@article{70b82113ef2a4f408a57cc2aa94774d7,
title = "A Simple Baseline for Travel Time Estimation using Large-scale Trip Data",
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-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.",
author = "Hongjian Wang and Xianfeng Tang and Kuo, {Yu Hsuan} and Daniel Kifer and Zhenhui Li",
note = "Funding Information: The work was supported in part by NSF Awards No. 1054389, No. 1228669, No. 1544455, No. 1652525, No. 1618448, and No. 1702760. Zhenhui Li acknowledges the support from Haile Family Early Career Professorship. The views and conclusions contained in this article are those of the authors and should not be interpreted as representing any funding agencies. Authors{\textquoteright} addresses: H. Wang, Twitter Inc. 1355 Market St. #900, San Francisco, CA, 94103, USA; email: hongjianw@ twitter.com; X. Tang and Y.-H. Kuo, Pennsylvania State University, Westgate Building, University Park, PA, 16802, USA, emails: xut10@ist.psu.edu, yzk5145@cse.psu.edu; D. Kifer, Pennsylvania State University, W333 Westgate Building, University Park, PA, 16802, USA; email: dkifer@cse.psu.edu; Z. Li, Pennsylvania State University, E331 Westgate Building, University Park, PA, 16802, USA; email: jessieli@ist.psu.edu. The work was done while H. Wang was a PhD candidate at Penn State University. Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. {\textcopyright} 2019 Association for Computing Machinery. 2157-6904/2019/01-ART19 $15.00 https://doi.org/10.1145/3293317 Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.",
year = "2019",
month = jan,
doi = "10.1145/3293317",
language = "English (US)",
volume = "10",
journal = "ACM Transactions on Intelligent Systems and Technology",
issn = "2157-6904",
publisher = "Association for Computing Machinery (ACM)",
number = "2",
}