Multi-agent preemptive longest queue first system to manage the crossing of emergency vehicles at interrupted intersections

Abstract

Favouring the crossing of Emergency Vehicles (EVs) through intersections in urban cities is very critical for people lives. There have been several efforts toward developing Traffic Signal Control Systems (TSCS) dedicated to control efficiently the traffic flow, but few are the efforts toward developing Traffic Signal Priority Systems (TSPS) dedicated to favour the crossing of EVs (such as ambulances, firefighters, police cars, etc.). Multi-Agent Systems were considered to develop several distributed TSCS, while very few works have developed distributed TSPS. Such systems lack on dealing with the EVs crossing issues while maintaining a fluid state of the traffic. In the literature, the Longest Queue First – Maximal Weight Matching (LQF-MWM) is proved to guarantee a stable TSCS. Recently, the LQF-MWM technique is increasingly used to benchmarck and assess adaptive TSCS. Moreover, the preemption is one of the most effective techniques used to prioritise the crossing of EVs. This paper is the first to rely on LQF-MWM assumptions, preemption technique, and Multi-Agent Systems to design a distributed TSPS. The suggested system has two main purposes, which are the guidance of EVs and the control of traffic signals. Nine agents are implemented to govern a network of nine intersections, where each agent uses the Multi Agent System based Preemptive Longest Queue First – Maximal Weight Matching. We have referred to VISSIM traffic simulation software for benchmarking and analysis. To assess the suggested system, we have developed a distributed and preemptive version of VISSIM Optimized Stage-Based Fixed-Time algorithm. Python is considered to develop the suggested systems, and Spade platform is considered as agents’ platform. Several Key Performance Indicators are considered to assess the performance of all controllers including delay time, travel time, vehicles queue occupancy, number of stops, distance traversed, and speed. Experimental results show a competitive performance of the developed system to maintain a fluid traffic and guide efficiency EVs.