Modeling highway travel time distribution with conditional probability models

Conference · Tue Dec 31 23:00:00 EST 2013

OSTI ID:1115374

Oliveira Neto, Francisco Moraes ^[1]; Chin, Shih-Miao ^[1]; Hwang, Ho-Ling ^[1]; Han, Lee ^[2]

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1. ORNL
2. University of Tennessee, Knoxville (UTK)

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).

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: ME USDOE - Office of Management, Budget, and Evaluation; ORNL work for others
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1115374
• Country of Publication: United States
• Language: English