Abstract

Predicting benefits of advanced traveler information systems before implementation is one of the challenges in the area of transport modeling. Taking into consideration the differences in commuting behavior of unequipped and would be equipped drivers, as well as their different level of perception error are the key factor. Accordingly, it seems that the multi-class approach of traffic assignment (TA) can be regarded as a possible solution to the problem. However, dealing with the challenge of lack of observed data before system installation is still a major challenge.To deal with this problem, a double-class stochastic TA approach is proposed in this work. The network loading procedure follows a paired combinatorial logit (PCL) model, which addresses the classical problem of path overlapping. In addition, the model is origin-destination (OD)-specific parameter, which enables the modeler to represent different levels of uncertainty and stochasticity involved in route decision-making between different OD pairs. A heuristic practical estimation method is also proposed, which exempts the modeler from resorting to route choice data and facilitates the challenges involved in estimation of route choice models to a considerable extent. Furthermore, in the approximate proposed method of estimation, the new perspective from which the estimation parameter is considered provides a more tangible interpretation than that of the classical approach. It allows manipulation of data to obtain some sort of synthesized information as to the route choice behavior of prospective equipped travelers. The estimation method is applied to an experimental data set and the TA method is tested on an illustrative network. Authors demonstrate that, given the market penetration of the system, how the analyst would be able to provide quantitative forecasts as to the expected improvements in the network performance as a result of being introduced to advanced travelers information systems.

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