Dynamic Origin - Destination Demand Flow Estimation under Congested Traffic Conditions
نویسندگان
چکیده
This paper presents a single-level nonlinear optimization model to estimate dynamic origindestination (OD) demand. A path flow-based optimization model, which incorporates heterogeneous sources of traffic measurements and does not require explicit dynamic link-path incidences, is developed to minimize (i) the deviation between observed and estimated traffic states and (ii) the deviation between aggregated path flows and target OD flows, subject to the dynamic user equilibrium (DUE) constraint represented by a gap-function-based reformulation. A Lagrangian relaxation modeling framework, which dualizes the difficult DUE constraint, is proposed and solved by an efficient gradient-projection-based path flow adjustment algorithm. Additionally, a dynamic network loading (DNL) model, based on Newell’s simplified kinematic wave theory, is employed in the DUE traffic assignment process to realistically capture congestion phenomena and shock wave propagation. This study also derives analytical gradient formulas for the changes in link flow, density and travel time as a function of the unit change of incoming time-dependent path flow rate in a general network under congestion conditions. Numerical experiments conducted on three different networks illustrate the effectiveness and shed some light on the properties of the proposed OD estimation method and the DNL model.
منابع مشابه
Dynamic Origin-Destination Demand Flow Estimation Utilizing Heterogeneous data sources under Congested Traffic Conditions
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تاریخ انتشار 2011