In order to improve the performances of transit modeling and in particular to assess the effects of the introduction of ITS technologies, which imply substantial changes in user behavior, in the last 10 years transit path choice and assignment models that use a schedule-based approach, instead of the traditional frequency-based one, have been developed. This paper presents a schedule-based path choice model for high-frequency transit networks, which allows us to consider the evolution in time of transit services, both within-day and day-to-day, as well as the day-to-day learning process of attributes by which users choose. On the basis of this path choice model a dynamic process assignment model, both within-day and day-to-day (from which the term “doubly dynamic” derives), was developed and tested on a realistically sized network to verify its applicability for operations planning.

This article presents a mesoscopic transit assignment model suitable for real-time prediction of on-board passenger numbers in transit networks with real-time individual predictive information on travel time components and also including on-board crowding. The path choice modeling framework is based on the reproduction of a travel strategy using random utility models that simulate both choices of departure time at origin and first access stop, and en-route choices of vehicle to board at stops. Such choices are based on attributes anticipated through a learning mechanism, which considers previous experiences and provides real-time predictive information. Within-day dynamic network loading considers vehicle capacity constraints, which allows the explicit modeling of fail-to-board events. Finally, results of an application on a real-size test network show the ability of the model to capture effects of providing individual predicted information on vehicle crowding.

This paper presents a system of models for the estimation of international (import/export) freight flows through a partial share approach. It allows us to simulate attraction, production, distribution and modal split for the estimation of modal Origin-Destination matrices in quantities. Aiming at predicting long term effects for strategic planning, the modelling system has been specified through easy-to-capture variables represented by level-of-service attributes and aggregate socio-economic variables. The calibration was carried out by using a set of available data in Italy that allowed us to consider import/export flows for different freight types given by the aggregation of classes provided by European NST/R classifications.

This paper presents some calibration developments of a system of models for the simulation of freight restocking journeys within urban areas. The model system allows us to define freight vehicle Origin-Destination (O-D) matrices. The journeys for restocking urban retail activities are modelled through the definition of the trip-chain order and the choice of the delivery location. The modelling system has been calibrated and validated on a dataset of more than 500 truck driver interviews carried out in the city centre of Rome.