Abstract
Project PRIN 2022 Prot. Nr. 20223MHHA8 Title: Time-dependent optimization for sustainable transportation Abstract Project Description Sustainable transportation is one of the major challenges that modern countries are facing. Modern societies demand a high degree of mobility and the present transportation systems must evolve to ensure that people can move and freight can be transported in ways that are safe, cost-effective, and environmentally friendly. The number of private vehicles has increased for decades, and the ever-growing rise of e-commerce is generating an enormous demand for mobility of freight. Several projections foresee a continued growth of such a demand for mobility, which has been accelerated by the COVID-19 pandemic. Moreover, mobility players are collecting large amounts of data, thanks to cheap and connected devices such as smart phones, RFID readers, web-cams, and wireless sensors. The continuous data flow generated by these devices makes it possible to infer and to update time-dependent scenarios related to demand for service and traveling times. Finally, technical and socio-economic advances have made available a range of options for sustainability mobility (e.g., electric vehicles, intelligent transport systems, shared mobility), but still important hurdles must be overcome for their mass market adoption. As a result, mobility players, both from public (such as local authorities) and private sectors, are (and will be) facing increasingly complex managerial challenges, which urge the use of appropriate decision-support tools that aim at contributing to sustainability of mobility systems. This project aims at developing, implementing, and validating a set of time-dependent optimization tools that embraces different facets of mobility in urban areas. These tools will be based on new mathematical programming models and optimization algorithms, and aim at supporting the decisions of mobility players, in the context of sustainable mobility, taking advantage of the availability of dynamic time-dependent data. More in details, we will tackle the problem of determining an optimal deployment of charging stations for electric vehicles, by studying the variation during the day of recharging needs and the impact on the solutions of different urban layouts; we will address the problem of bike rebalancing in bike-sharing systems, by employing machine learning techniques to forecast user demand; we will study a new problem in freight delivery where an automated truck, along with a fleet of unmanned aerial vehicles, is employed; finally, we will study the problem of coordinating, in a dynamic setting, the directions to give to vehicles with the goal of reducing traffic congestion. The algorithms to solve these problems will be based on the solution, as sub-problems, of basic shortest path and routing problems. Hence, we will also develop efficient algorithms for the solution of constrained and time-dependent variants of the latter problems. Objectives This project will embrace different facets of mobility in urban areas, providing a set of optimization tools that can contribute to make urban areas more sustainable in terms of road usage and safety, less polluted environment, and efficient use of green mobility modes. More particularly, this project aims at developing, implementing, and validating mathematical programming models and optimization algorithms, which provide mobility players with effective tools to support decisions in the context of sustainable mobility and transport. The set of optimization tools will provide support for different decisions, encompassing aspects related to both the mobility of people and freight. Expected Results The main deliverables of the project are as follows: 1. The development of models and solution methodologies, exact and/or heuristic algorithms, for solving the optimization problems detailed above, which are relevant from both a theoretical and a practic
Project details
Unibo Team Leader: Daniele Vigo
Unibo involved Department/s:
Dipartimento di Ingegneria dell'Energia Elettrica e dell'Informazione "Guglielmo Marconi"
Coordinator:
Università degli Studi di BRESCIA(Italy)
Total Unibo Contribution: Euro (EUR) 45.000,00
Project Duration in months: 24
Start Date:
28/09/2023
End Date:
28/02/2026
