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

Abstract

Abstract:

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.

Key words: intelligent transportation, travel time estimation, lane change trajectory model, carriage-level network, car networking

摘要：

在车联网环境下，为满足精细化的车辆诱导需求，提出基于换道轨迹规划模型的车道级行程时间估计方法。建立路网基础道路拓扑模型，对所构建的路网模型进行Link划分，并利用改进的5次多项式模型对车辆行驶轨迹进行描述，构建车辆在不同路段Link间行驶的换道轨迹规划模型；整合车辆在路段各个Link单元的行车轨迹与行程时间，实现车道级行程时间估计；选取单向4车道的城市道路为仿真算例，建立VISSIM仿真模型，验证本文模型性能。仿真结果表明，在不同的车辆行驶速度条件下，相比于传统行程时间估计方法，本文提出的改进5次多项式换道轨迹规划模型能够精确地得到车辆最短行程时间下的行车轨迹，实现车道级行程时间的准确估计。

关键词: 智能交通, 行程时间估计, 换道轨迹模型, 车道级路网, 车联网

CLC Number:

U121

GUAN De-yong, ZHANG Shu-peng, LIU Hai-qing. Lane-level Travel Time Estimation Method Based on Lane Change Trajectory Planning Model[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(1): 124-131.

管德永，张树鹏，刘海青. 基于换道轨迹规划模型的车道级行程时间估计方法研究[J]. 交通运输系统工程与信息, 2021, 21(1): 124-131.

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URL: http://www.tseit.org.cn/EN/abstract/abstract20194.shtml

http://www.tseit.org.cn/EN/Y2021/V21/I1/124

References

[1] GUO C, MEGURO J I, KOJIMA Y, et al. Automatic lane-level map generation for advanced driver assistance systems using low- cost sensors[C]. HongKong: 2014 IEEE International Conference on Robotics and Automation(ICRA), 2014: 3875-3982.

[2] 余志, 廖琼华, 何兆成. 基于车辆身份感知数据的路段轨迹重构方法研究[J]. 交通运输系统工程与信息, 2019, 19(4): 87-93. [YU Z, LIAO Q H, HE Z C. Vehicle trajectory reconstruction in signalized- link using vehicle identification data[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(4): 87-93.]

[3] 杨敏, 丁剑, 王炜. 基于ARIMA-SVM模型的快速公交停站时间组合预测方法[J]. 东南大学学报(自然科学版), 2016, 46(3): 651-656. [YANG M, DING J, WANG W. Hybrid dwell time prediction method for bus rapid transit based on ARIMA- SVM model[J]. Journal of Southeast University(Natural Science Edition), 2016, 46 (3): 651-656.]

[4] KAWABATA K, MA L, XUE J R. A path generation for automated vehicle based on Bezier curve and via-points [J]. Robotics and Autonomous Systems, 2015, 74: 243- 252.

[5] 邓建华, 冯焕焕. 基于换道决策机理的多车道元胞自动机模型[J]. 交通运输系统工程与信息, 2018, 18(3): 68- 73. [DENG J H, FENG H H. Multilane cellular automaton model based on the lane- changing mechanism[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(3): 68-73.]

[6] UCHIDA K. Estimating the value of travel time and of travel time reliability in road networks[J]. Transportation Research Part B: Methodological, 2014, 66: 129-147.

[7] 罗霞, 曹阳, 刘博, 等. 基于浮动车数据的城市道路行程时间估计[J]. 交通运输工程与信息学报, 2018, 16 (2): 1- 8. [LUO X, CAO Y, LIU B, et al. Estimation of urban road trip time based on floating vehicle data[J]. Journal of Transportation Engineering and Information, 2018, 16(2): 1-8.]

[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[J]. Journal of Big Data Analytics in Transportation, 2019, 1: 95-107.

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