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Models and measures to evaluate a traffic signal

system

L. Marescotti, L. Mussone

Department ofDisegno Industriale e Tecnologia dell 'Architettura,

Polytechnic of Milan, Via Bonardi, 3, 20133 Milano, Italy

Department ofSistemi di Trasporto e Movimentazione, Polytechnic

of Milan, P.zaL Da Vinci, 32, 20133 Milano, Italy

Email: mussone@cdc8g5. cdc.polimi. it

Abstract

The paper deals with the scientific problem of the evaluation of traffic signal systems and itanalyses, in the light of European experience, which variables must be collected and how toweigh their non linearity to create homogeneous comparisons between different systems andurban traffic networks. Variables and the measure of their accuracy must be clearly defined totransfer results from one urban traffic scenario to another.The Italian urban networks of Morbegno, Padua, Parma, Piacenza and Terni, are then analysedto single out the best experimental field collection for measurement methodology andcomparison application. Padua was selected on the basis of some peculiarities and a fieldcollection, which will be the subject of a further paper, has been going on since last winter(1996).

1 Introduction

The measurement of a traffic signal system capability of controlling traffic, for asingle intersection or a complex urban network, is neither easily carried out ortabulated except after one has resolved some theoretical and methodological issues.

Variables and the accuracy of their measurement must be clearly defined as it isimportant to understand how to transfer results from one urban traffic scenario toanother.

The necessity of a more general approach makes the problem a hard oneespecially because of frequent changes in road layout and regulation, and becausevehicular traffic is a stochastic process both in flow variables (speed, density, flow),its composition (such as percentage of heavy vehicles) and meteorological conditionsand where driver behaviour plays a role that is not yet well-defined.

Transactions on Modelling and Simulation vol 16, © 1997 WIT Press, www.witpress.com, ISSN 1743-355X

666 Computer Methods and Experimental Measurements

To answer to the several implications consequent to a public administrator choiceit is useful to prepare an operative scheme and a methodology adaptable to a largenumber of cases of interventions carried out parallel to the current signalisation suchas structural (for example a network change for a new road) or management (such asone-way roads) interventions.

In the above-mentioned cases an operative method needs to know also how toevaluate the characteristics of the road network to which the signalisation is appliedin such a way to reasonably limit the research of possible signalization effects. In thiscontext the use of "reasonably" means that the understanding of the system and theformulation of aims cannot be absolute; this does not mean that this approach is notcorrect but it points out that in many cases several solutions are feasible and not easycomparable: the question is not to identify a possible unique solution but to chooseamong the possible ones.

Concerning the evaluation of the effects of a particular type of signal controlhomogeneous data before and after intervention (or in other terms ex-ante and ex-post) are needed (Marescotti and Mussone, 1995 [9]; Mussone and Turrini, 1995[10]). The non-linearity of traffic and the trend of vehicle flow to adapt themselves tonew signal regulations implies several theoretical and practical difficulties.

The study of these aspects and the analysis of signal control effects are beingcarried out by European research (EVA, 1991[5]; QUARTET, 1993 [13]; Primavera,1993 [12]; CORD, 1994 [2]; ITS, 1995 [7]; Smith and Ghali, 1994 [14]) whichproposes also the variables to be used to evaluate the system performance (Tab. 1).

Some proposed techniques to measure signal control system efficacy are based onsoftware simulators which, set on field data, give the data base (substituting the fieldcollection) on which to carry out the evaluation. This approach is not withoutdifficulties and above all the resolution of evaluation cannot be less than theresolution of the model used; for this reason and because we want to do nohypotheses on flow characteristics and to have data with the necessary accuracy (itcan be evaluated only after the measures) in this work the measurable variables andthe measurement method are looked for by field collection.

The subject of evaluation has obviously a great impact also on public transport,on control strategies supporting it (Nelson et al., 1993) [11] and on regulationstrategies such as road pricing (Ghali and Smith, 1992) [6]. We must take intoaccount the efforts made by developers of traffic management integrated systems whohave to tackle the evaluation criteria of different management choices, such as eachreal time signal control policy (Brookes and Bell, 1991 [1]).

After analysing technical papers nothing was found about the proposal or theexperimentation of variables keeping into account traffic non linearity and, above all,the urban road network structure: the last concerns the measure of increase in theoverall performance related both to the total ex-ante (before intervention) flow and toits ratio with the network capacity (where capacity means maximum possible flow).

The last question is not negligible because it is possible to obtain similar resultsby signal control, having quite different performance, only changing to ownadvantage the above-mentioned flow/capacity ratio as described in the followingparagraphs. The methodological difficulty of evaluation consists mainly in comparingdifferent traffic patterns (before and after the intervention or among different


Computer Methods and Experimental Measurements 667

scenarios) and, hence (apart from operative measure difficulties) (Curd andMarescotti, 1995)[4] in singling out the variables through which to homogenise allthe elements affecting traffic flow. Besides, it is necessary to write the correctformula by which these variables must weigh non linearity in flow.

The methodological course must be based on the ratio flow/capacity for allthe network links calculated before and after the interventions. Starting fromthis assumption a series of preliminary operations follows such as to separateinterventions of real traffic control from those of the structural type whichcannot and must not be taken into account for this type of evaluation or fromtraffic calming or increase of public transport use.

Data to be collectedLink/Junction Route/networkMean Journey Time Mean Journey TimeVariance Journey Time Variance Journey TimeMean Speed Mean SpeedVariance Mean Speed Variance Mean Speed"Speeding" vehicles Mean Fuel ConsumptionNumber of stops Number of stopsAccident Statistics Accident StatisticsMean Queue Length Mean Traffic densityMean flow Mean flowPollution Emissions Pollution Emissions

Mean Vehicular OccupancyVariance of Mean Bus Travel Time

Table 1: List of criteria of evaluation to be collected on links androuts or networks in field trials (Primavera, 1993 [12]).

1.1 The scientific problem

The scientific problem of evaluation concerns fundamentally two linked andinteractive aspects depending on the same characteristics of the phenomenon. Trafficis strongly non linear and it depends not only on geometrical, technical and functionalcharacteristics of road network but also on driver behaviour.

Besides this last aspect driver self-learning and self-regulation must be taken intoaccount as in each social system; in fact, behaviour is not only, in some way,deterministically and statistically unpredictable but it is at the same time a function ofthe road network system which is characterised by different regulations and trafficcontrols.

Some solutions to vehicular traffic problems are solved by interventions notrelated to traffic control. This involves policies that reinforce and change public orprivate transport, protecting some areas from vehicular traffic (limiting or excludingtraffic), separating functionally, where possible, traffic types (Curti et al, 1992) [3].These aspects help to define the real relationship between traffic and pollution and, asa consequence, to better express and understand emergency policies and situations. In



evaluating results, therefore, the limits and intervention possibilities on traffic andpollution by central real time control systems must be very clear.

The question, therefore, must be put on the level of possible control and on whatmeasures to carry out to verify control efficacy. As a consequence of this statementcorrect activity both in the phase of analysis of the actual system and in the phase ofplanning of a new system is particularly important.

In the phase of analysis of an existing system it is necessary to study the wholesystem in such a way as to recognise the different parts which led to a certainfunctionality and type of working.

Because the variable "travel time" for a link in a network is itself a non linearfunction of flow it is important to try to order the elements which to a greater extentaffect it so as to single out and study only those defining system capacity both fromthe structural point of view and those interacting with traffic flow:- elements depending on morphological peculiarities of a place and not depending

on regulation interventions;- elements depending on user behaviour and not depending on regulation

interventions;- elements depending on user behaviour but also depending on regulation

interventions (adaptation or system self-regulation); this aspect is probably relatedto a short (some days) transient starting period when every type of behaviour triesto adapt itself to the introduction of new rules;

- road network changes;- changes in circulation regulation;- functional division of traffic;- interventions on modal split.

During the phase of system planning (besides that listed in the previous phase) itis necessary not only to recognise the correct aims and resources proper to differentoperative levels but also to apply step by step various policies which can be used toachieve the aim of traffic control.

1.2 Definition of variables to be controlled and measures to be adopted

On the basis of the identification of the aims and the consequent critical elements tobe solved or to be put under surveillance, methods and operative criteria can bedefined to calculate control variables.The quantitative analysis initially may be articulated into four levels:a) analysis and network scanning: hierarchy and functional levels in order to single

out at least two functional types of networks, the main and secondary network;traffic basin; environmental island; characterising of traffic directrixes; areas andcritical zones (homogeneous, for example, for congestion level); crossing areasonly (i.e. network zones with no interaction with other zones);

b) capacity and functionality of the network: maximum capacity; real capacity(detected in different traffic and meteorological conditions); network saturation;

c) travel time in some network sections, such as intersections and boundary zones:splitting up of travel times for each O/D in order to single out more significantand critical paths;



d) quantitative description of traffic in the urban environment, possibly subdividedinto zones: collection of O/D matrices, census data of commuters for work andstudy; computing of minimum route (to calculate the variable vehicle*kmnecessary to evaluate the network saturation).From these elements the definition of network condition can be worked out and

then it can be classified into classes related to the "network state". With the aim ofdefining these "network states" several activities are necessary:- topological interpretation of the network, concerning links and junctions of the

main network (urbanistic interpretation of land use);- quantitative singling out of variables describing network functionality (flow,

density on links and junctions, travel time on main junctions, mean speed on thewhole network and on each link);

- singling out of network functionality level as a product of the three previousaspects.Mean speed must be studied in order to ascertain its relationship with the traffic

level of service (TRB, 1985) [15] and its correlation with the state of service for eachlink.

1.3 How to weigh non linearity

Another problem related to traffic measurement deals with the manipulation of nonlinear variables (for example, flow). Nevertheless, since this aspect is quite complexin a motorway environment, it is even more complex in an urban environment.

Non linearity plays a significant role when a cost functional, describing theperformance of the whole network, must be defined: in fact each traffic parameter,such as travel time, queue length, fuel consumption, is a non linear function of flow.

From these observations it appears necessary to establish whether total queuetime could be evaluated by simple sums then averaged on the whole network withoutconsidering the number of signal cycles necessary to cross an intersection. Theevaluation would be more correct if it included user perception of the level service,different traffic evolution when long or short cycles are applied, different fuelconsumption and atmospheric and acoustic pollution.

In any case, if queue length exceeds the link length, non linearity assumescatastrophical characteristics and certainly a simple sum of travel times cannotdescribe the quality and quantity of this phenomenon. It is important when definingthe examined network functionality to separate the primary network, where flowshave high values and characterised by crossing and interurban O/D, from thesecondary one where flows are low and characterised by local O/D.

To homogenise the different possible situations related to vehicle queues andcharacteristics, it is preferable to observe the number of signal cycles necessary toleave the intersection in addition to the total queue time. In fact, if it is possible toincrease the cycle length to reduce the number of cycles, on the other hand theincrease in cycle length is limited by the geometrical dimension of non-favoured linksso that the number of necessary cycles to leave can represent a good estimator.


670

Flow [veh/lOmin]350

Computer Methods and Experimental Measurements

Tipical flow pattern in a weekly day

0 1 2 3 45 6 1 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Figure 1: Typical trend in flow values for a weekday with a 10 minute detectioninterval.

Assuming that a queue forms when the last vehicle arrived in the red phase cannotleave the intersection, a homogenisation function can be based on the ratio betweenthe queue length and the link length. Two possible cases may arise: the queue is eithermuch shorter than the link or quite close to it. In Fig. 2 the second case is faced. Thevalues LI, L2, ..., L,, represent the queue which can be discharged by 1,2,....,n signalcycles, L(Hnk) the link length, L,,y, L(,,.i)y the queue length taking into account vehiclesarriving from upstream intersection. This formulation is easy to make general andallows us, by changing parameter values, to investigate how L; values affect finalevaluation.

The analytical form of the function is:

if n <n;if n Sn;

where n; is the number of cycles, threshold between the two possible cases ofqueue length. (3 takes into account interaction among intersections though it is limitedto the queue exceeding the link. Obviously it is assumed that 6, a, (3 > 0. When thequeue becomes greater than the link the function should become unlimited.

Parameters a, P e 6 allow us to evaluate system performance when differentvalues are considered; this operation represents a sensitivity measure of the signalregulation process. If field data are treated in such a way, different control strategiescan be compared as a function of their sensitivity to multiplicative, exponential orinteraction parameters.

It must be underlined that LI, L2, ..., L̂ are dependent on the cycle and phaselength and hence they can be (in a dynamic system) time varying; this fact makesmeasurements more difficult because it is necessary to follow queue evolution andvehicle position inside the queue in order to single out the correct function value.

An alternative to the measuring of queue length is to measure waiting time atintersections (or similarly the time required to cross the intersection). This could becalculated as ratio between queue length and mean speed of flow (by means of usingqueue length and speed as an indirect measurement); in case of congestion



(overtaking of link length) a time interval equal to the congestion discharge time mustbe added avoiding besides the unlimited term when the queue length method is used.

f(Lcoda)

1 -(n-l)P

11- (5)0. e1 -r (4)0. 0

J

Lqaeue{m)

0 LI L2 L4 L5 L(n-l)y Lay Llink

Figure 2: Homogenisation function when the queue length is close to the link length.

The measurement of mean speed is not very difficult but it is probably not verysignificant because of the wide spread characterising this type of variable (Festa andNuzzolo, 1992 [8]) and contradicting, in some way, the aim of evaluating merely thedishomogeneity of the process. It could be more adapt a direct and contemporarymeasurement of density, flow and speed which, on the other hand, is not easy to carryout unless a video recording system is used.

1.4 Comparing performance of different systems

This problem is much more complex than the previous one and it requires thatnetworks and interventions are clear defined in order to compare system performancebefore and after interventions. However it is difficult to prepare direct comparisonsbetween different systems, but it might be possible, on the other hand, to comparelocal situations due to signalisation built by the same methodology.

Firstly, a unique evaluation criterion of network capacity must be adopted. Thismeans that it is possible to evaluate, even if only qualitatively, the network level ofservice with a similar daily traffic demand (but keeping into account traffic nonlinearity).

Secondly, a topological network analysis is necessary, capable of verifying ifsimilar planning criteria were applied to similar traffic demand.

Thirdly, plant and maintenance costs must be analysed because not only theefficacy must be evaluated but also its ratio to the related investment.



2 The signal system evaluation in the case of the city of Padua

Due to the complexity of elements affecting the final evaluation it is better that theproposed methodology is applied not only to a single scenario; in fact a single casemay have a particular urban and anthropic structure which cannot, obviously, bemodified for the experiment. Initially one homogenous system can be tackled andthen methods and results relating to other less homogenous applications can beverified.

By means of using the same traffic data collected by the traffic control systemboth the utility and significance of data in respect of the same traffic control andwhich data analysis to work out in order to increase knowledge about controlstrategies must be singled out. For this reasons only recent and homogeneous signalsystems applied in different urban environment were considered.

The main characteristics of urban networks singled out for this research can besummed up by the dimension of the plants and the urban planimetric schemes of thefollowing five sample cities: Morbegno, Padua, Parma, Piacenza and Terni. The finalchoice of Padua depends essentially on the long time interval of the working of itssystem, the type of maintenance and available documentation. In fact signal controlhas been in operation since 1987 and there are historical archives of traffic data whichcan be used to evaluate system performance in the past years.

In order to set up the analysis, four different research directives are carried out:retrieval of available documents concerning the progress of the signal regulationsystem (also of investments), set of aims, data collection, analysis and evaluation ofresults. The most crucial activity, for what concerning available resources, is surelydata collection which usually must be extended and complex preceded by a detailedstudy for the collection plan.

Because collection must be carried out also when the system is not working (tocompare results), the problem also concerns administrative red-tape which should notbe under-estimated.

As concerns the substance of the plan realisation schedule and the liability of theinitial system plan settings and working out, it must be well defined pursuing thefollowing aims: aims and reasons, traffic control, solution to specific traffic saturationor congestion, system maintenance, traffic control over the whole area, control of cityentries.

In order to analyse the case study two levels of information and knowledge mustbe collected:

-functionality of the plants in respect of recurrent breakdowns, times offunctioning of the system at optimal or limited level;

- verification by manual collection, or by using suitable detectors of systemperformance also for those areas in which the signal system will be installed in afuture phase.

Besides the study needs to measure not only the current functionality of thesystem but also the effects of investments made by the "Programma triennale per latutela dell'ambiente" (Triennial Program for environment protection) 1994-1996.



3 Conclusions

Because of the necessity of having analytical tools to evaluate the performance ofsignal systems, operators in this sector must be persuaded to define a set of criteriasuch as the homogenisation function, network capacity evaluation and its saturationdegree and other tools by means of which to carry out comparative evaluationsbetween various systems.

Probably, considering the complexity and variability of traffic, a singleformulation for solving the evaluation problem does not exist; what is more, thecontinuous interaction (or interference) between control elements and of an urbantype makes the singling out of an absolute reference difficult.

This paper proposes some methodological outlines of evaluation by means ofwhich to reach a more accurate and generalised definition of the concept ofevaluation. Quoted references may allow the reconstruction of the logical wayfollowed in order to set up the research and outline the aims that can be achieved.

Analysis shows the difficulty in defining system performance, because the aimsof signal control can arise from different necessities: traffic control, trafficfluidification, increase in network capacity, violation control, working control of fielddevices, simplification or reduction of maintenance costs.

This research is developed within the Progetto Finalizzato Trasporti 2 of CNR(National Council of Research) and it will be carried on with a series of fieldcollections during the coming years.

Acknowledgements

Thanks are due to City Administration and Technical Offices of Comune di Padova,Parma, Piacenza, Morbegno and Terni for their kindness and co-operation. This paperis funded by CNR, contract. n.96.00183.PF74.

References

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2. CORD : "Guidelines for assessment of transport telematics applications in urbantraffic management and information", Delivarable No. AC07, DRIVE II, ProjectV2056, June '94, R.Burton , edt, 1994.

3. Curti V.M., Marchente M., Marescotti L. : "Simulazione, controllo e regolazionedel trasporto. Metodo di regolazione del traffico e gestione della mobilita inaree metropolitan ", PFT2, 28 Settembre 1992.

4. Curti V.M., Marescotti L. : "La classificazione del traffico e i dati per lavalutazione", Firenze 31 Marzo 1995.

5. EVA : "Evaluation process for road transport informatics", EVA manual, DriveProject v 1036, December 1991.



6. Ghali M.O., Smith MJ. : "Comparisons of the performances of three responsivetraffic control policies, taking drivers' day-to-day route choices into account ",Traffic Engineering Control, pp.555-560, October 1994.

7. ITS : "Comparative performance of three traffic control systems: TRANSYT 9,SCOOT 2.3 and SPOT", Primavera Drive project, February 1994

8. Festa D.C., Nuzzolo A. : "La dispersione della velocita dei veicoli nelle stradeurbane", In Modelli e Metodi per Panalisi ed il controllo delle reti di trasporto,E. Cascetta, G. Salerno, eds., pp. 173-194, F.Angeli, 1992.

9. Marescotti L., Mussone L., a cura di :"La valutazione dell'efficacia dellepolitiche di controllo in tempo reale del traffico urbano", Firenze 1995.

10. Mussone L., Turrini M., a cura di : "Dalla sorveglianza al controllo in temporeale del trqffico veicolare urbano: Obiettivi, Specifiche funzionali e validazioneex-post nella defmizione dei capitolati", 31 Marzo 1995 Firenze.

11. Nelson J.D., Brookes D.W., Bell M.G.H., Silcock J.P. : "Approaches to theprovision of priority for public transport at traffic signals: a Europeanperspective", Traffic Engngn. &Control, pp. 426-428, 431, September 1993.

12. PRIMAVERA : "Data collection & evaluation methodology", Deliverable No.11, DRIVE project V2016, September 1993.

13. QUARTET : "Evaluation framework", Deliverable No. 11 A, DRIVE projectV2018,July 1993.

14. Smith MJ., Ghali M.O. : "Two models for assessing Urban Traffic Control androad pricing strategies ", Traffic Engineering Control, pp. 245-249, April 1992.

15. TRB, Transportation Research Board (1985): "Highway Capacity Manual",Special Report 209, National Research Council, Washington, D.C. 1985.


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