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Project Details

Title:Big Transportation Data Analytics
Principal Investigators:Xiaoyue Cathy Liu
University:University of Utah
Grant #:69A3551747108 (FAST Act)
Project #:MPC-543
RH Display ID:151312
Keywords:data analysis, estimating, forecasting, information processing, machine learning, traffic counting, traffic volume, vehicle miles of travel


The world of Big Data is moving fast. There are several private sector Big Data vendors using data from vehicle probes, and from Wi-Fi, cellular, and Bluetooth data traces to synthesize transportation information such as vehicle speeds and origin-destination travel flows. In addition, Utah Department of Transportation (UDOT)'s Traffic Operations Center recently just purchased HERE traffic probe data to assist traffic management and road network performance assessment. UDOT also possesses Big Data in the form of Performance Measurement System (PeMS), that has been archived since 2008. This historic data set should be mined to learn new things about the direct and proximate causes of traffic volume change and travel reliability. There is also an urging need to develop analytical approaches that leverage existing historic data to reveal methods for estimating traffic, with the potential to reduce the burden of the annual traffic count program.

UDOT maintains an annual traffic count program which requires the acquisition of hundreds of short-duration counts each year. This traffic count effort represents a significant cost to UDOT, while also exposing UDOT staff to the dangers inherent to being exposed to traffic. The proposed research will seek to determine whether statistical modeling and/or machine learning methods might be employed to partially or fully supplant the short-duration traffic count program and, in doing so, reduce the effort, cost, and staff exposure of UDOT's traffic count program.

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NDSU Dept 2880P.O. Box 6050Fargo, ND 58108-6050