Travel Time Estimation in the Age of Big Data
© 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 citie...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/136193 |
| _version_ | 1826203117131137024 |
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| author | Bertsimas, Dimitris Delarue, Arthur Jaillet, Patrick Martin, Sébastien |
| author_facet | Bertsimas, Dimitris Delarue, Arthur Jaillet, Patrick Martin, Sébastien |
| author_sort | Bertsimas, Dimitris |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T12:31:55Z |
| format | Article |
| id | mit-1721.1/136193 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:31:55Z |
| publishDate | 2021 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1361932022-03-29T20:43:01Z Travel Time Estimation in the Age of Big Data Bertsimas, Dimitris Delarue, Arthur Jaillet, Patrick Martin, Sébastien © 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. 2021-10-27T20:34:12Z 2021-10-27T20:34:12Z 2019 2019-05-31T18:45:48Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136193 en 10.1287/opre.2018.1784 Operations Research Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) MIT web domain |
| spellingShingle | Bertsimas, Dimitris Delarue, Arthur Jaillet, Patrick Martin, Sébastien Travel Time Estimation in the Age of Big Data |
| title | Travel Time Estimation in the Age of Big Data |
| title_full | Travel Time Estimation in the Age of Big Data |
| title_fullStr | Travel Time Estimation in the Age of Big Data |
| title_full_unstemmed | Travel Time Estimation in the Age of Big Data |
| title_short | Travel Time Estimation in the Age of Big Data |
| title_sort | travel time estimation in the age of big data |
| url | https://hdl.handle.net/1721.1/136193 |
| work_keys_str_mv | AT bertsimasdimitris traveltimeestimationintheageofbigdata AT delaruearthur traveltimeestimationintheageofbigdata AT jailletpatrick traveltimeestimationintheageofbigdata AT martinsebastien traveltimeestimationintheageofbigdata |