Dynamic traffic models require dynamic inputs, and one of the main inputs are the Dynamic Origin-Destinations (OD) matrices describing the variability over time of the trip patterns across the network. The Dynamic OD Matrix Estimation (DODME) is a hard problem since no direct full observations are available, and therefore one should resort to indirect estimation approaches. Among the most efficient approaches, the one that formulates the problem in terms of a bilevel optimization problem has been widely used. This formulation solves at the upper level a nonlinear optimization that minimizes some distance measures between observed and estimated link flow counts at certain counting stations located in a subset of links in the network, and at the lower level a traffic assignment that estimates these link flow counts assigning the current estimated matrix. The variants of this formulation differ in the analytical approaches that estimate the link flows in terms of the assignment and their time dependencies. Since these estimations are based on a traffic assignment at the lower level, these analytical approaches, although numerically efficient, imply a high computational cost. The advent of ICT applications has made available new sets of traffic related measurements enabling new approaches; under certain conditions, the data collected on used paths could be interpreted as an empirical assignment observed de facto. This allows extracting empirically the same information provided by an assignment that is used in the analytical approaches. This research report explores how to extract such information from the recorded data, proposes a new optimization model to solve the DODME problem, and computational results on its performance.
Document de recerca desenvolupat dins del Doctorat Industrial 2017-DI-041
