer to the stopline, the lower the number of vehicles in the link estimated for a given occupancy. For a compromise between early detection of growing queues and better ability to measure flow (in-stead of halted vehicles), midblock placement of sensors is indicated [1].

The green times for the phases of each junction result-ing from (2) will generally not add up to a cycle and may

also violate minimum-green constraints; a suitably designed knapsack optimization al-gorithm [1] modifies the green times so as to satisfy these constraints but keep the relative proportions of the green times as close to the ones produced by (2) as possible. As shown in [1], [15], algorithms with linear complexity are available to find global optimal solutions to the class of problem at hand.

B. Cycle ControlTUC employs a common cycle length for a given region of the network in order to enable coordination via suit-able offsets. The reasoning behind cycle control, as in all cyclic methods of traffic control, is that a longer cycle time reduces the proportion of the lost time (which is a fixed quantity) with respect to the cycle time, thus in-creasing the intersection capacity. On the other hand, if traffic volume is low, a longer cycle will result in wasted green time.

TUC’s cycle control acts so as to limit the maximum observed saturation level in the network. More specifical-ly, TUC applies a proportional-type feedback algorithm that uses the current maximum saturation level of a pre-specified percentage of the network links as a criterion for the cycle setting. The cycle control algorithm com-prises three steps: 1) The list of network links is ordered by decreas-

ing current loads sz 1k 2 5 xz /xzmax (xz

max is the jam capacity of link z). Then, given a user-defined p, the top p-percent links are taken and the corresponding loads are averaged to provide the average maximum load s 1k 2 ;

2) The network cycle is calculated by the feedback control law (proportional controller)

C 1k 2 5 CN1 KC 1s 1k 2 2 sN 2 , (4)

where CN is the nominal network cycle length; sN is a nominal average load; and KC is a control parameter. Af-ter the application of Eq. (4), the calculated cycle length is constrained within the range of permissible cycle lengths 3Cmin, Cmax 4, if necessary;

3) If the resulting network cycle C 1k 2 is sufficiently high while all links approaching specific intersections have sufficiently low saturation levels, then these undersatu-rated intersections may be double-cycled. The steps 1 and 2 seek to adjust the cycle time to cope

with the maximum levels of saturation observed in the net-work, while step 3 attempts to reduce delays that would oc-cur at certain intersections with low saturation levels due to high cycle times.

The justification for the proportional control of Eq. (4) is the direct proportionality between cycle time and

TUC’s cycle control acts so as to limit the maximum observed saturation level in the network.


intersection capacity. In turn, capacity and link loads are re-lated by an inverse first-order process whereby an increase in capacity leads to a continuous decrease in load until a new equilibrium is reached. As such, a proportional controller is well suited to regulate link loads around a desired value.

C. Offset ControlOffsets should be set taking into account the existence of queues. This fact is exploited by TUC’s offset controller which is based in the following assumptions:

■ Arterials are defined here as sequences of links that do not need to correspond to physical network arteri-als so that any route chosen by the traffic engineer as requiring good progression can be assigned for offset calculation;

■ In case of two-directional arterials, an offset is speci-fied for each direction, and the offset that will be fi-nally implemented is a weighted sum of the offsets of the two directions. Alternatively, the most loaded di-rection may be selected in real time to determine the arterial offsets;

■ In case of arterials that do intersect, TUC considers a pre-specified priority order of the arterials according to their relative importance regarding offset specifi-cation, and offset control is implemented to each ar-terial sequentially, starting from the arterial that has highest priority. TUC performs offset control in a decentralized way,

i.e., for successive pairs of junctions along the pre-defined arterials. Moreover, calculations are performed indepen-dently for each direction of traffic and, as stated above, the resulting offsets can be averaged to produce the final off-set between successive pairs of junctions [2]. For each pair of junctions, the offset specification changes the starting time of a specific “main” stage of the upstream junction, where this main stage is uniquely determined from the arterial composition.

TUC considers the possible existence of queues by means of a simple feedback control law, as follows. Con-sider two successive junctions j1 and j2 and link z that con-nects them in the j1 to j2 direction. Link z has length lz and average speed vz and receives right-of-way in the main stage of junction j2 (Fig. 2).

The queue length on link z is approximately sz 1k 2 # lz. If there are no vehicles in the link, the offset between the two intersections should be equal to the travel time under average speed for the link, that is, lz / vz. In other words, the cycle in j2 should start after the cycle in j1 (positive offset). As the number of vehicles in link z grows, the offset should decrease correspondingly in order to allow the partial dis-charge of the queue in j2. Then the cycle in the downstream intersection should begin earlier than in the no-queue case and, in some cases, even before the cycle in the upstream intersection (negative offset).

More specifically, an ideal offset would be obtained if the following two traffic waves meet exactly at the tail of the existing queue: 1) Traffic wave created due to the change to green in the

upstream intersection j1; this wave moves downstream with speed vz; hence, it reaches the queue tail at time 312 sz 1k 2 4 # lz / vz after the signal change to green;

2) Kinematic wave created by the change to green in the downstream intersection j2; this wave moves upstream (along the queue) with speed vc which is usually esti-mated around 15 km/h; this kinematic wave reaches the queue tail at time sz 1k 2 # lz / vc.

The ideal offset tj1,j2 can be calculated by considering both times above, which yields the following offset control law:

tj1, j21k 2 5 lz

vz2 lz Kz

o xz 1k 2xz

max , (5)

where Kzo is a control parameter equal to 1vc1 vz 2 / 1vc vz 2 .

D. Control ImplementationThe actual control implementation in this case study has been done as follows. Split control is activated at every cycle. The centralized computation is performed several seconds before the end of a reference cycle for the whole network and implemented in the following cycle.

Cycle control is evaluated every n seconds, with n be-ing a design parameter typically chosen as a multiple of the nominal cycle. Since Eq. (4) may cause large variations in two consecutive cycle times that can impact traffic due to transient effects, a modified form has been adopted in the current installation. In order to attenuate variations in cycle times, values computed by the proportional control law (4) are filtered by a nonlinear filter given by:

Cf 1k 2 5 Cf 1k2 1 2 1 k* 3C 1k 2 2 Cf 1k2 1 2 4, k*5 eku if c 1k 2 $ cf 1k21 2

kd if c 1k 2 , cf 1k21 2 , (6)

where Cf 1 # 2 is the filtered cycle time actually used, C 1k 2 is the cycle given by Eq. (4), and ku and kd are the “up” and “down” control gains. The two gains make it possible to allow the cycle to change more rapidly when traffic flows are increasing than when flows are decreasing. This is

lz

j1 j2

(1–σz) ⋅ lz σz⋅ lz

FIG 2 Link z with queue (gray) (adapted from [2]).


beneficial since (i) surges in traffic are more disruptive than sudden flow drops, and (ii) shorter-than-optimal cy-cle times are more detrimental than longer-than-optimal ones. In the present implementation, the values chosen are ku= 0.6 and kd 5 0.4 and the cycle time is updated every two nominal traffic signal cycles.

Offset control is applied every kn seconds, with k5 1, 2, c being a design parameter. Large offset chang-es may occur, which create transient traffic disturbances that may affect performance. Hence, it is recommended to allow at least 10 min between consecutive offset changes.

IV. The Implemented SystemThe CBD was divided in two regions for control purposes. Region 1, depicted in Fig. 3a, comprises the rectangular network seen next to the center of Fig. 1 plus junctions to the East and South. Region 2 corresponds to the “V” shaped network in the upper part of Fig. 1, and is shown in Fig. 3b. Each region has its own cycle time, thus coor-dination between intersections at the border of adjacent regions cannot be guaranteed. The main criterion for de-termining the regions were the distinct traffic flows in each, meaning that different cycle times are necessary. Region 1 is the busiest of the two, with traffic volumes al-most twice as large as region 2. The colored paths (bold arrows) in the pictures represent the routes for which off-set control is active, representing the major flows of the respective regions.

A. TUC Control ParametersThe entries bij of B in (1) are determined as follows. In the control regions, all intersections have two approaches. In this case, the dynamics of link z is given by [3]:

xz 1k1 1 2 5 xz 1k 2 1 ta,z Sa Dga 1k 2 1 tb,z Sb D gb 1k 2 2 Sz Dgz 1k 2 , (7)

where ta,z and tb, z are the turning rates into z from approach-es a and b, Sa and Sb are the saturation flows of the approach-es, Dga and Dgb are the respective incremental green times, Sz is the saturation flow that leaves link z, and Dgz is its in-cremental green time. Collecting the appropriate terms, (7) can be written in vector form as:

xz 1k1 1 2 5 xz 1k 2 1 3baz bbz bzz 4 £Dga 1k 2Dgb 1k 2Dgc 1k 2 § , (8)

where baz5 ta,z Sa, bbz5 tb, z Sb and bz z5 2 Sz are the ele-ments of the line of the B matrix associated with the dynam-ics of link z. For the whole control region, the entries of B are arranged such that the correct green times multiply the respective coefficients for each link.

Traffic parameters necessary for the entries of the input matrix B, for nominal control times, and for the LQR calcu-lation of matrix L were conured as follows:

■ Saturation flows: a field survey was conducted to esti-mate saturation flows per traffic lane according to the procedures described in [18]. The values found are in the range of [1200,1650] veh/h, lower than the expected values of above 1800 veh/h. The reason for the lower values is the existence of side street parking and the relatively high interference of pedestrians and bicycles in the streets;

■ Turning rates: although field surveys were available, the data were not adopted because of the time of the year when the surveys were conducted, mostly during school holidays. Traffic in the city changes consider-ably when there are no school classes. Rough empirical estimations were conducted in order to assign turn-ing rates in one out of four possibilities: 80/20; 70/30; 60/40; 50/50;

■ Nominal green times gN: the existing green splits of the TOD plans were adopted;

31 2

4 5

76

14

22

2120

1918

17

25 16

23 24

26

15

8

9 11

1012

13

(a) (b)

FIG 3 Schematic diagram of the two control regions comprised only of one-way streets. (a) Diagram of control region 1. (b) Diagram of control region 2.


■ Link storage capacities: used in the LQR calculation as weights for the quadratic cost of the states xz, the stor-age capacities where based on average vehicle lengths of 6 m for the control regions.

B. Communication and Computational InfrastructureThe overall physical architecture of the control system is presented in Fig. 4. Based on the requirements and restric-tions posed by the scenario at hand, the following technical solutions were adopted:

■ Communications: GSM/GPRS modems were installed in the traffic signal controllers. The bit rate between modem and controller is 9600 bps due to controller characteristics. On the control room side, an Inter-net connection by radio with 1 Mbps download rate and 250 kbps upload is the medium for the GSM/GPRS link. In terms of data traffic, although only occupancy measurements are required for control purposes, ve-hicle counts are also transmitted for evaluation and archival reasons, resulting in messages of 20 bytes for measurements (download) and 18 + [number of stages] bytes for actuation (upload);

■ Traffic sensors: inductive loop detectors are positioned in the middle of the respective blocks, one per lane. The average distance between detectors and the con-troller cabinet is 60 m, with cabling laid down under the sidewalks;

■ Traffic controllers: existing controllers in Macaé were not modular. In close cooperation with the controller vendor, the CPU card was redesigned, both in hardware

and software, for real-time operation. The time base of the controller is obtained periodically from a GPS mod-ule installed in each cabinet, thus assuring network-wide synchronization of controller clocks with the con-trol room;

■ Control room: servers for database, control algorithm and operator workstations with multi-display graphic-cards are configured as an intranet with restricted ac-cess from the outside world. For the necessary internet services, one computer without protection of the fire-wall security rules hosts the GPRS/GSM and web serv-ers. The web server provides the public with internet access to the information about the current traffic state (see Fig. 1).For the control loop, the basic data traffic occurs once

per traffic cycle. It consists of incoming traffic measure-ments and outgoing control commands that communicate over the GPRS/GSM service. GPRS/GSM modems send messages to the IP address of the server located in the con-trol room. The server, on its turn, must keep a list of the dynamic IP addresses assigned by the wireless telephone provider, which may change from time to time. One-way latency in the available GPRS network in Macaé can be as high as 5 s. Considering the round trip of the request/re-sponse messaging, total time for the communication can reach 10 s. Clearly, the control algorithm must be robust to message delay and inaccuracy in sampling time. Ideally, traffic data would be requested at the last second of the cy-cle, but in reality requests are originated 15 to 20 s before the end of the cycle. Note that even though it is possible to initiate the sampling process at a precise instant, latencies

Loop

Sensor

Loop

Sensor

GPRS

Modem

GPRS

Modem

Controller

Controller

Megabit

SwitchMegabit

Switch

Firewall

Operator

Workstation

Application

Server

Database

Server

GPRS/Web

Server

Internet

FIG 4 Simplified physical architecture of the control system.


make it impossible to guarantee that the obtained samples have the same precise interval. Still, as shown in the field results presented below, TUC is robust to such errors in the sampling period.

An alternative to GPRS/GSM that can be used in other sce-narios is the 3G (UMTS) wireless telephone service, which presents increased bandwidth and low latencies. However, the lack of availability of 3G networks in developing countries like Brazil should not mean that cities in these countries must

wait to have workable and effective real-time adaptive systems in place to better manage their traffic networks.

V. Field ResultsPresentation of the field results is based on the three most important junctions of control region 1, each with two approaches. Being the busiest of the two regions, region 1

benefited most from TUC operation. Comparison is made between day long operation of

TOD control and TUC. For the sake of performance evalu-ation, traffic counts from loop detectors are taken as well as occupancy. Since daily traffic flows are not the same, occupancy is not used directly for evaluation purposes. Rather, an equivalent spot speed Veq is computed in order to combine flow and occupancy in one indicator according to

Veq56Q

ot1000 km/h, (9)

where Q is the hourly flow, ot is the time occupancy of the detector, and vehicle lengths are assumed constant and equal to 6 m. For the purposes of the comparative analysis, it is sufficient to assume that traffic composition is similar on all days so that the vehicle length of 6 m represents a normalized quantity rather than the actual effective ve-hicle length. The daily averages presented are harmonic means of the hourly speeds.

Due to roadwork on pavements right after the comple-tion of the installation, there was a loss of around 30% in the number of sensors in region 1 (17 out of 60). Since the broken sensors were spread over a large area, it was pos-sible to use interpolation of information from neighboring sensors to estimate the missing occupancy data.

Table I summarizes the results for the intersections considered. Numbering fol-lows the convention in Fig. 3a. As can be seen, intersection 4, the busiest of the city, shows improvements in speed even in pres-ence of greater daily f lows, with 21% in-crease in speed for approach 1 and 15% for approach 2. Results for intersections 6 and 7 show a different trend. Both have approach-es 1 worse off in speed, but that is contrast-ed with more significant improvements in speeds for the respective approaches 2 (these are two segments along the same road that runs East-West in Fig. 3a). It can be seen that TUC achieves a better balance of speeds in the intersections in such a way that the overall intersection speeds are in-creased by 10% for intersection 6 and by 36% for intersection 7.

120

110

100

90

80

70

60

Cycle

(s)

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Time-of-day (h)

TUCTOD

FIG 5 Day trend of cycle time comparing time-of-day and TUC (note the maximum TOD cycle set to 90 s).

Intersection

Approach 1 Approach 2 Total

N Veq (km/h) N Veq (km/h) N Veq (km/h)

4 TOD 6458 5.1 7617 9.8 14075 7.8 TUC 7628 6.5 8676 11.3 16304 9.1

% 18.1 20.7 13.9 15.1 15.8 16.3

6 TOD 6311 14.7 6151 8.7 12462 11.7 TUC 6509 11.1 6794 14.7 13303 12.9 % 3.1 -24.5 10.5 69.0 6.7 10.2

7 TOD 5480 14.2 6160 13.1 11640 13.6 TUC 5256 11.7 7083 23.7 12339 18.6 % -4.1 -17.8 15.0 81.1 6.0 36.5

Table I. Comparison of time-of-day and TUC for three selected intersections

of region 1. Data is from 6:00 to 21:00; N is the vehicle count and Veq is

computed by (9).

TUC achieves a better balance of speeds in the intersections in such a way that the overall intersection speeds are increased by 10% for intersection 6 and by 36% for intersection 7.


One reason for such marked improvement is the inade-quacy of TOD offsets under certain traffic regimes. Although not shown in this paper, field data indicate that TOD offsets assume that no queues are present at downstream links even in peak hours, while TUC takes queues into account so that large negative offsets are in place when needed.

Another improvement comes from cycle control. TOD plans are restricted to 90 s maximum cycle time due to concerns about spillbacks on the rather short link lengths between intersections. TUC, by virtue of its restriction of flow release when downstream occupancies are high, is al-lowed to apply cycles up to 120 s. Figure 5 shows the cycle time excursions during one day. Clearly the trend of TOD is followed by TUC in early and later hours, but with different behavior during busier daytime traffic.

Finally, field observations also led to the conclusion that split control works well in distributing the available green times so as to avoid green starvation or unduly short green times.

More detailed traffic behavior can be seen in Fig. 6, which depicts hourly data for approaches 1 and 2 of in-tersection 6. As can be seen, traffic flow is higher on both approaches under TUC control. In terms of speeds, how-

ever, it is seen that TUC degrades approach 1 in order to better distribute flows, mainly during the busier hours of the day. Such balancing effects has been noted in many other intersections of the network, along with an overall improvement of traffic.

ConclusionThe paper reports on practical results from a field deploy-ment of the TUC strategy in the city of Macaé, Rio de Janei-ro state, Brazil. Aspects of the installation were described, highlighting the economic data communication structure and the expedited nature of the selection of input param-eters used for TUC configuration.

Analysis of performance for three major intersections show a marked improvement brought by TUC over TOD plans. Results are particularly encouraging given the con-ditions of the installation. GPRS/GSM communication in the city can reach round-trip latencies of up to 10 s, thus introducing uncertainties in the measurements. Moreover, some loops have been already lost due to inadvertent works in the pavement. Such shortcomings highlight TUC’s ro-bustness that arises from region-wide consideration of

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Day Average:

TOD: 8.7 km/h

TUC: 14.7 km/h

FIG 6 Comparison of time-of-day (TOD) and TUC, intersection 6. (a) Traffic flow, approach 1. (b) Equivalent speed, approach 1. (c) Traffic flow, approach 2. (d) Equivalent speed, approach 2.


measurements for local control decision and sampling pe-riod of one cycle.

In conclusion, the preliminary data shown in the paper indicate that it is possible to improve traffic management without necessarily using high-capacity data links and accurate traffic modeling for control de-sign. TUC has proven to be relatively cheap to install when compared to established commercial systems which, in other studies, have performed similarly well or slightly worse [12]. Overall, the TUC strategy is well appropriate for cities that would like to deploy high- performance real-time traffic control systems without the more stringent installation requirements of more traditional systems.

AcknowledgmentThe authors acknowledge the contribution of techni-cal personnel at Macaé Trânsito e Transportes (MAC-TRAN), Mrs. Lais Meirelles and Mr. Fabiano Lima and consultant Mr. Pedro Bortolotto. Werner Kraus Junior thanks CNPq and FINEP/SEBRAE of Brazil for partially supporting this work through grants 310374/07-3 and 024/07 respectively.

About the AuthorsWerner Kraus Jr. was born in Blu-menau, Brazil, in 1964. He holds B.El.Eng. (1986) and M.El.Eng. (1991) de-grees from the Federal University of Santa Catarina, Brazil, and a Ph.D. (1997) from the Autralian National University. Since 2000 he has been

with the Department of Automation and Systems Engi-neering at the Federal University of Santa Catarina. His main interests are control of urban mobility systems and cooperative systems for traffic management and control, with emphasys in the implementation of prototype sys-tems in real scenarios.

Felipe Augusto de Souza graduated in Control and Automation Engineering from the Federal University of Santa Catarina in 2007. He is currently a mas-ters student at the same university, with main interests in the area of urban traf-fic control and software engineering

applied to urban traffic management and control systems. He is a recipient with co-authors of the best paper award in 6th IEEE Conference on Automation Science and Engineer-ing (CASE 2010).

Rodrigo Castelan Carlson received the Bachelor degree in Control and Automation Engineering (2004) and the Master degree in Electrical Engineering (2006) from the

Federal University of Santa Catarina, Brazil; and the Bachelor degree in Business Administration (2006) from the University of the State of Santa Ca-tarina, Brazil. Since 2007 he is a PhD candidate and a research assistant at the Technical University of Crete,

Greece. His main research interests include automatic control and optimization theory and applications to traffic and transportation systems.

Markos Papageorgiou received the Diplom-Ingenieur and Doktor-Inge-nieur (honors) degrees in Electrical Engineering from the Technical Uni-versity of Munich, Germany, in 1976 and 1981, respectively. He was a Free Associate with Dorsch Consult, Mu-

nich (1982–1988), and with Institute National de Recher-che sur les Transports et leur Scurit (INRETS), Arcueil, France (1986–1988). From 1988 to 1994 he was a Profes-sor of Automation at the Technical University of Munich. Since 1994 he has been a Professor at the Technical Uni-versity of Crete, Chania, Greece. He was a Visiting Profes-sor at the Politecnico di Milano, Italy (1982), at the Ecole Nationale des Ponts et Chausses, Paris (1985–1987), and at MIT, Cambridge (1997, 2000); and a Visiting Scholar at the University of California, Berkeley (1993, 1997, 2001) and other universities. Dr. Papageorgiou is author or editor of 4 books and of over 350 technical papers. His research in-terests include automatic control and optimization theory and applications to traffic and transportation systems, wa-ter systems and further areas. He is the Editor-in-Chief of Transportation Research—Part C. He also served as an As-sociate Editor of IEEE Control Systems Society—Confer-ence Editorial Board, of IEEE Transactions on Intelligent Transportation Systems and other journals. He is a Fellow of IEEE. He received a DAAD scholarship (1971–1976), the 1983 Eugen-Hartmann award from the Union of Ger-man Engineers (VDI), and a Fulbright Lecturing/Research Award (1997). He was a recipient of the IEEE Intelligent Transportation Systems Society Outstanding Research Award (2007) and of the IEEE Control Systems Society Transition to Practice Award (2010). He was presented the title of Visiting Professor of the University of Belgrade, Ser-bia (2010).

Luciano Dionisio Dantas received the Diploma of Control and Automa-tion Engineer and M.Sc. in Electrical Engineering degree from the Universi-dade Federal de Santa Catarina, Brazil in 2003 and 2005 respectively. He was enrolled as project engineer in the pilot


implementation of TUC in Maca, Brazil from 2005 to 2008. He is currently, since 2009, seeking the PhD degree at the Institut fr Verkehr und Stadtbauwesen—Technische Uni-versitt Braunschweig, Germany in the area of Traffic Con-trol Systems.

Elias B. Kosmatopoulos received the Diploma, M.Sc. and Ph.D. de-grees from the Technical University of Crete, Greece, in 1990, 1992, and 1995, respectively. He is currently an Associate Professor with the De-partment of Electrical and Computer

Engineering, Democritus University of Thrace, Xanthi 67100, Greece and a Senior Researcher with the Infor-matics & Telematics Institute, Center for Research and Technology–Hellas (ITI-CERTH), Greece. He was a faculty member of the Department of Production Engi-neering and Management, Technical University of Crete (TUC), Greece, a Research Assistant/Associate Profes-sor with the Department of Electrical Engineering-Sys-tems, University of Southern California, CA, USA, and a Postdoctoral Fellow with the Department of Electrical & Computer Engineering, University of Victoria, B.C., Can-ada. Dr. Kosmatopoulos’ research interests are in the ar-eas of adaptive optimization and control, energy efficient buildings, robotics swarms and intelligent transportation systems. He is the author of over 40 journal papers. Cur-rently he is leading the intelligent control developments in 4 European Commission-funded projects involving swarms of f lying robots, swarms of underwater robots, positive-energy buildings and traffic control systems.

Eduardo Camponogara received the Ph.D. degree in Electrical and Comput-er Engineering from Carnegie Mellon University in 2000. After being a post-doctoral fellow at the Institute for Com-plex Engineered Systems, he joined the faculty of the Department of Automa-

tion and Systems Engineering at the Federal University of Santa Catarina, Brazil. His research interests include sys-tems optimization, distributed decision making, and traffic control engineering.

Konstantinos Aboudolas received the Diploma, M.Sc. and Ph.D. de-grees from the Technical University of Crete, Greece, in 1999, 2003, and 2009, respectively. He is currently a Postdoctoral Fellow in the Informatics and Telematics Institute at the Centre

for Research and Technology Hellas, Greece. From 1999 to 2009, he was a Research and Teaching Assistant at the Technical University of Crete. His research interests lie in the application of control and optimization techniques to transportation systems, multi-robot systems, and further areas. Dr. Aboudolas is the co-author of some 20 journal and conference papers. He is a member of the IEEE and the Technical Chamber of Greece.

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