SUN Mengting, WEI Haiping, LI Xingying, XU Li. Classification and Detection of Traffic Congestion Points Using CART[J]. Geomatics and Information Science of Wuhan University, 2022, 47(5): 683-692. DOI: 10.13203/j.whugis20190288
Citation: SUN Mengting, WEI Haiping, LI Xingying, XU Li. Classification and Detection of Traffic Congestion Points Using CART[J]. Geomatics and Information Science of Wuhan University, 2022, 47(5): 683-692. DOI: 10.13203/j.whugis20190288

Classification and Detection of Traffic Congestion Points Using CART

Funds: 

The National Natural Science Foundation of China 41901397

More Information
  • Author Bio:

    SUN Mengting, master, specializes in technology and application of geographic information system and traffic data mining. E-mail: smt_giser@163.com

  • Corresponding author:

    WEI Haiping, PhD, professor. E-mail: haipingwei@163.com

  • Received Date: October 06, 2019
  • Published Date: May 04, 2022
  •   Objectives  Traffic congestion detection is one of the key points and difficulties of urban traffic management. The existing congestion detection methods are based on road sections, which is not conducive to the extraction of spatiotemporal evolution information of congestion. Moreover, most of the detection only involves the degree of congestion but lacks the congestion type identification. With the classification and regression tree (CART) algorithm, this paper proposes a method for the classification and detection of traffic congestion points, which takes the road section point as the detection unit. In the practical application of this method, congestion points and their categories can be detected in real time according to the average running speed on the road section.
      Methods  Firstly, the road section is divided at a specific interval and mapped to be road section points. According to the abnormal rules and patterns of spatiotemporal road conditions, the spatiotemporal evolution patterns of four congestion types are analyzed with road section points as units. Secondly, the spatial and temporal sequence of road conditions of road section points is extracted on the basis of the road condition detection of road sections, and the spatial and temporal sequence of road conditions is classified and labeled according to different congestion types. Thirdly, four speed indexes are selected to constitute the attribute set of samples, and the speed of each road section point at each peri‍od is extracted according to the attribute set, which forms the dataset of decision tree learning. Finally, with the CART algorithm, the optimal model is obtained by the training with cross-validation to achieve the best generalization ability.
      Results  This paper proposes a classification and detection method of traffic congestion points based on CART. On one hand, congestion point detection is added to refine the basic unit of congestion detection, and on the other hand, congestion point type detection is also involved. Classification and detection of congestion points is helpful to improve the efficiency of traffic management.
      Conclusions  The proposed method is compared with the support vector machine classification model, and the experimental results show that the method in this paper has higher accuracy, higher recall rate, and better classification and detection timeliness.
  • [1]
    朱明皓. 城市交通拥堵对社会经济发展的动力学机制与疏导策略[M]. 北京: 电子工业出版社, 2017

    Zhu Minghao. Dynamic Mechanism and Dispersal Strategy of Social Economic Impact on Urban Traffic Congestion[M]. Beijing: Publishing House of Electronics Industry, 2017
    [2]
    Kerner B S, Demir C, Herrtwich R G, et al. Traffic State Detection with Floating Car Data in Road Networks[C]//IEEE Intelligent Transportation Systems, Vienna, Austria, 2005
    [3]
    付子圣, 李秋萍, 柳林, 等. 利用GPS轨迹二次聚类方法进行道路拥堵精细化识别[J]. 武汉大学学报·信息科学版, 2017, 42(9): 1264-1270 doi: 10.13203/j.whugis20150036

    Fu Zisheng, Li Qiuping, Liu Lin, et al. Identification of Urban Network Congested Segments Using GPS Trajectories Double-Clustering Method[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9): 1264-1270 doi: 10.13203/j.whugis20150036
    [4]
    孙超, 张红军, 陈小鸿. 基于多源浮动车数据融合的道路交通运行评估[J]. 同济大学学报(自然科学版), 2018, 46(1): 46-52 https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201801008.htm

    Sun Chao, Zhang Hongjun, Chen Xiaohong. Road Traffic Operation Assessment Based on Multi-source Floating Car Data Fusion[J]. Journal of Tongji University(Natural Science), 2018, 46 (1): 46-52 https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201801008.htm
    [5]
    曾伟良, 何兆成, 沙志仁, 等. 结合卡尔曼滤波的城市路段速度估计[J]. 测绘科学, 2013, 38(1): 96-99 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201301032.htm

    Zeng Weiliang, He Zhaocheng, Sha Zhiren, et al. Urban Link Speed Estimation with Kalman Filter [J]. Science of Surveying and Mapping, 2013, 38 (1): 96-99 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201301032.htm
    [6]
    Yin X T, Ma C Q, Qu L P. The Analysis of Urban Road Traffic State Based on Kinds Floating Car Data [J]. Applied Mechanics and Materials, 2014, 694: 80-84 doi: 10.4028/www.scientific.net/AMM.694.80
    [7]
    田薇, 张锦明. 拥堵路段的判断与分析[J]. 遥感信息, 2017, 32(1): 149-154 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXX201701029.htm

    Tian Wei, Zhang Jinming. Judgment and Analysis of Congested Road[J]. Remote Sensing Information, 2017, 32(1): 149-154 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXX201701029.htm
    [8]
    牛嘉郡. 基于视频处理的道路交通流跟踪统计系统设计与实现[D]. 成都: 电子科技大学, 2018

    Niu Jiajun. Design and Implementation of Traffic Flow Tracking Statistics System Based on Video Processing[D]. Chengdu: University of Electronic Science and Technology of China, 2018
    [9]
    宋茜萌. 基于公交车轨迹数据的交通拥堵区域检测[D]. 大连: 大连理工大学, 2017

    Song Ximeng. Detection of Traffic Congestion Regions Based on Bus Trajectory Data[D]. Dalian: Dalian University of Technology, 2017
    [10]
    沈敬伟, 周廷刚, 朱晓波. 基于GPS浮动车数据的交通信息时空分布研究[J]. 西南大学学报(自然科学版), 2015, 37(8): 157-162 https://www.cnki.com.cn/Article/CJFDTOTAL-XNND201508026.htm

    Shen Jingwei, Zhou Tinggang, Zhu Xiaobo. On the Spatial and Temporal Distribution of Traffic Information Based on GPS Floating Car Data[J]. Journal of Southwest University (Natural Science Edition), 2015, 37(8): 157-162 https://www.cnki.com.cn/Article/CJFDTOTAL-XNND201508026.htm
    [11]
    Persaud B N, Hall F L. Catastrophe Theory and Patterns in 30-Second Freeway Traffic Data: Implications for Incident Detection[J]. Transportation Research Part A: General, 1989, 23(2): 103-113 doi: 10.1016/0191-2607(89)90071-X
    [12]
    颜安. 基于GPS浮动车的城市道路交通事件检测技术研究[D]. 西安: 长安大学, 2010

    Yan An. Study on the Technology of Traffic Incident Detection for Urban Road Based on GPS Equipped Floating Car[D]. Xi?an: Chang?an University, 2010
    [13]
    隋靓, 党建武. 基于运动目标轨迹的高速公路异常事件检测算法研究[J]. 计算机应用与软件, 2018, 35(1): 246-252 https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201801044.htm

    Sui Jing, Dang Jianwu. Traffic Anomaly Detection Based on Moving Object Trajectory[J]. Computer Applications and Software, 2018, 35(1): 246-252 https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201801044.htm
    [14]
    庞根明. 城市道路交通事件自动检测方法研究[D]. 长春: 吉林大学, 2007

    Pang Genming. Study on the Methods of Automatic Incident Detection for Urban Road[D]. Changchun: Jilin University, 2007
    [15]
    余思远, 杜豫川. 城市快速路瓶颈拥堵分析[J]. 交通科学与工程, 2018, 34(1): 93-98 https://www.cnki.com.cn/Article/CJFDTOTAL-CSJX201801017.htm

    Yu Siyuan, Du Yuchuan. Analysis on the Congestion of Bottlenecks in Urban Expressway[J]. Journal of Transport Science and Engineering, 2018, 34 (1): 93-98 https://www.cnki.com.cn/Article/CJFDTOTAL-CSJX201801017.htm
    [16]
    Lozano A, Manfredi G, Nieddu L. An Algorithm for the Recognition of Levels of Congestion in Road Traffic Problems[J]. Mathematics and Computers in Simulation, 2009, 79(6): 1926-1934
    [17]
    方勇, 党倩, 万剑, 等. 基于深度学习的道路交通拥堵检测[J]. 智能城市, 2018, 4(23): 1-3 https://www.cnki.com.cn/Article/CJFDTOTAL-ZNCS201823002.htm

    Fang Yong, Dang Qian, Wan Jian, et al. Road Traffic Congestion Detection Based on Deep Learning[J]. Intelligent City, 2018, 4(23): 1-3 https://www.cnki.com.cn/Article/CJFDTOTAL-ZNCS201823002.htm
    [18]
    孙慧, 姜宝华. 道路拥堵视频监控信息智能检测仿真[J]. 计算机仿真, 2018, 35(5): 431-434 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJZ201805095.htm

    Sun Hui, Jiang Baohua. Intelligent Detection and Simulation of Road Congestion Video Surveillance Information[J]. Computer Simulation, 2018, 35 (5): 431-434 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJZ201805095.htm
    [19]
    王冬根, 孙冰夏, 宋璟璐. 利用被动式GPS数据的交通行为信息提取方法: 发展现状及趋势[J]. 武汉大学学报·信息科学版, 2014, 39(6): 671-681 doi: 10.13203/j.whugis20140136

    Wang Donggen, Sun Bingxia, Song Jinglu. Methods for Detecting Activity-Travel Behavior Information from Passive GPS Data: State-of-the-Art[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6): 671-681 doi: 10.13203/j.whugis20140136
    [20]
    唐炉亮, 杨雪, 阚子涵, 等. 一种基于朴素贝叶斯分类的车道数量探测[J]. 中国公路学报, 2016, 29(3): 116-123 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201603018.htm

    Tang Luliang, Yang Xue, Kan Zihan, et al. Traffic Lane Numbers Detection Based on the Naive Bayesian Classification[J]. China Journal of Highway and Transport, 2016, 29(3): 116-123 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201603018.htm
    [21]
    Adeli H, Karim A. Fuzzy-Wavelet RBFNN Model for Freeway Incident Detection[J]. Journal of Transportation Engineering, 2000, 126 (6) : 464-471
    [22]
    邓敏, 石岩, 龚健雅, 等. 时空异常探测方法研究综述[J]. 地理与地理信息科学, 2016, 32 (6): 43-50 https://www.cnki.com.cn/Article/CJFDTOTAL-DLGT201606008.htm

    Deng Min, Shi Yan, Gong Jianya, et al. A Summary of Spatio-Temporal Outlier Detection[J]. Geography and Geo-Information Science, 2016, 32(6): 43-50 https://www.cnki.com.cn/Article/CJFDTOTAL-DLGT201606008.htm
    [23]
    杨婷婷. 基于出租车GPS轨迹数据的实时交通状态获取和现有实时路况系统评估[D]. 上海: 华东师范大学, 2016

    Yang Tingting. The Real-Time Traffic Status Acquirement and System Assessment Based on Taxis GPS Trajectory Data[D]. Shanghai: East China Normal University, 2016
    [24]
    李国和, 王峰, 郑阳, 等. 基于决策树生成及剪枝的数据集优化及其应用[J]. 计算机工程与设计, 2018, 39(1): 205-211 https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201801036.htm

    Li Guohe, Wang Feng, Zheng Yang, et al. Optimization of Data Set and Its Application Based on Construction and Pruning of Decision Tree[J]. Computer Engineering and Design, 2018, 39(1): 205-211 https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201801036.htm
    [25]
    洪烨. 基于机器学习算法的糖尿病预测模型研究[D]. 哈尔滨: 哈尔滨工业大学, 2016

    Hong Ye. Research on Diabetes Prediction Models Based on Machine Learning Algorithm[D]. Harbin: Harbin Institute of Technology, 2016
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