1-s2.0-0377221796000100-main

E L S E V I E R European Joumal of Operational Research 92 (1996) 550-572

EUROPEAN JOURNAL

OF OPERATIONAL RESEARCH

New Operations Research and Artificial Intelligence approaches to traffic engineering problems

M a u r i z i o Biel l i a, *, P i e r f r a n c e s c o R e v e r b e r i b

a Istituto di Analisi dei Sistemi ed Informatica, Consiglio Nazionale delle Ricerche, Viale Manzoni 30, O0185 Rome, Italy b Department of Production, Systems and Computer University of Rome "Tor Vergata", Via della Ricerca Scientifica, O0 133 Rome, Italy

Abstract

The purpose of this paper is to review some of the main Operations Research (OR) and Artificial Intelligence (AI) research achievements in the field of traffic engineering, with particular reference to road traffic control. Therefore, the performances of the discussed approaches are illustrated and compared with respect to the features and the requirements which characterize some of the most relevant problems in the area of interest. The potential for a combined use of typical OR and AI methods and techniques when solving complex real-world, large-size traffic control problems is also emphasized. Finally, some concluding remarks are drawn that highlight the new challenges awaiting for the scientific community efforts.

Keywords: Mathematical programming; Simulation; Artificial Intelligence; Traffic engineering; Road traffic control

1. Introduction

The perception of the key role played by the transport sector in the development of a world econ- omy, together with the significant and continuous growth of the demand of mobility, have led to the activation of several research programs (such as DRIVE and PROMETHEUS in Europe, IVHS in the United States of America, and RACS, AMTICS, VICS and SVSS in Japan) that aim at finding new solutions to the main problems originating from the widespread use of traffic networks, based on the application of information technology and telecom- munications (Transportation Research Circular, 1993).

* Corresponding author.

Due to the large amount of expected benefits, the majority of cities are currently developing system architectures which incorporate functions such as on-line traffic control, public transport and parking management, traffic and travel information, demand management, route guidance and pollution monitoring. Technical advances in computer science offer traffic engineers an entire set of programming options and control tools to implement the various strategies required to cope with the broad range of situations originating from pattern changes in traffic behaviour. Presently, advanced telematic systems provide a data-rich, real-time environment which gives rise to a new challenge awaiting for the scientific community efforts (Bell, 1992).

In this framework, new Operations Research (OR) models are currently being developed, together with practical on-line heuristic algorithms for traffic engi-

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neering. If the application of such techniques to transportation network planning and design is not new (see, e.g. Florian, 1994), there is a growing interest in their use in the related areas of monitoring, management and control, in view of improving the safety and efficiency as well as reducing the environmental impact of the transportation system (Papageorgiou, 1991a). At the same time, traffic engineers have begun to recognize the significant progress made in the development and validation of Artificial Intelligence (AI) and knowledge-based approaches to transportation problems, both for the high flexibility, extensibility and interactivity de- grees shown by these methods in solving complex real-world tasks and for the computational burden that typical OR optimization procedures often impose on the available hardware (Bielli, Ambrosino and Boero, 1994).

Scientific research has promoted the application of OR and A/techniques in the study of any possible transportation mode operation; for instance, air traffic control problems are addressed by means of several OR models in Bianco and Bielli (1993), while AI methods are discussed by Goslin (1987), Nevertheless, road traffic has been recognized not only as the transport sector where benefits expected from the application o f OR methods and A / t ech - niques are potentially the most relevant, but also:as the most challenging sector for assessing feasibility, effectiveness and usefulness of s u c h approaches (Ambrosino et al., 1991; Sussman, 1992),

At present, conventional software systems based on OR models and mathematical programming techniques are largely in use to provide support in a variety of tasks including traffic analysis, signal timing; simulation and evaluation:of alternative traffic management strategies (McDonald and Hounsell, 1991). On the other hand, A/techniques can provide significant contributions in two complementary directions (Ambrosino et al., 1991):

(a) to overcome some of the major limitations of current traffic control technology and extend the range of situations the control system is able to deal with (e.g. traffic congestion, incidents e tc . ) ;

(b) to provide traffic control operators with better support for managing all the system facilities and coping with the increasing complexity and flexibility of available technology.

The focus of this paper is on the road traffic control problem, which requires to select the best set of controllable inputs (e.g. signal timings) from an admissible region, so as to achieve a desired process output (e.g. minimum travel delay). If the task of the control strategy is performed continuously during process operation, by using current measurements, the system performs a real-time (or traffic-responsive) control. In particular, this is feedback (closed loop) if it makes use of process variable measurements, or feedforward (open loop) if it makes use of disturbance measurements, both nonpredictable (e.g. incidents) and predictable (e.g. demand, Origin-De- stination patterns). On the other hand, if control input specification is made before operation, by using historical data, the system performs a fixed-time control. Public transport and fleet management needs are often taken into account, by allowing priorities at signalized intersections.

In face of the huge amount of articles and books dealing with OR and /or AI applications to traffic engineering problems, the authors felt to have no other choice than narrowing the scope of their pre- sentation to a proper subset. Therefore, this survey does not claim to be exhaustive, although it aims not to come up against too many relevant omissions. In any case, these are not intended to down play the significance of the proposed approaches; rather, it is to be hoped that they are possibly due to space limitations and not to the authors' negligence.

Based on the foregoing remarks, this paper is organized as follows. Section 2 presents some of the main OR mathematical and computer simulation models for road: traffic management, with particular reference to Origin-Destination (O-D) trip matrix estimation, traffic assignment and prediction. Section 3 analyses the features and the role of the major AI techniques - namely, Qualitative Reasoning (QR), Artificial Neural Networks (ANN), constraint programming and :others - in traffic engineering prob-

automatic incident most relevant OR

junction, arterial or discussed in detail.

Then, the rationale for AI Knowledge-Based (KB) approaches and their combined use with classic optimization procedures is remarked. Finally, in Section 5, some concluding remarks are drawn.

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2. Operations Research models and algorithms

Mathematical programming and computer simulation models have received great attention for many years in the field of road traffic engineering, both in urban traffic management (see, e.g. Bianco, Bielli and Speranza, 1993) and freeway traffic control (see, e.g. Papageorgiou and Schimdt, 1991; Papageorgiou, 1991b; Allsop, 1991a). In particular, this section reviews the most common approaches to traffic analysis, assignment and forecasting problems.

2.1. Origin-Destination (O-D) trip matrix estimation

Knowledge of O-D flow patterns is essential for traffic control to reduce congestion. Static O-D estimation methods use traffic counts on a subset of observed links and additional a priori information provided by a target O-D matrix in order to set up a system of equations which in general takes the following form:

Min Fl( t, t ) + F2( v, ~) /), t

s.t. v = M ( t ) ,

where t is a target matrix, represented as the column vector of trips between each O-D pair, ~ is the vector of observed link flows, t and v are vectors representing the O-D matrix and link flows to be estimated respectively, Fl(t,-t) and F2(v, ~) are generalized distance measurement functions and M(t) is the assignment map describing the relationship between the predicted link flows and O-D matrix. Common methods include entropy maximization (Van Zuylen and Willumsen, 1980), maximum likelihood (Spiess, 1987), generalized least squares (Cascetta, 1984) and Bayesian inference (Maher, 1983) (for a comprehensive review, see Cascetta and Nguyen, 1988).

In the uncongested network case, a flow-independent, proportional assignment can be performed. Moreover, when traffic counts and the assignment proportions are assumed to be error free, the reference model family turns out to be greatly simplified, as follows:

Min Fl( t, i~) /. ',t

s.t. ~ = Pt,

where P is the assignment proportion matrix. Such models have computational advantages, since they represent single convex optimization problems. However, inaccurate estimates are produced in the general case when their underlying assumptions are not fulfilled and several methods have been devised to cope with more realistic practical situations (see, e.g. the bilevel programming formulation for congested networks given by Yang et al., 1992).

Static methods cannot track time-varying O-D patterns, as traffic-responsive on-line control systems would require. Therefore, dynamic estimation methods have been defined which take into account the short-time variation of traffic measurements, while providing a unique and bias-free solution for O-D flows. Thus, such methods avoid the waste of information typical of static methods, that consider only accumulated traffic counts (Cremer, 1991).

Dynamic O-D estimation procedures are based on causal relationships between time records of traffic network entry and exit flows. Therefore, it seems that they can be applied to small-scale systems (such as intersections), where these records may be suitably assumed to be correlated. On the other hand, it is reasonable to believe that the same causal relationships get weaker in large networks, because of various effects (e.g., the impact of signals on traffic f low/t ime functional relationship). Hence, the rationale for dynamic estimation procedures will also diminish and a combination of dynamic and static methods represents the most promising approach in such cases (Keller and Ploss, 1987).

Since international research activities aim at equipping vehicles with communication devices for route guidance and driver information systems for traffic control purposes, O-D trip matrix estimation procedures ought to be proposed in the near future that make effective use of the additional information both provided to and gathered by individual cars (Cremer, 1991).

2.2. Traffic assignment

Traffic assignment models are mathematical programming schemes intended to simulate the interaction between transportation supply (that is, the set of

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facilities and means available to the users of the network) and travel demand (expressed by the number of users of the network at a given time of the day), producing as a result a flow pattern on the links of the network. Travel demand depends on travel costs on links and paths, which in turn depends on flow patterns. Therefore, taking the costs as a measure of supply, a mutual interaction is generated between demand and costs that defines simultaneously the demand level for each O-D pair and the flow pattern resulting from the optimal distribution of demand on available paths. An equilibrium is reached when the flow pattern produces a set of costs which induce a choice of paths generating the same flow pattern (Cantarella and Sforza, 1991).

Depending on the behavioral assumptions con- cerning individual route choice, it is possible to conceive either a user optimal traffic assignment, when users attempt to minimize individual travel cost, or a system optimal traffic assignment, when users cooperate in minimizing total transportation cost over the road network (Wie, 1991). Costs are usually defined as an increasing function of travel time and expected delay with respect to desired destination arrival time. According to the Wardrop's principles, at equilibrium each commuter going from a given origin to a given destination cannot reduce his total travel cost by changing departure time and/or route, given all other commuters' decisions.

Deterministic models, assuming that travelers are perfectly informed about network attributes and conditions, have to be distinguished from stochastic models, allowing the more realistic hypotheses of limited information, perception errors or specific habit for road users' behaviour. Stochastic approaches rely on discrete choice models which are built based on the main principles of random utility theory. Although stochastic models are the most appealing ones from a theoretical viewpoint, they

• greater computational effort in searching require a for an equilibrium solution (Cascetta and Nguyen, 1988; Cantarella and Sforza, 1991).

Dynamic traffic assignment models have also been devised based on mathematical programming, computer simulation or optimal control theory (de Palma, 1991). These models attempt to predict the temporal evolution of traffic flows on congested networks, where travel demands and costs vary over time and

space, not necessarily seeking an equilibrium condition. Thus, they are concerned with the way travelers adjust their route and departure time decisions, either within day or from day to day (Cascetta and Cantarella, 1991). Adjustment processes that de- scribe traffic pattern dynamics are characterized formally in order to give account of minor changes affecting constantly traffic networks, at both the system and the individual traveler level.

In this framework, there is an urgent need for software programs that may take into account the potentialities of new information technologies, together with emerging socio-economical requirements related to energy and the environment, In fact, theoretical and empirical models have been developed in order to include route guidance and information system impacts on travelers' behaviour in optimal assignment procedures (Bell et al., 1991; Ben-Akiva, Koutsopoulos and Mukundan, 1992; Mahmassani, Hu and Jayakrishnan, 1992; Barcelo' and Martin, 1994; Ben-Akiva, de Palma and Kaysi, 1994). Other models try to evaluate changes in pollution levels with respect to alternative driving cycles, through microscopic recording of time and speed in floating car measurements (Bruno and Improta, 1992).

D u e the great complexity o f the considered problems, no satisfactory model o f general u se has been produced so far. Indeed, it is very difficult and costly to evaluate dynamic processes, so that Mah- massani and Chang (1986) prefer to conduct microsimulation procedures where road users make daily decisions according to some exogenously given rules. Then, a special purpose traffic simulation pro- g r ~ gives the resulting congestion levels: and provides commuters with the actual travel times, that are the most impo~ant decision variables: regulating travelers' choices relative to the next day. According to the so-called bounded-rationality behaviour, ~ v e r s are assUmed to have anindifference band that includes the:range of acceptable arrival times at their destination, s0 that they will not modify: their subse- quent choices as long as such band is met (Bellei and Bielli,: 1992):

Computer simulation models and tools are well suited for traffic studies, either when analysing and understanding traffic behaviour, or when testing and evaluating alternative management and control strategies. Several simulation approaches and labora-

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tory experiments have been carded out that show great potentialities in the field of dynamic approaches to behavioral models in transportation, although they should necessarily be calibrated on real data (Barcelo', 1991a). At a macroscopic level, models of multimodal traffic flow assignment on large transportation networks (such as EMME-2 and SAT- URN) are currently used; a mesoscopic approach (such as CONTRAM) is suitable for medium-size networks; finally, fully microscopic simulation models (such as SITRA-B, MISSION, AIMSUN) are necessary to work at a local level, in particular at intersections (Bielli, 1992).

Macroscopic simulation models consider group- ings of vehicles and apply flow relationships to determine successive traffic states. The main parameters to be calibrated, apart from the physical description of the roadway, concern flow-density relationships, time-slice length, mean vehicle length, occupancy rate and speed thresholds between free flow and congestion (Barcelo', 1991b). On the other hand, microscopic simulation models deal with movement of single vehicles. Generally, they include a car-following and a lane-changing model that govern the traffic process and are programmed in a modular framework, allowing the user to modify easily all the underlying submodels in an interactive way (Barcelo', 1991a).

For instance, AIMSUN performs microsimula- tions on a discrete time sample basis by splitting the reference horizon into short fixed intervals called 'simulation cycles', in which all the elements of the system (namely: vehicles, traffic signal settings, etc.) are successively updated. The outputs of AIMSUN are an animated graphical representation of traffic network state as well as link flow and occupancy rate data collected by simulated detectors. Moreover, a set of statistics are produced about traffic flow, speed, vehicle delay and number of stops that could not be obtained in on-street trials (Barcelo', Ferrer and Montero, 1989). System improvements have taken to the development of a new version of the model, called AIMSUN 2, that includes the possibil- ity of modifying signal plans and other parameters (such as external flows and turning movement proportions at junctions) during the course of a simulation run.

2.3. Traffic forecasting

One of the major concerns in traffic engineering is the development, implementation and evaluation of mathematical models providing real-time short- term forecasts of traffic flows and travel times along all links of urban and interurban networks. In fact, traffic management and control centers need accurate information about traffic parameters and variables in order to maintain stable traffic patterns and reduce expected near-future congestions. Three types of traffic volume predictions can be distinguished, depending on time scale (Lesort, 1991):

(a) long term predictions (a few months), that can be used to forecast the required capacity of transportation facilities, or else compute fixed-time coordination signal plans;

(b) short term predictions (a few minutes), that can be used in adaptive regulation systems, where the implemented coordination plan is successively selected from a pre-stored package;

(c) very short term predictions (a few seconds to a minute), that can be used by real time regulation algorithms, which are highly responsive to traffic variations.

Several kinds of predictive models based on techniques of time series analysis are used for short term prediction: these include linear models using smoothed information (McShane, Lieberman and Goldblatt, 1976), spectral analysis (Nicholson and Swarm, 1974), autoregressive integrated moving average (ARIMA) models (Eldor, 1977) and filtering methods (Baras et al., 1979). All such models are endogenous, namely, their input is represented by previous values of the parameter to be predicted. On the other hand, very short term prediction models use previous measurements at upstream locations, rather than at investigated sites. Given that the relationships between flow volume and travel time cannot be identified precisely in an urban network, the most relevant problem becomes to estimate travel time from upstream to downstream points on the basis of flow volume information. As Lesort (1991) states, linear or piecewise linear models are usually built for this purpose. These models are first fitted to a repre- sentative data set and then corrected on-line.

Prediction models are also crucial to the develop-


ment and application of advanced transport telematics strategies, because they are related to within-day decisions on mode, route, time and even parking destination choice. Several prediction models have been studied and applied for specific purposes, par- ticularly within the DRIVE project, but they generally need to be cross validated (McDonald, 1994).

3. Artificial intelligence methods and techniques

Artificial Intelligence (AI) research field has been developed with the aim of emulating human problem-solving behaviour in complex real-world tasks. In recent years, the potentialities of expert systems and AI techniques in transport applications have received considerable and increasing attention within the traffic engineering community (Bonsall and Kirby, 1986; Bielli, Ambrosino and Boero, 1994). Essentially AI means knowledge processing. A knowledge base contains a set of facts and rules relevant to a specific domain, while the inference engine provides the methods for using the knowledge base through heuristic, largely nonalgorithmic procedures.

3.1. Qualitative reasoning and applications

Qualitative Reasoning (QR) represents a viable approach to tackle real-world traffic problems, where one is often dealing with uncertain, missing and erroneous data and has to incorporate knowledge which is present only as human expertise or heuristics and expressed in non-numerical or symbolic form. In such cases, it is not possible or even not necessary to know the exact value of the different parameters, while plausible confidence intervals for them are enough in light of proper decisions to be made. These may be related to analysis, interpretation, diagnosis, prediction, advising, p l ~ n g and design tasks and, in particular, to da~ completion, congestion monitoring, traffic assignment and forecasting problems (Wild, 1994).

Qualitative techniques greatly reduce some of the major disadvantages of quantitative approaches, Ac- tually, they are simpler than physical models based on the mathematics of continuous variables and differential equations, which characterize quantitative

methods. Moreover, they generate causal explana- tions of observed phenomena based on mechanisms that can be easily understood by humans, thus providing the basis for common sense modeling (Martin, Toledo and Moreno, 1994).

A qualitative representation of the fundamental diagram of traffic theory is needed in order to apply this method. The diagram has to relate the macroscopic traffic variables -flow, density and speed- through the definition of a proper 'quantity space', that identifies the range of traffic states (free flow, undersaturation, saturation, oversaturation, congestion). QR implies thinking about problems in terms of the involved concepts and then building the se- mantic network of interrelationships among variables. As a consequence, analyses can be carried out based on propagation mechanisms that are required to satisfy some locally and empirically defined constraints (according to the constraint propagation approach). Some advanced qualitative traffic control systems have been developed and partially implemented; the most remarkable among these include AURA and TRYS in Spain (Cuena, Molina and Martin, 1992; Cuena, 1989).

AURA is an expert system for traffic control on urban motorways by ramp metering and variable message signs. Incident and congestion detection, as well as traffic prediction, are achieved through a QR approach based on the theoretical method developed by de Kleer and Brown (1984). Thus, free-flow traffic dynamics is modeled by the continuity and dynamic differential equations, that can be trans- formed into corresponding qualitative relationships called confluences. These in turn are represented by sets of rules that generate possible state variable changes and alternative hypotheses about future traffic states. Since the confluence approach lacks an explicit representation of time and events, AURA is suitable for motorways, where continuous flow and unfrequent stops are prevailing features, but not so much for urban road networks (Cuena and Molina, 1994).

In order to overcome some of the main limitations of currently used QR approaches when applied to urban traffic control, an alternative (k, t)-formalism that is able to deal with temporal reasoning and qualitative representation of multidimensional parameters has been developed (Martin, Toledo and

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Moreno, 1994). According to such formalism, an adequate representation of urban traffic behaviour can be achieved only if traffic density values are known for each instant and each point in the network. The proposed model consists in a knowledge representation hierarchical method and an inference engine capable of managing time-space parameters, thus extending usual qualitative paradigms dealing with unidimensional parameters, such as those proposed by de Kleer and Brown (1984), Forbus (1984) and Kuipers (1986).

3.2. Neural networks and applications

Artificial Neural Networks (ANN) is another attempt to model associative reasoning and pattern matching typical of human brain. At present, these networks only model the process that connects input data with output data by exploiting computer ability to perform an iterative series of fast numerical computations (Hajek and Hurdal, 1993). Unlike rule- based systems, ANN do not require detailed encoding of causal relationships and existing expertise. Neural networks are parallel distributed information processing architectures suited to hardware implementation and real-time operation. They consist of the following elements (Dougherty, Kirby and Boyle, 1994):

- a set of nodes that receive a vector of inputs and compute an analogue output according to a transfer function; - connection links of various strength - measured by proper weights - joining the different nodes; - a transfer function and an associated threshold level for the weighted sum of each node inputs, that possibly triggers its activation (that is, a switch between the two node states 'on/of f ' ) ; - a set of layers, that gives rise to a topological arrangement of nodes such that all nodes in adjacent layers are connected to each other. A neural network thus has an input layer, an output layer and possibly one or more hidden (or internal) layers.

One of the most used learning technique to train ANN is the back-propagation paradigm (Hecht-Niel- sen, 1990). It'presents input data to the input layer of the network and computes an outcome that emerges from the output layer based on current connection weights. This output is compared with the one that was expected for the given input data by means of a

global error function. This in tum is 'back-propagated' in the network via a set of partial derivatives that smoothly update the connection weights, to dis- place the output towards the desired level. If the training is successful, the value of the function re- duces over time as input data are repeatedly presented. Various methods exist that improve the rate of convergence, such as 'variable momentum', that regulate step sizes in successive updating iterations.

Neural networks represent a valuable methodological tool in the field of transportation research, par- ticularly in the areas of traffic pattern recognition, classification and prediction, congestion and incident detection, driver route choice modeling (Dougherty, Kirby and Boyle, 1994). For instance, as far as traffic distribution is concerned, conventional systems are based on time interval-dependent actions. In other words, owing to the difficulty of dynamic traffic assignment modelling, it is generally assumed that network traffic state (i.e., the traffic pattern) is static in a given time interval. The Adaptive Reso- nance Theory (ART) is an ANN model able to provide more reasonable traffic pattern recognition results than the most common methods, by allowing parallel processing and tolerance adjustability (Faghri and Hua, 1992).

Moreover, ANN have been used in signal timing control, through a network-based pattern classification and evaluation procedure of monitoring and control strategies. Back-propagation has been used also in O-D matrix forecasting, where trip generation and attraction zones can be assumed as external input, while the neural network output gives future O-D distribution. Noise present in past O-D patterns can be removed through proper training (Hua and Faghri, 1993).

Although large data sets are needed for ANN effective training, it can be assumed that road monitoring systems are currently in position to meet this requirement. However, several points need to be further investigated in order to assess the performance of ANN models, among which there are the extensibility of ANN traffic predictions made at specific sites (that is heavily connected with implementation costs of ANN systems), as well as the sensitivity of the achieved results on the representa- tiveness of input data and traffic pattern changes over time.

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3.3. Other methods and applications

Rule-based programming is the best method developed so far in order to capture expert knowledge about some specific domain. In particular, production systems, which represent the most common rule-based approach, consist of:

(a) a knowledge base comprising a set of rules formed by a precondition and a set of actions that capture some heuristics of the expert;

(b) one or more data bases that contain information about the domain and the problem-solving state (the so-called working memory);

(c) a control strategy, that is, a way of controlling and directing the reasoning steps performed by a rule interpreter in a 'recognize-act cycle'.

Production systems are normally employed when relevant knowledge can be given in terms of situation-action pairs. Thus, they are suitable for expressing the heuristic knowledge that is necessary for interpreting a set of sensor readings or assessing the effectiveness of a given signal plan (Bielli, 1992). Model-based reasoning is a deeper form of encoding and manipulating knowledge about the domain, that implies the use of models describing the underlying system structure and the interactions among its com- ponents. It can be employed successfully for reasoning on hypothetical consequences of actions or making diagnoses with respect to the observed conditions (Bobrow, 1985).

Constraint programming is a recent AI paradigm. It is based on techniques for representing functional relationships among variables as normative rules that regulate the propagation of values over the reference network, according to constraint satisfaction mechanisms (McDermott and Chamiak, 1985). Given some initial assignment of values to a subset of elements, a constraint satisfaction mechanism tries to achieve an assignment of values to all elements that is consistent with the constraints. Depending on the kind of propagated values, one can have symbolic or nu- meric constraint propagation. This method can be suitably used for data completion problems, where one is asked to derive traffic flows on nonequipped links and intersections on the basis of the information provided by sensor-detected links. In such a case, constraints could be inherent to the structure of the network, acquired traffic data, operating signal plans and so on (Bielli et al., 1991).

3.4. The case o f automatic incident detection

Automatic Incident Detection (AID) has been developed mainly for freeway traffic, due to the expected high safety improvements deriving from incident prevention and /o r management. So-called Cali- fornia AID algorithms are among the most widely used tools that enable to perform such a complex task (Payne, Helfenbein and Knobel, 1976). How- ever, since congestion is a widespread phenomenon in the majority of large urban areas, it is highly desirable to detect early an incident also in such contexts, in order to take effective actions that aim at alleviating its direct and induced impacts on traffic flows.

The effect of an incident is an immediate change in the quantitative relationship of the macroscopic traffic variables (volume, density and speed) in the neighborhood. Hence, as long as the inconveniences last, a different fundamental traffic diagram holds in the affected area. Classical AID algorithms are based on filtering techniques and stochastic process analysis, but also pattern recognition, cluster analysis and probabilistic approaches have been used for the case.

Although these algorithms are generally conceived to be integrated into traffic control systems, very simple procedures are employed in practice which do not reflect the state of the art. Moreover, the confidence in such methods is relatively low, so that human control and supervision is still considered necessary (Busch, 1991). At present, no method has proved to be clearly superior to the others. Due to the whole possible specla'um of traffic conditions, a single algorithm can scarcely find the optimal solution; therefore, a mixture of various algorithms in a multimodei approach has been proposed and investigated (Morelio and Sala, 1993).

An Incident Management Expert System (IMES) has been developed for freeways and motorways, where unexpected congestion may occur even when surveillance, communication and control systems are in operation (Chang and Huarng, 1993). It aims at evaluating off-line control strategies and assisting the overall decision making process, which includes incident detection, confirmation, prediction, management and response. It operates with AID algorithms, a production rule editor, a rule-based reasoning mechanism that perform forward-chaining to formu- late responses as advice to users.


The same features are addressed by the set of real-time expert systems which have been integrated in a distributed problem-solving blackboard architecture called FRED (Freeway Real-time Expert system Demonstration) (Ritchie, 1990), that aims at providing an effective support to detecting and verifying traffic incidents, as well as identifying proper response strategies. Further developments have taken to a more comprehensive real-time KBES prototype, that works as a decision support system assisting traffic engineers in designing and selecting strategies for variable message signs, highway advisory radio and adaptive signal control. The system, called ARTIST (Arterial Real-time Traffic Incident Re- Sponse Tool), attempts to replicate three stages of human response to operational problems: verifica- tion/classification, action and impact monitoring. The basic parameters to be taken into account in making decisions are time of day/day of week, traffic phenomena (number of vehicles, arrival rate) and traffic restraints both in the reference area and in its surroundings (Deeter and Ritchie, 1993).

As far as urban traffic management and control are concerned, short term forecasting, congestion monitoring and incident detection have been recognized as highly interdependent fields. In fact, incidents are usually identified by comparing current network conditions with expected conditions. These are derived from traffic predictions formulated on the basis of data collected by properly located traffic detectors and the operating signal control system (McDonald, 1994). Some prototype systems have been developed in the framework of DRIVE programme (e.g. in projects INVAID I, II and LLAMD-LEADER). Extensive field tests are planned in order to assess their performance (Sellam and Boulmakoul, 1994).

4 . O R a n d A I a p p r o a c h e s t o r o a d t r a f f i c c o n t r o l

Traffic control has been defined as " a non-capital intensive technique to promote safe, efficient and convenient movement of persons and goods, making a better use of existing roads" (Gartner and Gersh- win, 1983). Urban traffic control (UTC) systems differ in a number of aspects. The major distinctive features are related to:

1) spatial organization of control measures (centralized or distributed systems);

2) aggregation level of control measures (isolated intersection, arterial or network);

3) operation mode (fixed-time, traffic flow actuated, demand-responsive).

First generation control systems choose signal timing plans among those stored in a library, that are computed off-line by means of historical traffic data. A plan can be selected according to the time-of-day, by direct operator choice or by matching plans to current traffic conditions. Second generation control is an on-line strategy that computes and implements real-time signal plans based on surveillance data and predicted traffic volumes. Third generation control was conceived to implement and evaluate a fully responsive, on-line, traffic control system. Timing plans are revised in a shorter period than second generation (every 3-5 minutes) and cycle length (that is, the minimum time in which a complete succession of signals occurs) is required a priori to vary during the control period. Last generation control systems take account of the strict interdepen- dence between the system architecture design (number, kind and location of detectors and computers) and the implemented control strategy. Even if actua- tion methods cannot guarantee the best possible performance per se, properly calibrated traffic-actuated signals can provide considerable advantages over fixed-time control (Bruno and Improta, 1994).

4.1. OR approaches

Mathematical programming methods for UTC can be grouped in four classes, corresponding to problems of increasing complexity: - single junction control; - arterial coordination and synchronization; - network coordination and synchronization; - combined traffic assignment and signal setting. These models are presented, analysed and discussed in the following subsections.

4.1.1. Single junction control The main goals of single junction control can be

listed as follows: - capacity factor maximization; - total rate of delay minimization;

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- cycle time minimization. These can be subject to the following kinds of (generally linear) constraints:

- undersaturation constraints; - minimum green-time constraints; - maximum red-time constraints; - constraints on minimum and maximum values for the cycle time; - compliance constraints between stage cycle and green times (for stage-based approaches); - incompatibility crossing constraints (for phase- based approaches).

Stage-based approaches seek optimal green times after requiring the sequence of stages and the duration of transition periods to be specified in advance (a stage is that part of a plan where signals do not change). No direct method is available to constrain the duration of the effective red and green times for the groups (which are sets of streams receiving the same signals from the controller) (Improta, 1991; for further insights, see Allsop, 1991b).

Such approaches have evolved to allow the computation of optimal timings by using knowledge about crossing compatibilities among the different streams. This implies to analyse possible sequences of compatibility cliques, whose edges correspond to a set of groups that are mutually compatible. Any clique is a possible stage, while any ordered set of cliques that satisfies some imposed constraints is a possible stage sequence. The optimization problem consists of calculating the optimal clique sequence, the green time for each clique and the cycle time (Stoffers, 1968; Zuzarte Tully, 1977; Zuzarte Tully and Murchland, 1978). All maximal sequences of stages which give a single green interval for each group in each cycle are generated, although no automatic procedure has been developed yet in order to remove superfluous stages. Hence , a considerable amount o f manual intervention is required to refor- mulate the constraints when zero-green-time cliques and corresponding transition periods are deleted (Heydecker and Dudgeon, 1987).

Phase-based approaches determine optimal cycle time, green timing and scheduling by directly ad- dressing group signal timings through knowledge of the incompatibilities among the streams, rather than through the stage mediation. This allows for constraints such as minimum green times and group-to-

group clearance times to be expressed explicitly. Furthermore, the stage sequence and structure of the transition periods need not be specified before the optimization takes place (Heydecker and Dudgeon, 1987; Moller, 1987). Mathematical programming models have been proposed that optimize control variables simultaneously. Such variables are represented on a time axis that can be scaled in time units (Improta and Cantarella, 1984) or in proportion to the cycle time (Cantarella and Improta, 1988). Bi- nary variables are required for compatibility constraints. Phase-based approaches, however, require a preliminary decision about the composition of the streams and their assignment to the lanes; generally, the flow ratios of the streams have to be assumed constant during the cycle. SICCO (Single Intersec- tion Capacity factor and Cycle time Optimization) (Cantarella and Improta, 1988) and SIGSIGN (SIG- nal deSIGN) (Silcock, 1990) are among the computer codes that use a phase-based approach.

Signal timing optimization presents both theoretical and computational difficulties. Actually, it is necessary to select the best sequence of signal changes and the associated timings. These decisions are based upon estimates of current queue lengths as well as on vehicle detector data about the traffic flow that is arriving/leaving the junction within the next few seconds. Actually, the state of traffic at a junction is specified by both the number of vehicles queuing in each of the streams, that is influenced by the signal control plan, and the number of vehicles arriving in the next future.

On the other hand, the state of the controller is specified by the set of green signals, the changes - if any - which are currently underway, the times at which they will be completed and the expire times of minimum or maximum allowed durations. It follows that the state space to be investigated is huge, since it is given by the product of all possibilities for each of these state variables. Several techniques have been devised to abate the computational complexity of the signal timing optimization problem. Generally, these work on a state space reduction achieved through simplifying assumptions such as those of uniform vehicular arrivals beyond the detection period, the restriction of the signal control states to a few stages and time discretization.

Many researchers approached the single junction-


optimizing traffic responsive control as a Dynamic Programming (DP) problem (Robertson and Brether- ton, 1974; Gartner, 1982; Henry, Farges and Tuffal, 1983). This optimization process divides the horizon length into N stages, where by horizon is meant any period for which flows arriving at the stop lines are known or can be predicted. At each stage the state of the intersection is defined by the state of the signal (green or red) and the queue length on each of the approaches.

The decision variable indicates whether the signal is to be switched at the current stage or not. The final objective is to determine the optimal sequence of switching decisions at all stages of the horizon, whose typical length is 5-15 minutes. A recursive optimization function that aims at minimizing the total delay based on the queuing discharge processes occurring at the intersection is provided. The main shortcomings of dynamic programming methods are that they require future arrival information for the entire horizon and an extensive computational effort. Moreover, since DP optimization is carried out back- wards, it precludes the opportunity for modifying control decisions in response to real-time updated traffic data (Gartner, 1991).

Hence, a procedure that uses only available flow data has been carried out in which a 'projection horizon' of k intervals is the period for which traffic flow information is needed. The procedure calculates an optimal policy for the entire horizon, but implements it only for the first r < k intervals. Next, the projection horizon is shifted forward of r intervals and the process is repeated, thus realizing the so- called 'rolling horizon' optimization (Wagner, 1977).

4.1.2. Arterial system control Traffic lights tend to group vehicles in platoons

with fairly uniform headways. It is desirable to set up control actions that maintain a continuous movement of vehicle platoons through successive intersections. Little (1966) proposed a general formulation of the arterial coordination as a mixed-integer linear program, taking as objective function a weighted sum of the bandwidths of all arterials, where each bandwidth was assumed to be linearly dependent on the offsets (a bandwidth measures the length of the platoon that passes a junction and does not have to stop at the next one due to the traffic ligh0.

The presence of integer variables as well as the introduction of several redundant variables and constraints generated a lot of troubles in the practical use of the method. Nevertheless, Litfle's methodology has been implemented in an off-line computer procedure, named MAXBAND (Little, Kelson and Gartner, 1981). The procedure uses Webster's theory (Webster, 1958), stating that total delay at an intersection is minimized by assigning the available cycle time to the competing traffic streams in proportion to their saturation ratios. If b (b ') denotes the outbound (inbound) bandwith and k is the target ratio of inbound-to-outbound bandwith, this can be expressed as follows:

Max ( b + k b ' )

s.t. ( 1 - k ) b ' > _ ( 1 - k ) k b , k ~ l,

b = b ' , k= 1.

Note that the traffic engineer may wish to favor one direction of traffic over the other.

Given splits, queue clearances, bandwidth target ratio as well as lower and upper bounds for cycle time, link speeds and speed changes among the different links, MAXBAND finds cycle time, offsets, bandwidths, link progression speeds and left-turn phase patterns in order to maximize the objective function subject to cycle time, bandwidth-ratio, inter- ference, loop-integer, speed and speed-change constraints. It must be specified that loop-integer constraints are a set of consistency constraints on signal offsets, requiring that the algebraic sum of relative offsets associated with the links of any closed path (loop) has an integer value. Gartner (1972) demon- strated that, for a connected network of v vertices and l links, only l - v + 1 loop constraints are linearly independent.

A recent improvement of MAXBAND is the procedure MULTIBAND, in which traffic volume variations along an arterial (due to turn-in and tum-out traffic) are considered. Thus, it is possible to assign a different bandwidth to each directional road section of the arterial, that is weighted with respect to its contribution to the overall objective function (Gartner et al., 1991).

Signal timing optimization is generally carried out by means of a sequential decision process involving the following steps (Gartner, 1976):


(a) a master cycle is found depending on the requirements of the most loaded junctions;

(b) based on the identified master cycle, green splits are computed for the various junctions;

(c) a set of optimal offsets among signals is determined. This procedure cannot ensure a globally optimal solution, that could be found only through a simultaneous optimization process. However, experience has shown that sequential procedures provide good solutions for arterial and network signal coordination problems in practice.

4.1.3. The network synchronization problem The bandwidth-based methods present some prac-

tical advantages. In fact, they use relatively little input and provide space-time diagrams that represent a simple graphic tool to evaluate and /or modify the results obtained. However the bandwidth-based regulation is often inadequate when applied to urban networks, given that it does not take into account road users' delay. On the other hand, mathematical programming models for network synchronization of traffic control generally assume as ultimate goal the total rate of delay minimization (usually a non-linear function that is approximated by a piecewise linear function). With respect to single junction control, these methodologies introduce the following additional sets of constraints: - congruence constraints between green and red times for each pair of conflicting streams; - congruence constraints between cycle time, green and red times for each stream; - loop-integer constraints.

MITROP (Mixed-Integer TRaffic Optimization Program) represents the only simultaneous network optimization model of all control variables (cycle time, green splits and offsets) proposed so far (Gartner, Little and Gabbay, 1976). It aims at minimizing the total users' delay by using convex piecewise linear delay functions. Nevertheless, the con- vexity assumption may be violated in real cases; moreover, the program requires large computer memory and high running time.

Link performance functions used in most mathematical programming schemes are based on the assumption that the delay on a link depends only on the offset between signals at upstream and down-

stream junctions. This hypothesis is not true in general. It is then necessary to define new formulations, expressing the delay as a more general function of network regulation parameters. At present, only heuristic procedures allow to remove the limitative assumption conceming the independence of the delay on a link on signal control features of other links.

Among these, the TRANSYT method (Robertson, 1969; Vincent, Mitchell and Robertson, 1980) is surely the most widely used in real-world applications for fixed-time plan calculations. The method works on the basis of the following assumptions:

(a) All major network junctions are controlled by signals or priority rules.

(b) All signals have a common cycle time or a multiple of it.

(c) Traffic pattern is known and constant. TRANSYT follows a hill-climbing strategy for

minimizing a performance index (PI) given by a weighted sum of delay and stops on the network, expressed as follows:

N

PI = ~ (Wwid i + K / I O 0 -- k iPi) , i = 1

where: N = Number of links of the network. W = Overall cost per average passenger car unit

(pcu) of delay. K = Overall cost per 100 pcu stops. d i = delay on link i. wi = Delay weighting on link i. k~ = Stop weighting on link i. Pg = Number of stops on link i.

A demand-responsive approach to traffic network control is represented by SCOOT (Split, Cycle and Offset Optimization Technique), developed at TRRL in the United Kingdom, whose success is testified by over forty installations both in Europe and overseas (Hunt et al., 1981; McDonald and Hounsell, 1991). SCOOT control system has flow and occupancy detectors placed on specific links, that allow arrival patterns at the downstream signals to be predicted; these in turn represent the primary input of the signal setting optimization model (Bell, 1992). It adopts a traffic model similar to the one used in TRANSYT, attempting to determine the signal timings that mini-


mize a performance index based on a linear combination of delays and stops. Signal timings are ad- justed smoothly based on the latest traffic situation, thus avoiding abrupt changes in control plans.

Each junction is treated by the green split optimizer independently from other junctions. A few seconds before each stage changes, it decides whether the change should be advanced or delayed by a small amount of time based on the minimization of the maximum degree of saturation on the approaches to the junction. The offset optimizer decides whether or not to alter all scheduled change times for the stages at a junction, for every cycle. Any such change has the effect of altering the offsets between that junction and the adjacent ones. This decision is made by comparing the values of the sum of the PIs on all adjacent streets for the assessed offsets. The cycle time optimizer computes the common cycle time for the region under control every 2.5 or 5 minutes (in some cases, SCOOT can assign a junction a cycle time equal to half of the common cycle time, if this provides better results). The adopted criterion is that the most heavily loaded junction in the region should operate at a maximum degree of saturation of about 90%.

Various demand-responsive systems have been realized or are currently being developed in several countries; these include, among others, SCATS (Lowrie, 1982) in Australia, OPAC (Gartner, 1983) in USA, PRODYN (Henry, Farges and Tuffal, 1983) in France, UTOPIA (Mauro and Di Taranto, 1989) in Italy. PRODYN has represented for a long time the state-of-the-art in real-time traffic control with conventional techniques. Although it has been tested in a limited number of areas, it resulted in about 16% improved performance with respect to the fixed-time plans of TRANSYT (Wild, 1994a). It is a decentralized system based on a decomposition of the road network into a number of small traffic subareas, where it is possible to apply a Dynamic Program- ming approach to signal optimization that aims at minimizing total delay. For this purpose, a Bayesian estimation of queue lengths within a time horizon of 70 seconds is used and a signal plan generation is made in real-time (every 5 seconds). Flexible signal phase times and acyclic signal settings are additional features of the system.

4.1.4. Combined traffic assignment and signal optimization

From the pioneering work of Allsop (1974), it has been widely recognized that vehicle delays at junctions highly depend on signal setting. As a consequence, changing the adopted control strategy does have a redistributional effect on traffic flows, given that traveling costs for road users are altered conse- quently. Conversely, if changes in routes selected by road users take place, the optimality of operating signal settings is compromised (van Vuren, 1991). Based on the foregoing observations, traffic signal setting models have been defined where user behaviour is explicitly taken into account; these can be referred to as equilibrium network traffic signal setting models and viewed as network design models where regulation parameters assume the role of design variables.

When the flow pattern is assigned, link flows as well as path choice are assumed independent of the adopted regulation. Under this hypothesis, traffic signal setting models for single junction as well as network control can be used to compute regulation parameters (Cantarella, Improta and Sforza, 1991). Such models can be formally expressed as

Min Z ( p , q ) , pEP

where q is the assigned flow pattern, p is the vector of control parameters, P is the set of feasible vectors p and Z(p , q) is the system performance index. If this index relates to the total user delay, the previous expression becomes

Min Z( p, q ) = D ( p , q ) = r , di( P, q)ql, p~P

where di(p, q) is the unitary delay on link (or junction approach) i.

On the other hand, when traffic signal setting is fixed, the link flow pattem can be found through a traffic assignment model q = qb(p). According to Wardrop's first principle (Wardrop, 1952), for which each driver chooses the path that minimizes his own generalized travel cost, such model results in the following convex program:

Min I ( p , q ) , qEQ


where Q is the set of feasible flow pattern and I (p , q) is the integral travel cost:

I( p, q) = ~ foqici( p, t) dr,

where ci( p, q) is the cost-flow function for link i. Hence, the travel cost is assumed to be a function of flow on the different links and vector p of network control system parameters.

When both signal setting parameters and link flows are not fixed, it is necessary to perform a combined evaluation that requires modelling the interaction between flow pattern and signal setting. This interaction can be seen as a Stackelberg two- player game between a leader (maybe a traffic agency), that chooses a control strategy in order to optimize a system performance index, and a follower (in this case road users), that aims at minimizing its individual travel costs (Fisk, 1984). Formally:

Min Z ( p , q ) , p ~ P , q ~ Q . P , q

Such combined assignment and signal optimization problem can be formulated by means of a bilevel programming model in which the upper level relates to the leader's decisions, while the lower level concerns the follower's decisions in light of the choices made by the leader. Note that the manager's criteria for evaluating his decision are based on public interest concerns, while the individual trip- maker is assumed to select routes for his own exclu- sive benefit. Signalized network equilibrium is. therefore defined by control variables that minimize the collective travel cost (system optimum), according to the Wardrop's second principle for a normative system, together with a traffic flow pattern that guaran- tees the minimization of user travel cost for each assigned regulation (user optimum), thus satisfying Wardrop's first principle for a descriptive system. The bilevel optimization problem requires to find the minimum total cost solution among all those which respect Wardrop's first principle. Therefore, it can be formalized through

M i n { Z ( p , q * ) : [ l ( p , q * ) = Min l ( p , q) ]}

subject to conservation, additivity and non-negativity constraints on traffic flows, as well as further constraints on regulation parameters.

Bilevel programming problems are generally hard to solve because evaluation of the upper-level objective function requires solving the lower-level optimization problem. Since the lower-level problem is in effect a nonlinear constraint, the whole problem turns out to be nonconvex, so that a global optimum might be difficult to find (Yang et al., 1992). Exact and approximate algorithms developed so far in order to solve bilevel programming models can be applied to small or, at best, medium-size problems with a limited number of variables and constraints, but they cannot be used to cope with large-scale network problems, for computational burden limitations (Hansen, Jaumard and Savard, 1992; Bard and Moore, 1990; Dempe, 1987).

With reference to traffic network control, global optimization models usually assume green times as the only signal setting variablesi This represents a strongly limitative assumption in urban areas, where signal coordination between adjacent junctions has the potential for greatly improving system performance and thus plays a fundamental ro le in the link travel cost-function definition. The values of all other var iablescan only be determined through heuristic procedures that iteratively perform signal setting and traffic assignment until a fixed convergence criterion is met. These methods are at present the most viable approach to bilevel programming problems, both for their computational features and their closeness to the actual decision-making process in a S~ackelberg game. In fact, they dramatically reduce the computational effort needed to achieve a solution; although an optimal Solution:: is not guaranteed t o b e found (Cant~ella, Improta and Sforza, 1991), so tha t Dick- so n (1981) showed that the total t ravel cost can increase its value during the execution of such procedures.

In: order, to compute a mutually consistent flow pattern and control policy for a set :of coordinated junctions, Allsop a n d Charlesworth (1977) introduced a heuristic method consisting of the following steps:

Step 1. Perform an initial assignment. Step 2. Determine the signal setting which mini-

mizes delay at each junction. Step 3. Estimate cost-flow relationships, by using

TRANSYT simulation module.


Step 4. Perform an equilibrium assignment, according to TRAFFIC method (Nguyen, 1976).

Step 5. Perform a new signal setting, according to TRANSYT.

Step 6. Repeat Steps 3 -5 till a negligible change in traffic assignment pattern does occur.

Based on the same scheme, Gartner et al. (1980) proposed two iterative procedures, the first using the Webster's method and an equilibrium assignment, the second based on MITROP method and an equilibrium assignment. Further developments of the same kind of procedure may be found in Cantarella, Improta and Sforza (1991).

4.2. AI approaches

Mathematical programming models for road network control are generally developed based on the assumption of traffic flow undersaturation, while temporary oversaturations do occur frequently in urban networks, especially during peak hours. More- over, OR methods are often used for off-line computations, while in recent years there has been an increasing need for methodological tools suitable for real-time traffic control. Yagar (1991) identifies four major reasons explaining the failure of conventional control systems: - high frequency of signal timing changes; - low accuracy and reliability of traffic predictions; - inadequacy of traffic performance indices; - inability to deal with occasional events.

The main factors affecting the performance of conventional traffic control systems and approaches can be briefly summarized as follows:

1) Congestion. None of the conventional systems is able to cope with queuing phenomena that expand locally when these start interacting in different network areas. In such cases, some higher-level, strate- gic control is necessary, where local problems are treated simultaneously, various options for the different areas are evaluated, priorities are set up and remedial control actions are implemented accord- ingly;

2) Sudden traffic pattern changes. Conventional methods are not able to respond effectively to unforeseen, significant non-recurrent traffic flow redis- tributions on the road network (possibly due to acci- dents, roadworks, etc.), given that they are designed either to cope with predefined 'average' traffic con-

ditions (fixed-time, traffic actuated systems) or react to small changes in traffic flows (demand responsive systems);

3) Traffic data. Conventional signal control methods are able to deal with traffic flow counts measured by roadside detectors, but they cannot take account of structural changes in travel demand and users' route choice, as reflected by other kinds of information sources, like, for instance, Origin-De- stination (O-D) trip matrices;

4) System integration. A major drawback of current UTC systems is that, in most cases, signal control is applied as an isolated control strategy which has little or no interaction with other traffic management measures, like variable message signs, demand management systems, route guidance and navigation, motorway control, ramp metering, public transport management, pollution monitoring, etc. In order to achieve the highest benefits, such systems should be integrated in a common road transport environment that makes some new functions possible, by implementing the principles of cooperative control and cooperative equilibrium (Mauro, Morello and Wrathall, 1993). Most of the current research and development in the area of traffic and transportation systems is pointing towards such an integrated view.

It follows from the foregoing statements that current UTC systems, no matter how sophisticated, are unable to operate totally unsupervised. Actually, traffic engineers still play a fundamental role in designing and managing the systems in order to cope with real traffic conditions through day-by-day operations. By means of various tools and devices (such as TV cameras, sensor-reading databases or radio links), UTC operators monitor traffic behaviour, identify critical events, evaluate the performance of currently implemented control strategies and adopt remedial actions when necessary (e.g., by forcing a particular signal plan). It is this 'man in the loop' paradigm, that is a prevailing feature of today UTC installations, which motivates the need for the development of AI and knowledge-based (KB) methods for on-line traffic control.

Relevant contributions are expected from AI and KB techniques with respect to different levels of application to urban traffic control, such as transportation network analysis and planning, integration of UTC with other traffic management systems and

M. B&lli, P. Reverberi / European Journal of Operational Research 92 (1996)550-572 565

UTC system management. Focusing our attention on the latter point, it is worth to distinguish the following phases:

(a) Traffic data acquisition. A comprehensive view of current traffic flow volume, composition and distribution on the network is required to improve on-line management actions. Some advanced systems, like SCOOT, already include a database (called ASTRID) connected to signal control. However, this is meant as a tool for data collection in light of planning purposes, rather than as a support to on-line traffic analysis and real-time control decisions. The major requirements to achieve a complete and reli- able information set include:

- inference on missing data, to provide plausible traffic flow values where sensors are not installed and /o r misfunctioning;

- estimation of turning movement coefficients at intersections, to infer the distribution of measured flows with respect to different directions;

- estimation of the O-D trip matrix relative to the reference area.

(b) Traffic monitoring. Improved methods are required for traffic data interpretation. Traffic actuated control strategies classify detected traffic patterns and select proper signal plans based either on a state space description (vector method) or, more empirically, on a set of threshold values. However, this simple approach needs to be extended in several respects to better account for the complexity and variability of traffic phenomena. Further improvements are needed in order to analyse congestion over the dimensions of space and time, thusi understanding its causes. Critical intersections and other congestion sources in the network should be identified according to empirical criteria to set up priorities when solving traffic problems. Finally, predicting traffic situations on a short-term basis (5-10 minutes)would enable to define improved control strategies, capable of anticipating and regulating network flow distribution. Since it is very difficult to develop quantitative models for short-term prediction, especially in large networks, qualitative approaches that aim a t predicting only relevant changes can be seen as an effective alternative (Cuena, 1989; Martin et al,, 1990).

(c) Supervision. Control quality and effectiveness depend largely on data collected in phases (a) and (b). However, 'expert' control requires additional

capabilities, such as evaluating continuously the performance of currently implemented control strategies, individuating where and when remedial actions are needed, assessing the relative merit and likely effects of alternative control actions. AI techniques enable to deal simultaneously with multiple problems occurring in the traffic network (e.g. congestion and critical situations at different sites), that may require to manage conflicting objectives (e.g. favour- ing traffic in an area vs. restraining access to reduce environmental pollution). Moreover, they allow at the same time to make decisions on the basis of a temporal analysis of traffic behaviour (and not merely as a reaction to current travel demand) and to cope with non-recurring congestion due to unforeseen events.

Hierarchically intelligent traffic control systems have been proposed for both urban areas and flee- ways, based on the same theoretical approach. A significant reduction in computational complexity is achieved by logically decomposing the control problem into three easier-to-solve decision-making levels, arranged in ascending order of required intelligence (Saridis, 1991). The on-line control level is the low- est level in the hierarchy and corresponds to single intersections for urban roads or entry ramps for freeways. At this level, the operating system opti- mizes signal cycles while taking into account relevant physical constraints. Some of the regulating parameters are set by the higher coordination level of the hierarchy, that supervises a group of intersection or access ramp controllers in order to allow efficient traffic progressions.

The coordinator receives the traffic assignment task from a centrally located organization level, that represents the highest level of the hierarchy. It aims at improving the overall system performance by establishing a communication link between human operators (traffic engineers) and the control system. This kind o f architectural design allows to adapt control strategies to the different traffic patterns with little computational effort, due to its high learning capabilities.

4.2.1. Knowledge-based architecture for traffic signal control

In the framework of the DRIVE programme, several projects have been launched in order to develop

566 M. Bielli, P. Reverber i / European Journal o f Operational Research 92 (1996) 5 5 0 - 5 7 2

and test M-based prototypes to be integrated in current UTC system operational processes (some of them are presented in Bielli, Ambrosino and Boero, 1994). The objectives of the projects are to assess potential benefits of KB methods in several kinds of traffic management tasks. Generally, the proposed model builds an 'intelligent layer' on top of conventional UTC systems. Such layer would have to take real-time traffic flow data from the existing infras- tructures via a traffic control computer (TCC) and provide suggestions about control actions, to be transmitted back to the roadside receivers through the TCC.

Most of the prototypes consist in a blackboard architecture that is made of several knowledge-based modules (or knowledge sources, KS) associated to different key traffic control tasks, including data completion, traffic evaluation, classification and diagnosis, traffic prediction and signal control. A cen- tral object database provides a common representation of the traffic network and a data exchange facility for the various modules. Finally, an action scheduler controls individual knowledge source operations in response to some changes introduced in the blackboard-stored data.

The essential features of blackboard problem- solving models are the integration of different knowledge representation methods and the execution of procedures in a distributed computing environment, that allows real-time traffic control and management systems to achieve greater efficiency (Bi- elli, 1992), especially in the field of signal plan selection (Ambrosino et al., 1991) and incident detection (Ritchie, 1990). In particular, prototype IUTCS (Intelligent Urban Traffic Control System) works by reasoning on a symbolic and qualitative model of the real world that is maintained in the global working memory and updated according to the output of some KS and traffic data coming from roadside sensors (Ambrosino, Bielli and Boero, 1994).

Traffic situation on the network is evaluated through a quite straightforward rule-based shallow reasoning system. At a local level, traffic flows on each link are classified according to the degree of saturation (or occupancy rate, or) v/C, where v is the actual flow and C is the capacity of the link, indicating how near the link is at its maximum

Table 1

Quantity space for traffic flow analysis in the IUTCS test case of Hamburg city

Link load levels:

Flow Occupancy Load level

v < 20%C

20%C < v < 50% 50%C < v < 80% 80%C < u

or < 0.2 very low or < 0.2 low or < 0.2 undersaturation

or < 0.2 saturation 0.2 < or < 0.3 saturation

0.3 < or < 0.4 preeongestion

0.4 < or congestion

Intersection load levels:

Capacity factor Load level

CF > 5 very low 5 > CF > 2 low

2 > CF > 1.25 undersaturation 1.25 > CF > 1 precongestion

1 > CF congestion

capacity. Analogously, traffic conditions at any intersection are evaluated through a global indicator called capacity factor (CF), that, for a given signal setting, measures the maximum common multiplier of traffic flow values relative to a junction for which undersaturation still holds.

Qualitative indicators for both parameters are defined by setting up a classification of load levels based on proper correspondences to a quantity space (Table 1 shows the instantiation of such classification in the IUTCS test case of Hamburg city center). Traffic condition trends are also defined based on a three-valued qualitative scale (increasing, decreasing or steady flows), so that a complete qualitative description of traffic flow on a link at a given time would have the following form: (low, + ), meaning ' low traffic with increasing volume'.

The Traffic Control (TC) KS of IUTCS models decision-making processes underlying the selection of proper control actions in face of dynamically changing traffic conditions. Actually, it evaluates the performance of the traffic network under the current control setting, identifies remedial actions for critical intersections and assesses the impact of proposed local changes on adjacent links and intersections. Control actions are available in the form of a predefined library of Signal Plans (SP) for intersections. A global performance index based on parameter CF -


as provided by the Data analysis KS - is continuously evaluated to assess the adequacy of the adopted control strategy with respect to the current traffic situation on the network. This evaluation enables TCKS to assess current and available signal plans at intersections for identifying a candidate SP list. In order to handle the combinatorial explosion arising in the solution search process, some heuristic mechanisms are needed. These consist in compatibility constraints that embody both general, domain-independent SP selection criteria and specific, site-dependent rules. Examples of site-independent criteria are shown in Table 2, with reference to a given set of network links and intersections.

IUTCS environment has been developed by implementing several knowledge representation schemes and inference mechanisms on top of LISP, including object-oriented, rule-based and constraint- based reasoning. Object-oriented programming has also been extensively used to implement higher-level representation functionalities and is supported by CLOS, the Common LISP Object System (Steele, 1990). IUTCS has been tested in laboratory using both detectors data collected in the test site of Ham- merbrook area (in Hamburg city center) and: MIS- SION microsimulation environment. Although further experimentation is needed, promising results have already been obtained in terms of the signal control system performance (Ambrosino, Bielli and Boero, 1994).

IUTCS prototype is intended to provide a first step towards the development of a general model for the integration of knowledge processing functionalities in the next generation of UTC systems. The blackboard architecture is open with respect to fur-

ther developments. For instance, in a centralized control approach, different modules can be run in parallel on different processors, while in a distributed control architecture the same theoretical approach could be hierarchically replicated at the intersection, area and network levels.

M-based traffic-actuated control systems have also been developed in the framework of the DRIVE programme, for instance in CLAIRE (Bell, Scemama and Ibbetson, 1991) and SAPPORO (Wild, 1994) projects. Such systems aim at establishing a feedback loop between traffic flows on the network and control actions. Since no exact mathematical relationship between traffic behaviour and control does exist, traffic flow simulations are carried out by using currently observed data and a model of time-dependent traffic evolution. These result in an estimated flow distribution within a time horizon of 1 up to 10 minutes, on which evaluation of current signal plans and - if necessary - signal adjustments are performed and compared with the original plans. This cycle can be repeated several times until a final decision is made based on a performance index value and corresponding signal plans for the next time horizon are switched to the network junctions.

5. Conclusions

Traffic engineering problems have been usually faced through optimization approaches that require the development of complex mathematical models. When dealing with real-world problems, defined on medium and large-size networks, these models often impose an excessive computational burden on the

Table 2 Site-independent SP selection rules

Rule 1:

Rule 2:

Rule 3:

i f CFi > 2 in all key junctions then restrict candidates SPs to those with shorter cycle times

i f CFi < 2 in all key junctions then restrict candidates SPs to those with longer cycle times

i f CF i < 2 on junction i since t > n- T (T = sampling time; n = 3) and SP k I(cycle-time(SPk)) = cycle time (current SP) and direction of coordination (SP) - current direction of coordination and CFi > 2

then SPk is a candidate else constrain selection to SPs with longer cycle times.


available hardware. In general, numerical computation techniques are not able to find a solution in reasonable time except for the single intersection level. As a consequence, it has been necessary to devise a set of practical heuristic problem-solving algorithms that, for their very nature, cannot guarantee to achieve the optimum (as it has been shown for the case of combined traffic assignment and signal setting).

A significant reduction in computational complexity can also be obtained by decomposing logically a general traffic engineering problem into easier-to- solve decision-making levels, arranged in ascending order of required intelligence. A centrally located organization level, representing the top of the hierarchically intelligent traffic control system, aims at improving the overall system performance by establishing a communication link between human operators and the control system. At a local level (namely, network links and intersections), the control system would have to deal with elementary problem in- stances that can be addressed by common optimization approaches.

Moreover, in recurring congested situations, that cause oversaturation flows to affect some links of the network, pure quantitative optimization models show some inconveniences. Hence, traffic engineers still play a significant role as supervisors of the control system, since they can decide the implementation of proper strategies or remedial actions in response of the observed traffic network conditions, with the aim of improving the values taken by a set of established performance criteria.

Qualitative reasoning patterns of AI-based techniques represent a viable approach in dealing with uncertain, missing and erroneous data. In such cases, that occur frequently in practice, determining the optimal value of all the relevant parameters is either not possible or not necessary in light of the correct decisions to be made. In fact, qualitative methods allow to incorporate knowledge which is present only as human expertise or heuristics and is expressed in non-numerical or symbolic form, thus overcoming some of the major limitations of conventional mathematical and algorithmic procedures.

However, since AI techniques are not appropriate for dealing with every subtask comprised in a complex real-world problem, research efforts in the near

future should be directed towards the identification of a correct combined use of such methods with conventional OR approaches. This would allow to exploit the large amount of expected benefits deriving from the possible sinergies between the two disciplines in different practical applications, as it has been shown for the case of road traffic control.

The current research trend is to combine computer simulation and AI tools in hybrid systems that include advanced methodologies and knowledge-based systems, for the purpose of designing and building a traffic simulation intelligent front end. This is a kind of expert system interfacing the user and performing both symbolic and algorithmic computations. Com- bined systems provide a range of facilities that include different modelling paradigms (among others, object-oriented, logic, data-oriented and rule-based programming) and interactive access to the model building and experiment running phases.

Enhancements originating from the integration of such different approaches could be realized effectively in the framework of Decision Support Sys- tems, taking into account the large number of con- flic*ang goals and objectives arising from the hetero- geneous actors that are affected by the traffic management and control strategies evaluated in the decision-making process.

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