Traffic Signal Control System Based on Intelligent Transportation System and Reinforcement Learning

Intelligent Traffic Signal Control System Using Embedded System

A development of an intelligent traffic signal control (ITSC) system needed because present traffic light controllers are based on old microcontroller such as AT89C51 which has very less internal memory and no in-built ADC. These systems have limitation because they will use the predefined program that does not have the flexibility of modification on real time application. The present traffic s...

Reinforcement learning-based multi-agent system for network traffic signal control

A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi-agent system and reinforcement learning (RL) framework to obtain an efficient traffic signal control policy. The latter is aimed at minimising the average delay, congestion and likelihood of intersection cr...

Real-time Reinforcement Learning in Traffic Signal System

Real-time optimization of a traffic signal system is a difficult decision-making problem with no considerable model given. To achieve this optimization, a perfect tradeoff should be made between the distributed learning in each individual intersection, and the integrated cooperation in the whole traffic grid. This paper explores such a tradeoff and designs an efficient traffic signal system by ...

Reinforcement learning in neurofuzzy traffic signal control

Reinforcement Learning For Adaptive Traffic Signal Control

By 2050, two-thirds of the world’s 9.6 billion people will live in urban areas [2]. In many cities, opportunities to expand urban road networks are limited, so existing roads will need to more efficiently accommodate higher volumes of traffic. Consequently, there is a pressing need for technologically viable, low-cost solutions that can work with existing infrastructure to help alleviate increa...