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,
[email protected]
Abstract. Since the traffic management requires clear comprehension of the flows, especially jam cases, researchers are encouraged to have recourse to traffic simulations. Indeed, they demonstrate their ability to predict efficient solutions to complex problems. This paper attests once more how agent-based simulation is among best achievable options used to model and simulate the complex behavior of both urban and interurban road network. We present and discuss a multiagent approach and simulation for both route choice and lane change problems. These simulations, considered as representation of a hierarchical road network multiagent architecture, are realized using ‘TurtleKit’ tool under the generic multiagent platform ‘MadKit’. Comprehension and discussion of the evolving behavior of each agent demonstrate the adaptability and effectiveness of a multiagent simulation in such natural distributed problem. Key words: Multiagent simulation, Traffic simulation.
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Introduction
In view of the sharp increase of vehicle number, accidents and traffic jam situations in all road networks have become wide spread all over the world. An accurate management will allow more efficient vehicle routing over time and space, in order to improve traffic efficiency, etc. Dynamic interventions means the need of an auto detection of jam situations or incidents, so vehicles will be adapted according to the new road network situation. Since the traffic management requires clear comprehension of the flows, especially jam cases, researchers are encouraged to have recourse to traffic simulations [16] [22]. The traffic simulation is the best achievable option to make predictions in 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. The applications are designed to inform drivers about the traffic situation and give recommendations, regulate the traffic with signals and messages, and so on. In fact, since some years, various successful experiments notified the advantages of combining transportation field with artificial intelligence and soft computing [2].
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However, a road network, with its natural geographic distribution, can contain some million travelers of entities or more that need to be simulated and controlled. The main difficulties in such problem are the complexity and the dynamicity of road networks. Furthermore, the road traffic management system must provide an adaptive behavior and a flexible interaction between components. Therefore, the use of decentralized approach is very interesting. Actually, the multiagent (MA) approach allow to model complex systems where numerous autonomous entities interact to produce global solutions. The global system behavior is made of several emergent phenomena that result from the behavior of individual entities and their interactions [25]. The use of MA methodology for modeling, simulating and analyzing traffic has a particular interest for researchers due to their ability to efficiently solve complex problems [12]. In addition, the MA simulation model has more advantages than classic simulation [5]. It consists of a set of agents which encapsulate the behavior of the whole system. In this case, it is possible to represent both entities behaviors in road network and agent’s interaction phenomenon in order to test and to value several use cases. In this sense, this paper presents a MA approach and simulation of road traffic network. The proposed approach is different from the existing ones in terms of hierarchical MA architecture for road networks. We implement two common problems: route choice problem and lane change problem. Theses simulations are realized using ‘TurtleKit’ tool under the MA platform ‘MadKit’. The paper is organized as follows: Next section presents an overview on the use of MA simulation in Intelligent Transportation Systems (ITS). The third section describes our hierarchical MA architecture with putting the emphasis on its organization and the intelligent vehicle agent behavior. The forth section recalls some features of Madkit platform. The simulation part detailed in the fifth section presents and discusses the MA simulation for two common problems: route choice problem and lane change problem. Finally, we conclude by summarizing the obtained results and pointing to some directions for future work.
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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 the behavior of the system entities along time, as well as their interactions, at a high level of detail; Memoscopic simulation represents most entities at high level of detail, 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 have already been reported in the literature. Most of them are still under development or at experimental stages, but they clearly demonstrate the potential of implementing this technology to improve dynamic routing performances and traffic management by employing cooperative and distributed MA System (MAS).
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Many published works contribute to grow the use of agents as an emerging technology for transportation as well as the usability of MAS [3] [4] [8]. We can quote 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 behavior as a result of individual actions. The MA simulation is based on the idea that it is possible to represent entities behaviors in one environment, and agent’s interaction phenomenon. At each simulation step, each agent can receive a set of information describing the surrounding situation in the environment [5]. Simulation of traffic networks and transportation systems has especially important practice in transportation engineering. Models based on agents are applied to traffic simulation, and have been proposed in the literature for quite a long time. Wahle et al. [24] simulate two route scenarios with different types of information and study the impact of real-time information. The behavior of the simulated agents is controlled by two layers: the tactical and the strategic layer. The authors present a positive influence of dynamic drivers representing conservative or flexible route selection. An additional problem occurs in the road network: the organized traffic is less dynamic and erratic than unorganized traffic because of the presence of traffic rules. For this reason, Paruchuri et al. apply MA simulation for unorganized traffic [21]. They model the behavior agents’ drivers, as being cautious, normal, and aggressive. So, they could explain results about average speed of vehicles in traffic, number of overtakes, and number of accidents occurring with different proportions of aggressive and cautious drivers. The simulation deals with virtual maps implemented with C++ language. Some other works treat with more details cooperation and coordination in simulation entities. Hall´e and Chaib-Draa propose in [10] a Collaborative Driving System (CDS) and compare different coordination models using MA simulation scenarios. The simulated vehicle model is built in CDS simulator, called HESTIA, developed by authors, and based on Java 3D simulation engine. In [17], Mandiau et al. describe a MA coordination mechanism to simulate an urban network, and in particular critical situations, at intersections. Authors use a tool of road traffic simulation, called ARCHISIM developed at INRETS, to simulate the real case of intersection between the Via Roma and the Via Zerbi in the city of Reggio Calabria in Italy. Other MA simulations deal with public transportation such as urban bus networks developed by Meignan et al. [18]. The authors adopt a MA approach to describe the global system operation as behaviors resulting from numerous autonomous entities such as buses and travelers. The proposed simulation tool has been entirely implemented in Java language. These works prove the success of MA simulation compared to other traffic simulations models. But, no one of the cited works is developed under a generic MA platform.
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Hierarchical Multiagent Architecture for Road Network
Before presenting our MA architecture for road networks, it would seem wise to explain in which sense this approach is different from previous works. On the one hand, our approach proposes a new hierarchical MA architecture. In fact, all vehicles are regrouped by city and road since this is the natural geographic distribution of road networks. With this decomposition, our architecture becomes well adapted to urban and interurban road networks. On the other hand, our approach can be easily implemented for the real world because it is based on information collected for example from Global Positioning Systems (GPS). However, most of previous works’ information are collected on data 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 in each road. The GPS can collect data on position, speed and direction, store them and send reports at regular time intervals. In this paper, we look for a simple and efficient representation of road network. By modeling the separate tasks as intelligent agents, it will be possible to adapt the actions of vehicle’s driver through the concept of agent cooperation in order to achieve a common goal: improving the traffic roads. Applying MA approach to traffic simulation presents several interests in modeling as well as simulation level. In fact, it has been proved that MA modeling is 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 leads to the use of a reactive architecture with different types of agents. At the opposite of cognitive agents, reactive agents have not any representation of their environment. Reactive agents can cooperate and communicate by means of their interactions (direct communication) or through perception of environment (indirect communication). As a consequence, such reactive systems present some global intelligent behaviors resulting from numerous interactions [6]. We propose a model involving three types of agents for the road traffic architecture [14]: – City agent CA: manages the connected city to obtain better road network exploitation. It can communicate and cooperate with other city agents according to the RSA claim. It collaborates to receive the state values of traffic flows over the road network; – Road supervisor agent RSA: there are many RSA in one city. Each one supervises the state of traffic flow in the corresponding road, implements the control action of road, and achieves coordination control and integrated management by coordinating with the corresponding CA; – Intelligent Vehicle agent IVA: the vehicles are considered as reactive agents evolving 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, positioning system, sensors and an onboard driving system. Human driver only introduces his destination through the onboard system (by interface agent). Each agent communicates both with a GPS to receive its coordinates and with a Geographic Information System (GIS) to obtain a routing table. All agents have the same functioning cycle and exchange messages based on asynchronous point-to-point communication. Each agent lives according to a cycle bound to an iterative process of reception / deliberation / action detailed in [13]. The reception represents the identification and the interpretation of all received messages in the mailbox. In addition, it reconstitutes them according to the agent internal beliefs. The deliberation expresses the whole internal process so that an agent accomplishes its action according to its internal rules while taking into account static and dynamic knowledge. The action describes the operation that an agent executes in order to be able to update its dynamic knowledge, to send a message to another agent, or to act in the environment. It is at this phase that each IVA executes their movement commands after route choice decision making. 3.2
Organizational Model
The first idea is that the problem of road supervision can be naturally distributed and well carry itself for a hierarchical vertical architecture. Then, to take better advantage from agents’ cooperative characters while minimizing the risk of objective conflict, we choose to represent our system with a hierarchical organizational structure. Figure 1 presents three levels of the proposed system as well as the acquaintance links between CA, RSA and IVA. To give a better organization, the vehicles’ groups are decomposed in subgroups. A change of groups can occur in the level 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 in groups. The organizational model is based on the AGRE (Agent - Group - Role - Environment) suggested in [7]. This meta-model is one of the frameworks proposed to define the organizational dimension of MAS, and it is well appropriate to the transportation context. Several reasons justify our interest of this meta-model: – Adding dynamically a software component into the kernel of the application is easy because creating a new group or playing a new role may be seen as a plug-in process when a software component is integrated into an application; – It eases security: what happens in a group cannot be viewed from agents that do not belong to that group; – It supports coherent exchange because a role describes the constraints that an agent should satisfy to obtain a role.
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Fig. 1. Hierarchical organizational architecture of road network
Compared to other architecture, Hern´andez et al. decompose the road network into ‘problem areas’ [13]. Each problem area is managed by an agent. However, this decomposition needs to be achieved by an expert who has some previous information about road networks. Whereas, our supervisor agent is independent of road number: one agent for each road.
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Simulation Platform: MadKit
Since few years, we note the birth of some MA platforms. These platforms provide both a model for developing MAS and an environment for running distributed agent-based applications. To develop our simulator, we choose the MadKit platform (MultiAgent Development Kit) [9] as a generic MA platform. Among its advantages, the possibility to make the traffic services fully extensible and easily replaceable. In addition, Madkit is a free platform with open source, and it can be programmatically extended using Java programming language. We use 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 providing standard services for communication and life cycle management of the agents. It can support thousands of agents interact and perform tasks together 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 simulation model while proposing flexibility, modularity and extensibility; – TurtleKit tool provide advantages to use agent observer, agent viewer, agent scheduler, and agent launcher; – MadKit allows high heterogeneity in agent architectures and communication languages. For the interaction model, Madkit provide FIPA-ACL and KQML. The FIPA-ACL interaction protocol is currently sufficient for our cooperating traffic agents.
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Road Network Management Simulations
As we need to take into account the road traffic of one or many city and visualize the evolution of the road network, we chose to develop a hybrid traffic simulation model. Vehicles are simulated both with a macroscopic model to visualize road traffic and with a microscopic model to follow the route choice process. The hierarchical organizational architecture of MAS based road network described in section 3 is considered as the simulation kernel. Figure 2 presents the static structure, as a class diagram, of the whole system. The launcher has the role to set up, launch and manage turtles (see figure 3a). At each simulation step, an agent receives a set of information describing the surrounding environment situations. Figure 3b presents two examples of virtual maps, created by the observer from TurtleKit tool.
Fig. 2. Class diagram of our system
To well represent the reality, we add an other kind of agent called jammed vehicle 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 free path. For reasons of simplicity, all IVAs are randomly generated and have the same speed moving in a one-way-street. Congested areas are randomly generated in some roads. Interactions between RSAs and IVAs accomplished in a series of running simulations show the influence of an early detection of congestion and jam cases. 5.1
Route Choice Problem
The route choice, or route assignment, concerns the selection of alternative set of routes between origin and destination in road networks. The route choice process improves the fluency of road network, reduces the number of traffic congestion and allows a dynamic assignment of traffic flows. A better understanding of route choice decision-making behavior will make possible to explain the phenomena. The decision making agent of IVA selects the best alternative while avoiding congested areas compromising time and route length. The behavior of this agent is based on a cooperative route choice published in pervious work [14]. This algorithm is executed before each crossroad which allows the IVA to select the best 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 algorithm with a simple example of environment. Figure 4a presents eleven roads numbered from 1 to 11. Let consider an IVA in the road 1 before intersection having the road 5 as destination; this IVA has three possible alternatives: by road 4, 3 then 6, 3 then 10 then 9. Thanks to observer agent, we can observe the traffic variation in each road resulting from the computation of Path Flow Index (PFI) for each possible alternative in each step (see figure 4b). This figure shows the traffic variation using observer agent. It is also possible to display the traffic variation in each road. The simulation has been done every 450 seconds when updating the 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 possible path
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 alternatives, at the request moment (see figure 5). The PFI in the first alternative is high because of the jam situation in road 4. The PFI in the third alternative is high compared to second one due to the effect of path length. Moreover, the RSA detects this jam and informs all other agents. Figure 5 presents a sample of 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 road network management in terms of optimizing the route choice. As regards the technical features, java programming language has some limitations to associate a thread to each agent. So, the management of a great number of concurrent threads is not efficient. We counteract this problem by adopting 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 larger scenarios with a large number of agents. 5.2
Lane Change Problem
Lane change models are also fundamental component of microscopic traffic flow theory. The lane changing is defined by modification in the vehicle lateral position relative to the road lanes. It is one of frequent driving action, but it is the most important 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 vehicle detects the behavior of each close vehicle through its exchanged parameters via communication with exteroceptive agent. We realize a MA simulation environment with three lanes’ road. Vehicles are added randomly according to an initial density and traffic security laws. If it is possible to change the lane, the vehicle has firstly to move in left direction and then to increase its speed according to IVA commands. If it is not, IVA handles the speed reduction of the vehicle. Figure 6 presents all steps during change lane maneuver (captions from left to right) starting by the detection of possible change. After several simulations running, 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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Conclusion and Future Work
In this paper, we presented a hierarchical organizational multiagent architecture as well as a description of agents’ behavior. This model has the advantage to be applied in urban and interurban road networks. Several multiagent simulations for road traffic network have been achieved while using ‘TurtleKit’ tool under the generic multiagent platform ‘MadKit’. We tested the coordination mechanism on the basis of various virtual road networks and randomly generated congested area. The microscopic simulations of both route choice and lane change problems show good results in terms of realism and cooperative behavior. Since this work is part of an on-going research project to develop a multiagent intelligent transportation system in an integrated soft computing framework, we intend as perspectives, to investigate in multiobjective optimization path planning. This work is currently being continued considering the most possible dynamic information coming from environmental conditions. Acknowledgment. The authors thank the Tunisian General Direction of Scientific Research and Technological Renovation (DGRSRT), under the ARUB program 01/UR/11-02, Tunisia.
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