Volume 39 Issue 1
Feb.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(1): 64-75
MA Tianyi, WEN Jiaqiang, WANG Liyuan, LYU Nengchao, WANG Yugang. An Estimation Model of Section Traffic Parameters Based on Connected ADAS Spatiotemporal Data[J]. Journal of Transport Information and Safety, 2021, 39(1): 64-75. doi: 10.3963/j.jssn.1674-4861.2021.01.008
Citation: MA Tianyi, WEN Jiaqiang, WANG Liyuan, LYU Nengchao, WANG Yugang. An Estimation Model of Section Traffic Parameters Based on Connected ADAS Spatiotemporal Data[J]. Journal of Transport Information and Safety, 2021, 39(1): 64-75. doi: 10.3963/j.jssn.1674-4861.2021.01.008
shu

An Estimation Model of Section Traffic Parameters Based on Connected ADAS Spatiotemporal Data

doi: 10.3963/j.jssn.1674-4861.2021.01.008
  • 1.

    Second Highway Consultant Company LTD., CCCC, Wuhan 430056, China

  • 2.

    Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

  • 3.

    Engineering Research Center for Transportation Safety of Ministry of Education, Wuhan University of Technology, Wuhan 430063, China

  • Received Date: 2020-11-26
  • Publish Date: 2021-02-28
    Abstract
  • Real-time acquisition of traffic parameters is an essential basis for road traffic control. A method for estimating section traffic parameters using the connected ADAS data is studied for the limited observation range of fixed detectors and the great demand for floating vehicles. A traffic parameter-estimation model under unsteady traffic conditions is established by analyzing the relationship between forward target parameters perceived by on-board ADAS and traffic parameters, the definition of generalized traffic volume, and the relative motion characteristics of the ADAS vehicle and its neighboring vehicle in a multi-lane environment. According to the simulation, the calibration data set and the verification data set are obtained to complete the parameter calibration and verification of the model. Also, the paper discusses the influences of time and space resolutions, and ADAS vehicle penetration rates on the estimation accuracy of the model. The analysis shows that when the time resolution is reduced by 5 min, the estimation error is reduced by 3.4% on average; reducing the time resolution can improve the estimation accuracy of the proposed model. When the space resolution is reduced by 500 m, the estimation error of flow and density is reduced by 1.68% on average; however, it may lead to an average increase of 5.19% in speed estimation error. The increased penetration rate of ADAS vehicles can enhance the overall fit between estimated traffic parameters and observed traffic parameters in the time-space area of the road sections. In the context of the gradual application of ADAS, the proposed model of traffic parameter estimation can quickly obtain the traffic volume information in the continuous time-space range of the road sections.

     

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    • References(30)
  • [1]
    GEROLIMINIS N, DAGANZO C F. Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings[J]. Transportation Research Part B: Methodological, 2008, 42(9): 759-770. doi: 10.1016/j.trb.2008.02.002
    [2]
    张亮亮, 贾元华, 牛忠海, 等. 交通状态划分的参数权重聚类方法研究[J]. 交通运输系统工程与信息, 2014, 14(6): 147-151. doi: 10.3969/j.issn.1009-6744.2014.06.023

    ZHANG Liangliang, JIA Yuanhua, NIU Zhonghai, et al. Traffic state classification based on parameter weighting and clustering method[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(6): 147-151. (in Chinese) doi: 10.3969/j.issn.1009-6744.2014.06.023
    [3]
    QU X, WANG S, ZHANG J. On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models[J]. Transportation Research Part B: Methodological, 2015(73): 91-102. http://www.sciencedirect.com/science/article/pii/S0191261515000041
    [4]
    DURET A, YUAN Y. Traffic state estimation based on Eulerian and Lagrangian observations in a mesoscopic modeling framework[J]. Transportation Research Part B: Methodological, 2017 (101): 51-71. http://www.sciencedirect.com/science/article/pii/S0191261516305240
    [5]
    SEO T, KUSAKABE T. Probe vehicle-based traffic state estimation method with spacing information and conservation law[J]. Transportation Research Part C: Emerging Technologies, 2015 (59): 391-403. http://www.sciencedirect.com/science/article/pii/S0968090X15002132
    [6]
    ALJAMAL M A, ABDELGHAFFAR H M, RAKHA H A. Realtime estimation of vehicle counts on signalized intersection approaches using probe vehicle data[J/OL].(2020-02)[2021- 01-22]. https://ieeexplore.ieee.org/document/9007043.
    [7]
    唐克双, 徐天祥, 董可然, 等. 基于低频定点检测数据的交叉口交通状态估计[J]. 同济大学学报(自然科学版), 2017, 45 (5): 705-713. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201705013.htm

    TANG Keshuang, XU Tianxiang, DONG Keran, et al. Traffic state estimation based on low frequency detection data at intersections[J]. Journal of Tongji University(Natural Science Edition), 2017, 45(5): 705-713. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201705013.htm
    [8]
    SEO T, KUSAKABE T, ASAKURA Y. Estimation of flow and density using probe vehicles with spacing measurement equipment[J]. Transportation Research Part C: Emerging Technologies, 2015(53): 134-150. http://www.sciencedirect.com/science/article/pii/S0968090X15000443
    [9]
    SINGH K, LI B. Estimation of traffic densities for multilane roadways using a Markov model approach[J]. IEEE Transactions on Industrial Electronics, 2012, 59(11): 4369-4376. doi: 10.1109/TIE.2011.2180271
    [10]
    SEO T, BAYEN A M, KUSAKABE T, et al. Traffic state estimation on highway: a comprehensive survey[J]. Annual Reviews in Control, 2017(43): 128-151. http://smartsearch.nstl.gov.cn/paper_detail.html?id=b9166f0ad576da7a787b473f4b0383a7
    [11]
    陈喜群, 周凌霄, 曹震. 基于图卷积网络的路网短时交通流预测研究[J]. 交通运输系统工程与信息, 2020, 20(4): 49-55. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202004008.htm

    CHEN Xiqun, ZHOU Lingxiao, CAO Zhen. Short-term network-wide traffic prediction based on graph convolutional network[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 49-55. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202004008.htm
    [12]
    HERRERA J C, WORK D B, HERRING R, et al. Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment[J]. Transportation Research Part C: Emerging Technologies, 2010, 18(4): 568-583. doi: 10.1016/j.trc.2009.10.006
    [13]
    QIU T Z, LU X Y, CHOW A H F, et al. Estimation of freeway traffic density with loop detector and probe vehicle data[J]. Transportation Research Record: Journal of the Transportation Research Board, 2010(2178): 21-29. http://www.researchgate.net/publication/277392609_Estimation_of_Freeway_Traffic_Density_with_Loop_Detector_and_Probe_Vehicle_Data
    [14]
    BHASKAR A, TSUBOTA T, KIEU L M, et al. Urban traffic state estimation: Fusing point and zone based data[J]. Transportation Research Part C: Emerging Technologies, 2014 (48): 120-142. http://www.sciencedirect.com/science/article/pii/S0968090X14002319
    [15]
    ZHANG J, HE S, WANG W, et al. Accuracy analysis of freeway traffic speed estimation based on the integration of cellular probe system and loop detectors[J]. Journal of Intelligent Transportation Systems, 2015, 19(4): 411-426. doi: 10.1080/15472450.2014.1000456
    [16]
    MONTERO L, PACHECO M, BARCELO J, et al. Case study on cooperative car data for estimating traffic states in an urban network[J]. Transportation Research Record: Journal of the Transportation Research Board, 2016(2594): 127-137. http://www.researchgate.net/publication/309517357_Case_Study_on_Cooperative_Car_Data_for_Estimating_Traffic_States_in_an_Urban_Network
    [17]
    YUAN Y, VAN LINT J W C, WILSON R E, et al. Real-time Lagrangian traffic state estimator for freeways[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(1): 59-70. doi: 10.1109/TITS.2011.2178837
    [18]
    林晓辉, 徐建闽. 基于自适应加权平均的路网MFD估测融合方法[J]. 交通运输系统工程与信息, 2018, 18(6): 102-109. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201806015.htm

    LIN Xiaohui, XU Jianmin. Macroscopic fundamental diagram estimation fusion method of road networks based on adaptive weighted average[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(6): 102-109. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201806015.htm
    [19]
    李晨朋, 韩印, 王馨玉. 车联网环境下公交路径交通状态估计方法研究[J]. 交通运输研究, 2018, 4(5): 29-34. https://www.cnki.com.cn/Article/CJFDTOTAL-JTBH201805004.htm

    LI Chenpeng, HAN Yin, WANG Xinyu. Traffic state estimation method of bus route in connected vehicle environment[J]. Transport Research, 2018, 4(5): 29-34. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTBH201805004.htm
    [20]
    符旭, 欧梦宁, 闫旭普, 等. 基于分布式车辆速度检测信息的城市快速路交通状态估计[J]. 交通运输工程与信息学报, 2016, 14(4): 105-112. doi: 10.3969/j.issn.1672-4747.2016.04.017

    FU Xu, OU Mengning, YAN Xupu, et al. Traffic state estimation of urban freeway traffic based on distributed speed detecting information networks[J]. Journal of Transportation Engineering and Information, 2016, 14(4): 105-112. (in Chinese) doi: 10.3969/j.issn.1672-4747.2016.04.017
    [21]
    HOU W, LYU N, LIU Z, et al. Modeling large vehicle operating speed characteristics on freeway alignment based on aggregated GPS data[J]. IET Intelligent Transport Systems, 2020, 14 (8): 857-865. doi: 10.1049/iet-its.2019.0563
    [22]
    SEO T, KUSAKABE T. Probe vehicle-based traffic flow estimation method without fundamental diagram[J]. Transportation Research Procedia, 2015(9): 149-163. http://www.sciencedirect.com/science/article/pii/s2352146515001696
    [23]
    GRUMERT E F, TAPANI A. Traffic state estimation using connected vehicles and stationary detectors[J/OL].(2018-01)[2021-01-22]. https://www.hindawi.com/journals/jat/2018/4106086/.
    [24]
    LYU N, DUAN Z, MA C, et al. Safety margins: A novel approach from risk homeostasis theory for evaluating the impact of advanced driver assistance systems on driving behavior in near-crash events[J]. Journal of Intelligent Transportation Systems, 2020, 25(1): 93-106.
    [25]
    BENGLER K, DIETMAYR K, FARBER B, et al. Three decades of driver assistance systems: Review and future perspectives[J]. IEEE Intelligent Transportation Systems Magazine, 2014, 6(4): 6-22. doi: 10.1109/MITS.2014.2336271
    [26]
    WILBY M R, DIAZ J J V, GONZALEZ A B R, et al. Lightweight occupancy estimation on freeways using extended floating car data[J]. Journal of Intelligent Transportation Systems, 2014, 18(2): 149-163. doi: 10.1080/15472450.2013.801711
    [27]
    刘永涛, 华珺, 赵俊玮, 等. 场景风险引导下驾驶人应激反应能力研究[J]. 交通信息与安全, 2019, 37(3): 35-41. https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS201903005.htm

    LIU Yongtao, HUA Jun, ZHAO Junwei, et al. Emergency response ability of drivers under risk guidance situations[J]. Journal of Transport Information and Safety, 2019, 37(3): 35-41. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS201903005.htm
    [28]
    王祺, 李力, 胡坚明, 等. 不同车头间距下交通流的速度分布[J]. 清华大学学报(自然科学版), 2011, 51(3): 309-312. https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201103004.htm

    WANG Qi, LI Li, HU Jianming, et al. Traffic velocity distributions for different spacings[J]. Journal of Tsinghua University (Science and Technology), 2011, 51(3): 309-312. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201103004.htm
    [29]
    EDIE L C, FOOTE R S, HERMAN R, et al. Analysis of single lane traffic flow[J]. Traffic Engineering, 1962, 33(4): 21-27. http://www.researchgate.net/publication/284577535_Analysis_of_single_lane_traffic_flow
    [30]
    唐克双, 梅雨, 李克平. 基于浮动车数据的交通状态估计精度仿真评价[J]. 同济大学学报(自然科学版), 2014, 42(9): 1347-1351. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201409007.htm

    TANG Keshuang, MEI Yu, LI Keping. A simulation-based evaluation of traffic state estimation accuracy by using floating car data in complex road networks[J]. Journal of Tongji University(Natural Science Edition), 2014, 42(9): 1347-1351. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201409007.htm
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