Ernesto Cipriani

TL;DR: The solving procedure consists of a set of heuristics, which includes a first routine for the route generation based on the flow concentration process and a parallel genetic algorithm for finding a sub-optimal set of routes with the associated frequencies.

TL;DR: A new method to solve the simultaneous adjustment of a dynamic traffic demand matrix is presented, searching for a reliable solution with acceptable computational times for off-line applications and using as an input traffic counts and speeds, prior O–D matrices and other aggregate demand data.

TL;DR: A common evaluation and benchmarking framework is proposed, providing a synthetic test bed, which enables implementation and comparison of OD estimation/updating algorithms and methodologies under “standardized” conditions.

TL;DR: In this paper, the problem of clean vehicle allocation in an existing public fleet is faced by introducing a method for solving the transit network design problem in a multimodal, demand elastic urban context dealing with the impacts deriving from transportation emissions.

TL;DR: Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models, thus overcoming most of the limitations of current DDEP approaches found in the literature.