01373458

Component

This paper presents a single-level nonlinear optimization model to estimate dynamic origin-destination (OD) demand. In contrast to the majority of previous studies, a path-flow based optimization model, which 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 dynamic user equilibrium constraints represented by a gap-function-based reformulation. A Lagrangian relaxation algorithmic framework, which dualizes the difficult user equilibrium constraints to construct a tractable nonlinear programming model, is proposed and solved by an efficient gradient-based path flow adjustment algorithm. This study also derives analytical gradient formulas for the link flow, density and travel time changes as a function of incoming time-dependent path flow rate changes in a general network under congestion conditions.

This paper was sponsored by TRB committee ADB30 Transportation Network Modeling

01362476

12-4339

English

17p

2012

Transportation Research Board 91st Annual Meeting

Digital/other

Figures; References

Dynamic models; Lagrangian functions; Nonlinear programming; Optimization; Origin and destination; Traffic flow rate

Heterogeneous traffic; Lagrangian relaxation approach; Path flow estimator

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2012 Paper #12-4339

TRIS, TRB

Feb 8 2012 5:23PM