计算机科学 ›› 2019, Vol. 46 ›› Issue (1): 271-277.doi: 10.11896/j.issn.1002-137X.2019.01.042

• 人工智能 • 上一篇    下一篇

基于出租车GPS大数据的城市区域间可达性评估模型

王英博1, 单晓晨2, 孟煜3   

  1. (辽宁工程技术大学创新实践学院 辽宁 阜新123000)1
    (辽宁工程技术大学软件学院 辽宁 葫芦岛 125105)2
    (东北大学计算机科学与工程学院 沈阳110819)3
  • 收稿日期:2017-12-19 出版日期:2019-01-15 发布日期:2019-02-25
  • 作者简介:王英博(1964-),男,博士,教授,CCF会员,主要研究领域为软件工程、数字矿山、大数据,E-mail:wybustb@126.com;单晓晨(1994-),女,硕士生,CCF会员,主要研究领域为大数据、数字图像处理,E-mail:867352646@qq.com(通信作者);孟 煜(1990-),男,博士生,CCF会员,主要研究领域为数据挖掘、云计算,E-mail:mengyu@stumail.neu.edu.cn。
  • 基金资助:
    国家科技支撑计划基金资助项目(2013BAH12F00,MK2013008),辽宁省教育厅科学技术研究一般项目(L2015216)资助

Inter-regional Accessibility Evaluation Model of Urban Based on Taxi GPS Big Data

WANG Ying-bo1, SHAN Xiao-chen2, MENG Yu3   

  1. (College of Innovation and Practice,Liaoning Technical University,Fuxin,Liaoning 123000,China)1
    (School of Software,Liaoning Technical University,Huludao,Liaoning 125105,China)2
    (School of Computer Science and Engineering,Northeastern University,Shenyang 110819,China)3
  • Received:2017-12-19 Online:2019-01-15 Published:2019-02-25

摘要: 区域间可达性的评估对城市地面交通出行效率的提高有着重要作用。传统区域间可达性评估方法使用区域间直线距离计算区域间的平均旅行时间,其平均值与实际值的偏差较高,而且基于出租车乘降热点统计的区域间可达性量化方法对于旅行目的地分布不均的区域量化结果过低。针对以上两点不足导致的区域间可达性评估不准确的问题,文中构建了基于GPS的区域间可达性评估模型,从出租车GPS数据中提炼出完整的旅行来计算实际的旅行时间,以提高平均旅行时间的准确性。在此基础上还提出了一种基于四维OD矩阵的可达率计算模型,并以此可达率作为可达性量化标准,从而解决部分区域因发生旅行的目的地分布不均而导致的区域可达性评估不准确的问题。实验表明,提出的可达性评估模型较传统方法而言评估的准确性提高了9.4%~28.7%,特别是在旅行目的地分布不均的结果区域中,可达性评估准确性的提高更为显著。

关键词: GPS, OD矩阵, 大数据, 交通, 可达性

Abstract: The evaluation of inter-regional accessibility plays an important role in improving the efficiency of ground traffic in cities.Traditional inter-regional accessibility evaluation methods make use of the inter-regional linear distance to calculate the regional average travel time,leading to big error between average value and actual value,and the result of inter-regional accessibility measurement method based on hotspot statistics of taxi boarding area quantifying the areas with uneven travel destination distribution is unsatisfactory.In order to solve the problem of inaccurate inter-regional accessibility evaluation caused by the above two points,this paper constructed an inter-area accessibility evaluation modelbased on GPS,and extracted a complete trip from the taxi GPS data to calculate the actual travel time,so as to improve the accuracy of average travel time.On this basis,this paper proposed a quantitative calculation model of accessibility rate based on four-dimensional OD matrix,and used the accessibility rate as the quantification standard of accessibility to solve the problem of inaccurate evaluation of inter-regional accessibility caused by uneven travel destination distribution of some areas.Experiments show that the accuracy of the proposed accessibility evaluation model is 9.4%~28.7% higher than the traditional method,especially in the area with uneven distributed travel destination,the improvement of accessibility evaluation is significant.

Key words: Accessibility, Big data, GPS, OD matrix, Transportation

中图分类号: 

  • TP391
[1]CHENG Y,LIU L,REN J L,et al.Study on Traffic Satisfaction and Economic Development Level Measurement and Spatial Pattern of Jinan Metropolitan Area[J].Chinese Economic geography,2013,33(3):59-64.(in Chinese)<br /> 程钰,刘雷,任建兰,等.济南都市圈交通可达性与经济发展水平测度及空间格局研究[J].经济地理,2013,33(3):59-64.<br /> [2]CUI J X,LIU F,JANSSENS D,et al.Detecting urban road network accessibility problems using taxi GPS data[J].Journal of Transport Geography,2016,51(C):147-157.<br /> [3]WANG X W,XI Y T,TAO J Q,et al.Study on the Accessibility of Traffic Road Network in the Main Urban Area of Xuzhou City Based on[J].Chinese Shanxi Architecture,2016,42(14):13-15.(in Chinese)<br /> 王晓薇,奚砚涛,陶季奇,等.基于GIS的徐州市主城区交通道路网络可达性研究[J].山西建筑,2016,42(14):13-15.<br /> [4]LI S K.Study on Road Route Selection and Urban Traffic Network Evaluation Based on GIS[D].Chongqing:Chongqing University,2005.(in Chinese)<br /> 李石科.基于GIS的道路选线及城市交通路网评价研究[D].重庆:重庆大学,2005.<br /> [5]KONING J G.Indicators of urban accessibility:Theory and application[J].Transportation,1980,9(2):145-172.<br /> [6]MORRIS J M,DUMBLE P L,WIGAN M R.Accessibility indication for transport planning[J].Transportation Research A,1978,13:19-109.<br /> [7]LANGFORD M,HIGGS.Accessibility and public service provision:evaluating the impacts of the Post Office Network Change Programme in the UK[J].Transactions of the Institute of British Geographers,2010,35(4):585-601.<br /> [8]NOVAK D C,SULLIVAN J L.A link-focused methodology for evaluating accessibility to emergency services.Decis[J].Decision Support Systems,2014,57:309-319.<br /> [9]ANDERSON P,LEVINSON D,PARTHASARATHI P.Accessibility futures[J].Transactions in GIS,2013,17(5):683-705.<br /> [10]CURL A,NELSON J D,ANABLE.Does accessibility planning address what matters? a review of current practice and practitioner perspectives[J].Research in Transportation Business & Management,2011,2:3-11.<br /> [11]PÁEZ A,SCOTT D M,MORENCY C.Measuring accessibility:positive and normative implementations of various accessibility indicators[J].Journal of Transport Geography,2012,25:141-153.<br /> [12]HUA S Y,BAO D W,JIA J H.Research on Measurement Method of Accessibility of Airport Network Based on Impe-dance Function[J].Journal of Wuhan University of Technology,2016,40(5):885-890.(in Chinese)<br /> 华松逸,包丹文,贾俊华.基于阻抗函数的机场集疏运道路网可达性测度方法研究[J].武汉理工大学学报,2016,40(5):885-890.<br /> [13]LI L,WANG Z H,LI B C,et al.A Study on the Spatial Accessibility of Star Hotels in Shanghai and Its Driving Forces[J].Journal of Hainan Normal University,2016,29(4):425-434.(in Chinese)<br /> 李龙,王朝辉,李保超,等.上海市星级酒店空间可达性及其驱动力研究[J].海南师范大学学报,2016,29(4):425-434.<br /> [14]LU Y,LI S.An empirical study of with-in day OD prediction using taxi GPS data in Singapore[C]//Transportation Research Board 93<sup>rd</sup> Annual Meeting.2004.<br /> [15]GÜHNEMANN A,SCHÄFER R P,THIESSENHUSEN K U.Monitoring traffic and emissions by floating car data[J].Institute of Transport Studies Working Paper,2004,3:4-7.<br /> [16]MUSTARY N R,CHANDER R P,BAIG.A performance evaluation of VANET for intelligent transportation system.WorldJournal of Science and Technology,2012,2(10):89-93.<br /> [17]ZHANG H,WANG X M,GUO X C,et al.Taxi GPS track large data in intelligent traffic applications.Journal of Lanzhou University of Technology,2016,42(1):109-114.(in Chinese)<br /> 张红,王晓明,过秀成,等.出租车GPS轨迹大数据在智能交通中的应用.兰州理工大学学报,2016,42(1):109-114.<br /> [18]DING G H,XU Y N,GUO J H.Multi-pattern Matching Based on DBSCAN Clustering Algorithm.Chinese Computer applications and software,2016(2):25-29.(in Chinese)<br /> 丁国辉,许莹南,郭军宏.基于DBSCAN聚类算法的多模式匹配.计算机应用与软件,2016(2):25-29.<br /> [19]RONG Q S,YAN J B,GUO G Q.Research and Implementation of DBSCAN Clustering Algorithm[J].Chinese Computer Application,2004,24(4):45-46.(in Chinese)<br /> 荣秋生,颜君彪,郭国强.基于DBSCAN聚类算法的研究与实现[J].计算机应用,2004,24(4):45-46.<br /> [20]SCHUBERT E,SANDER J,ESTER M,et al.DBSCAN Revisited,Revisited:Why and How You Should (Still) Use DBSCAN [J].ACM Transactions on Database Systems (TODS),2017,42(3):19.
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