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

TL;DR

This paper introduces a simple yet effective baseline for estimating travel times using large-scale trip data, outperforming complex existing methods and aiding navigation and trip planning.

Contribution

The paper proposes a straightforward baseline for travel time estimation that surpasses more complex state-of-the-art approaches, demonstrating the value of simplicity in big data applications.

Findings

The baseline outperforms Bing Maps and Baidu Maps in travel time estimation.

Simple methods can be more effective than complex algorithms in large-scale trip data analysis.

The approach has practical applications in navigation and trip planning.

Abstract

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).