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A cellular automata model for car–truck heterogeneous traffic flow considering the car–truck following combination effect

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  • Yang, Da
  • Qiu, Xiaoping
  • Yu, Dan
  • Sun, Ruoxiao
  • Pu, Yun

Abstract

To better understand the characteristics of car–truck heterogeneous traffic flow that is very common on freeway, a cellular automata-based traffic flow model is proposed for single lane traffic in this paper. The proposed model discriminates the four types of car–truck following combination, car-following-car (CC), car-following-truck (CT), truck-following-car (TC) and truck-following-truck (TT). The four combinations are considered in terms of the safety distance, reaction time and randomization probability. The parameter values in the proposed model are derived from NGSIM data. Simulations are conducted based on the new model and some new conclusions about the characteristics of the car–truck traffic flow are drawn. First, in the density range of (23–36) vehs/km, the fundamental diagram mainly depends on the car–truck following combination, especially, on the proportion of CC combination. In this range, the fundamental diagram curves with the same proportion of CC gather into a cluster, and the flow rate increases with the increment of the proportion of CC for the same traffic density. Second, traffic congestion can be effectively reduced up to 6.3% by increasing the proportion of TC or CT combination. This finding provides a possible way to alleviate traffic congestion on freeway. Third, reducing randomization probability of the four combinations can effectively increase traffic capacity and alleviate traffic congestion.

Suggested Citation

  • Yang, Da & Qiu, Xiaoping & Yu, Dan & Sun, Ruoxiao & Pu, Yun, 2015. "A cellular automata model for car–truck heterogeneous traffic flow considering the car–truck following combination effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 62-72.
    • Handle: RePEc:eee:phsmap:v:424:y:2015:i:c:p:62-72
    DOI: 10.1016/j.physa.2014.12.020
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