Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicles guidance

Abstract

Authorities in modern cities are facing daily challenges related to traffic control. Due to the problem complexity caused by the urbanization growth, investing in developing traffic signal control systems (TSCS) to guarantee better mobility has taken more attention by these authorities. In the existing literature, the majority of TSCS offers only a real-time control for a detected traffic problem without considering early prediction and estimation of its occurrence. Furthermore, traffic problems related to the arrival and guidance of emergency vehicles are rarely considered. Based on these gaps, we rely on concepts and mechanisms from both, the Artificial and the convolution neural networks (ANN and CNN), coupled with the longest queue first maximal weight matching algorithm (LQF-MWM), to develop PANNAL, a predictive and reactive TSCS. PANNAL is a Multi-Agent based System, where each individual agent has ANN, CNN, and LQF-MWM to adapt signal sequences and durations and favor the crossing of emergency vehicles. Agents have a heterarchical architecture considered for coordination. We leant on V1SSIM, a state-of-the-art traffic simulation software for simulation and evaluation. We adopted algorithms, scenarios, key performance indicators, and evaluation results from the recent literature for benchmarking. These algorithms are pre-emptive and have a high performance and competitive results in traffic control of disturbed traffic condition.