A simple baseline for travel time estimation using large-scale trip data

Hongjian Wang, Yu Hsuan Kuo, Daniel Kifer, Zhenhui Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Citations (Scopus)

Abstract

The increased availability of large-scale trajectory data provides rich information for the study of urban dynamics. For example, New York City Taxi & Limousine Commission regularly releases source/destination information of taxi trips, where 173 million taxi trips released for Year 2013 [1]. Such a big dataset provides us potential new perspectives to address the traditional traffic problems. In this paper, we study the travel time estimation problem. Instead of following the traditional route-based travel time estimation, we propose to simply use a large amount of taxi trips without using the intermediate trajectory points to estimate the travel time between source and destination. Our experiments show very promising results. The proposed big data-driven approach significantly outperforms both state-of-the-art route-based method and online map services. Our study indicates that novel simple approaches could be empowered by the big data and these approaches could serve as new baselines for some traditional computational problems.

Original languageEnglish (US)
Title of host publication24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
EditorsMatthias Renz, Mohamed Ali, Shawn Newsam, Matthias Renz, Siva Ravada, Goce Trajcevski
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450345897
DOIs
StatePublished - Oct 31 2016
Event24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 - Burlingame, United States
Duration: Oct 31 2016Nov 3 2016

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Other

Other24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
CountryUnited States
CityBurlingame
Period10/31/1611/3/16

Fingerprint

taxis
Travel Time
Travel time
travel time
Baseline
Trajectories
Trajectory
trajectory
Data-driven
Availability
Traffic
Estimate
Experiment
Experiments
Big data
experiment

All Science Journal Classification (ASJC) codes

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Wang, H., Kuo, Y. H., Kifer, D., & Li, Z. (2016). A simple baseline for travel time estimation using large-scale trip data. In M. Renz, M. Ali, S. Newsam, M. Renz, S. Ravada, & G. Trajcevski (Eds.), 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 [61] (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). Association for Computing Machinery. https://doi.org/10.1145/2996913.2996943
Wang, Hongjian ; Kuo, Yu Hsuan ; Kifer, Daniel ; Li, Zhenhui. / A simple baseline for travel time estimation using large-scale trip data. 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. editor / Matthias Renz ; Mohamed Ali ; Shawn Newsam ; Matthias Renz ; Siva Ravada ; Goce Trajcevski. Association for Computing Machinery, 2016. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).
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abstract = "The increased availability of large-scale trajectory data provides rich information for the study of urban dynamics. For example, New York City Taxi & Limousine Commission regularly releases source/destination information of taxi trips, where 173 million taxi trips released for Year 2013 [1]. Such a big dataset provides us potential new perspectives to address the traditional traffic problems. In this paper, we study the travel time estimation problem. Instead of following the traditional route-based travel time estimation, we propose to simply use a large amount of taxi trips without using the intermediate trajectory points to estimate the travel time between source and destination. Our experiments show very promising results. The proposed big data-driven approach significantly outperforms both state-of-the-art route-based method and online map services. Our study indicates that novel simple approaches could be empowered by the big data and these approaches could serve as new baselines for some traditional computational problems.",
author = "Hongjian Wang and Kuo, {Yu Hsuan} and Daniel Kifer and Zhenhui Li",
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Wang, H, Kuo, YH, Kifer, D & Li, Z 2016, A simple baseline for travel time estimation using large-scale trip data. in M Renz, M Ali, S Newsam, M Renz, S Ravada & G Trajcevski (eds), 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016., 61, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Association for Computing Machinery, 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016, Burlingame, United States, 10/31/16. https://doi.org/10.1145/2996913.2996943

A simple baseline for travel time estimation using large-scale trip data. / Wang, Hongjian; Kuo, Yu Hsuan; Kifer, Daniel; Li, Zhenhui.

24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. ed. / Matthias Renz; Mohamed Ali; Shawn Newsam; Matthias Renz; Siva Ravada; Goce Trajcevski. Association for Computing Machinery, 2016. 61 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Wang H, Kuo YH, Kifer D, Li Z. A simple baseline for travel time estimation using large-scale trip data. In Renz M, Ali M, Newsam S, Renz M, Ravada S, Trajcevski G, editors, 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016. Association for Computing Machinery. 2016. 61. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). https://doi.org/10.1145/2996913.2996943