A continuous traffic flow model considering predictive headway variation and preceding vehicle’s taillight effect

Abstract

The car taillights in the deceleration process of preceding vehicle may greatly affect the acceleration and deceleration processes of the following vehicle. Additionally, the driver can predict the headway at the next time period from surrounding traffic information, and adjust the vehicle acceleration based on the difference between the predicted headway and the current headway information. To analyze these combined effects, we propose a new continuous traffic flow model taking into account the predictive headway variation and preceding vehicle’s taillight. The stability condition and KdV–Burgers equation of the continuum model are derived in linear and nonlinear stability analysis. The density wave solution obtained by solving the KdV–Burgers equation can be used to describe the formation and propagation mechanism of traffic jams near stable conditions. The complex traffic phenomena such as shock waves, rarefaction waves and local cluster effects are reproduced by simulation. Results show that the taillight information of preceding vehicle and the driver’s prediction time step both contribute greatly to the stability of the traffic flow and energy consumption.