Soft Computing

, Volume 23, Issue 19, pp 9397–9412 | Cite as

An improved two-lane cellular automaton traffic model based on BL-STCA model considering the dynamic lane-changing probability

  • Zheng-Tao Xiang
  • Zhan Gao
  • Tao Zhang
  • Kai Che
  • Yu-Feng ChenEmail author


Based on the brake light (BL) model, Knospe et al. proposed a symmetric two-lane cellular automaton (BL-STCA) model, which could reproduce various empirically observed two-lane phenomena. In real traffic, the effect of brake light on the lane-changing behavior cannot be ignored. Therefore, BL-STCA model is interesting. However, there are two problems with BL-STCA model, too strong exchange of vehicles between lanes and unreasonable phenomenon in some special scenarios, such as a broken-down vehicle parked on one lane due to traffic accident. In order to solve the problems, we introduce the dynamic lane-changing probability and proposed an improved BL-STCA model with modification of lane-changing rules. The simulation results show as below. (1) Our new model effectively solves the above two problems of BL-STCA model. In addition, the lane-changing frequency is consistent with the real traffic data, which means the validity of our new model. (2) Compared with single-lane scenario, the lane-changing behaviors in two-lane scenario can effectively suppress the emergence of wide moving jam. (3) From the microcosmic level, the lane-changing behaviors can well explain the moving blank phenomenon within wide moving jam.


Traffic flow Cellular automaton model Lane-changing rule Dynamic lane-changing probability 



This work was financially supported by Local Science and Technology Development Project Guided by Central Government (Grant No. 2018ZYYD007), CERNET Innovation Project (Grant No. NGII20180615), Natural Science Foundation of Hubei Province (Grant No. 2013CFA054).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zheng-Tao Xiang
    • 1
  • Zhan Gao
    • 1
  • Tao Zhang
    • 1
  • Kai Che
    • 1
  • Yu-Feng Chen
    • 1
    Email author
  1. 1.School of Electrical and Information EngineeringHubei University of Automotive TechnologyShiyanChina

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