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2011, 2011 Ieee Intelligent Vehicles Symposium
In rapidly growing transportation networks, traffic congestion can result from inefficient traffic control infrastructure or ineffective traffic control measures. Existing congestion management techniques in Intelligent Transportation Systems (ITS) have not been very effective due to lack of autonomous and collaborative behavior of the constituent traffic control entities involved in these techniques. Moreover, these entities cannot easily adapt to the traffic dynamics and the traffic control intelligence is mostly centralised making it susceptible to overload and failures. The autonomous and distributed nature of multi-agent systems is well-suited to the transportation domain which is dynamic and geographically distributed. This paper reviews existing congestion management techniques and discusses their limitations. The paper, further, comprehensively surveys multiagent techniques for congestion management in ITS and describes their advantages over other existing techniques. The paper classifies the multi-agent techniques based on the locus of decision control intelligence and focuses on their suitability of application in congestion management. We conclude with outstanding issues and challenges.
2009 IEEE International Conference on Control Applications, 2009
Due to the strong interrelations between traffic situations at different locations of a road network the traffic control actions applied for solving a local traffic problem can create another traffic congestion at a different location in the network. This can result the average travel time on the network level, even after the application of the control actions, to be the same or worse. Therefore, coordinative control strategies are required to make sure that all available control actions serve the same objective. In this paper, an intelligent traffic control system based on multi-agent approach is proposed to assist the human operator of the road traffic centre to manage and control the current traffic state. In the proposed system, the total network is divided in sub-networks, each of which has its own evaluation agent. In the proposed system the agent will be able to react with other (affected) agents to find the optimal global traffic control action using an intelligent traffic control. The capability of the proposed multi-agent-based system was tested for a case study of a part of the traffic network in the Riyadh city of Saudi Arabia. The obtained results show the merits and capabilities of the proposed multi-agent-based system to identify the optimal global control action.
The continuous Advancement in the technology is past few years is refurbishing the standards & the way of living, it has effected almost every aspect of life, from learning and teaching to the thinking, traveling, analyzing, managing and controlling. Everything has been upgraded to better or in some conditions fully new techniques and standards. Life is now being controlled by the smart and intelligent devices. This research provides a detailed proposal about the use of Multi Agent technology to build the system to manage and control the traffic on real-time. Distributing complex traffic network into small easily manageable subsystems and responsibility based Agents, to perform a well-coordinated, real-time and accurate processing of events / incidents into a suitable and onetime solution.
2014
The rapid growth experienced by many cities has created huge pressures on their traffic and transport systems. One common solution consists of increasing the number of roads available, but is not always the most appropriate. The application of Information and Communications Technologies enabling more efficient and effective use of the existing transport infrastructure is necessary (also known as Intelligent Transportation Systems). Furthermore, cooperation between vehicles and infrastructure is currently studied under the name of Cooperative Intelligent Transportation Systems. This paper proposes a traffic management framework based on agents that enable users to make better use of traffic and transport infrastructure by cooperating with infrastructure. We also propose a parallel algorithm to determine the K-routes for the drivers. Keywords—Multiagent; Traffic Management; Cooperative Intelligent Transportation Systems.
Proceedings of the 7th …, 2008
Current traffic management measures increasingly exhibit dynamic features by taking into account the dynamics in traffic demand and transportation system supply. Demand actuated traffic signal settings or variable message signs are examples of traffic management devices driven by the dynamic characteristics of the traffic. In most cases however, these traffic management devices are implemented as stand-alone systems, meaning that there is no, or hardly any, co-ordination between the various traffic management measures taken. The lack of co-ordination carries within it the risk of reduced effectiveness. The various measures could, for example, serve opposing objectives or even generate a negative impact on traffic flows that or not in any way related to the problem that the traffic management device was meant to solve in the first place. The uncoordinated application of dynamic traffic management measures thus could possibly be counter-productive. The setbacks of uncoordinated control can be avoided by carrying out the control task in two different ways: in a detailed way by focusing on the problem(s) that need(s) to be solved (distributed control), and in a more generic way by controlling the overall traffic performance in the network (generic control). In this paper we analyse the possibility of combining both distributed and generic control in one control strategy using hierarchic agents. In effect the approach tries to match local and global impacts by using autonomous agents interacting with each other in a horizontal and in a vertical (hierarchical) way. The local agents (defined in terms of network links or network nodes) control the traffic in their specific area according to predefined performance goals. One layer higher in the hierarchy another agent controls the traffic performance in a part of the network, checking the results of individual control strategies against the overall performance goal of that specific part of the network. We present the results of a modelling experiment featuring a control system with two layers. The first layer consists of link agents directly serving the traveller by guaranteeing reliable travel times and/or maximal throughput. The second layer consists of node agents that try to harmonize conflicting goals of the various link agents. An important characteristic of our approach is that the higher level agent is dominant in the negotiation process (i.e. a higher weight is attached to the decision of the higher level agent). The multi-agent control strategy described above is applied to a test network consisting of a part of the road network around the city of Antwerp. The results show that it can easily deal with the goals of the various agents. In the case of conflicts, the attached control priority determines how differences will be settled. An interesting feature of the above approach is the lack of a central mechanism controlling the various agents. The global optimum that is established in the system is the result of selfish behaviour on the part of the various agents combined with some co-ordination based on pre-set priorities. Actually the system is finding this optimum in a self-organising way. This is a very interesting feature as it allows us to apply a large range of control strategies.
Birkhäuser Basel eBooks, 2005
In this paper we present a test bed for multiagent control systems in road traffic management. As the complexity of traffic control on a network grows it becomes more difficult to coordinate the actions of the large number of heterogeneous traffic management instruments that are available in the network. One way of handling this complexity is to divide the coordination problem into smaller coherent subproblems that can be solved with a minimum of interaction. Multiagent systems can aid in the distribution of the problem (over the various agents that comprise the multiagent system) and facilitate the coordination of the activities of these agents when required. In the literature no consensus exists about the best configuration of the traffic managing multiagent system and how the activities of the agents that comprise the multiagent system should be coordinated. The decomposition of a problem into various subproblems is an active field of research in the world of distributed artificial intelligence. This paper starts out with a survey of the approaches as they are reported in the literature. Subsequently the test bed is introduced and the modules it is comprised of. Finally an application is presented that illustrates some of the research the test bed has made possible.
2007 IEEE Congress on Evolutionary Computation, CEC 2007, 2007
2021
To favor emergency vehicles, promote collective modes of transport in Moroccan cities, we propose in this paper a control system to manage traffic at signalized intersections with priority links in urban settings. This system combines multi-agent technology and fuzzy logic to regulate traffic flows. The traffic system flow is divided into two types of vehicles; priority and regular vehicles. The regular vehicles can use only the regular links, while the priority vehicles may use both priority and the regular links. This approach aims to favor emergency vehicles and promote collective modes of transport, it acts on the traffic light phases length and order to control all traffic flows. We proposed a decentralized system of regulation based on real-time monitoring to develop a local inter-section state, and intelligent coordination between neighboring intersections to build an overview of the traffic state. The regulation and prioritization decisions are made through cooperation, comm...
… Research Part C: …, 2002
This paper reports our experiences with agent-based architectures for intelligent traffic management systems. We describe and compare integrated TRYS and TRYS autonomous agents, two multiagent systems that perform decision support for real-time traffic management in the urban motorway network around Barcelona. Both systems draw upon traffic management agents that use similar knowledge-based reasoning techniques in order to deal with local traffic problems. Still, the former achieves agent coordination based on a traditional centralized mechanism, while in the latter coordination emerges upon the lateral interaction of autonomous traffic management agents. We evaluate the potentials and drawbacks of both multiagent architectures for the domain, and develop some conclusions respecting the general applicability of multiagent architectures for intelligent traffic management.
Progress in Artificial …, 1998
This paper presents the experimental TRYSA 2 distributed decision support system, that has been designed for the management of the urban motorway network around Barcelona. It shows how different technologies in the area of intelligent agents can be combined to solve this real-world decision support problem: on the one hand, a preestablished distribution of the loci of decision-making and the nature of local traffic management tasks, suggested to apply "deliberate" problem-solving agents; on the other, the complexity of the co-ordination task called for an "emergent" approach, in order that the overall decision support functionality be the result of non-benevolent agent interactions. The paper sets out from a description of our particular traffic management problem. Subsequently, the architecture of TRYSA 2 is outlined, pointing out the design strategy followed and describing how the different design steps have been realised. Finally, we discuss the lessons learnt from building this multiagent application.
Progress in Artificial Intelligence, 2012
This paper proposes a bimodal urban traffic control strategy based on a multi-agent model. We call bimodal traffic, a traffic which takes into account both private vehicles and public vehicles such as buses. The objective of this research is to improve global traffic, to reduce bus delays and to improve bus regularity in congested areas of the network. In our agent-based approach, traffic regulation is obtained thanks to communication, collaboration and negotiation between heterogeneous agents. An important feature of our system is that it allows regulation at two levels: macroscopic and microscopic levels. To model in depth regulation procedures, we have introduced special features such as priority levels for buses, computation and update of traffic signal plans, urgency index of intersection stages depending on the level of congestion on the arcs. We have tested our strategy on a small network of six intersections, using the JADE platform. The simulation is described and preliminary results are presented. They show that our MAS strategy improves bus travel time while improving also private vehicles' travel time, decreases bus delays and improves its regularity compared to a classical strategy called fixed-time control strategy.
Advances in Intelligent and Soft Computing, 2011
This paper proposes a bimodal urban traffic control strategy based on a multi-agent model. We call bimodal traffic a traffic which takes into account private vehicles and public transport vehicles such as buses. The objective of this strategy is to improve global traffic and reduce the time spent by buses in traffic jams so that buses cope with their schedule. Reducing bus delays is done by studying time length of traffic lights, giving priority to buses, more precisely to buses running late. Regulation is obtained thanks to communication, collaboration and negotiation between the agents of the system. The implementation has been done using the JADE platform. We have tested our strategy on a small network of six junctions. The first results of the simulation are given. They show that our MAS control strategy improves both bus traffic and private vehicle traffic, decreases bus delays and improve its regularity compared to a classical strategy called fixedtime control.
Road Traffic presents a high dynamism which makes necessary the development of traffic management and control strategies to improve traffic flows and more important, road safety. So it is needed the use of intelligent systems to support traffic organizations and road operators to cope with incidents. In this paper we introduce a local autonomous system for traffic management. This way, though there could be a breakdown in the communications between the local system and the TCC, the local system will be able to warn the road users in case of incidents. The system uses multiagent technology to work with the specific characteristics of traffic domain. The expert MAS system is ruled-based that implies each agent has a knowledge and a fact base in order to reach their objectives.
Transportation Research Record, 2005
In this paper we present a test bed for multiagent control systems in road traffic management. As the complexity of traffic control on a network grows it becomes more difficult to coordinate the actions of the large number of heterogeneous traffic management instruments that are available in the network. One way of handling this complexity is to divide the coordination problem into smaller coherent subproblems that can be solved with a minimum of interaction. Multiagent systems can aid in the distribution of the problem (over the various agents that comprise the multiagent system) and facilitate the coordination of the activities of these agents when required. In the literature no consensus exists about the best configuration of the traffic managing multiagent system and how the activities of the agents that comprise the multiagent system should be coordinated. The decomposition of a problem into various subproblems is an active field of research in the world of distributed artificial intelligence. This paper starts out with a survey of the approaches as they are reported in the literature. Subsequently the test bed is introduced and the modules it is comprised of. Finally an application is presented that illustrates some of the research the test bed has made possible.
hicss, 2001
This paper reports our experiences with agent-based architectures for Intelligent Traffic Management. We describe and compare TRYS and TRYSA 2 , two multiagent systems that perform decision support for real time traffic management in the urban motorway network around Barcelona. Both systems draw upon similar traffic management knowledge, but the former is based on a centralised architecture, while in the latter co-ordination emerges upon the lateral interaction of autonomous traffic management agents. We conclude that the centralised approach applied by TRYS promotes efficiency for real time operation, whereas the decentralised approach used in TRYSA 2 promotes scalability.
International Journal of Innovative Research in Technology, 2019
For resolving traffic management problems it requires a coordinative control solutions where controls like traffic signals, vehicles progressing direction, roads capacity and many other parameters needs to be carefully analyzed and decisions to be made individually and collectively. Multi-agents systems have the potential and framework to define several agents to monitor various parameters across various locations and help co-ordinate agents to resolve individual decision for every single problem and collectively decisions could be made from the available individual decisions. This paper focuses on the various capabilities available in Multi-agents framework to resolve complex issues like traffic management system.
Journal européen des systèmes automatisés, 2021
With the development, almost all the sectors, countries tend to grow adapting to latest technologies. The transport sector also has a huge impact on this development sphere. When it comes to traffic, it is a huge problem in the world. In Sri Lanka traffic is a problem that exists for a long period. Annually there is a loss of Rs. 400 billion due to traffic congestions. Over the past years various solutions have been proposed using different methods for traffic control. These solutions are based on different trending technologies such as machine learning, image processing, fuzzy logic and the Internet of Things. However, those systems are not able to handle traffic congestions problems in complex environments. Therefore, a real-time traffic controlling system with the ability to handle traffic congestions in dynamic environment is highly valued. This study aims to design and build a real-time traffic management system using Multi Agent technology. Simulations for both existing system and Agent-Based system are implemented using NetLogo simulation tool and compared for various traffic situations. According to the results obtained from the two simulations, Agent-Based systems provide more accuracy and efficiency than the existing fixed scheduling systems.
Transportation Research Part B-methodological, 2005
This paper explores the use of cooperative, distributed multi-agent systems to improve dynamic routing and traffic management. On the supply-side, real-time control over the transportation network is accomplished through an agent-based distributed hierarchy of system operators. Allocation of network capacity and distribution of traffic advisories are performed by agents that act on behalf of information service providers. Driver needs and preferences are represented by agents embedded in intelligent in-vehicle route guidance systems. Negotiation between ISP and driver agents seek a more efficient route allocation across time and space. Results from simulation experiments suggest that negotiation can achieve more optimal network performance and increased driver satisfaction.
International Journal of Advanced Research in Computer Science
According to the multi-agent, our design was based on three main layers: decomposition, modeling, and communication protocol. Through decomposition, the design was decomposed into five sub-agents: source, queue, server, WFQ/FCFS hierarchal scheduler, and controller. Defining the functionalities of each sub-agent was through the modeling layer, while communication protocol defines the interaction
Intelligenza Artificiale, 2022
Intelligent transport systems have efficiently and effectively proved themselves in settling up the problem of traffic congestion around the world. The multi-agent based transportation system is one of the most important intelligent transport systems, which represents an interaction among the neighbouring vehicles, drivers, roads, infrastructure and vehicles. In this paper, two traffic management models have been created to mitigate congestion and to ensure that emergency vehicles arrive as quickly as possible. A tool-chain SUMO-JADE is employed to create a microscopic simulation symbolizing the interactions of traffic. The simulation model has showed a significant reduction of at least 50% in the average time delay and thus a real improvement in the entire journey time.
International Journal of Engineering and Advanced Technology (IJEAT), 2019
Abstract: Traffic control system is an imperative instrument in traffic management and smart urban development. However, most of the current traffic control systems cannot intercommunicate nor interact with each other. Most importantly, none of these systems are proactive and reactive to their immediate traffic environment. Thus, this study explores the design of an agent-based traffic control system where traffic lights can interact and inter-communicate to take prompt traffic control decisions within a traffic area. The study presents an agent-based traffic control system known as ATC. ATC system design was done using Design Science Research Process Model while the system was evaluated using qualitative research methodology. The study argues that there is need for traffic control system to be more reactive and proactive to their immediate traffic environment in order to limit traffic jam in urban areas. Index Terms: Agent-based system, agent-based traffic system, interactive system, traffic control, urban traffic control.
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