Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane

Abstract

Abstract Traffic lights monitoring that considers only traffic volumes is not necessarily the optimal way to time the green/red allocation in a junction. A “smart” allocation should also consider the necessities of the vehicle’s passengers and the needs of the people those passengers ought to serve. This paper deals with a “smart” junction, where several cars approach the intersection from different directions and a traffic light is set to comply to a sequence of time intervals of red and green lights in each direction. The novel approach presented here is based not only on traffic congestion parameters, but also on the social and economic characteristics of the passengers (e.g. a handicapped person, a medical doctor, an employee who is extremely required in a certain organization due to an emergency situation). This paper proposes to enhance the smart junction with a fast lane, which has a flexible entry permit based on social and economic criteria. Machine learning (specifically, Reinforcement Learning (RL)) is added to the junction’s algorithm with the aim of optimizing the social utility of the junction. For the purposes of this study, the utility of the junction is defined by the total social and economic potential benefits given a certain red/green time allocation is set. This is defined as the measure of the reward function which contains positive factors for vehicles which crossed the junction or advanced their position and a negative factor for vehicles which remains in their positions. In addition, a weight value for the vehicles with high priority is also part of the equation. A simplified version of the smart junction has been used, serving as a model for incorporating RL into the “smart’ junction with Fast Lane (FL). Specifically, the Q-Learning algorithm is used to maximize the reward function. Simulation results show that prioritizing high priority vehicles via FL is influenced by the weights and factors given to the reward components. Farther research should enhance the “Smart” junction with FL to a more complex and realistic one using a varying amount of vehicles crossing the junction.