A state dependent mandatory lane-changing model for urban arterials with hidden markov model method

Abstract

The inherent intention and decision process of lane changes are complex. Though the external environment and traffic conditions are changing along the traveling direction, the drivers’ characteristics and preferences may lead to persistence of preferable lane choice. The lane-changing decision process is commonly separated into two steps after the decision to consider a lane change: lane choices and acceptance of available gaps, while the outcome of first step is unobservable. To better represent the mandatory lane-changing behaviors on arterials, a two-stage lane-changing model with Hidden Markov Model (HMM) method is proposed. The proposed model is estimated and validated using detail Next Generation Simulation (NGSIM) vehicle trajectory data from an urban arterial. Comparison between generated lane position sequences and original trajectories validated the model’s capability of representing mandatory lane changes.