Estimating Time-Dependent Origin-Destination Trip Tables with Trips Originating in Multiple Time Slices

Abstract

Intelligent transportation system initiatives, such as travel information systems and travel demand management systems, have been used to ameliorate problems with congestion due to increasing travel demand. The success of these initiatives depends on the ability to accurately estimate the temporal variations in travel demand. A modeling framework based on arrival patterns of trips at workplaces and travel times in urban areas is presented. The peak period is divided into short, discrete intervals of time called time slices. Origin-destination trip tables for each time slice are estimated on the basis of when a trip with a certain travel time must have started to arrive at the destination in a specified time slice. There are two possibilities for the time slice in which a trip might have originated: ( a) starting in time slice k - 1 and ending in time slice m and ( b) starting in time slice k and ending in time slice m ( m ≥ k). A model is presented to estimate the probability that the trip might have originated in time slice k - 1 and time slice k. On the basis of these probabilities, the performance of the model is tested using data for the Las Vegas metropolitan area.