History-based Self-Organizing Traffic Lights

Managing traffic in cities is nowadays a complex problem involving considerable physical and economical resources. Multi-agent Systems (MAS) consist of a set of distributed, usually co-operating, agents that act autonomously. The traffic in a city can be simulated by a MAS with different agents, cars and traffic lights, that interact to obtain an overall goal: to reduce average waiting times for the traffic users. In this paper, we describe an agent-based simulator to model traffic in cities.Using this simulator, we present a self-organizing solution to efficiently manage urban traffic. We compare our proposal with recent approaches, providing better results than classical and alternative self-organizing methods, with lower resources and investments

A multi-agent Framework for dynamic traffic management Considering Priority Link

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, communication, and coordination between different agents. The performance of the proposed system is investigated and instantiated in ANYLOGIC simulator, using a section of the Marrakesh road network that contains priority links. The results indicate that the designed system can significantly develop the efficiency of the traffic regulation system

Adaptive traffic signal control for real-world scenarios in agent-based transport simulations

This study provides an open-source implementation of a decentralized, adaptive signal control algorithm in the agent-based transport simulation MATSim, which is applicable for large-scale real-world scenarios. The implementation is based on the algorithm proposed by Lämmer and Helbing (2008), which had promising results, but was not applicable to real-world scenarios in its published form. The algorithm is extended in this paper to cope with realistic situations like different lanes per signal, small periods of overload, phase combination of non-conflicting traffic, and minimum green times. Impacts and limitations of the adaptive signal control are analyzed for a real-world scenario and compared to a fixed-time and traffic-actuated signal control. It can be shown that delays significantly reduce and queue lengths are lower and more stable than with fixed-time signals. Another finding is that the adaptive signal control behaves like a fixed-time control in overload situations and, therefore, ensures system-wide stability