Using Drift Diffusion Model to Analyze Cars' Lane Change Decisions behind Heavy Vehicles

Abstract

Heavy vehicles (HVs) pose a significant challenge to maintaining a smooth traffic flow on the freeway because they are slower moving and create large blind spots. It is therefore desirable for the followers of HVs to perform lane changes (LCs) to achieve a higher speed and a safer driving environment. Understanding LC behaviors of vehicles behind HVs is important because LCs can lead to highway capacity drop and induce safety risks. In this paper, a drift-diffusion model (DDM) is proposed to model the LC behavior of cars behind HVs. In this drift-diffusion (DD) process, vehicles consider the surrounding traffic environment and accumulate evidence over time. A LC is made if the evidence threshold is exceeded. By obtaining vehicle trajectories with LC intentions in the Third Generation Simulation (TGSIM) dataset through clustering and fitting them with the DDM, we find that a lower initial headway makes the drivers more likely to LC. Furthermore, a larger distance to the follower on the target lane, an increasing target gap size, and a higher speed difference between the target lane and the leading HV increases the rate of evidence accumulation and leads to a LC execution sooner.