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

Hongjian Wang

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 [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-datadriven 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.