• Energy Research

The road network design problem is widely studied in the literature, both in urban and rural areas; most papers deal with the solution algorithms and with the mathematical formulation of the problem. The road network design problem is NP-hard and is very difficult to solve in acceptable computing times for real-scale networks. In urban areas, the objective of the design is usually the minimisation in total travel time on the network, while in rural areas it is usually a sum of total travel time and total (i.e. construction and maintenance) road costs. Very few papers consider other terms in the objective function and/or propose a multicriteria approach. In our best knowledge, no papers consider as objective of the design to minimise the fuel consumption, even if it is an actual objective of road network planning and design. In this paper, we compare the results obtained by solving some road network design problems adopting the classic objective functions and adopting as objective the minimisation in fuel consumptions on the network. In order to obtain this result, in the paper a fuel consumption function is proposed, based on CORINAIR models and depending on the vehicle fleet of the study area; this function is used both for estimating the reduction in fuel consumption due to the design obtained with the classic objective functions and for designing the road network using as objective function the total consumption on the network. The results obtained with these approaches are compared in terms of fuel consumed and total travel time spent on the network. The minimisation in fuel consumption is very important since greenhouse gas emissions and air pollutants are directly related to the consumption. Initial tests highlighted that the results obtained with the two different approaches are more significant in rural areas, where higher average flow speeds may produce higher consumption, according to the CORINAIR models.

We propose a model for optimising driving speed profiles on metro lines to reduce traction energy consumption. The model optimises the cruising speed to be maintained on each section between two stations; the functions that link the cruising speed to the travel time on the section and the corresponding energy consumption are built using microscopic railway simulation software. In addition to formulating an optimisation model and its resolution through a gradient algorithm, the problem is also solved by using a simulation model and the corresponding optimisation module, with which stochastic factors may be included in the problem. The results are promising and show that traction energy savings of over 25% compared to non-optimised operations may be achieved.

Sustainable transport frameworks are gaining attention within national and international transportation policies, given the key role that decarbonisation plays in making urban environments people-friendly. Within this context, several shared services and micro-mobility options are being developed, especially as first/last mile facilities, further increasing public transport coverage levels. We present an overview of the environmental impacts of different transport modes and compare them from different perspectives, namely, CO2 emission levels, total costs (also including the user generalised cost) and service life of vehicles involved. The proposed methodology is applied to an urban context, using real trip data and showing the main findings under real conditions.

Energy-saving and energy-recovery strategies represent key factors to achieve operational cost reductions within rail systems’ management tasks. However, in altering service features, they also affect passenger satisfaction. This paper investigates the effect of implementing such measures in the case of rolling stock unavailability. Numerous operational scenarios were explored by analysing different planned headway and rolling stock configurations. The scenarios were simulated with and without the adoption of Energy-Saving Strategies (ESS), both in ordinary and in disruption conditions. Our results show that, in ordinary conditions, the optimal scenarios are those that minimise the planned headway. By contrast, in disrupted conditions, due to greater passenger inconvenience, the use of a time-optimal condition is preferable if a real-time adjustment of ESS is not feasible. However, if the ESS can be updated in real-time, use of ESS is preferable only if the adopted headway is the smallest of those associated with the rolling stock scheme considered.

AbstractThe 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.

The sustainable design of public transport services is a crucial issue that may affect the economy of local areas and the social inclusion of their inhabitants. Indeed, transport accessibility of households, businesses, and retail activities is one of the main drivers that influence location choices and sustainable development and where the public transport services may play a key (positive) role. Furthermore, the economic crisis of the 2009 and the current ones caused by the COVID-19 pandemic has further limited the funds available for public services, including the transport sector. In this critical context, sound design of public transport services assumes great importance, especially in light of financial constraints. Starting from these considerations, the aim of this paper has been to propose an innovative methodology for designing public transport services based on a rational decision-making process with stakeholder engagement, aiming to perform a sustainable development perspective. The proposed methodology combines technical aspects, social and political issues, equity perspectives, and it is aimed in obtaining a design solution that can be accepted by the whole community. The proposed theoretical design methodology has also been applied to a case study in order to verify its applicability to a real context, consisting of the design of the public transport plan of the province of Foggia in southern Italy. Applications results show that the sustainable design scenario is not only the best ones from a technical point of view, but also participated, equitable, and approved by both policymakers and stakeholders.

This paper describes the first results of a research project where the main focus is to implement a Decision Support System (DSS) to optimise energy consumption of rail systems. In order to achieve this objective, the authors implement an optimisation module for the design of energy-efficient driving strategies, in terms of speed profiles, that requires a railway simulation model as a subroutine. Here the authors focus on the general framework of the optimisation module and on the calibration of the railway simulation model. All elaborations are implemented in a MatLab environment, aiming at defining possible energy-efficient speed profiles, in accordance with energy-saving strategies, through optimised speed profile parameters, in terms of acceleration, target speed, deceleration, coasting phase, and driving behaviour, represented by the jerk. The model is calibrated on real data recorded on a double track section of a railway line in the city of Naples (Italy). Initial results show that consumption is very variable with the speed profile and with driver behaviour, but the model is able to reproduce the average consumption of each driving strategy and should be able, within the DSS, to suggest the best driving strategies for each rail section.