This project was funded by the National Institute for Transportation and Communities (NITC; grant number NITC-RR-1082) a U.S. DOT University Transportation Center. The project also benefitted from matches from the University of Utah and the Utah Department of Transportation.
Traffic engineering, Traffic congestion, Traffic signs and signals -- Control systems, Traffic -- Data sets
Current traffic management strategies are based on expected conditions caused by recurring congestion (e.g., by time of day, day of week), and can be very effective when provisions are also given for reasonable variations from such expectations. However, traffic variations due to non-recurrent events (e.g., crashes) can be much larger and difficult to predict, making also challenging efforts to identify, measure, and forecast their disruptive effects. This project explores a proactive approach to deploy a tool for managing non-recurrent congestion by identifying and quantifying the effects of disruptive traffic events at a microscopic level using a comprehensive set of data sources. A combination of resources including detailed near-time crash records, high-resolution vehicle detection activations and deactivations, as well as traffic signal phasing and timing, are combined together to build an understanding of standard traffic patterns, store this knowledge, and compare it with new incoming data for event identification. The team explored the use of high-resolution data for this purpose at surface street and arterial levels, and the outcomes from model fittings in such scenarios. Upon deployment using virtual servers and interfaces developed by the University of Utah team, ingestion of daily data and event detection will build up of a library of events and their effects, and this process will continue over time to strengthen the knowledge base on the corridors analyzed. Further outcomes from this research could lead to detailed event-based spatio-temporal congestion and safety models, ultimately enabling informed proactive traffic management and safety countermeasures. This project uses the Salt Lake Valley as a testbed and could open new opportunities for research that relies on the integration of large and disaggregated datasets.
Medina, J. C, Liu, X. L Network Effects of Disruptive Traffic Events. NITC-RR-1082. Portland, OR: Transportation Research and Education Center (TREC), 2022. https://doi.org/10.15760/trec.285