Double-head car-following and lane-changing combined model

被引:6
作者
Wang, Hao [1 ,2 ]
Liu, Zhenquan [3 ]
Zhang, Zhixue [4 ]
Li, Ye [1 ,2 ]
Wang, Wei [1 ,2 ]
机构
[1] Jiangsu Key Laboratory of Urban Intelligent Transportation System, Southeast University, Nanjing
[2] Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing
[3] Liaoning Provincial Communication Planning and Design Institute, Shenyang
[4] Tianjing Municipal Engineering Design and Research Institute, Tianjing
来源
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2015年 / 45卷 / 05期
关键词
Car-following model; Double-head; Lane-changing model; Traffic flow;
D O I
10.3969/j.issn.1001-0505.2015.05.029
中图分类号
U41 [道路工程];
学科分类号
0814 ;
摘要
Due to the fact that the traditional car-following and lane-changing combined models fail to take the driver's anticipation into consideration, based on full velocity difference car-following model and probabilistic lane-changing model, a double-head car-following and lane-changing combined model was proposed, and the method of model calibration was presented as well. The traffic data of Emeryville section in American freeway I 80 from next generation simulation (NGSIM) open data source were used to calibrate the proposed model. The trajectory-based method and maximal likelihood method were carried out to calibrate car-following model and lane-changing model, respectively. Numerical simulation according to the real freeway traffic scenario in NGSIM was designed. The results indicate that both the average speed and the speed dispersion in the simulation have the errors around 5.0%, and the errors for both the times and rate of lane changes are less than 20.0%. Therefore, the proposed model can describe the microscopic characteristics of traffic flow appropriately and simulate the real freeway traffic dynamics successfully. ©, 2015, Southeast University. All right reserved.
引用
收藏
页码:985 / 989
页数:4
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