A Simple Baseline for Travel Time Estimation using Large-scale Trip Data
A Simple Baseline for Travel Time Estimation using Large-scale Trip Data
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DOI：
10.1145/3293317
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发表时间：
2019-02-01
影响因子：
5
通讯作者：
Li, Zhenhui
中科院分区：
文献类型：
--
作者：
Wang, Hongjian;Tang, Xianfeng;Li, Zhenhui
大规模轨道数据的增加为城市动态研究提供了丰富的信息。例如，纽约市出租车和豪华轿车委员会定期发布出租车出行的来源/目的地信息，2013年公布的出租车出行次数为1.73亿人次[29]。如此庞大的数据集为我们解决传统的交通问题提供了潜在的新视角。本文研究行程时间估计问题。与传统的基于路径的行程时间估计方法不同，我们提出了简单地使用大量的出租车出行，而不是使用中间轨迹点来估计来源和目的地之间的行程时间。我们的实验显示了非常有希望的结果。提出的大数据驱动的方法显著优于最先进的基于路线的方法和在线地图服务。我们的研究表明，大数据可以赋予新的简单方法以能力，这些方法可以作为一些传统计算问题的新基线。
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.