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Evolution and Future of Urban Road Incident Detection Algorithms

  • Published:2020-06 Issue:6 Volume:146 Page:03120001
  • ISSN:2473-2907
  • Container-title:Journal of Transportation Engineering, Part A: Systems
  • language:en
  • Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Evans Jonny1ORCID,Waterson Ben2,Hamilton Andrew3

Affiliation:

1. Ph.D. Student, Transportation Research Group, Univ. of Southampton, Burgess Rd., Southampton SO16 7QF, UK (corresponding author). ORCID: .

2. Lecturer, Transportation Research Group, Univ. of Southampton, Burgess Rd., Southampton SO16 7QF, UK.

3. Lead Product Owner, Siemens Limited, Sopers Ln., Poole BH17 7ER, UK.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Reference41 articles.

1. A threshold-based real-time incident detection system for urban traffic networks;Ahmed F.;Procedia Social Behav. Sci.,2012

2. Automated accident detection in intersections via digital audio signal processing;Bruce L.;Transp. Res. Rec.,2003

3. Traffic management parameters from single inductive loop detectors;Cherrett T.;Transp. Res. Rec.,2000

4. Improved network monitoring using UTC detector data ‘RAID’;Cherrett T.;Traffic Eng. Control,2002

5. The economic costs of road traffic crashes: Australia, states and territories;Connelly L. B.;Accid. Anal. Prev.,2006

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1. Graph Spatiotemporal Pattern Learning Network for Real-Time Road Network Traffic Abnormal Incident Detection;Transportation Research Record: Journal of the Transportation Research Board;2023-05-28

2. Improved Two-Layer Stacking Model for Prediction of the Level of Delay Caused by Crashes: An Empirical Analysis of Texas;Journal of Transportation Engineering, Part A: Systems;2023-02

3. Vision-Based Traffic Accident Detection and Anticipation: A Survey;IEEE Transactions on Circuits and Systems for Video Technology;2023

4. A Cross-modal Attention Model for Fine-Grained Incident Retrieval from Dashcam Videos;MultiMedia Modeling;2023

5. Prediction of Traffic Incident Duration Using Clustering-Based Ensemble Learning Method;Journal of Transportation Engineering, Part A: Systems;2022-07
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