Campus Units

Civil, Construction and Environmental Engineering, Electrical and Computer Engineering, Institute for Transportation

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

8-2019

Journal or Book Title

Transportation Research Part C: Emerging Technologies

Volume

105

First Page

81

Last Page

99

DOI

10.1016/j.trc.2019.05.034

Abstract

Automatic incident detection (AID) is crucial for reducing non-recurrent congestion caused by traffic incidents. In this paper, we propose a data-driven AID framework that can leverage large-scale historical traffic data along with the inherent topology of the traffic networks to obtain robust traffic patterns. Such traffic patterns can be compared with the real-time traffic data to detect traffic incidents in the road network. Our AID framework consists of two basic steps for traffic pattern estimation. First, we estimate a robust univariate speed threshold using historical traffic information from individual sensors. This step can be parallelized using MapReduce framework thereby making it feasible to implement the framework over large networks. Our study shows that such robust thresholds can improve incident detection performance significantly compared to traditional threshold determination. Second, we leverage the knowledge of the topology of the road network to construct threshold heatmaps and perform image denoising to obtain spatio-temporally denoised thresholds. We used two image denoising techniques, bilateral filtering and total variation for this purpose. Our study shows that overall AID performance can be improved significantly using bilateral filter denoising compared to the noisy thresholds or thresholds obtained using total variation denoising.

Research Focus Area

Transportation Engineering

Comments

This is a manuscript of an article published as Chakraborty, Pranamesh, Chinmay Hegde, and Anuj Sharma. "Data-driven parallelizable traffic incident detection using spatio-temporally denoised robust thresholds." Transportation Research Part C: Emerging Technologies 105 (2019): 81-99. DOI: 10.1016/j.trc.2019.05.034. Posted with permission.

Copyright Owner

Elsevier Ltd.

Language

en

File Format

application/pdf

Published Version

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