Predicting traffic speed in urban transportation subnetworks for multiple horizons
Traffic forecasting is increasingly taking on an important role in many intelligent transportation systems (ITS) applications. However, prediction is typically performed for individual road segments and prediction horizons. In this study, we focus on the problem of collective prediction for multiple...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| 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/1721.1/100457 https://orcid.org/0000-0002-8585-6566 |
| _version_ | 1811073727514804224 |
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| author | Dauwels, Justin Aslam, Aamer Asif, Muhammad Tayyab Zhao, Xinyue Vie, Nikola Mitro Cichocki, Andrzej Jaillet, Patrick |
| 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 Dauwels, Justin Aslam, Aamer Asif, Muhammad Tayyab Zhao, Xinyue Vie, Nikola Mitro Cichocki, Andrzej Jaillet, Patrick |
| author_sort | Dauwels, Justin |
| collection | MIT |
| description | Traffic forecasting is increasingly taking on an important role in many intelligent transportation systems (ITS) applications. However, prediction is typically performed for individual road segments and prediction horizons. In this study, we focus on the problem of collective prediction for multiple road segments and prediction-horizons. To this end, we develop various matrix and tensor based models by applying partial least squares (PLS), higher order partial least squares (HO-PLS) and N-way partial least squares (N-PLS). These models can simultaneously forecast traffic conditions for multiple road segments and prediction-horizons. Moreover, they can also perform the task of feature selection efficiently. We analyze the performance of these models by performing multi-horizon prediction for an urban subnetwork in Singapore. |
| first_indexed | 2024-09-23T09:37:38Z |
| format | Article |
| id | mit-1721.1/100457 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:37:38Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1004572022-09-26T12:46:08Z Predicting traffic speed in urban transportation subnetworks for multiple horizons Dauwels, Justin Aslam, Aamer Asif, Muhammad Tayyab Zhao, Xinyue Vie, Nikola Mitro Cichocki, Andrzej Jaillet, Patrick Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Jaillet, Patrick Traffic forecasting is increasingly taking on an important role in many intelligent transportation systems (ITS) applications. However, prediction is typically performed for individual road segments and prediction horizons. In this study, we focus on the problem of collective prediction for multiple road segments and prediction-horizons. To this end, we develop various matrix and tensor based models by applying partial least squares (PLS), higher order partial least squares (HO-PLS) and N-way partial least squares (N-PLS). These models can simultaneously forecast traffic conditions for multiple road segments and prediction-horizons. Moreover, they can also perform the task of feature selection efficiently. We analyze the performance of these models by performing multi-horizon prediction for an urban subnetwork in Singapore. Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobility Program) 2015-12-21T17:17:06Z 2015-12-21T17:17:06Z 2014-12 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-5199-4 http://hdl.handle.net/1721.1/100457 Dauwels, Justin, Aamer Aslam, Muhammad Tayyab Asif, Xinyue Zhao, Nikola Mitro Vie, Andrzej Cichocki, and Patrick Jaillet. “Predicting Traffic Speed in Urban Transportation Subnetworks for Multiple Horizons.” 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) (December 2014). https://orcid.org/0000-0002-8585-6566 en_US http://dx.doi.org/10.1109/ICARCV.2014.7064363 Proceedings of the 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) 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 | Dauwels, Justin Aslam, Aamer Asif, Muhammad Tayyab Zhao, Xinyue Vie, Nikola Mitro Cichocki, Andrzej Jaillet, Patrick Predicting traffic speed in urban transportation subnetworks for multiple horizons |
| title | Predicting traffic speed in urban transportation subnetworks for multiple horizons |
| title_full | Predicting traffic speed in urban transportation subnetworks for multiple horizons |
| title_fullStr | Predicting traffic speed in urban transportation subnetworks for multiple horizons |
| title_full_unstemmed | Predicting traffic speed in urban transportation subnetworks for multiple horizons |
| title_short | Predicting traffic speed in urban transportation subnetworks for multiple horizons |
| title_sort | predicting traffic speed in urban transportation subnetworks for multiple horizons |
| url | http://hdl.handle.net/1721.1/100457 https://orcid.org/0000-0002-8585-6566 |
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