Skip to the main content

Original scientific paper

https://doi.org/10.7307/ptt.v36i1.218

A Reliability-Based Network Equilibrium Model with Electric Vehicles and Gasoline Vehicles

Qiang Tu ; School of Traffic and Transportation, Chongqing Jiaotong University; Engineering Research Center for Waste Oil Recovery Technology and Equipment, Ministry of Education, Chongqing Technology and Business University; Chongqing Urban Investment Gold Card Information Industry (Group) Co. Ltd.
Manman Li ; School of Automobile, Chang'an University
Yongjun Wu ; School of Traffic and Transportation, Chongqing Jiaotong University


Full text: english pdf 2.126 Kb

page 83-99

downloads: 73

cite


Abstract

With the popularity of electric vehicles, they have become an indispensable part of traffic flow on the road network. This paper presents a reliability-based network equilibrium model to realise the traffic flow pattern prediction on the road network with electric vehicles and gasoline vehicles, which incorporates travel time reliability, electric vehicles’ driving range and recharge requirement. The mathematical expression of reliable path travel time is derived, and the reliability-based network equilibrium model is formulated as a variational inequality problem. Then a multi-criterion labelling algorithm is proposed to solve the reliable shortest path problem, and a column-generation-based method of the successive average algorithm is proposed to solve the reliability-based network equilibrium model. The applicability and efficiency of the proposed model and algorithm are verified on the Nguyen-Dupuis network and the real road network of Sioux Falls City. The proposed model and algorithm can be extended to other road networks and help traffic managers analyse traffic conditions and make sustainable traffic policies.

Keywords

transportation engineering; reliability-based network equilibrium; electric vehicle; driving range; recharge requirement

Hrčak ID:

318694

URI

https://hrcak.srce.hr/318694

Publication date:

1.3.2024.

Visits: 261 *