Title: A two-lane mixed traffic flow model with drivers' intention to change lane based on cellular automata

Authors: Changbing Jiang; Ruolan Li; Tinggui Chen; Chonghuan Xu; Liang Li; Shufang Li

Addresses: HangZhou College of Commerce; School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China ' HangZhou College of Commerce; School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China ' School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China ' Business Administration College, Zhejiang Gongshang University, Hangzhou, China ' School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China ' School of Accounting and Finance, Zhejiang Vocational College of Commerce, Hangzhou; 310053, China

Abstract: Aiming at the shortcomings of the classic NaSch model and the two-lane lane change model (STCA), the viaduct was used as the simulation object on the basis of improving the rules and lane changing rules. Under the open boundary condition, a two-lane mixed traffic flow model considering the intention to enter the lane is established. Aim at the willingness of driver to enter lane research, this model puts forward the vehicle enter rules, which take into account the relevant factors that affect driver's intention to enter the road and quantify the influencing factors through the method of average input rate in queuing theory. Computer numerical simulation shows that the average input rate λ is determined by input rate λ and output rate μ, and is affected by the sensitivity parameter β, and is not greater than μ. The passing state of a road vehicle is determined by the output rate μ and is affected by β. At the same time, compared with the model that ignores the driver's intention to enter the road, the model proposed in this paper is closer to the actual traffic situation.

Keywords: average arrival rate; cellular automata; changed lane; mixed traffic flow.

DOI: 10.1504/IJBIC.2020.112328

International Journal of Bio-Inspired Computation, 2020 Vol.16 No.4, pp.229 - 240

Received: 26 Dec 2019
Accepted: 07 Feb 2020
Published online: 12 Jan 2021 *

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