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Modeling Link Travel Time Reliability with Conditional Probability Models

ChinShih-Miao Chin, PhD
The Center for Transportation Analysis
Oak Ridge National Laboratory
January 24, 2014, 2:30 – 3:30 PM
500 John D. Tickle Engineering Building

Dr. Shih-Miao Chin has been a member of the research staff in the Center for Transportation Analysis at Oak Ridge National Laboratory since 1984.  Main concentration of his work has been in the area of traffic and transportation applications particularly those involving geographic information systems, web-based application developments, and advanced information technologies.  Dr. Chin has performed extensive research in the areas of national freight/passenger transportation, transportation security, military transportation logistics, and energy/environmental policy-related studies. Currently, Dr. Chin is concentrating on freight transportation models research and developing transportation performance measures.  Dr. Chin earned his Ph.D. in Civil Engineering from Rensselaer Polytechnic Institute in Troy, New York in 1983.  He also holds a Master of Mathematics from Utah State University, Logan, Utah and a Master of Science in Civil Engineering from University of Utah.

Talk Abstract: Under the sponsorship of the Federal Highway Administration’s Office of Freight Management and Operations, the American Transportation Research Institute (ATRI) has developed performance measures through the Freight Performance Measures (FPM) initiative. Under this program, travel speed information is derived from data collected using wireless based global positioning systems. These telemetric data systems are subscribed and used by trucking industry as an operations management tool.  More than one telemetric operator submits their data dumps to ATRI on a regular basis.  Each data “transmission” contains truck location, its travel time, and a clock time/date stamp.  Data from the FPM program provides a unique opportunity for studying the upstream-downstream speed distributions at different locations, as well as different time of the day and day of the week. This research is focused on the stochastic nature of successive link travel speed data on the continental United States Interstates network. Specifically, a method to estimate route probability distributions of travel time is proposed. This method uses the concepts of convolution of probability distributions and bivariate, link-to-link, conditional probability to estimate the expected distributions for the route travel time. Major contribution of this study is the consideration of speed correlation between upstream and downstream contiguous Interstate segments through conditional probability.  The established conditional probability distributions, between successive segments, can be used to provide travel time reliability measures. This study also suggests an adaptive method for calculating and updating route travel time distribution as new data or information is added. This methodology can be useful to estimate performance measures as required by the recent Moving Ahead for Progress in the 21st Century Act (MAP 21).

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