Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction
The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2015
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| Online Access: | http://hdl.handle.net/1721.1/100436 https://orcid.org/0000-0003-0064-6568 https://orcid.org/0000-0002-8585-6566 |
| _version_ | 1826211351609999360 |
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| author | Asif, Muhammad Tayyab Dauwels, Justin Oran, Ali Fathi, Esmail Dhanya, Menoth Mohan Mitrovic, Nikola Jaillet, Patrick Goh, Chong Yang Xu, Muye |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Asif, Muhammad Tayyab Dauwels, Justin Oran, Ali Fathi, Esmail Dhanya, Menoth Mohan Mitrovic, Nikola Jaillet, Patrick Goh, Chong Yang Xu, Muye |
| author_sort | Asif, Muhammad Tayyab |
| collection | MIT |
| description | The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can significantly vary across the network and during different time periods. Insight into those spatiotemporal trends can improve the performance of intelligent transportation systems. Traditional prediction error measures, such as the mean absolute percentage error, provide information about the individual links in the network but do not capture global trends. We propose unsupervised learning methods, such as k-means clustering, principal component analysis, and self-organizing maps, to mine spatiotemporal performance trends at the network level and for individual links. We perform prediction for a large interconnected road network and for multiple prediction horizons with an SVR-based algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of the SVR. |
| first_indexed | 2024-09-23T15:04:32Z |
| format | Article |
| id | mit-1721.1/100436 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:04:32Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1004362022-09-29T12:31:53Z Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction Asif, Muhammad Tayyab Dauwels, Justin Oran, Ali Fathi, Esmail Dhanya, Menoth Mohan Mitrovic, Nikola Jaillet, Patrick Goh, Chong Yang Xu, Muye Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Massachusetts Institute of Technology. Operations Research Center Goh, Chong Yang Jaillet, Patrick The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can significantly vary across the network and during different time periods. Insight into those spatiotemporal trends can improve the performance of intelligent transportation systems. Traditional prediction error measures, such as the mean absolute percentage error, provide information about the individual links in the network but do not capture global trends. We propose unsupervised learning methods, such as k-means clustering, principal component analysis, and self-organizing maps, to mine spatiotemporal performance trends at the network level and for individual links. We perform prediction for a large interconnected road network and for multiple prediction horizons with an SVR-based algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of the SVR. Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobility Program) 2015-12-18T16:14:40Z 2015-12-18T16:14:40Z 2014-04 Article http://purl.org/eprint/type/JournalArticle 1524-9050 1558-0016 http://hdl.handle.net/1721.1/100436 Asif, Muhammad Tayyab, Justin Dauwels, Chong Yang Goh, Ali Oran, Esmail Fathi, Muye Xu, Menoth Mohan Dhanya, Nikola Mitrovic, and Patrick Jaillet. “Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction.” IEEE Transactions on Intelligent Transportation Systems 15, no. 2 (April 2014): 794–804. https://orcid.org/0000-0003-0064-6568 https://orcid.org/0000-0002-8585-6566 en_US http://dx.doi.org/10.1109/tits.2013.2290285 IEEE Transactions on Intelligent Transportation Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Asif, Muhammad Tayyab Dauwels, Justin Oran, Ali Fathi, Esmail Dhanya, Menoth Mohan Mitrovic, Nikola Jaillet, Patrick Goh, Chong Yang Xu, Muye Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction |
| title | Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction |
| title_full | Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction |
| title_fullStr | Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction |
| title_full_unstemmed | Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction |
| title_short | Spatial and Temporal Patterns in Large-Scale Traffic Speed Prediction |
| title_sort | spatial and temporal patterns in large scale traffic speed prediction |
| url | http://hdl.handle.net/1721.1/100436 https://orcid.org/0000-0003-0064-6568 https://orcid.org/0000-0002-8585-6566 |
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