Dynamic Origin-Destination Demand Flow Estimation under Congested Traffic Conditions: A General Framework

摘要

This paper presents a single-level nonlinear optimization model to estimate dynamicorigin-destination (OD) demand. In contrast to the majority of previous studies, a path5flow based optimization model, which does not require explicit dynamic link-pathincidences, is developed to minimize (ⅰ) the deviation between observed and estimatedtraffic states and (ⅱ) the deviation between aggregated path flows and target OD flows,subject to dynamic user equilibrium constraints represented by a gap-function-basedreformulation. A Lagrangian relaxation algorithmic framework, which dualizes thedifficult user equilibrium constraints to construct a tractable nonlinear programmingmodel, is proposed and solved by an efficient gradient-based path flow adjustmentalgorithm. This study also derives analytical gradient formulas for the link flow, densityand travel time changes as a function of incoming time-dependent path flow rate changesin a general network under congestion conditions. Numerical results illustrate theeffectiveness and shed some light on the properties of the proposed OD estimationmethod and the DNL model.