Volume 39 Issue 3
Jun.  2021
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(3): 128-135
CUI Hongjun, SUN Wanru, ZHAO Rui, ZHU Minqing, LI Xia. A Quantitative Analysis on Repeatability of Residents'Bus Trip Chain and Travel Regularity[J]. Journal of Transport Information and Safety, 2021, 39(3): 128-135. doi: 10.3963/j.jssn.1674-4861.2021.03.016
Citation: CUI Hongjun, SUN Wanru, ZHAO Rui, ZHU Minqing, LI Xia. A Quantitative Analysis on Repeatability of Residents'Bus Trip Chain and Travel Regularity[J]. Journal of Transport Information and Safety, 2021, 39(3): 128-135. doi: 10.3963/j.jssn.1674-4861.2021.03.016
shu

A Quantitative Analysis on Repeatability of Residents'Bus Trip Chain and Travel Regularity

doi: 10.3963/j.jssn.1674-4861.2021.03.016
  • 1.

    School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China

  • 2.

    School of Architecture and Art Design, Hebei University of Technology, Tianjin 300401, China

  • Received Date: 2020-06-16
    Abstract
  • As a hub system of urban transportation, public transportation carries a large number of residents'travel.The IC card data collected by the automatic data collection system contains a large number of passenger-travel information, which can be analyzed to optimize the public transport service.The information entropy and entropy rate are introduced to quantify the repeatability of the trip chain of public transport, with the method of analyzing the law of public transport travel based on the quantitative index studied.The trip chain of passengers is transformed into a discrete travel sequence through the state calibration of the travel place.Information entropy and the entropy rate are used to quantitatively analyze the travel sequence, thus obtaining the relationship between travel repeatability and quantitative index.In other words, the higher the information entropy of the travel sequence, the lower the entropy rate, the higher the passengers'travel repeatability, and the stronger the travel rule.Based on the repeatability of quantitative processing, the work takes the travel data of smart card passengers in Shijiazhuang as a case study to analyze the travel rules of bus passengers from group and individual.The results show that the quantification index of trip-chain repeatability can intuitively judge the strength of travel rules.When the information entropy is higher than the sample mean(2.53 bits)and the entropy rate is lower than the sample mean(1.13 bits/event)with the unobvious travel rules of passengers, the potential travel rules of passengers can be mined through further analysis.

     

    • FullText(HTML)
  • loading
    • References(19)
  • [1]
    呙娟. 基于公交数据的乘客出行特征分析[D]. 广州: 华南理工大学, 2016.

    GUO Juan. The travel characteristics analysis of passengers based on the bus data. [D]. Guangzhou: South China University of Technology, 2016. (in Chinese).
    [2]
    何兆成, 余畅, 许敏行. 考虑出行模式和周期性的公交出行特征分析[J]. 交通运输系统工程与信息, 2016, 16(6): 135-141. doi: 10.3969/j.issn.1009-6744.2016.06.021

    HE Zhaocheng, YU Chang, XU Minxing. Analyzing methods of residents'travel characteristics considering travel patterns and periodicity[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(6): 135-141. (in Chinese). doi: 10.3969/j.issn.1009-6744.2016.06.021
    [3]
    王俊兵. 基于出行链的公交乘客出行特征分析[D]. 北京: 北京交通大学, 2017.

    WANG Junbing. Analysis of bus passenger travel characteristics based on trip chain. [D]. Beijing: Beijing Jiaotong University, 2017. (in Chinese).
    [4]
    朱亚迪, 陈峰, 王子甲等. 基于概率图模型的乘客出行链提取方法[J]. 吉林大学学报(工学版), 2019, 49(1): 60-65. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201901008.htm

    ZHU Yadi, CHEN Feng, WANG Zijia, et al. Passengers'trip chains extraction method based on probabilistic graphical[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(1): 60-65. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201901008.htm
    [5]
    KUSAKABE T, ASAKURA Y. Behavioural data mining of transit smart card data: A data fusion approach[J]. Transportation Research Part C: Emerging Technologies, 2014(46): 179-191. http://www.sciencedirect.com/science/article/pii/S0968090X14001612
    [6]
    MA Xiaolei, WU Yaojan, WANG Yinhai, et al. Mining smart card data for transit riders'travel patterns[J]. Transportation Research Part C: Emerging Technologies, 2013(36): 1-12. http://www.sciencedirect.com/science/article/pii/S0968090X13001630
    [7]
    张晚笛, 陈峰, 王子甲, 等. 基于多时间粒度的地铁出行规律相似性度量[J]. 铁道学报, 2018, 40(4): 9-17. doi: 10.3969/j.issn.1001-8360.2018.04.002

    ZHANG Wandi, CHEN Feng, WANG Zijia, et al. Similarity measurement of metro travel rules based on multi-time granularities[J]. Journal of the China Railway Society, 2018, 40(4): 9-17(in Chinese). doi: 10.3969/j.issn.1001-8360.2018.04.002
    [8]
    杨光. 快速公交乘客出行的时空规律特征研究[D]. 成都: 西南交通大学, 2017.

    YANG Guang. Research on temporal and spatial characteristics of bus rapid transit. [D]. Chengdu: Southwest Jiaotong University, 2017. (in Chinese).
    [9]
    CHU K K A, CHAPLEAU R. Enriching archived smart card transaction data for transit demand modeling[J]. Transportation Research Record: Journal of the Transportation Research Board, 2008(2063): 63-72. http://www.researchgate.net/publication/237903429_Enriching_Archived_Smart_Card_Transaction_Data_for_Transit_Demand_Modeling
    [10]
    MEDINA S A O. Inferring weekly primary activity patterns using public transport smart card data and a household travel survey[J]. Travel Behaviour and Society, 2018(12): 93-101.
    [11]
    冯树民, 贾佃精, 胡宝雨. 中小城市居民出行链特征分析[J]. 武汉理工大学学报, 2015, 37(1): 51-55+94. https://www.cnki.com.cn/Article/CJFDTOTAL-WHGY201501012.htm

    FENG Shumin, JIA Dianjing, HU Baoyu. Analysis of small and medium sized city residents trip chain characteristics[J]. Journal of Wuhan University of Technology, 2015, 37(1): 51-55+94. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WHGY201501012.htm
    [12]
    李海波, 陈学武. 基于公交IC卡和AVL数据的换乘行为识别方法[J]. 交通运输系统工程与信息, 2013, 13(6): 73-79. doi: 10.3969/j.issn.1009-6744.2013.06.012

    LI Haibo, CHEN Xuewu. Transfer behavior identification method based on public transport IC card and AVL data[J]. Journal of Transportation Systems Engineering and Informa-tion Technology, 2013, 13(6): 73-79. (in Chinese). doi: 10.3969/j.issn.1009-6744.2013.06.012
    [13]
    李莹, 翁小雄. 基于公交IC卡和GPS数据的换乘识别方法[J]. 广西大学学报(自然科学版), 2017, 42(2): 579-586. https://www.cnki.com.cn/Article/CJFDTOTAL-GXKZ201702021.htm

    LI Ying, WENG Xiaoxiong. Transfer identification method based on bus IC card and GPS data[J]. Journal of Guangxi University(Natural Science Edition), 2017, 42(2): 579-586. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GXKZ201702021.htm
    [14]
    BARRY J, NEWHOUSER R, RAHBEE A, et al. Origin and destination estimation in New York City with automated fare system data[J]. Transportation Research Record: Journal of the Transportation Research Board, 2002(1817): 183-187. http://www.researchgate.net/publication/239438647_Origin_and_Destination_Estimation_in_New_York_City_with_Automated_Fare_System_Data
    [15]
    MUNIZAGA M A, PALMA C. Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile[J]. Transportation Research Part C: Emerging Technologies, 2012(24): 9-18. http://smartsearch.nstl.gov.cn/paper_detail.html?id=d878caffe1164ca06b70136ef3ca11fc
    [16]
    CAI Haixiao, KULKARNI S R, VERDU S. Universal entropy estimation via block sorting[J]. IEEE Transactions on Information Theory, 2004, 50(7): 1551-1561. doi: 10.1109/TIT.2004.830771
    [17]
    GAO Yun, KONTOYIANNIS I, ELIE B. Estimating the entropy of binary time series: methodology, some theory and a simulation study[J]. Entropy, 2008, 10(2): 71-99. http://arxiv.org/abs/0802.4363
    [18]
    BARRY J J, FREIMER R, SLAVIN H The Burrows-Wheeler Transform: Data compression, suffix arrays, and pattern matching Boston Springer 2008 ADJEROHD, BELLT C, MUKHERJEE A. The Burrows-Wheeler Transform: Data compression, suffix arrays, and pattern matching[M]. Boston: Springer, 2008.
    [19]
    BARRY J J, FREIMER R, SLAVIN H. Use of entry-only automatic fare collection data to estimate linked transit trips in New York city[J]. Transportation Research Record: Journal of the Transportation Research Board, 2009(2112): 53-61. http://www.researchgate.net/publication/238197099_Use_of_Entry-Only_Automatic_Fare_Collection_Data_to_Estimate_Linked_Transit_Trips_in_New_York_City
    • 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(7)  / Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (276) PDF downloads(19) 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