Supervised machine learning for jamming transition in traffic flow with fluctuations in acceleration and braking

Abstract

Jamming transition in traffic flow refers to the sudden transition from a free-flowing state to a jammed state as the traffic density increases. This transition is of great interest to traffic engineers and physicists, as it can have significant implications for traffic safety, efficiency, traffic management, and urban planning. Homogeneous car following models is a popular framework used to study the jamming transition phenomenon. The mathematical structure of the problem is governed by the classical Lorenz system to consider the fluctuational effects. The analytical solution of such nonlinear oscillatory differential equations does not exist. Therefore, this study aims to utilize the machine learning approach with the optimization technique that could be used to fine-tune the weights/ parameters of a neural network model to predict the accurate and reliable solutions for the jamming transition in traffic flow. The headway deviations have been studied by considering the multiple scenarios based on the acceleration and braking of the vehicle.