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In this paper we propose a model for optimising bus frequencies under the assumption of elastic demand, considering explicitly the effects of changes in transit supply on modal split. Indeed, neglecting demand elasticity may lead to solutions that may not represent actual design objectives. Using an objective function that is a weighted sum of user costs on all transportation systems (car and bus), operation costs and external costs, we propose a heuristic solution algorithm that is able to solve the problem in acceptable computing times, also for real-scale problems. The model and the algorithm are tested on a large urban multimodal network.

The objective of this paper is to propose a hyper-network model for simulating intermodal trips, such as park-and-ride trips, assuming elastic travel demand as regards mode choice. In particular, user flows on both road and transit networks can be estimated by the proposed model that is based on dummy links, with suitable cost functions, which are able to simulate virtually ‘on-network’ modal split and transfer operations. In particular, the authors of the paper state that with these suitable functions, mathematical equivalence is ensured between the results obtained on the hypernetwork and those obtained by applying a general assignment model with elastic demand at mode choice level.

This paper will propose an ant colony optimization (ACO)-based algorithm that can be used to simulate mass-transit networks; this algorithm imitates the behavior of public transport users. In particular, the authors show that the proposed algorithm, which is an extension of that proposed by D’Acierno et al. (A stochastic traffic assignment algorithm based on ACO, Lecture Notes in Computer Science 4150, pp. 25–36, 2006), allows mass-transit systems to be simulated in less time but with the same accuracy compared with traditional assignment algorithms. Finally, the authors state theoretically the perfect equivalence in terms of hyperpath choice behavior between artificial ants (simulated with the proposed algorithm) and mass-transit users (simulated with traditional assignment algorithms).

A model for the optimization of transit fares is proposed and tested on a trial network in this paper. This model considers a multimodal transportation system under the assumption of elastic demand for simulating the impacts of fare policies on modal split. The model takes into account all system and social costs: (transit and road) user costs, firm costs and external costs. First tests on a trial network show the non-negligible impacts of transit fare design on the overall transportation system conditions and on social costs.