Predicting the duration of non-recurring road incidents by cluster-specific models
In metropolitan areas, about 50% of traffic delays are caused by non-recurring traffic incidents. Hence, accurate prediction of the duration of such events is critical for traffic management authorities. In this paper, we study the predictability of the duration of traffic incidents by considering v...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/83995 http://hdl.handle.net/10220/42471 |
| _version_ | 1811692751033466880 |
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| author | Ghosh, Banishree Muhammad Tayyab Asif Dauwels, Justin Cai, Wentong Guo, Hongliang Fastenrath, Ulrich |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ghosh, Banishree Muhammad Tayyab Asif Dauwels, Justin Cai, Wentong Guo, Hongliang Fastenrath, Ulrich |
| author_sort | Ghosh, Banishree |
| collection | NTU |
| description | In metropolitan areas, about 50% of traffic delays are caused by non-recurring traffic incidents. Hence, accurate prediction of the duration of such events is critical for traffic management authorities. In this paper, we study the predictability of the duration of traffic incidents by considering various external factors. As incident data is typically sparse, training a large number of models (for instance, model for each road) is not possible. On the other hand, training one
model for the entire network may not be a suitable solution, as such a model will be too generalized and consequently unsuitable for many relatively rare scenarios. Therefore, we propose to solve this issue by first grouping incidents through common latent similarities among them and then training data-driven predictors for each group. In our numerical analysis we consider incident data from Singapore and the Netherlands. Our results show that by training cluster-specific models we can reduce the prediction error by 19.41% for incidents in Singapore and by 17.8% for incidents in the Netherlands. |
| first_indexed | 2024-10-01T06:40:45Z |
| format | Conference Paper |
| id | ntu-10356/83995 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:40:45Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/839952022-09-23T01:41:19Z Predicting the duration of non-recurring road incidents by cluster-specific models Ghosh, Banishree Muhammad Tayyab Asif Dauwels, Justin Cai, Wentong Guo, Hongliang Fastenrath, Ulrich School of Electrical and Electronic Engineering School of Computer Science and Engineering Interdisciplinary Graduate School (IGS) 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) Energy Research Institute @ NTU (ERI@N) Electric breakdown Accidents In metropolitan areas, about 50% of traffic delays are caused by non-recurring traffic incidents. Hence, accurate prediction of the duration of such events is critical for traffic management authorities. In this paper, we study the predictability of the duration of traffic incidents by considering various external factors. As incident data is typically sparse, training a large number of models (for instance, model for each road) is not possible. On the other hand, training one model for the entire network may not be a suitable solution, as such a model will be too generalized and consequently unsuitable for many relatively rare scenarios. Therefore, we propose to solve this issue by first grouping incidents through common latent similarities among them and then training data-driven predictors for each group. In our numerical analysis we consider incident data from Singapore and the Netherlands. Our results show that by training cluster-specific models we can reduce the prediction error by 19.41% for incidents in Singapore and by 17.8% for incidents in the Netherlands. Accepted version 2017-05-23T07:49:11Z 2019-12-06T15:36:06Z 2017-05-23T07:49:11Z 2019-12-06T15:36:06Z 2016 Conference Paper Ghosh, B., Muhammad Tayyab Asif, Dauwels, J., Cai, W., Guo, H., & Fastenrath, U. (2016). Predicting the duration of non-recurring road incidents by cluster-specific models. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1522-1527. https://hdl.handle.net/10356/83995 http://hdl.handle.net/10220/42471 10.1109/ITSC.2016.7795759 en © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [https://dx.doi.org/10.1109/ITSC.2016.7795759]. 6 p. application/pdf |
| spellingShingle | Electric breakdown Accidents Ghosh, Banishree Muhammad Tayyab Asif Dauwels, Justin Cai, Wentong Guo, Hongliang Fastenrath, Ulrich Predicting the duration of non-recurring road incidents by cluster-specific models |
| title | Predicting the duration of non-recurring road incidents by cluster-specific models |
| title_full | Predicting the duration of non-recurring road incidents by cluster-specific models |
| title_fullStr | Predicting the duration of non-recurring road incidents by cluster-specific models |
| title_full_unstemmed | Predicting the duration of non-recurring road incidents by cluster-specific models |
| title_short | Predicting the duration of non-recurring road incidents by cluster-specific models |
| title_sort | predicting the duration of non recurring road incidents by cluster specific models |
| topic | Electric breakdown Accidents |
| url | https://hdl.handle.net/10356/83995 http://hdl.handle.net/10220/42471 |
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