Network Effects of Disruptive Traffic Events

Juan Medina, University of Utah



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 measure and forecast their disruptive effects. This project explores a proactive approach to manage non-recurring congestion by quantifying and modeling the effects of disruptive traffic events (crashes, major sporting events, weather, etc.) at a microscopic level using a comprehensive set of data sources. A combination of resources at the Utah Traffic Lab will enable collection of a number of data sources that will be integrated for a more comprehensive network analysis. Data includes high-resolution vehicle detection and traffic controller data, live video feeds, real-time weather data, and near-real-time crash records, which can be accessed through a unique content management system created by the UofU. Outcomes from this research will lead to detailed event-based spatio-temporal congestion and safety models, ultimately enabling informed and proactive traffic management and safety countermeasures. Network operators and traffic and public safety agencies may adopt such policies to improve travel time reliability, environmental outcomes, and user safety. This project will use the Salt Lake valley as a testbed and will open new opportunities for research using integration of large datasets of disaggregated data.


Results from this project are intended to enhance understanding on the spatio-temporal effects of events in traffic networks. Researchers and Traffic Operations Centers are expected to utilize outcomes from this research to improve network reliability and resiliency. As the effects of events are better understood by operators and researchers, their ability to develop countermeasures is also improved. This in turn, opens new avenues to implement strategies aimed at prevent and mitigate congestion, and therefore reduce societal costs due to such events. Immediate impacts of the research will be produced in the form of publications available through transportation-related journals, but a possibility of further interaction with Utah DOT operators will be pursued in order to implement or validate some of the proposed countermeasures. 

Project Details

Project Type:
Project Status:
In Progress
End Date:
December 31,2019
UTC Grant Cycle:
NITC 16 Initial Projects
UTC Funding:

Other Products

  • Wu, Y. Y., Wei, Y. H. D., & Li, H. (2020). Analyzing Spatial Heterogeneity of Housing Prices Using Large Datasets. Applied Spatial Analysis and Policy, 13(1), 223-256. doi:10.1007/s12061-019-09301-x (PUBLICATION)
  • Wei, Y. D., Xiao, W. Y., Medina, R., & Tian, G. Effects of neighborhood environment, safety, and urban amenities on origins and destinations of walking behavior. Urban Geography. doi:10.1080/02723638.2019.1699731 (PUBLICATION)
  • None to date (PRESENTATION)