A Simple Baseline for Travel Time Estimation using Large-scale Trip Data

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

Research output: Contribution to journalArticlepeer-review

88 Scopus citations


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 [29]. Such a big dataset provides us potential new perspectives to address the traditional traffic problems. In this article, 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 big data and these approaches could serve as new baselines for some traditional computational problems.

Original languageEnglish (US)
Article numbera19
JournalACM Transactions on Intelligent Systems and Technology
Issue number2
StatePublished - Jan 2019

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Artificial Intelligence


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