Sample Expansion Model of Household Travel Survey Using Cellphone Data
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1.Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China;2.Information and Modelling Department, Guangzhou Transport Planning and Research Institute, Guangzhou 510030, China

Clc Number:

U491

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    Abstract:

    In this paper, the causes of errors in household travel surveys were analyzed, the traditional weighted sample expansion model was introduced, and its limitations, especially the lack of unreported trip records were analyzed. Combining the advantages and characteristics of the research method using cellphone data on travel behaviors, a novel model of using cellphone data to expand the sample of household travel surveys and mining unreported trip records was proposed. A sample expansion model was designed including five steps, combining the household travel survey data, traffic operation monitoring data, and cellphone data. Based on the stationary point classification technology, a travel behavior distribution model based on cellphone data was established. The parameters of the model split model were calibrated based on the revealed preference data of household travel surveys. The difference between the results of the survey sample, the traditional weighted sample expansion model, and the proposed model was analyzed from the aspects of travel purpose composition, time of day, and travel distance distribution. The results show that the model proposed reveals the unreported trip record caused by false reports and omissions.

    Table 6
    Table 4
    Table 2
    Table 3
    Fig.1 Population classification for HTS
    Fig.2 Procedure of multilevel expansion with loop
    Fig.3 Expansion model framework of HTS based on cellphone data
    Fig.4 Model framework of stay-points identification and classification
    Fig.5 Logic diagram for classification of stationary points based on cellphone data
    Fig.6 Framework of Nested Logit model
    Fig.7 Comparison of time of day of different models
    Table 1
    Table 5
    Reference
    [1] BOYCE D, WILIAMS H. Forecasting urban travel: Past, present and future[M]. Northampton: Edward Elgar Publishing Limited, 2015.
    [2] CARROLL J D. Detroit metropolitan area traffic study part I: Data summary and interpretation[R]. Detroit:
    [3] WEINER E. Urban transportation planning in the united states: History, policy, and practice[M]. 5th ed. [s.l.]: Springer, 2016.
    [4] CARROLL J D. Chicago area transportation study final report volume I: Survey findings[R]. Chicago:
    [5] 佐佐木纲, 饭田恭敬. 交通工程学[M]. 邵春福, 杨海, 史其信, 等译. 北京: 人民交通出版社, 1994.
    [6] 马小毅,龙小强,金安等. 广州市新一轮交通综合交通调查总报告[R]. 广州: 广州市交通运输委员会, 2018.
    [7] WESTAT. 2017 NHTS data user guide[R]. Washington D C: Federal Highway Administration, 2018.
    [8] ROTH S B, DEMATTEIS J, DAI Y. 2017 NHTS weighting report[R]. Washington D C: Federal Highway Administration, 2017.
    [9] CASAS J, ZMUD M, LANINI L, et al. 2010/2011 Regional household travel survey: Final report[R]. New York: NYSDOT / NYMTC, 2014.
    [10] HOBBS F D. Traffic planning and engineering[M]. 2nd. [s.l.]: Pergamon Press, 1979.
    [11] RICHARDSON A J, AMPT E S, MEYBURG A H. Survey methods for transport planning[M]. [s.l.]: Eucalyptus Press, 1995.
    [12] EVANS A, KELLY A, SLOCOMBE M. National Travel Survey: England 2018[R]. London: Department of Transport, 2019.
    [13] CORNICK P, CANT J, BYRON C, et al. National Travel Survey 2018: Technical Report[R]. London: Department for Transport, 2019.
    [14] Ministry of Transport New Zealand. Household travel survey[EB/OL]. [2020-04-12] https://www.transport.govt.nz/mot-resources/household-travel-survey/.
    [15] MOONEY R, HEALY S, DULAT E. National household travel survey 2017: Final report[R]. Dublin City: Amárach Research, 2018.
    [16] LEHOHLA P. National household travel survey 2013: Technical report[R]. Pretoria: Statistics South Africa, 2014.
    [17] ARGIROPOULOS I, ANASTASSAKI A, DELOUKAS A. A longitudinal household travel survey: Issues of design, nonresponse and variation in travel behaviour[J]. IFAC Proceedings Volumes, 1997.
    [18] FORSMAN ? , GUSTAFSSON S, VADEBY A. Impact of nonresponse and weighting in Swedish travel survey [J]. Transportation Research Record: Journal of the Transportation Research Board, 2007,1993(1):80.
    [19] HEATHCOTE E A. The role of validation, weighting and expansion in travel surveys[J]. Publication of Australian Road Research Board, 1985:235.
    [20] Delaware Valley Regional Planning Commission. 2012—2013 Household travel survey for the Delaware valley region[R]. Delaware: Delaware Valley Regional Planning Commission, 2014.
    [21] 邹哲, 蒋寅, 朱海明, 等. 天津市综合交通模型框架及关键技术探索[J]. 城市交通, 2013(5): 28.
    [22] 李春艳, 郭继孚, 安志强, 等. 城市综合交通调查发展建议——基于北京市第五次综合交通调查[J]. 城市交通, 2016(2): 29.
    [23] 李娜, 董志国, 薛美根, 等. 上海市第五次综合交通调查新技术方法实践[J]. 城市交通, 2016, 14(2): 35.
    [24] 马小毅. 居民出行调查数据扩样方法研究[J]. 交通运输工程与信息学报, 2010(1):14.
    [25] 中华人民共和国住房和城乡建设部. 城市综合交通体系规划交通调查导则[S]. 北京:中国建筑工业出版社. 2014.
    [26] 中华人民共和国住房和城乡建设部. 城市综合交通调查技术标准[S]. 北京: 中国计划出版社,2018.
    [27] 广州市居民出行调查领导小组. 1984广州市居民出行调查总报告[R].广州:
    Leading Group of Guangzhou Household Travel Survey. 1984 Guangzhou household travel survey report[R]. Guangzhou:
    [28] 陈声洪. 上海城市交通分析和预测[M]. 上海: 上海科学技术出版社, 1998.
    [29] ARUP. 二零一一年交通習慣調查研究報告[R]. 香港: 香港运输署, 2014.
    [30] ARUP. 二零零二年交通習慣調查研究報告[R]. 香港: 香港运输署, 2003.
    [31] 李炬. 城市居民出行调查的理论研究与实践[D]. 西安: 长安大学, 2011.
    [32] 彭泽宇. 居民出行调查数据组合扩样方法及评价研究[D]. 武汉: 华中科技大学, 2016.
    [33] 李元. 基于多源大数据的居民出行调查校核体系研究[D]. 西安: 长安大学, 2017.
    [34] 苏跃江, 陈先龙, 吴德馨. 大数据在广州市第三次交通综合调查中的应用[J]. 城市交通, 2019,17(3): 30.
    [35] WEISBERG H F. The total survey error approach: A guide to the new science of survey research[M]. Chicago: University Of Chicago Press, 2005.
    [36] 胡永恺, 宋璐, 张健, 等. 基于手机信令数据的交通OD提取方法改进[J]. 交通信息与安全, 2015,33(5): 84.
    [37] 王德, 王灿, 谢栋灿, 等. 基于手机信令数据的上海市不同等级商业中心商圈的比较——以南京东路、五角场、鞍山路为例[J]. 城市规划学刊, 2015(3): 50.
    [38] 张天然. 基于手机信令数据的上海市域职住空间分析[J]. 城市交通, 2016,14(1): 15.
    [39] GREENE W. Discrete choice modeling[M]//MILLS T C, PATTERSON K. Palgrave Handbook of Econometrics: Volume 2: Applied Econometrics. London: Palgrave Macmillan UK, 2009:473-556.
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CHEN Xiaohong, CHEN Xianlong, LI Caixia, CHEN Jiachao. Sample Expansion Model of Household Travel Survey Using Cellphone Data[J].同济大学学报(自然科学版),2021,49(1):86~96

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  • Received:May 23,2020
  • Online: February 26,2021
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