Estimating Time-Dependent Freeway Origin–Destination Demands with Different Data Coverage

Abstract

To obtain more credible estimates of time-dependent travel demand, various data sources should be exploited jointly to improve the observability of origin–destination (O-D) trip tables. A comprehensive case study uses a real freeway network to reveal how different data coverage affects the quality of estimated O-D tables. The dynamic O-D estimation problem is formulated as a variational inequality (VI) problem that provides a flexible framework to incorporate different data sources and to encapsulate realistic traffic flow dynamics. Traffic surveillance data considered include traffic counts from vehicle detectors, historical O-D tables (static or dynamic), and travel time measurements on subpaths. A novel method is employed to evaluate marginal path travel times, which is a key procedure to properly incorporate travel time measurements into the VI formulation. Numerical experiments generate a number of guidelines to the proper selection of data coverage for obtaining improved O-D estimates.

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