Methods based on equilibrium traffic assignment (such as the one by Nielsen) seek consistency with route choice behavior so that an assigned origindestination (OD) matrix closely mirrors the measured counts. Though computationally more intensive, these have become practical due to improvementsin computing power. Despite widespread use, static OD estimation has limitations. Each matrix is assumed to be assigned and the trips completed within the same time period, leading to inconsistencies from trips that span more than an interval. Conventional static assignment also cannot account for over-saturated conditions. Further, equilibrium assignment path flows are not unique, suggesting that the estimated OD matrices are also not unique. Finally, since link volumes and speeds are dynamic over time, there is aggregation error in the use of long time periods. Short-term planning and traffic simulation require dynamic OD tables in order to accurately model peaking, queue formation and dissipation and spillbacks. Such tables represent trip departure rates during short time intervals such as 5 or 15 minutes. Dynamic OD profiles derived from one or more static OD tables are often not based on traffic measurements; such methods need not reflect real-world traffic dynamics, and may be unrealistic. Time-varying traffic sensor measurements are collected automatically, represent recent network conditions and contain information about dynamic OD patterns. It is thereforelogical to divide the analysis period into short departure time intervalsand estimate a set of OD matrices that replicate the time-varying data. The general approach thus has been to estimate the OD matrices one intervalat a time while fixing the estimated flows in all previous intervals. This sequential method, though computationally attractive, can break down on congested networks with long trips or short time intervals. Further, the assignment matrix provides an intuitive mapping between OD flows and link counts, but is harder to employ in the context of other data such as speedsor travel times. Gradient-based and response surface methods have recently been used to solve for dynamic OD flows without imposing linear approximations or using the sequential approach. The mappings between any general traffic data (counts, speeds, travel times, etc.) and the OD flows are directly captured with an assignment model of any fidelity desired by the modeler. The method provides a practical solution to simultaneously solve forthe OD flows of several departure time intervals. Two dynamic OD estimation methods were recently compared on the same dataset from downtown Los Angeles. The numerical results illustrate the benefits of simultaneously estimating the OD matrices for all departure time intervals at once, and moving away from linear approximations. On-going tests on another example in California involving two classes of vehicles (single- and high-occupancy) also indicate the potential of the non-linear approach. Nevertheless, dynamic OD estimation remains a challenging exercise owing to sparse sensor coverage, poor data quality and limited real-world applications. It is considered that more tests on a wide range of networks are required before any method (existing or new) can be reliably adopted in practice. For the covering abstract see ITRD E145999
Samenvatting