Prediction of Traffic Flow considering Electric Vehicle Market Share and Random Charging

This paper mainly studies the multiclass stochastic user equilibrium problem considering market share of battery electric vehicles (BEVs) and random charging behavior (RCB) in a mixed transport network containing gasoline (GVs). In order to analyze path choice behaviors BEV users extract differences travel between GV users, an improved logit-based model, multilabel algorithm, queuing theory are applied. The influencing factors possibility include initial state charge (SOC), SOC at beginning charging, psychologically acceptable safe threshold arriving destination. Diversity choices paths locations will result changes traffic time. Conversely, different stations have dwell times, which also affect routing for BEVs with RCB. path-based method successive averages (MSA) is adopted solve model. Through simulation test Sioux Falls, flow possible under shares predicted, properties model feasibility algorithm verified.