A Road Traffic MultiAgent Simulation using

TurtleKit under MadKit

Habib M. Kammoun1, Ilhem Kallel1, Jorge Casillas2, and Adel M. Alimi1

1 REGIM, Research Group on Intelligent Machines,university of Sfax, BP W 3038, Tunisia

2 Dept. Computer Science and Artificial Intelligence,university of Granada, E 18071, Spain

{Habib.Kammoun, Ilhem.Kallel, Adel.Alimi}@ieee.org, Casillas@decsai.ugr.es

Abstract. Since the traffic management requires clear comprehensionof the flows, especially jam cases, researchers are encouraged to haverecourse to traffic simulations. Indeed, they demonstrate their abilityto predict efficient solutions to complex problems. This paper attestsonce more how agent-based simulation is among best achievable optionsused to model and simulate the complex behavior of both urban andinterurban road network. We present and discuss a multiagent approachand simulation for both route choice and lane change problems. Thesesimulations, considered as representation of a hierarchical road networkmultiagent architecture, are realized using ‘TurtleKit’ tool under thegeneric multiagent platform ‘MadKit’. Comprehension and discussion ofthe evolving behavior of each agent demonstrate the adaptability andeffectiveness of a multiagent simulation in such natural distributed prob-lem.Key words: Multiagent simulation, Traffic simulation.

1 Introduction

In view of the sharp increase of vehicle number, accidents and traffic jam sit-uations in all road networks have become wide spread all over the world. Anaccurate management will allow more efficient vehicle routing over time andspace, in order to improve traffic efficiency, etc. Dynamic interventions meansthe need of an auto detection of jam situations or incidents, so vehicles will beadapted according to the new road network situation.

Since the traffic management requires clear comprehension of the flows, espe-cially jam cases, researchers are encouraged to have recourse to traffic simulations[16] [22]. The traffic simulation is the best achievable option to make predictionsin a scientifically proven way. It may be very expensive to carry out the real plan.Simulation results allow researchers and manufactures to make better decisions,understand and optimize the performance or reliability of complex systems. Theapplications are designed to inform drivers about the traffic situation and giverecommendations, regulate the traffic with signals and messages, and so on. Infact, since some years, various successful experiments notified the advantages ofcombining transportation field with artificial intelligence and soft computing [2].

However, a road network, with its natural geographic distribution, can con-tain some million travelers of entities or more that need to be simulated andcontrolled. The main difficulties in such problem are the complexity and thedynamicity of road networks. Furthermore, the road traffic management systemmust provide an adaptive behavior and a flexible interaction between compo-nents. Therefore, the use of decentralized approach is very interesting. Actually,the multiagent (MA) approach allow to model complex systems where numer-ous autonomous entities interact to produce global solutions. The global systembehavior is made of several emergent phenomena that result from the behaviorof individual entities and their interactions [25]. The use of MA methodologyfor modeling, simulating and analyzing traffic has a particular interest for re-searchers due to their ability to efficiently solve complex problems [12].

In addition, the MA simulation model has more advantages than classic sim-ulation [5]. It consists of a set of agents which encapsulate the behavior of thewhole system. In this case, it is possible to represent both entities behaviors inroad network and agent’s interaction phenomenon in order to test and to valueseveral use cases.

In this sense, this paper presents a MA approach and simulation of road trafficnetwork. The proposed approach is different from the existing ones in terms ofhierarchical MA architecture for road networks. We implement two commonproblems: route choice problem and lane change problem. Theses simulationsare realized using ‘TurtleKit’ tool under the MA platform ‘MadKit’.

The paper is organized as follows: Next section presents an overview on theuse of MA simulation in Intelligent Transportation Systems (ITS). The third sec-tion describes our hierarchical MA architecture with putting the emphasis on itsorganization and the intelligent vehicle agent behavior. The forth section recallssome features of Madkit platform. The simulation part detailed in the fifth sec-tion presents and discusses the MA simulation for two common problems: routechoice problem and lane change problem. Finally, we conclude by summarizingthe obtained results and pointing to some directions for future work.

2 Intelligent Transportation Simulation Based on MA

Approach

The implementation of ITS may use different kinds of simulations. Essentially,there are three kinds of approaches [16]: Microscopic simulation describes thebehavior of the system entities along time, as well as their interactions, at a highlevel of detail; Memoscopic simulation represents most entities at high level ofdetail, but describes their activities and interactions at a lower level of detail;and Macroscopic simulation is usually associated to global descriptions of traffic.

Recently, a number of ITS based on MA approach come into being and havealready been reported in the literature. Most of them are still under developmentor at experimental stages, but they clearly demonstrate the potential of imple-menting this technology to improve dynamic routing performances and trafficmanagement by employing cooperative and distributed MA System (MAS).

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Many published works contribute to grow the use of agents as an emergingtechnology for transportation as well as the usability of MAS [3] [4] [8]. We canquote the works of [1] [10] [11]. It exists major categories of ITS based on MAS:Urban Traffic Control Systems, Advanced Transportation Information System,Advanced Vehicle Control System, Transportation Simulation Systems, etc. [26]

Moreover, the MA simulation is very helpful in explaining collective behavioras a result of individual actions. The MA simulation is based on the idea thatit is possible to represent entities behaviors in one environment, and agent’sinteraction phenomenon. At each simulation step, each agent can receive a setof information describing the surrounding situation in the environment [5].

Simulation of traffic networks and transportation systems has especially im-portant practice in transportation engineering. Models based on agents are ap-plied to traffic simulation, and have been proposed in the literature for quite along time.

Wahle et al. [24] simulate two route scenarios with different types of informa-tion and study the impact of real-time information. The behavior of the simu-lated agents is controlled by two layers: the tactical and the strategic layer. Theauthors present a positive influence of dynamic drivers representing conservativeor flexible route selection.

An additional problem occurs in the road network: the organized traffic is lessdynamic and erratic than unorganized traffic because of the presence of trafficrules. For this reason, Paruchuri et al. apply MA simulation for unorganizedtraffic [21]. They model the behavior agents’ drivers, as being cautious, normal,and aggressive. So, they could explain results about average speed of vehicles intraffic, number of overtakes, and number of accidents occurring with differentproportions of aggressive and cautious drivers. The simulation deals with virtualmaps implemented with C++ language.

Some other works treat with more details cooperation and coordination insimulation entities. Halle and Chaib-Draa propose in [10] a Collaborative DrivingSystem (CDS) and compare different coordination models using MA simulationscenarios. The simulated vehicle model is built in CDS simulator, called HESTIA,developed by authors, and based on Java 3D simulation engine. In [17], Mandiauet al. describe a MA coordination mechanism to simulate an urban network, andin particular critical situations, at intersections. Authors use a tool of road trafficsimulation, called ARCHISIM developed at INRETS, to simulate the real caseof intersection between the Via Roma and the Via Zerbi in the city of ReggioCalabria in Italy.

Other MA simulations deal with public transportation such as urban busnetworks developed by Meignan et al. [18]. The authors adopt a MA approachto describe the global system operation as behaviors resulting from numerousautonomous entities such as buses and travelers. The proposed simulation toolhas been entirely implemented in Java language.

These works prove the success of MA simulation compared to other trafficsimulations models. But, no one of the cited works is developed under a genericMA platform.

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3 Hierarchical Multiagent Architecture for Road

Network

Before presenting our MA architecture for road networks, it would seem wise toexplain in which sense this approach is different from previous works. On theone hand, our approach proposes a new hierarchical MA architecture. In fact,all vehicles are regrouped by city and road since this is the natural geographicdistribution of road networks. With this decomposition, our architecture becomeswell adapted to urban and interurban road networks.

On the other hand, our approach can be easily implemented for the real worldbecause it is based on information collected for example from Global PositioningSystems (GPS). However, most of previous works’ information are collected ondata from stationary equipments (i.e. detector giving the number of vehicles).Unfortunately, these equipments can cover only some road sections. Moreover,the implementation of vehicle interface is better than installing notice board ineach road. The GPS can collect data on position, speed and direction, store themand send reports at regular time intervals.

In this paper, we look for a simple and efficient representation of road net-work. By modeling the separate tasks as intelligent agents, it will be possible toadapt the actions of vehicle’s driver through the concept of agent cooperation inorder to achieve a common goal: improving the traffic roads.

Applying MA approach to traffic simulation presents several interests in mod-eling as well as simulation level. In fact, it has been proved that MA modelingis better than standard one in terms of individual and collective behaviors [15].

3.1 Different Agents

The necessity of a dynamic simulation with a very big number of vehicles leadsto the use of a reactive architecture with different types of agents. At the op-posite of cognitive agents, reactive agents have not any representation of theirenvironment. Reactive agents can cooperate and communicate by means of theirinteractions (direct communication) or through perception of environment (in-direct communication). As a consequence, such reactive systems present someglobal intelligent behaviors resulting from numerous interactions [6]. We proposea model involving three types of agents for the road traffic architecture [14]:

– City agent CA: manages the connected city to obtain better road networkexploitation. It can communicate and cooperate with other city agents ac-cording to the RSA claim. It collaborates to receive the state values of trafficflows over the road network;

– Road supervisor agent RSA: there are many RSA in one city. Each onesupervises the state of traffic flow in the corresponding road, implementsthe control action of road, and achieves coordination control and integratedmanagement by coordinating with the corresponding CA;

– Intelligent Vehicle agent IVA: the vehicles are considered as reactive agentsevolving in dynamic environment. In the real case, a key assumption for

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our system is that vehicles are equipped with communicating devices, posi-tioning system, sensors and an onboard driving system. Human driver onlyintroduces his destination through the onboard system (by interface agent).Each agent communicates both with a GPS to receive its coordinates andwith a Geographic Information System (GIS) to obtain a routing table.

All agents have the same functioning cycle and exchange messages basedon asynchronous point-to-point communication. Each agent lives according to acycle bound to an iterative process of reception / deliberation / action detailedin [13]. The reception represents the identification and the interpretation of allreceived messages in the mailbox. In addition, it reconstitutes them according tothe agent internal beliefs. The deliberation expresses the whole internal processso that an agent accomplishes its action according to its internal rules whiletaking into account static and dynamic knowledge. The action describes theoperation that an agent executes in order to be able to update its dynamicknowledge, to send a message to another agent, or to act in the environment. Itis at this phase that each IVA executes their movement commands after routechoice decision making.

3.2 Organizational Model

The first idea is that the problem of road supervision can be naturally dis-tributed and well carry itself for a hierarchical vertical architecture. Then, totake better advantage from agents’ cooperative characters while minimizing therisk of objective conflict, we choose to represent our system with a hierarchicalorganizational structure.

Figure 1 presents three levels of the proposed system as well as the acquain-tance links between CA, RSA and IVA. To give a better organization, the vehi-cles’ groups are decomposed in subgroups. A change of groups can occur in thelevel of every IVA if it moves to another road. To adapt this model to our system,we add a composition relation that links vehicle, road and city to physical world.These three types of agents inherit from the abstract agent that plays roles ingroups.

The organizational model is based on the AGRE (Agent - Group - Role - En-vironment) suggested in [7]. This meta-model is one of the frameworks proposedto define the organizational dimension of MAS, and it is well appropriate to thetransportation context. Several reasons justify our interest of this meta-model:

– Adding dynamically a software component into the kernel of the applicationis easy because creating a new group or playing a new role may be seen as aplug-in process when a software component is integrated into an application;

– It eases security: what happens in a group cannot be viewed from agentsthat do not belong to that group;

– It supports coherent exchange because a role describes the constraints thatan agent should satisfy to obtain a role.

507A Road Traffic MultiAgent Simulation

Fig. 1. Hierarchical organizational architecture of road network

Compared to other architecture, Hernandez et al. decompose the road net-work into ‘problem areas’ [13]. Each problem area is managed by an agent.However, this decomposition needs to be achieved by an expert who has someprevious information about road networks. Whereas, our supervisor agent is in-dependent of road number: one agent for each road.

4 Simulation Platform: MadKit

Since few years, we note the birth of some MA platforms. These platformsprovide both a model for developing MAS and an environment for runningdistributed agent-based applications. To develop our simulator, we choose theMadKit platform (MultiAgent Development Kit) [9] as a generic MA platform.Among its advantages, the possibility to make the traffic services fully extensibleand easily replaceable. In addition, Madkit is a free platform with open source,and it can be programmatically extended using Java programming language. Weuse the version 4.1.2 of November 2005. The platform is stable since this date.

Our choice is firstly based on comparison with other know MA platforms [23][20]. The MadKit platform fulfills our requirements by the following features:

– MadKit allows a fast development of distributed agent system by provid-ing standard services for communication and life cycle management of theagents. It can support thousands of agents interact and perform tasks to-gether by using a simple agent with reactive tasks;

– MadKit is built upon the AGR organizational model used in our architecture;– MadKit offers the TurtleKit tool [19] presented as a reactive agent execution

tool that runs on the ‘synchronous engine’ of MadKit platform. This tool

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aims at providing to advanced users the simplicity of a Logo simulationmodel while proposing flexibility, modularity and extensibility;

– TurtleKit tool provide advantages to use agent observer, agent viewer, agentscheduler, and agent launcher;

– MadKit allows high heterogeneity in agent architectures and communica-tion languages. For the interaction model, Madkit provide FIPA-ACL andKQML. The FIPA-ACL interaction protocol is currently sufficient for ourcooperating traffic agents.

5 Road Network Management Simulations

As we need to take into account the road traffic of one or many city and visualizethe evolution of the road network, we chose to develop a hybrid traffic simulationmodel. Vehicles are simulated both with a macroscopic model to visualize roadtraffic and with a microscopic model to follow the route choice process.

The hierarchical organizational architecture of MAS based road network de-scribed in section 3 is considered as the simulation kernel. Figure 2 presents thestatic structure, as a class diagram, of the whole system. The launcher has therole to set up, launch and manage turtles (see figure 3a). At each simulationstep, an agent receives a set of information describing the surrounding environ-ment situations. Figure 3b presents two examples of virtual maps, created bythe observer from TurtleKit tool.

Fig. 2. Class diagram of our system

To well represent the reality, we add an other kind of agent called jammedvehicle agent. It is simply an IVA having a high probability of broking down.

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Fig. 3. Examples of simulation environments

A vehicle stops when perceiving halted ones and it continues moving in a freepath. For reasons of simplicity, all IVAs are randomly generated and have thesame speed moving in a one-way-street. Congested areas are randomly generatedin some roads. Interactions between RSAs and IVAs accomplished in a series ofrunning simulations show the influence of an early detection of congestion andjam cases.

5.1 Route Choice Problem

The route choice, or route assignment, concerns the selection of alternative set ofroutes between origin and destination in road networks. The route choice processimproves the fluency of road network, reduces the number of traffic congestionand allows a dynamic assignment of traffic flows. A better understanding of routechoice decision-making behavior will make possible to explain the phenomena.The decision making agent of IVA selects the best alternative while avoidingcongested areas compromising time and route length. The behavior of this agentis based on a cooperative route choice published in pervious work [14]. Thisalgorithm is executed before each crossroad which allows the IVA to select thebest next road to reach vehicle’s destination.

By reflexive reasoning, The IVA is composed itself of three agents:

– Interface Agent: ensures the link between the driver’s vehicle and the system;– Decision Making Agent: encapsulates the cooperative route choice algorithm;– Effector Agent: moves the vehicle (forward, turn left, turn right).

In this microscopic simulation, we present the use of route choice algorithmwith a simple example of environment. Figure 4a presents eleven roads numberedfrom 1 to 11. Let consider an IVA in the road 1 before intersection having theroad 5 as destination; this IVA has three possible alternatives: by road 4, 3 then6, 3 then 10 then 9. Thanks to observer agent, we can observe the traffic variationin each road resulting from the computation of Path Flow Index (PFI) for eachpossible alternative in each step (see figure 4b). This figure shows the trafficvariation using observer agent. It is also possible to display the traffic variationin each road. The simulation has been done every 450 seconds when updatingthe road flow index table after every 60 seconds.

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Fig. 4. (a) Simulated congestion in road 4 (b) Path flow index observer in each possiblepath

Considering a jam situation in road 4, the IVA chooses the second alternative(by road 3) because it has the smallest PFI compared with first and third alter-natives, at the request moment (see figure 5). The PFI in the first alternativeis high because of the jam situation in road 4. The PFI in the third alternativeis high compared to second one due to the effect of path length. Moreover, theRSA detects this jam and informs all other agents. Figure 5 presents a sampleof communication messages between different agents.

Fig. 5. Sample of communication messages between IVA and RSA

Series of simulations are performed when varying maps and jam positions.We note that IVA always reaches its destination according to the best path.The results show once more the effectiveness of MA approach to improve roadnetwork management in terms of optimizing the route choice.

As regards the technical features, java programming language has some lim-itations to associate a thread to each agent. So, the management of a greatnumber of concurrent threads is not efficient. We counteract this problem byadopting Madkit platform. In fact, Madkit proposes a synchronous engine which

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can manage a big number (hundreds or even thousands) of agents in one thread.In spite of IVA agent is a MAS itself, Madkit can support simulation in largerscenarios with a large number of agents.

5.2 Lane Change Problem

Lane change models are also fundamental component of microscopic traffic flowtheory. The lane changing is defined by modification in the vehicle lateral positionrelative to the road lanes. It is one of frequent driving action, but it is the mostimportant cause of accidents.

Analogically to route choice problem, this IVA is composed of forth agents:

– Interface Agent: ensures the link between the driver’s vehicle and the system;– Proprioceptive Agent: receives information about position and motion from

the internal sensory system;– Exteroceptive Agent: receives information from external sensors. This agent

controls the safety level of the distance separating vehicles;– Effector Agent: moves the vehicle (increase speed, decrease speed, turn left,

turn right).

In order to detect the beginning of a lane change manoeuvre, the vehicledetects the behavior of each close vehicle through its exchanged parameters viacommunication with exteroceptive agent.

We realize a MA simulation environment with three lanes’ road. Vehicles areadded randomly according to an initial density and traffic security laws. If it ispossible to change the lane, the vehicle has firstly to move in left direction andthen to increase its speed according to IVA commands. If it is not, IVA handlesthe speed reduction of the vehicle.

Figure 6 presents all steps during change lane maneuver (captions from leftto right) starting by the detection of possible change. After several simulationsrunning, we can confirm the advantages of an automatic MA lane changing,especially when many vehicles change lane at the same time.

Fig. 6. Lane change simulation

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6 Conclusion and Future Work

In this paper, we presented a hierarchical organizational multiagent architectureas well as a description of agents’ behavior. This model has the advantage to beapplied in urban and interurban road networks.

Several multiagent simulations for road traffic network have been achievedwhile using ‘TurtleKit’ tool under the generic multiagent platform ‘MadKit’. Wetested the coordination mechanism on the basis of various virtual road networksand randomly generated congested area. The microscopic simulations of bothroute choice and lane change problems show good results in terms of realismand cooperative behavior.

Since this work is part of an on-going research project to develop a multiagentintelligent transportation system in an integrated soft computing framework,we intend as perspectives, to investigate in multiobjective optimization pathplanning. This work is currently being continued considering the most possibledynamic information coming from environmental conditions.

Acknowledgment. The authors thank the Tunisian General Direction of Sci-entific Research and Technological Renovation (DGRSRT), under the ARUBprogram 01/UR/11-02, Tunisia.

References

1. Adler, J.L., Satapathy, G., Manikonda, V., Bowles, B., Blue, V.J.: A multi-agentapproach to cooperative traffic management and route guidance. Transportation Re-search Part B 39(4) (2005) 297–318

2. Avineri, E.: Soft Computing Applications in Traffic and Transport Systems: A Re-view. Advances in Soft Computing 1 (2005) 17–25

3. Bazzan, A.L.C., Klugl, F., Ossowski, S.: Agents in traffic and transportation: Explor-ing autonomy in logistics, management, simulation, and cooperative driving. Trans-portation Research Part C 13 (2005) 251–254

4. Burmeister, B., Haddadi, A., Matylis, G.: Application of multi-agent systems intraffic and transportation. IEE Proceedings: Software, 144(1) (1997) 51–60

5. Drogoul, A., Vanbergue, D., Meurisse, T.: Multi-Agent Based Simulation: Whereare the Agents?. Multi-Agent-Based Simulation II, LNCS, 2581 (2003) 43–49

6. Ferber, J.: Multi-agent system: an introduction to distributed artificial intelligence.Addison-Wesley (1999)

7. Ferber, J., Michel, F., Baez, J.: AGRE: Integrating environments with organizations.Environments for Multi-Agent Systems, LNCS, 3374 (2005) 48–56

8. Gruer, P., Hilaire, V., Koukam, A.: Multi-agent approach to modeling and sim-ulation of urban transportation systems. In: IEEE Conference Systems, Man, andCybernetics (2001) 2499–2504

9. Gutknecht, O., Ferber, J.: The madkit agent platform architecture. Infrastructurefor Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems, LNCS, 1887

(2001) 48–5510. Halle, S., Chaib-Draa, B.: A collaborative driving system based on multiagent

modelling and simulations. Transportation Research Part C 13 (2005) 320–345

513A Road Traffic MultiAgent Simulation

11. Hernandez, J.Z., Ossowski, S., Garcıa-Serrano, A.: Multiagent architectures for in-telligent traffic management systems. Transportation Research Part C 10(5-6) (2002)473–506

12. Jennings, N.R.: An agent-based approach for building complex software systems.Communication of the ACM, 44(4) (2001) 35–41

13. Kallel, I., Alimi, M.A.: MAGAD-BFS: A learning method for beta fuzzy systemsbased on a multi agent genetic algorithm. I. J. of Soft Computing 10(9) (2006) 757–772

14. Kammoun, H.M., Kallel, I., Alimi, A.M.: A Joint Hierarchical Fuzzy-MultiAgentModel Dealing with Route Choice Problem RoSFuzMAS. In: 4th Int. Conf. on Infor-matics in Control, Automation and Robotics ICINCO (2007) 394–397

15. Klugl, F., Oechslein, C., Puppe, F., Dornhaus A.: Multi agent modelling in compar-ison to standard modeling. In: Proc. of Artificial Intelligence Simulation and Planningin High Autonomous Systems AIS (2002) 105–110

16. Lieberman, E., Rathi, A.K.: Traffic Simulation. In Traffic flow theory, Ch.10 (1997)17. Mandiau, R., Champion, A., Auberlet, J.M., Espie, S., Kolski, C.: Behaviour based

on decision matrices for a coordination between agents in a urban traffic simulation.Applied Intelligence, 28(2) (2008) 121–138

18. Meignan, D., Simonin, O., Koukam, A.: Simulation and evaluation of urban bus-networks using a multiagent approach. Simulation Modelling Practice and Theory,15(6) (2007) 659–671

19. Michel, F., Beurier, G., Ferber, J.: The TurtleKit Simulation Platform: Applicationto Complex Systems. In: 1st IEEE Int. Conf. on Signal-Image Technology & Internet-Based Systems SITIS (2005) 122–127

20. Mulet, L., Such, J.M., Alberola, J.M.: Performance evaluation of open-source Mul-tiagent platforms. In: 5th Int. joint Conf. on Autonomous Agents and MultiAgentSystems AAMAS (2006) 1107-1109

21. Paruchuri, P., Pullalarevu, A.R., Karlapalem, K.: Multi agent simulation of un-organized traffic. In: 1st Int. joint Conf. on Autonomous Agents and MultiAgentSystems AAMAS (2002) 176-183

22. Pursula M.: Simulation of Traffic Systems - an Overview. J. of Geographic Infor-mation and Decision Analysis 3(1) (1999) 1–8

23. Ricordel, P.M., Demazeau, Y.: From analysis to deployment: A multi-agent plat-form survey. Engineering Societies in the Agents World, LNCS, 1972 (2000) 93–105

24. Wahle, J., Bazzan, A.L.C., Klugl, F., Schreckenberg, M.: The impact of real-timeinformation in a two-route scenario using agent-based simulation. TransportationResearch Part C 10 (2002) 399–417

25. Wooldridge, M.: An introduction to multiagent systems. John Wiley and Sons(2002)

26. Zhang, J., Wang, H., Li, P.: Towards the applications of multi-agent techniques inintelligent transportation systems. IEEE Intelligent Transportation Systems 2 (2003)1750–1754

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