Stochastic OD demand estimation using stochastic programming

Abstract

Understanding the origin–destination (OD) demand of travelers can help traffic operators and mobility service providers form more efficient mobility planning and operation decisions. Large quantities of high-dimensional spatial and temporal data that are becoming increasingly available for urban transportation systems present opportunities as well as new challenges to this end. Approaching from a fresh angle of stochastic programming, we present a modeling framework for OD demand estimation based on observed traffic flow data in a transportation network. The proposed two-stage stochastic programming-based method is flexible to incorporate various design principles and risk preferences and domain knowledge regarding travel behavioral and physical rules. Additionally, a benefit comes from the scenario representation, where the point estimate can be combined with estimation of the discrete approximation to the demand distribution. As a result, we simultaneously incorporate demand parameter estimation and trip table reconstruction processes. In addition, we demonstrate that under the proposed framework, well-established theories and methods for stochastic programming, including epi-convergence and scenario-decomposition, can be exploited to advance the analytical and computational capabilities of the estimation model. The applicability and efficiency of the proposed method are illustrated through numerical examples based on highway and transit networks of various sizes.

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