Traffic simulation using agent-based models (original) (raw)
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An agent based approach for modeling traffic flow
Informatics and Systems ( …
Driver behavior is a key factor that gives rise to traffic congestion. In this paper we make use of agent based modeling (ABM) in simulating a traffic system. To investigate how drivers' behavior effect on the performance of the traffic system, we modeled four different types of car drivers. In order to enhance the traffic system's performance, we used a genetic algorithm to optimize the schedule of traffic lights to maximize the overall network throughput. Moreover, we built our model using a 3D modeling environment (Blender) for a more realistic simulation of vehicles' motion.
Distributed Agent-Based Traffic Simulations
IEEE Intelligent Transportation Systems Magazine, 2018
Modeling and simulation play an important role in transportation networks analysis. With the widespread of personalized real-time information sources, it is relevant for the simulation model to be individual-centered. The agent-based simulation is the most promising paradigm in this context. However, representing the movements of realistic numbers of travelers within reasonable execution times requires significant computational resources. It also requires relevant methods, architectures and algorithms that respect the characteristics of transportation networks. In this paper, we tackle the problem of using high-performance computing for agent-based traffic simulations. To do so, we define two generic agent-based simulation models, representing the existing sequential agent-based traffic simulations. The first model is macroscopic, in which travelers do not interact directly and use a fundamental diagram of traffic flow to continuously compute their speeds. The second model is microscopic, in which travelers interact with their neighbors to adapt their speeds to their surrounding environment. We define patterns to distribute these simulations in a high-performance environment. The first distributes agents equally between available computation units. The second pattern splits the environment over the different units. We finally propose a diffusive method to dynamically balance the load between units during execution. The results show that agent-based distribution is more efficient with macroscopic simulations, with a speedup of 6 compared to the sequential version, while environmentbased distribution is more efficient with microscopic simulations, with a speedup of 14. Our diffusive load-balancing algorithm improves further the performance of the environment based approach by 150%.
A Multiagent urban traffic simulation
We built a multiagent simulation of urban traffic to model both ordinary traffic and emergency or crisis mode traffic. This simulation first builds a modeled road network based on detailed geographical information. On this network, the simulation creates two populations of agents: the Transporters and the Mobiles. Transporters embody the roads themselves; they are utilitarian and meant to handle the low level realism of the simulation. Mobile agents embody the vehicles that circulate on the network. They have one or several destinations they try to reach using initially their beliefs of the structure of the network (length of the edges, speed limits, number of lanes etc.). Nonetheless, when confronted to a dynamic, emergent prone environment (other vehicles, unexpectedly closed ways or lanes, traffic jams etc.), the rather reactive agent will activate more cognitive modules to adapt its beliefs, desires and intentions. It may change its destination(s), change the tactics used to reach the destination (favoring less used roads, following other agents, using general headings), etc. We describe our current validation of our model and the next planned improvements, both in validation and in functionalities.
An Agent-Based Simulation Model for Urban Traffic System
Computer and Information Science, 2011
In this paper, we propose a model for vehicle traffic based on multi-agent systems and account suppositions and its issues. Traffic is an ever-growing problem as population and the number of drivers around the world increase exponentially. Previously, fluid flow models have been used in an attempt to model traffic. Based on recent studies, only agent based models can accurately model a traffic scenario. This is because small perturbations could have a butterfly-like effect, which causes a rapid change in the entire system.
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/a-review-paper-on-modeling-of-traffic-simulation-system-for-dynamic-traffic-with-multi-agent-framework https://www.ijert.org/research/a-review-paper-on-modeling-of-traffic-simulation-system-for-dynamic-traffic-with-multi-agent-framework-IJERTV9IS090205.pdf Traffic in developing and developed countries mostly having heterogeneous traffic conditions. It comprises with wide range of physical dimension of vehicles and its weight. Due to dynamic traffic it causes series stain on road infrastructure and also effect the road condition (Structure & Surface condition), this affect the increasing the road accident. At intersection point due to heavy traffic jam density increases and it reflect the increasing of travelling time (Time & Distance Headway) for the driver it also affect the total cost of the trip as the vehicles always starting in position during jam occur at intersection. For better traffic movement traffic simulation need to be implemented for distribution the traffic lane wise or by size of the vehicles. This article focuses on the study and modeling of dynamic traffic using multi-agent framework. In this review paper different keywords have been consider by every individual researcher, which can be useful for the improvement of traffic condition and reduction of accidents at intersection point or at crossing. There are different types of modal used by the researcher like single regime linear modal, dual regime modal, polynomial modal, exponential modal for the distribution of traffic by considering vehicular characteristic on and geometric design. Intelligent transport can be used for the improvement of traffic condition and it uses the multi agent can be maintained or utilized according to the traffic conditions.
Agents in traffic modelling—from reactive to social behaviour
KI-99: Advances in Artificial Intelligence, 1999
In modern societies the demand for mobility is increasing daily. Hence, one challenge to researchers dealing with traffic and transportation is to find efficient ways to model and predict traffic flow, even if the behaviour of people in traffic is not a trivial problem. Increasingly more people travel longer distances and choose more complex routes and transportation means. Thus, the social nature of traffic (e.g. coordinated decisions) seems to be a key question, not well explored. There are already systems designed to help drivers to make traffic decisions (broadcast, internet, etc.). However, such systems cannot process any feedback from the users. We aim at creating a model of drivers as social agents, thus allowing their behaviour to be predicted and considered in the simulation. This may, on its turn, improve the accuracy of the existing Advanced Travel Information Systems (ATIS).
Multilayered Multiagent System for Traffic Simulation: (Demonstration)
2016
We propose a multilayered multiagent simulator that can simulate traffic in any urban environment on earth, subject to specific weather conditions. We adopt an agent-based approach for the behaviors of the vehicles and the drivers. We additionally propose a behavioral model to realistically emulate the driving behaviors of humans.
A behavioral multi-agent model for road traffic simulation
Engineering Applications of …, 2008
Multi-agent systems allow the simulation of complex phenomena that cannot easily be described analytically. Multi-agent approaches are often based on coordinating agents whose actions and interactions are related to the emergence of the phenomenon to be simulated. In this article, we focus on road traffic simulation, specifically the design of a road traffic simulation tool able to deal realistically with road junctions. We propose a multi-agent behavioral model based on (i) the opportunistic individual behaviors that describe the norm violation and (ii) the anticipatory individual abilities of simulated drivers that allow critical situations to be detected. Our proposition has been validated for different traffic scenarios. Specifically, we simulated the traffic in a real intersection and then compared the simulated traffic flow with the real flow to highlight the relevance of our approach.
The Multi-Agent System and Scholastic Process in the Road Traffic
International Journal of Computer Applications, 2013
In recent years, the rapid growth of road traffic density generates a rising request for tools that can be used to analyze and control the traffic networks. The Microscopic traffic simulation is one of the major tools used in the analysis of traffic systems; it provides a very detailed study of the interaction between the elements of the traffic network. Thus microscopic traffic simulation has become an ever increasing field of research and development. The main aim if this paper is to provide a new model for microscopic traffic simulation; Traditional traffic simulation models neglect some real-life factors that need to be considered, such as the effect of random distribution in the entry of lane. This paper combines the Multi-Agent Systems (MAS) and the stochastic process to model the randomness of vehicles arrival at the entry of the lane. The second contribution of this paper is about the internal structure of mobile agents[8] which initially reacts according to the instructions of the Main agent (MA); in the case of a lack of dynamic information, the mobile agents take decisions based on their experiences accumulated during previous interactions. The obtained results illustrate that using the randomness in the reaction of agent enhanced greatly the performance of simulation.
Multi-agent Architecture for Simulation of Traffic with Communications
Inter-vehicle communications, in the context of Intelligent Transportation Systems, will probably bring a significant improvement in both traffic safety and efficiency. In order to evaluate in what measure this is true, traffic simulations that take into account the communications between vehicles are needed. In this paper, we propose an agent-based architecture, in which the simulation and management of the intervehicle communications are integrated in the simulation of vehicles, in a hierarchical multi-agent environment. An overview of multi-agent methodologies, platforms, among other, is also presented.