Asymmetric Behavior and Traffic Flow Characteristics of Expressway Merging Area in China

  • Weihua Zhang
  • Siping Wei
  • Changsheng Wang Hefei University of Technology
  • Meng Qiu Hefei University of Technology
Keywords: Expressway; Merging area; Asymmetric behavior; Traffic hysteresis; Capacity

Abstract

The urban expressway confluence area is selected as the research object. Through video aerial photography, image processing and artificial auxiliary technology to obtain high precision trajectory data. Based on the measured data, the driver response mode is divided into multiple sub-models from the macro and micro levels. Based on the measurement of driver type, asymmetric characteristic index and driver response mode, the type and distribution of traffic delay and the influence of asymmetric behavior on the traffic capacity of confluence area are further revealed. The results show that the radical, conservative and ordinary drivers mainly adopt non-decreasing mode, concave mode 4-1 and convex mode 2-1 respectively in the process of oscillation disturbance.The traffic delay caused by the driver ' s asymmetric behavior in the confluence area of the expressway is generally positive, and the average reaction time of the driver is larger than that of the equilibrium state. And asymmetric behavior leads to the average decrease of bottleneck outflow rate by about 7 %, which leads to the increase of delay and the decrease of traffic capacity.

References

Sun Ping,Wang Xuesong,Zhu Meixin. Modeling Car-Following Behavior on Freeways Considering Driving Style[J]. Journal of Transportation Engineering, Part A: Systems,2021,147(12). doi:10.1061/JTEPBS.0000584.

LEUTZBACH D. Introduction to the theory of traffic flow [M]. Berlin: Springer, 1988.

LI X, et al. An improved car- following model considering the influence of space gap to the response[J]. Physica A: Statistical Mechanics and its Applications, 2018, 509: 536-545. doi:10.1016/j.physa.2018.06.069.

Li Xiang-chen. Modeling and analysis of car-following behavior considering the asymmetry[D]. Southwest Jiaotong University Master Degree Thesis,2019.

Wan Q., et al. Using Asymmetric Theory to Identify Heterogeneous Drivers’ behavior Characteristics Through Traffic Oscillation [J]. IEEE Access, 2019, 7: 106284-106294. doi:10.1109/ACCESS.2019.2930762

Newell, G.F. Instability in dense highway traffic, a review [J]. Proceedings of the Second International Symposium on the Theory of Traffic Flow, 1965: 9-54.

Wong, G.C.K., Wong, S.C. A multi-class traffic flow model – an extension of LWR model with heterogeneous drivers [J]. Transportation Research Part A, 2002, 36(9): 827–841.

Wei D, Liu H. Analysis of asymmetric driving behavior using a self-learning approach[J]. Transportation Research Part B: Methodological, 2013, 47: 1-14.

doi:10.1016/j.trb.2012.09.003.

Chen D., et al. A behavioral car-following model that captures traffic oscillations [J]. Transportation Research Part B: Methodological, 2012, 46(6): 744-761.

doi:10.1016/j.trb.2012.01.009.

Chen D., et al. Microscopic traffic hysteresis in traffic oscillations: A behavioral perspective [J]. Transportation Research Part B: Methodological, 2012, 46(10): 1440–1453. doi:10.1016/j.trb.2012.07.002

Chen D., et al. On the periodicity of traffic oscillations and capacity drop: the role of driver characteristics[J]. Transportation research part B: methodological, 2014, 59: 117-136. doi:10.1016/j.trb.2013.11.005

Published
2023-02-13
How to Cite
1.
Zhang W, Wei S, Wang C, Qiu M. Asymmetric Behavior and Traffic Flow Characteristics of Expressway Merging Area in China. Promet [Internet]. 2023Feb.13 [cited 2024Dec.3];35(1):12-6. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/4200
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Articles