textDeployment of Intelligent Transportation Systems (ITS) is providing
researchers and practitioners with an unprecedented amount of valuable on-line and
archived traffic data. To date, ITS data have been used primarily to support real-time
operational applications, while other potential uses of these data have been largely
ignored.
In this research, the effort to extract knowledge from the on-line or archived
data gathered by Advanced Transportation Management Systems (ATMS) is focused
on the estimation of dynamic origin-destination (OD) flows using optimization
methods. In addition to their use for planning purposes, time-dependent OD flows can
be used as an input to Dynamic Traffic Assignment (DTA) systems. However,
gathering OD demand flow information directly by conducting surveys is very costly
and time consuming.
To estimate the OD flows, a methodology is developed to minimize an overall
measure of the deviation of estimated link-flows from the time-varying link-flow
observations, subject to a set of constraints. The set of constraints could include nonnegativity
constraints, initial condition constraints, cordon line counts and the user’s
route-choice behavior or traffic assignment rules. The traffic assignment solution,
itself, is often obtained by optimizing an objective function. This objective function
can explicitly be included in the constraints of the main or upper minimization
problem. This formulation results in a bi-level optimization or theoretical game
problem.
In this dissertation, the upper-level problem is formulated alternatively as
linear and non-linear optimization problems. To solve the lower-level traffic
assignment problem, a DTA simulation program, namely DYNASMART-P, is used
to find the equilibrium flows. The suggested algorithm iterates between the upperlevel
and the lower-level optimization problems for a pre-specified number of times
or until convergence in terms of the estimated OD flows or the simulated link flows is
achieved.
To integrate the a priori information on OD demand flows with the
information extracted from the link flow observations, adoption of the Bayesian
inference method is proposed. If such information on OD flows is available, Bayesian
inference treats the old information as the target values to update the estimated OD
flows from the sample of the link flow observations.Civil, Architectural, and Environmental Engineerin
