Volume 41 Issue 2
Apr.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(2): 28-35
YANG Dongfeng, DAI Jie, ZHANG Yueyan, HAN Lei, YU Rongjie. Effects of Spacing of Highway Roadside Millimeter-wave Radar Detectors on the Accuracy of a Crash Risk Evaluation Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 28-35. doi: 10.3963/j.jssn.1674-4861.2023.02.003
Citation: YANG Dongfeng, DAI Jie, ZHANG Yueyan, HAN Lei, YU Rongjie. Effects of Spacing of Highway Roadside Millimeter-wave Radar Detectors on the Accuracy of a Crash Risk Evaluation Model[J]. Journal of Transport Information and Safety, 2023, 41(2): 28-35. doi: 10.3963/j.jssn.1674-4861.2023.02.003
shu

Effects of Spacing of Highway Roadside Millimeter-wave Radar Detectors on the Accuracy of a Crash Risk Evaluation Model

doi: 10.3963/j.jssn.1674-4861.2023.02.003
  • 1.

    Zhejiang Hangshaoyong Expressway Company Limited, Hangzhou 310000, China

  • 2.

    Zhejiang Highway Information Engineering Technology Company Limited, Hangzhou 310000, China

  • 3.

    Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China

  • Received Date: 2022-09-28
    Available Online: 2023-06-19
    Abstract
  • Freeways equipped with new sensing equipment such as millimeter-wave radar detectors can accurately monitor traffic operation and well support active traffic management measures. However, due to the high deployment expenditure, there is a need to consider the cost constraints and the effectiveness of traffic state detection. To investigate the impacts of millimeter-wave radar deployment spacing on crash risk evaluation performance, this study is conducted based on the empirical data of the Hangshaoyong highway in Zhejiang Province. A crash risk evaluation model based on deep forest (DF) is developed. Specifically, sliding spatio-temporal windows are employed to extract the features of traffic operation while the correlation relationships between the features and crash risk are established through the integrations of multi-layer cascaded random forests. Considering the sensing range of the millimeter-wave radar detectors, multiple traffic operation datasets are developed by assuming different deployment spacings. Sensitivity analyses of radar deployment spacing on the evaluation accuracy of crash risk are conducted. Analyses results show that: The area under curve (AUC) of DF model is 0.849 with 80.9% recall on crash samples, which is higher than traditional convolutional neural network model (AUC is 0.741, recall is 75.2%) and random forest model (AUC is 0.715, recall is 70.8%). An inverse relationship between radar deployment spacing and evaluation accuracy of crash risk is captured, and the marginal effects of the improvement to the model accuracy decreases under dense deployment conditions. If the radar deployment spacing is reduced from 1 500 m to 750 m, the AUC of crash risk evaluation model shows a substantial increase (from 0.794 to 0.853), but there is no obvious change in AUC values when the radar deployment spacing is reduced from 750 m to 250 m. In conclusion, the radar deployment spacing of 750 m can balance the deployment cost and the evaluation performance of crash risk, which could be used to support the decisions related to the installment of traffic sensing equipment.

     

    • FullText(HTML)
  • loading
    • References(27)
  • [1]
    交通运输部, 2021年交通运输行业发展统计公报[R]. 北京: 交通运输部, 2022.

    Ministry of Transport. Statistical bulletin on the development of the transportation industry(2021)[R]. Beijing: Ministry of Transport, 2022. (in Chinese)
    [2]
    焦蕴平. 向交通强国奋进[J]. 中国公路, 2019(19): 16-19. https://www.cnki.com.cn/Article/CJFDTOTAL-GLZG201919010.htm

    JIAO Y P. Forge towards a country with strong transportation network[J]. China Highway, 2019(19): 16-19. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLZG201919010.htm
    [3]
    王少飞, 谯志, 付建胜, 等. 智慧高速公路的内涵及其架构[J]. 公路, 2017, 62(12): 170-175. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL201712045.htm

    WANG S F, QIAO Z, FU J S, et al. Connotation and architecture of smart expressway[J]. Highway, 2017, 62(12): 170-175. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL201712045.htm
    [4]
    王虹. 新基建模式下智慧高速的"破"与"建"[J]. 中国交通信息化, 2021(7): 22-26. https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202107003.htm

    WANG H. The"break"and"build"of smart highway in the new infrastructure model[J]. China ITS Journal, 2021(7): 22-26. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202107003.htm
    [5]
    公安部交通管理局. 中国道路交通事故统计年报(2021)[R]. 北京: 公安部交通管理局, 2022.

    Ministry of Public Security, Transportation Bureau. The road traffic accidents statistics report in China(2021)[R]. Beijing: Ministry of Public Security, Transportation bureau, 2022. (in Chinese)
    [6]
    王锐, 高磊. 智慧高速主动交通管控策略探究[J]. 中国交通信息化, 2022(增刊): 109-111. https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC2022S1028.htm

    WANG R, GAO L. Exploration of active traffic control strategy on smart highway[J]. China ITS Journal, 2022(SUP1): 109-111. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC2022S1028.htm
    [7]
    崔录库, 刘金伟. 主动交通管理在智慧高速中的应用探讨[J]. 中国交通信息化, 2021(12): 131-133. https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202112021.htm

    CUI L K, LIU J W. Exploring the application of active traffic management in smart highway[J]. China ITS Journal, 2021 (12): 131-133. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202112021.htm
    [8]
    赵祥模, 高赢, 徐志刚, 等. IntelliWay-变耦合模块化智慧高速公路系统一体化架构及测评体系[J]. 中国公路学报, 2023, 36(1): 176-201. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202301015.htm

    ZHAO X M, GAO Y, XU Z G, et al. IntelliWay: An integrated architecture and testing methodology for intelligent highway using varied coupling modularization[J]. China Journal of Highway and Transport, 2023, 36(1): 176-201. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202301015.htm
    [9]
    ABDEL-ATY M A, CAI Q, ELURU N, et al. Integrated freeway/arterial active traffic management[R]. Orlando: Department of Transportation, 2019.
    [10]
    刘星良, 单珏, 刘唐志, 等. 基于交通流稳定性系数的高速公路交通事故实时风险预测[J]. 交通信息与安全, 2022, 40 (4): 71-81. doi: 10.3963/j.jssn.1674-4861.2022.04.008

    LIU X L, C Y, LIU T Z, et al. Real-time forecast models for traffic accidents on expressways using stability coefficients of traffic flow[J]. Journal of Transport Information and Safety, 2022, 40(4): 71-81. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.04.008
    [11]
    王西. 新基建背景下智慧高速多元融合感知技术应用浅谈[J]. 中国交通信息化, 2020(6): 125-126. https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202006017.htm

    WANG X. Discussion on the application of smart highway multi-integration sensing technology under the background of new infrastructure construction[J]. China ITS Journal, 2020(6): 125-126. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202006017.htm
    [12]
    ROSHANDEL S, ZHENG Z, WASHINGTON S. Impact of real-time traffic characteristics on freeway crash occurrence: systematic review and meta-analysis[J]. Accident Analysis & Prevention, 2015, 79: 198-211.
    [13]
    杜豫川, 刘成龙, 吴荻非, 等. 新一代智慧高速公路系统架构设计[J]. 中国公路学报, 2022, 35(4): 203-214. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202204017.htm

    DU Y C, LIU C L, WU D F, et al. Framework of the new generation of smart highway[J]. China Journal of Highway and Transport, 2022, 35(4): 203-214. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL202204017.htm
    [14]
    金峰, 顾永鑫, 胡飞. 沪杭甬高速毫米波雷达事件检测能力分析[J]. 中国交通信息化, 2022(8): 122-124, 139. https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202208012.htm

    JIN F, GU Y X, HU F. Analysis of millimeter wave radar event detection capability for the Huhangyong highway[J]. China ITS Journal, 2022(8): 122-124, 139. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTXC202208012.htm
    [15]
    CHEN H, DOUGHERTY M, KIRBY H. An investigation of detector spacing and forecasting performance using neural networks[C]. 4th World Congress on Intelligent transport systems, Berlin, German: Mobility for everyone, 1997.
    [16]
    KWON J, PETTY K, VARAIYA P. Probe vehicle runs or loop detectors? effect of detector spacing and sample size on accuracy of freeway congestion monitoring[J]. Transportation research record, 2007, 2012(1): 57-63.
    [17]
    刘政威. 考虑交通事件检测的固定型交通检测器布设方法研究[D]. 南京: 东南大学, 2011.

    LIU Z W. Research on the fixed traffic detectors deployment method considering traffic event detection[D]. Nanjing: Southeast University, 2011. (in Chinese)
    [18]
    CAO Q, LI Z, MA Y, et al. Optimal layout of heterogeneous sensors for traffic accidents detection and prevention[J]. IEEE transactions on instrumentation and measurement, 2022, 71: 1-13.
    [19]
    刘昕, 刘志远, 聂品, 等. 微观交通仿真模型参数标定研究综述[J]. 铁道科学与工程学, 2022, 19(11): 3179-3189. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202211007.htm

    LIU X, LIU Z Y, NIE P, et al. A survey of microscopic traffic simulation calibration methods[J]. Journal of Railway Science and Engineering, 2022, 19(11): 3179-3189. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202211007.htm
    [20]
    HOSSAIN M, ABDEL-ATY M, QUDDUS M A, et al. Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements[J]. Accident Analysis & Prevention, 2019, 124: 66-84.
    [21]
    YU R, ABDEL-ATY M. Utilizing support vector machine in real-time crash risk evaluation[J]. Accident Analysis & Prevention, 2013, 51: 252-259.
    [22]
    孙剑, 孙杰. 城市快速路实时交通流运行安全主动风险评估[J]. 同济大学学报(自然科学版), 2014, 42(6): 873-879. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201406008.htm

    SUN J, SUN J. Proactive assessment of real-time traffic flow accident risk on urban expressway[J]. Journal of Tongji University(Natural Science), 2014, 42(6): 873-879. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201406008.htm
    [23]
    ZHOU Z H, FENG J. Deep forest: towards an alternative to deep neural networks[C]. IJCAI Conference, Melbourne, Australia: IJCAI, 2017.
    [24]
    夏恒, 汤健, 乔俊飞. 深度森林研究综述[J]. 北京工业大学学报, 2022, 48(2): 182-196. https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD202202010.htm

    XIA H, TANG J, QIAO J F. Review of deep forest[J]. Journal of Beijing University of Technology, 2022, 48(2): 182-196. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJGD202202010.htm
    [25]
    YU R, WANG Y, ZOU Z, et al. Convolutional neural networks with refined loss functions for the real-time crash risk analysis[J]. Transportation Research Part C: Emerging Technologies, 2020(119): 102740.
    [26]
    LI P, ABDEL-ATY M, YUAN J. Real-time crash risk prediction on arterials based on LSTM-CNN[J]. Accident Analysis & Prevention, 2020(135): 105371.
    [27]
    高珍, 高屹, 余荣杰, 等. 连续数据环境下的道路交通事故风险预测模型[J]. 中国公路学报, 2018, 31(4): 280-287. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201804033.htm
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(5)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (893) PDF downloads(53) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return