Lane-level Travel Time Estimation Method Based on Lane Change Trajectory Planning Model

被引：0
|
作者：
Guan D.-Y. [1 ]
Zhang S.-P. [1 ]
Liu H.-Q. [1 ]
机构：
[1] College of Transportation, Shandong University of Science and Technology, Qingdao
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2021年 / 21卷 / 01期
关键词：
Car networking; Carriage-level network; Intelligent transportation; Lane change trajectory model; Travel time estimation;
D O I：
10.16097/j.cnki.1009-6744.2021.01.019
中图分类号：
学科分类号：
摘要：
In the context of the Internet of vehicles, to satisfy the requirements of precise vehicle guidance, a lane-level travel time estimation method is proposed based on a lane change trajectory planning model. Firstly, the lane-level topology model of road networks is established, and the Link division is carried out. An improved quintic polynomial is used to model the vehicles' travel trajectory, and the lane change trajectory planning model is constructed for the vehicle trajectory between links of different sections. Then, the travel trajectory and travel time of each Link in the road section are integrated to estimate the lane-level travel time. Finally, a four-lane road is selected as an example in the VISSIM simulation to verify the performance of the proposed method. The simulation results show that, compared with the traditional travel time estimation method, the improved quintic polynomial lane change trajectory planning model can accurately obtain the travel trajectory with the shortest travel time and realize the accurate estimation of lane-level travel time under different vehicle speeds. Copyright © 2021 by Science Press.
引用
收藏
页码：124 / 131
页数：7
相关论文
共 8 条
  • [1] GUO C, MEGURO J I, KOJIMA Y, Et al., Automatic lane-level map generation for advanced driver assistance systems using low-cost sensors, HongKong: 2014 IEEE International Conference on Robotics and Automation(ICRA), pp. 3875-3982, (2014)
  • [2] YU Z, LIAO Q H, HE Z C., Vehicle trajectory reconstruction in signalized-link using vehicle identification data, Journal of Transportation Systems Engineering and Information Technology, 19, 4, pp. 87-93, (2019)
  • [3] YANG M, DING J, WANG W., Hybrid dwell time prediction method for bus rapid transit based on ARIMA-SVM model, Journal of Southeast University(Natural Science Edition), 46, 3, pp. 651-656, (2016)
  • [4] KAWABATA K, MA L, XUE J R., A path generation for automated vehicle based on Bezier curve and via-points, Robotics and Autonomous Systems, 74, pp. 243-252, (2015)
  • [5] DENG J H, FENG H H., Multilane cellular automaton model based on the lane-changing mechanism, Journal of Transportation Systems Engineering and Information Technology, 18, 3, pp. 68-73, (2018)
  • [6] UCHIDA K., Estimating the value of travel time and of travel time reliability in road networks, Transportation Research Part B: Methodological, 66, pp. 129-147, (2014)
  • [7] LUO X, CAO Y, LIU B, Et al., Estimation of urban road trip time based on floating vehicle data, Journal of Transportation Engineering and Information, 16, 2, pp. 1-8, (2018)
  • [8] LIN L, LI W, PEETA S., Efficient data collection and accurate travel time estimation in a connected vehicle environment via real-time compressive sensing, Journal of Big Data Analytics in Transportation, 1, pp. 95-107, (2019)
1