Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/136590
Title: Dynamic prediction of the incident duration using adaptive feature set
Authors: Ghosh, Banishree
Muhammad Tayyab Asif
Dauwels, Justin
Fastenrath, Ulrich
Guo, Hongliang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Ghosh, B., Muhammad Tayyab Asif, Dauwels, J., Fastenrath, U., & Guo, H. (2019). Dynamic prediction of the incident duration using adaptive feature set. IEEE Transactions on Intelligent Transportation Systems, 20(11), 4019-4031. doi:10.1109/TITS.2018.2878637
Journal: IEEE Transactions on Intelligent Transportation Systems
Abstract: Non-recurring incidents such as accidents and vehicle breakdowns are the leading causes of severe traffic congestions in large cities. Consequently, anticipating the duration of such events in advance can be highly useful in mitigating the resultant congestion. However, availability of partial information or ever-changing ground conditions makes the task of forecasting the duration particularly challenging. In this paper, we propose an adaptive ensemble model that can provide reasonable forecasts even when a limited amount of information is available and further improves the prediction accuracy as more information becomes available during the course of the incidents. Furthermore, we consider the scenarios where the historical incident reports may not always contain accurate information about the duration of the incidents. To mitigate this issue, we first quantify the effective duration of the incidents by looking for the change points in traffic state and then utilize this information to predict the duration of the incidents. We compare the prediction performance of different traditional regression methods, and the experimental results show that the Treebagger outperforms other methods. For the incidents with duration in the range of 36 - 200 min, the mean absolute percentage error (MAPE) in predicting the duration is in the range of 25% - 55%. Moreover, for longer duration incidents (greater than 65 min), prediction improves significantly with time. For example, the MAPE value varies over time from 76% to 50% for incidents having a duration greater than 200 min. Finally, the overall MAPE value averaged over all incidents improves by 50% with elapsed time for prediction of reported as well as effective duration.
URI: https://hdl.handle.net/10356/136590
ISSN: 1524-9050
DOI: 10.1109/TITS.2018.2878637
Rights: © 2018 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://doi.org/10.1109/TITS.2018.2878637
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Journal Articles

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