Macroscopic Traffic Flow Modeling Under Heterogeneous Traffic Condition

被引：0
|
作者：
Guo Y. [1 ]
Hou D. [1 ]
Li Y. [1 ]
Yi Q. [1 ]
Huang Y. [1 ]
机构：
[1] Research Institute of Highway, Ministry of Transport, Beijing
来源：
Tongji Daxue Xuebao/Journal of Tongji University | 2021年 / 49卷 / 07期
关键词：
Automated driving vehicle; Cell basic parameters; Fundamental diagram; Heterogeneous traffic condition; Traffic flow model;
D O I：
10.11908/j.issn.0253-374x.21147
中图分类号：
学科分类号：
摘要：
A modeling method of dynamic graph hybrid automata combined with an improved cell transmission model was proposed. Based on the triangular fundamental diagram fitting results of flow volume and density of road segment under different mixing ratios of automated driving vehicles, the variation rules of critical congestion density, traffic capacity, reverse wave velocity and other main parameters were discussed, and the traditional cell transmission model was improved. The dynamic graph hybrid automata was used to characterize the hierarchical topology of road network, and the improved cell transmission model was embedded into the dynamic graph hybrid automata to establish a macroscopic traffic flow model under heterogeneous traffic condition. Finally, simulation platform was built by using the OpenModelica software to verify the effectiveness of the modeling method. The results show that with the increase of the mixing ratio of automated driving vehicles, the critical congestion density, maximum traffic capacity and reverse wave velocity of road segment all have significant changes. © 2021, Editorial Department of Journal of Tongji University. All right reserved.
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页码：949 / 956
页数：7
相关论文
共 22 条
  • [1] QIN Yanyan, Study on analytical method of heterogeneous traffic flow characteristics under connected and autonomous environment, (2019)
  • [2] QIN Yanyan, WANG Hao, WANG Wei, LWR model for mixed traffic flow in connected and autonomous vehicular environments, China Journal of Highway and Transport, 31, 11, (2018)
  • [3] NGODUY D., Application of gas-kinetic theory to modelling mixed traffic of manual and ACC vehicles, Transportmetrica, 8, 1, (2012)
  • [4] FAN S, HERTY M, SEIBOLD B., Comparative model accuracy of a data-fitted generalized Aw-Rascle-Zhang model, Networks & Heterogeneous Media, 9, 2, (2014)
  • [5] WANG R, LI Y, WORK D B., Comparing traffic state estimators for mixed human and automated traffic flows, Transportation Research, Part C: Emerging Technologies, 78, (2017)
  • [6] DAGANZO C F., The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory, Transportation Research, Part B: Methodological, 28, 4, (1994)
  • [7] DAGANZO C F., The cell transmission model, PartⅡ: network traffic, Transportation Research, Part B: Methodological, 29, 2, (1995)
  • [8] TIAPRASERT K, ZHANG Y, ASWAKUL C, Et al., Closed-form multiclass cell transmission model enhanced with overtaking, lane-changing, and first-in first-out properties, Transportation Research, Part C: Emerging Technologies, 85, (2017)
  • [9] LI Zhiwei, Study on methods of traffic estimation under connected and autonomous vehicles and manual vehicles mixed traffic flow, (2017)
  • [10] LEVIN M W, BOYLES S D., A cell transmission model for dynamic lane reversal with autonomous vehicles, Transportation Research, Part C: Emerging Technologies, 68, (2016)
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