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Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

PUBLISHED
25.04.2017
LICENSE
Copyright (c) 2024 Jiangfeng Wang, Chang Gao, Zhouyuan Zhu, Xuedong Yan

Multi-lane Changing Model with Coupling Driving Intention and Inclination

Authors:

Jiangfeng Wang
Beijing Jiaotong University

Chang Gao
Beijing Jiaotong University

Zhouyuan Zhu
Beijing Jiaotong University

Xuedong Yan
Beijing Jiaotong University

Keywords:lane changing model, driver intention, driving inclination, cellular automaton,

Abstract

Considering the impact of drivers’ psychology and behaviour, a multi-lane changing model coupling driving intention and inclination is proposed by introducing two quantitative indices of intention: strength of lane changing and risk factor. According to the psychological and behavioural characteristics of aggressive drivers and conservative drivers, the safety conditions for lane changing are designed respectively. The numerical simulations show that the proposed model is suitable for describing the traffic flow with frequent lane changing, which is more consistent with the driving behaviour of drivers in China. Compared with symmetric two-lane cellular automata (STCA) model, the proposed model can improve the average speed of vehicles by 1.04% under different traffic demands when aggressive drivers are in a higher proportion (the threshold of risk factor is 0.4). When the risk factor increases, the average speed shows the polarization phenomenon with the average speed slowing down in big traffic demand. The proposed model can reflect the relationship among density, flow, and speed, and the risk factor has a significant impact on density and flow.

References

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How to Cite
Wang, J. (et al.) 2017. Multi-lane Changing Model with Coupling Driving Intention and Inclination. Traffic&Transportation Journal. 29, 2 (Apr. 2017), 185-192. DOI: https://doi.org/10.7307/ptt.v29i2.2085.

SPECIAL ISSUE IS OUT

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Marko Matulin, PhD, Dario Babić, PhD, Marko Ševrović, PhD


Accelerating Discoveries in Traffic Science |
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