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Study on traffic characteristics for a typical expressway on-ramp bottleneck considering various merging behaviors

Author

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  • Sun, Jie
  • Li, Zhipeng
  • Sun, Jian

Abstract

Recurring bottlenecks at freeway/expressway are considered as the main cause of traffic congestion in urban traffic system while on-ramp bottlenecks are the most significant sites that may result in congestion. In this paper, the traffic bottleneck characteristics for a simple and typical expressway on-ramp are investigated by the means of simulation modeling under the open boundary condition. In simulations, the running behaviors of each vehicle are described by a car-following model with a calibrated optimal velocity function, and lane changing actions at the merging section are modeled by a novel set of rules. We numerically derive the traffic volume of on-ramp bottleneck under different upstream arrival rates of mainline and ramp flows. It is found that the vehicles from the ramp strongly affect the pass of mainline vehicles and the merging ratio changes with the increasing of ramp vehicle, when the arrival rate of mainline flow is greater than a critical value. In addition, we clarify the dependence of the merging ratio of on-ramp bottleneck on the probability of lane changing and the length of the merging section, and some corresponding intelligent control strategies are proposed in actual traffic application.

Suggested Citation

  • Sun, Jie & Li, Zhipeng & Sun, Jian, 2015. "Study on traffic characteristics for a typical expressway on-ramp bottleneck considering various merging behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 440(C), pages 57-67.
    • Handle: RePEc:eee:phsmap:v:440:y:2015:i:c:p:57-67
    DOI: 10.1016/j.physa.2015.08.007
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    Citations

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    Cited by:

    1. Shen Li & Qiaojun Xiang & Yongfeng Ma & Xin Gu & Han Li, 2016. "Crash Risk Prediction Modeling Based on the Traffic Conflict Technique and a Microscopic Simulation for Freeway Interchange Merging Areas," IJERPH, MDPI, vol. 13(11), pages 1-14, November.
    2. Jin, Zhizhan & Li, Zhipeng & Cheng, Rongjun & Ge, Hongxia, 2018. "Nonlinear analysis for an improved car-following model account for the optimal velocity changes with memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 278-288.

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