Simultaneous estimation of individual coefficient, mean and covariance of origin-destination demands from day-to-day parking data and traffic counts

The urban Intelligent Transportation System has been rapidly developed in decades, especially in large cities, such as sensing, cyber-physical infrastructures and crowdsourcing technologies for its effective use in improving the performance of transportation system. In this research, we propose a method to simultaneously estimate the transportation network state including means, covariances of origin-destination demands and travelers' route-choice-related coefficient in the logit-model-based stochastic user equilibrium from day-to-day data including link flow data and parking data. A bi-level optimization problem is formulated. A new weighted least squares method is used in the upper-level problem to estimate the means and covariances of the origin-destination demands. In the lower-level problem, the stochastic traffic assignment model, i.e. logit model, is adopted. A constrained local metric stochastic response surface method with link-based equilibrium algorithm is proposed. Numerical examples are presented to illustrate the applications of the proposed method.