skip to main content
10.1145/3139958.3140006acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
poster

Link Travel Time Prediction from Large Scale Endpoint Data

Published:07 November 2017Publication History

ABSTRACT

Existing systems for travel time estimation either use data collected from loop detectors and probe vehicle locations, or from GPS traces from cellphones of "online" users. The former methods of data acquisition are expensive, while the latter turns out to be infeasible in connectivity-poor regions. However, many crowdsourced taxi trip datasets (from Boston, Beijing, Rome, etc.) are publicly available which, despite containing limited information, can be made useful for inferring meaningful insights by certain amount of data engineering. The datasets are both cheap to acquire (hence available in large volumes), and impose less heavy connectivity requirements on the end user. One such crowdsourced dataset is the NYC (New York City) Taxi dataset, which contains only the end-point information for each trip. In this paper, a link (road segment) travel time estimation algorithm named Least Square Estimation with Constraint (LSEC) has been developed from such end-point data, which estimates travel time 20% more accurately than existing algorithms. The key idea is to augment a subset of trips with unique paths using logged distance information, as opposed to fitting adhoc "route-choice" models.

References

  1. Mohammad Asghari, Tobias Emrich, Ugur Demiryurek, and Cyrus Shahabi. 2015. Probabilistic estimation of link travel times in dynamic road networks. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 47. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Yanying Li. 2008. Short-term prediction of motorway travel time using ANPR and loop data. Journal of Forecasting 27, 6 (2008), 507--517.Google ScholarGoogle ScholarCross RefCross Ref
  3. Bei Pan, Ugur Demiryurek, and Cyrus Shahabi. 2012. Utilizing real-world transportation data for accurate traffic prediction. In 2012 IEEE 12th International Conference on Data Mining. IEEE, 595--604. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Mahmood Rahmani, Erik Jenelius, and Haris N Koutsopoulos. 2015. Nonparametric estimation of route travel time distributions from low-frequency floating car data. Transportation Research Part C:Emerging Technologies 58 (2015), 343--362.Google ScholarGoogle ScholarCross RefCross Ref
  5. Irum Sanaullah. 2013. Real-time estimation of travel time using low frequency GPS data from moving sensors. Ph.D. Dissertation. © Irum Sanaullah.Google ScholarGoogle Scholar
  6. Philip B Stark and Robert L Parker. 1995. Bounded-variable least-squares: an algorithm and applications. Computational Statistics 10 (1995), 129--129.Google ScholarGoogle Scholar
  7. Jameson L Toole, Serdar Colak, Bradley Sturt, Lauren P Alexander, Alexandre Evsukoff, and Marta C Gonzalez. 2015. The path most traveled: Travel demand estimation using big data resources. Transportation Research Part C: Emerging Technologies 58 (2015), 162--177.Google ScholarGoogle ScholarCross RefCross Ref
  8. Jin Y Yen. 1971. Finding the k shortest loopless paths in a network. management Science 17, 11 (1971), 712--716.Google ScholarGoogle Scholar
  9. Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, and Yan Huang. 2010. T-drive: driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL International conference on advances in geographic information systems. ACM, 99--108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Xianyuan Zhan, Samiul Hasan, Satish V Ukkusuri, and Camille Kamga. 2013. Urban link travel time estimation using large-scale taxi data with partial information. Transportation Research Part C: Emerging Technologies 33 (2013), 37--49.Google ScholarGoogle ScholarCross RefCross Ref
  11. Xianyuan Zhan, Satish V Ukkusuri, and Chao Yang. 2015. ABayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data. Automation in Construction (2015).Google ScholarGoogle Scholar
  12. Fangfang Zheng and Henk Van Zuylen. 2013. Urban link travel time estimation based on sparse probe vehicle data. Transportation Research Part C: Emerging Technologies 31 (2013), 145--157.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Link Travel Time Prediction from Large Scale Endpoint Data

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SIGSPATIAL '17: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
        November 2017
        677 pages
        ISBN:9781450354905
        DOI:10.1145/3139958

        Copyright © 2017 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 November 2017

        Check for updates

        Qualifiers

        • poster
        • Research
        • Refereed limited

        Acceptance Rates

        SIGSPATIAL '17 Paper Acceptance Rate39of193submissions,20%Overall Acceptance Rate220of1,116submissions,20%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader