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Travel Time Estimation in the Age of Big Data
Name
travel-time-18.pdf
Description
Submitted version
Size
3.72 MB
Format
Adobe PDF
Checksum (MD5)
c712779362f43901a7207156d47170c5
Author(s)
Bertsimas, Dimitris
Delarue, Arthur
Jaillet, Patrick
Martin, Sébastien
Date Issued
2019
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Version
Original manuscript
Abstract
© 2019 INFORMS. Twenty-first century urban planners have identified the understanding of complex city traffic patterns as a major priority, leading to a sharp increase in the amount and the diversity of traffic data being collected. For instance, taxi companies in an increasing number of major cities have started recording metadata for every individual car ride, such as its origin, destination, and travel time. In this paper, we show that we can leverage network optimization insights to extract accurate travel time estimations from such origin–destination data, using information from a large number of taxi trips to reconstruct the traffic patterns in an entire city. We develop a method that tractably exploits origin–destination data, which, because of its optimization framework, could also take advantage of other sources of traffic information. Using synthetic data, we establish the robustness of our algorithm to high variance data, and the interpretability of its results. We then use hundreds of thousands of taxi travel time observations in Manhattan to show that our algorithm can provide insights about urban traffic patterns on different scales and accurate travel time estimations throughout the network.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1287/opre.2018.1784
