Management and Analysis of Michigan Intelligent Transportation Systems Center Data with Application to the Detroit Area I-75 Corridor

Principal Investigators

Dr. Charles R. Standridge
Grand Valley State University

Co-Investigators

Dr. Shabbir Choudhuri
Grand Valley State University
Dr. Snehamay Khasnabis
Wayne State University

Project Dates:

09/01/2008 to 12/31/2011

Project year: Year 2

MIOH-UTC Project Identifier: TS-21; Projects 1 and 2

Focus Area:

Research: Intelligent Transportation Systems

Abstract:

An understanding of traffic flow in time and space is fundamental to the development of strategies for the efficient use of the existing transportation infrastructure in large metropolitan areas. Thus, this research involves developing the methods necessary to systematically describe, explain, and predict the flow of traffic with respect to time and space. The utility of this knowledge will be demonstrated in routing voluminous traffic. Achieving these objectives requires the collection, management, and analysis of traffic data concerning volume, speed, and traffic sensor occupancy. Management of this data requires the design and implementation of a large scale database management system as well as assuring the quality of the collected data. Descriptive, explanatory, and predictive statistical models will be used in developing the desired understanding of traffic flow. Application efforts will focus on the Detroit metropolitan area. Traffic data will be obtained from the Michigan Intelligent Transportation System Center.

Statistical models of traffic flow in the Detroit area I-75 corridor will be constructed. A previously developed routing model will be adapted to the I-75 corridor and the newly developed statistical models incorporated to compute traffic flow metrics. Both a software solver and a hardware solver for the model will be implemented. Using the World Wide Web, the database management system, including selective data retrieval as well the statistical modeling procedures and routing models will be made accessible to traffic experts, students, and researchers.