The importance of a mobility system based on railway technology as the backbone of public transport is now widely acknowledged. Indeed, rail systems are green, high performing, smart and able to ensure a high degree of safety. Therefore, modal split should be steered towards rail transport by increasing the attractiveness of this transport mode. In this context, a key element is represented by the timetabling design phase, which must aim to guarantee an appropriate degree of robustness of rail operations in order to ensure a high degree of system reliability and increase service quality. A crucial factor in the task of timetabling entails evaluating dwell times at stations. The innovative feature of this paper is the analytical definition of dwell times as flow dependent. Our proposal is based on estimating dwell times according to the crowding level at platforms and related interaction between passengers and the rail service in terms of user behaviour when a train arrives. An application in the case of a real metro system is provided in order to show the feasibility of the proposed approach.

The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown.

Forecasting user flows on transportation networks is a fundamental task for Intelligent Transport Systems (ITSs). Indeed, most control and management strategies on transportation systems are based on the knowledge of user flows. For implementing ITS strategies, the forecast of user flows on some network links obtained as a function of user flows on other links (for instance, where data are available in real time with sensors) may provide a significant contribution. In this paper, we propose the use of Artificial Neural Networks (ANNs) for forecasting metro onboard passenger flows as a function of passenger counts at station turnstiles. We assume that metro station turnstiles record the number of passengers entering by means of an automatic counting system and that these data are available every few minutes (temporal aggregation); the objective is to estimate onboard passengers on each track section of the line (i.e., between two successive stations) as a function of turnstile data collected in the previous periods. The choice of the period length may depend on service schedules. Artificial Neural Networks are trained by using simulation data obtained with a dynamic loading procedure of the rail line. The proposed approach is tested on a real-scale case: Line 1 of the Naples metro system (Italy). Numerical results show that the proposed approach is able to forecast the flows on metro sections with satisfactory precision.

The aim of this paper is to provide an analytical approach for determining operational parameters for metro systems so as to support the planning and implementation of energy-saving strategies. Indeed, one of the main targets of train operating companies is to identify and implement suitable strategies for reducing energy consumption. For this purpose, researchers and practitioners have developed energy-efficient driving profiles with the aim of optimising train motion. However, as such profiles generally entail an increase in travel times, the operating parameters in the planned timetable need to be appropriately recalibrated. Against this background, this paper develops a suitable methodology for estimating reserve times which represent the main rate of extra time needed to put ecodriving strategies in place. Our proposal is to exploit layover times (i.e., times spent by a train at the terminus waiting for the next trip) for energy-saving purposes, keeping buffer times intact in order to preserve the flexibility and robustness of the timetable in case of delays. In order to show its feasibility, the approach was applied in the case of a real metro context, whose service frequency was duly taken into account. In particular, after stochastic analysis of the parameters involved for calibrating suitable buffer times, different operating schemes were simulated by analysing the relationship between layover times, number of convoys, and feasible headway values. Finally, some operation configurations are analysed in order to quantify the amount of energy that can be saved.

Cooperative-Intelligent Transportation Systems (C-ITSs) aim to connect vehicles, both with one another and with road infrastructures, so as to increase traffic safety and efficiency. This paper focuses on the European framework for supporting the development of Cooperative, Connected, and Automated Mobility, and aims to shed light on the current state of testing and deployment activities in the field at the start of 2019. This may be considered particularly timely given that the year 2019 was identified as the starting date for the deployment of mature services, and the Community legislation is currently paying great attention to the matter. In order to present a concise (but comprehensive) picture, we consulted and analysed the most diverse sources comprising more than 2000 pages.