Volume 14 Issue 6
. 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2014  >  14(6): 100-109
LI Hui-bing, YANG Xiao-guang, LUO Li-hua. Mining method of floating car data based on link travel time estimation[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 100-109.
Citation: LI Hui-bing, YANG Xiao-guang, LUO Li-hua. Mining method of floating car data based on link travel time estimation[J]. Journal of Traffic and Transportation Engineering, 2014, 14(6): 100-109.
shu

Mining method of floating car data based on link travel time estimation

  • 1.

    School of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China

  • 2.

    School of Transportation Engineering, Tongji University, Shanghai 201804, China

More Information
  • Author Bio:

    LI Hui-bing (1983-), male, lecturer, PhD, +86-21-38282347, hbli@shmtu.edu.cn

  • Received Date: 2014-06-28
  • Publish Date: 2014-12-25
    Abstract
  • Based on floating car data, a link travel time estimation method without signal timing data was proposed.The method consisted of four modules, which were intersection boundary dynamic partition module, link influence range partition module, floating car data extraction module, and link travel time estimation module, and the implementation of each module relied greatly on the output of previous one.According to vehicle travel state under the influence of signal control, link unit was divided into different segments by using density method in intersection boundary dynamic partition module and link influence range partition module.According to link travel time estimation mechanism, floating car data that were seriously affected by signal control were filtered off in floating car data extraction module, so the target floating car data could be obtained.Historical floating car data were excavated in link travel time estimation module, and floating car data were divided into 3 types according to different exsited regions of target data.Corresponding section travel time estimation methods were used for different types of data, and corresponding section travel time estimation models were established.Link travel time estimation method was simulated and verified by using software VISSIM, and its result was compared with the results of direct and indirect methods.Analysis result indicates that for coarse-grained floating car data, the average absolute error and average relative error of link travel time estimation method are 12 sand 8.67% respectively, so it performs better than traditional direct and indirect methods.

     

    • FullText(HTML)
  • loading
    • References(20)
  • [1]
    WANG Jian-qiang, NIU Hui-min. Graded-information feedback strategy in two-route systems under ATIS[J]. Journal of Traffic and Transportation Engineering: English Edition, 2014, 1 (2): 138-145. doi: 10.1016/S2095-7564(15)30098-2
    [2]
    WU C H, HO J M, LEE D T. Travel time prediction with support vector regression[J]. IEEE Transactions on Intelligent Transportation Systems, 2004, 5 (4): 276-281. doi: 10.1109/TITS.2004.837813
    [3]
    SUN Lu, YANG Jun, MAHMASSANI H. Travel time estimation based on piecewise truncated quadratic speed trajectory[J]. Transportation Research Part A: Policy and Practice, 2008, 42 (1): 173-186. doi: 10.1016/j.tra.2007.08.004
    [4]
    ZHENG Fang-fang, ZUYLEN H. Comparison of urban link travel time estimation models based on probe vehicle data[C]∥MAO Bao-hua, TIAN Zong-zhong, HUANG Hai-jun, et al. Seventh International Conference on Traffic and Transportation Studies. Kunming: ASCE, 2010: 615-626.
    [5]
    ZHENG Fang-fang, ZUYLEN H. Urban link travel time estimation based on sparse probe vehicle data[J]. Transportation Research Part C: Emerging Technologies, 2013, 31 (1): 145-157. https://www.sciencedirect.com/science/article/pii/S0968090X12000575
    [6]
    BI Song, WANG Zhi-jian, HAN Cun-wu, et al. Estimation of left-turning travel time at traffic intersection[J]. The Journal of China Universities of Posts and Telecommunications, 2013, 20 (S1): 10-14. https://www.sciencedirect.com/science/article/pii/S1005888513602575
    [7]
    HAN Shu, LIN Hang-fei, CHEN Xiao-hong. The application of link aggregating algorithm in travel time estimate on signalized arterial network[C]∥PENG Qi-yuan, WANG K C P, QIU Yan-jun, et al. Second International Conference on Transportation Engineering. Chengdu: ASCE, 2009: 2315-2321.
    [8]
    JIANG Zhou, ZHANG Cun-bao, XIA Yin-xia. Travel time prediction model for urban road network based on multisource data[J]. Procedia-Social and Behavioral Sciences, 2014, 138: 811-818. doi: 10.1016/j.sbspro.2014.07.230
    [9]
    李进燕, 朱征宇, 刘琳, 等. 基于简化路网模型的卡尔曼滤波多步行程时间预测方法[J]. 系统工程理论与实践, 2013, 33 (5): 1289-1297. doi: 10.3969/j.issn.1000-6788.2013.05.026

    LI Jin-yan, ZHU Zheng-yu, LIU Lin, et al. Multi-step Kalman filtering travel time estimation method based on simplified road network model[J]. Systems Engineering—Theory and Practice, 2013, 33 (5): 1289-1297. (in Chinese). doi: 10.3969/j.issn.1000-6788.2013.05.026
    [10]
    ZHENG Fang-fang, WAN Yu, WU Ping-heng. Link traveltime prediction using extended exponential smoothing and Kalman filter in dynamic networks[C]∥LIU Rong-fang, ZHANG Jin, GUAN Chang-qian. The Eighth International Conference of Chinese Logistics and Transportation Professionals. Chengdu: ASCE, 2008: 3753-3759.
    [11]
    LIU H X, MA Wen-teng. A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials[J]. Transportation Research Part C: Emerging Technologies, 2009, 17 (1): 11-26. doi: 10.1016/j.trc.2008.05.002
    [12]
    JENELIUS E, KOUTSOPOULOS H N. Travel time estimation for urban road networks using low frequency probe vehicle data[J]. Transportation Research Part B: Methodological, 2013, 53: 64-81. doi: 10.1016/j.trb.2013.03.008
    [13]
    李嘉, 刘春华, 胡赛阳, 等. 基于交通数据融合技术的行程时间预测模型[J]. 湖南大学学报: 自然科学版, 2014, 41 (1): 33-38. https://www.cnki.com.cn/Article/CJFDTOTAL-HNDX201401006.htm

    LI Jia, LIU Chun-hua, HU Sai-yang, et al. A travel time prediction model based on traffic data fusion technology[J]. Journal of Hunan University: Natural Sciences, 2014, 41 (1): 33-38. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HNDX201401006.htm
    [14]
    方路平, 陈仕骁, 赵飞帆. 基于小样本浮动车系统的平均行程时间估计[J]. 计算机仿真, 2012, 29 (9): 367-370. doi: 10.3969/j.issn.1006-9348.2012.09.091

    FANG Lu-ping, CHEN Shi-xiao, ZHAO Fei-fan. Average link travel time estimation based on floating car of small sample size[J]. Computer Simulation, 2012, 29 (9): 367-370. (in Chinese). doi: 10.3969/j.issn.1006-9348.2012.09.091
    [15]
    姜桂艳, 常安德, 张玮. 基于GPS浮动车的路段行程时间估计方法比较[J]. 吉林大学学报: 工学版, 2009, 39 (增2): 182-186. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY2009S2039.htm

    JIANG Gui-yan, CHANG An-de, ZHANG Wei. Comparison of link travel-time estimation methods based on GPS equipped floating car[J]. Journal of Jilin University: Engineering and Technology Edition, 2009, 39 (S2): 182-186. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY2009S2039.htm
    [16]
    姜桂艳, 常安德, 张玮, 等. 基于GPS浮动车的自然路段行程时间估计方法[J]. 公路, 2009 (11): 87-90. https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL200911019.htm

    JIANG Gui-yan, CHANG An-de, ZHANG Wei. Physical link travel-time estimation method based on GPS equipped floating car[J]. Highway, 2009 (11): 87-90. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GLGL200911019.htm
    [17]
    姜桂艳, 常安德, 吴超腾. 基于GPS浮动车的交通信息采集方法[J]. 吉林大学学报: 工学版, 2010, 40 (4): 971-975. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201004017.htm

    JIANG Gui-yan, CHANG An-de, WU Chao-teng. Traffic information collection method based on GPS equipped floating car[J]. Journal of Jilin University: Engineering and Technology Edition, 2010, 40 (4): 971-975. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201004017.htm
    [18]
    韩舒, 林航飞, 辛飞飞. 浮动车采集系统中城市道路分段方法研究[J]. 交通与计算机, 2007, 25 (5): 105-109. https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200705031.htm

    HAN Shu, LIN Hang-fei, XIN Fei-fei. Method of merging urban road subsections based on floating car data[J]. Computer and Communications, 2007, 25 (5): 105-109. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200705031.htm
    [19]
    熊英格, 徐卓立, 刘好德. 基于浮动车数据的交叉口范围动态划分方法[J]. 交通信息与安全, 2009, 27 (5): 38-43. https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200905010.htm

    XIONG Ying-ge, XU Zhuo-li, LIU Hao-de. Methods of intersection dynamic subsections based on floating car data[J]. Journal of Transport Information and Safety, 2009, 27 (5): 38-43. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200905010.htm
    [20]
    胡小文. 基于探测车数据和固定检测器数据的路段行程时间估计[D]. 上海: 同济大学, 2008.

    HU Xiao-wen. Link travel time estimation based on probe data and fixed detector data[D]. Shanghai: Tongji University, 2008. (in Chinese).
    • 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
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (659) PDF downloads(1028) Cited by()
    Proportional views
    Related

    Copyright《Journal of Traffic and Transportation Engineering》编辑部陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang 'an University, Middle Section of South Second Ring Road, Xi 'an, Shaanxi（710064） Tel：029-82334388 Email：jygc@chd.edu.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return