Nonlinear Complementarity Model for Mixed-User Equilibrium Traffic Assignment and Mode Choice of Electric and Gasoline Vehicles

Abstract

In the early years of 21st century, the ever-increasing volume of greenhouse gases from fossil fuel consumption have made humans seek alternative, non-polluting fuels as an effective strategy to reduce pollution and prevent related environmental issues. Electric vehicles (EVs) are today known as one of the most effective solutions for this purpose. To inform transportation planning and policies pertinent to EVs, models are needed to capture travelers’ behavior for vehicle type and route choice and the impacts on traffic congestion. The present study proposes a mixed complementarity traffic assignment model for the networks involving both EVs and gasoline vehicles so that the demand for each vehicle class depends on the characteristics of the paths and availability of electric charging stations on the paths. To determine the demand of each class, this study applies a logit choice model, which incorporates the effect of ownership and operating costs on demand of each class under different subsidy policies. This paper further investigates the charging behavior of EVs by considering private and public charging facilities in urban areas. To this end, the complementary traffic assignment algorithm has been used to solve the mentioned assignment problem. Besides, we used a label-setting algorithm for solving the constraint shortest path problem. The results of applying the mixed-user equilibrium to Sioux Falls and Chicago sketch networks demonstrate that our proposed algorithm outperforms existing algorithms for both solution time and accuracy across multiple networks.

Smart dynamic evacuation planning and online management using vehicular communication system

“…The upper level was dedicated to sheltering site selection in an SO fashion, and the lower level was for static traffic assignment in a UE manner. The presented model was solved by employing a simulated annealing heuristic, and for calculating the arc's congestion level, they used the Bureau of Public Roads (BPR) static function (Davazdah Emami & Khani, 2023). Li et al (2012) presented a scenario-based model.…”