Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (1): 271-277.doi: 10.11896／j.issn.1002-137X.2019.01.042

• Artificial Intelligence • Previous Articles     Next Articles

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

PDF (PC)

828

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

CLC Number: 

  • 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.
[1] CHEN Jing, WU Ling-ling. Mixed Attribute Feature Detection Method of Internet of Vehicles Big Datain Multi-source Heterogeneous Environment [J]. Computer Science, 2022, 49(8): 108-112.
[2] HE Qiang, YIN Zhen-yu, HUANG Min, WANG Xing-wei, WANG Yuan-tian, CUI Shuo, ZHAO Yong. Survey of Influence Analysis of Evolutionary Network Based on Big Data [J]. Computer Science, 2022, 49(8): 1-11.
[3] LI Hao-dong, HU Jie, FAN Qin-qin. Multimodal Multi-objective Optimization Based on Parallel Zoning Search and Its Application [J]. Computer Science, 2022, 49(5): 212-220.
[4] SUN Xuan, WANG Huan-xiao. Capability Building for Government Big Data Safety Protection:Discussions from Technologicaland Management Perspectives [J]. Computer Science, 2022, 49(4): 67-73.
[5] SHI Dian-xi, LIU Cong, SHE Fu-jiang, ZHANG Yong-jun. Cooperation Localization Method Based on Location Confidence of Multi-UAV in GPS-deniedEnvironment [J]. Computer Science, 2022, 49(4): 302-311.
[6] WANG Mei-shan, YAO Lan, GAO Fu-xiang, XU Jun-can. Study on Differential Privacy Protection for Medical Set-Valued Data [J]. Computer Science, 2022, 49(4): 362-368.
[7] WANG Jun, WANG Xiu-lai, PANG Wei, ZHAO Hong-fei. Research on Big Data Governance for Science and Technology Forecast [J]. Computer Science, 2021, 48(9): 36-42.
[8] YU Yue-zhang, XIA Tian-yu, JING Yi-nan, HE Zhen-ying, WANG Xiao-yang. Smart Interactive Guide System for Big Data Analytics [J]. Computer Science, 2021, 48(9): 110-117.
[9] WANG Li-mei, ZHU Xu-guang, WANG De-jia, ZHANG Yong, XING Chun-xiao. Study on Judicial Data Classification Method Based on Natural Language Processing Technologies [J]. Computer Science, 2021, 48(8): 80-85.
[10] NIU Kang-li, CHEN Yu-zhang, ZHANG Gong-ping, TAN Qian-cheng, WANG Yi-chong, LUO Mei-qi. Vehicle Flow Measuring of UVA Based on Deep Learning [J]. Computer Science, 2021, 48(6A): 275-280.
[11] ZHOU Xin, LIU Shuo-di, PAN Wei, CHEN Yuan-yuan. Vehicle Color Recognition in Natural Traffic Scene [J]. Computer Science, 2021, 48(6A): 15-20.
[12] WANG Xue-cen, ZHANG Yu, LIU Ying-jie, YU Ge. Evaluation of Quality of Interaction in Online Learning Based on Representation Learning [J]. Computer Science, 2021, 48(2): 207-211.
[13] ZENG Wei-liang, HAN Yu, HE Jin-yuan, WU Miao-sen, SUN Wei-jun. Simulation Analysis on Dynamic Ridesharing Efficiency of Shared Autonomous Taxi [J]. Computer Science, 2021, 48(2): 257-263.
[14] TENG Jian, TENG Fei, LI Tian-rui. Travel Demand Forecasting Based on 3D Convolution and LSTM Encoder-Decoder [J]. Computer Science, 2021, 48(12): 195-203.
[15] ZHANG Yu-long, WANG Qiang, CHEN Ming-kang, SUN Jing-tao. Survey of Intelligent Rain Removal Algorithms for Cloud-IoT Systems [J]. Computer Science, 2021, 48(12): 231-242.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.