Analysis and Prediction of Spatiotemporal Impact of Traffic Incidents for Better Mobility and Safety in Transportation Systems
In this proposal, the authors propose to study a machine learning approach to predict the spatiotemporal impact of traffic accidents on the upstream traffic and the surrounding region. The main objective of the authors' research is to forecast how and when the travel-time delays - caused by road accidents - will occur on the transportation network in both time and space. Towards this end, the authors will conduct fundamental research in mining and correlation of traffic incidents and sensor datasets that they have been collecting and archiving in the last past three years. Furthermore, to demonstrate the benefits of their research, the authors will develop a novel proof-of-concept mobile application and extend their existing web based application to enable monitoring and querying of the incident impacts on real-time and historical datasets. This research will exploit the real-world Los Angeles traffic sensor data and California Highway Patrol (CHP) accident logs collected from Regional Integration of Intelligent Transportation Systems (RIITS) under Archived Traffic Data Management System (ADMS) project. The mobile application developed as a result of this proposal will be released for public use.
- Record URL:
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Supplemental Notes:
- http://infolab.usc.edu/research.php#traffic_prediction
Language
- English
Project
- Status: Completed
- Funding: $99,999
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Contract Numbers:
65A0533
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Sponsor Organizations:
Research and Innovative Technology Administration
University Transportation Centers Program
1200 New Jersey Avenue, SE
Washington, DC United States 20590California Department of Transportation
1120 N Street
Sacramento, CA United States 95814 -
Project Managers:
Valentine Deguzman, Victoria
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Performing Organizations:
National Center for Metropolitan Transportation Research
University of Southern California
650 Childs Way, RGL 107
Los Angeles, CA United States 90089-0626 -
Principal Investigators:
Demiryurek, Ugur
Shahabi, Cyrus
- Start Date: 20150101
- Expected Completion Date: 0
- Actual Completion Date: 20151231
- Source Data: RiP Project 37357
Subject/Index Terms
- TRT Terms: Data mining; Intelligent transportation systems; Machine learning; Mobile applications; Sensors; Traffic crashes; Traffic delays; Traffic incidents; Traffic safety
- Identifier Terms: California Highway Patrol
- Uncontrolled Terms: Spatiotemporal analysis
- Geographic Terms: Los Angeles (California)
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;
Filing Info
- Accession Number: 01602382
- Record Type: Research project
- Source Agency: National Center for Metropolitan Transportation Research
- Contract Numbers: 65A0533
- Files: UTC, RIP
- Created Date: Jun 21 2016 1:00AM