Resumen de Towards sustainable and efficient road transportation development of artificial intelligence solutions for urban and interurban mobility
Pasqual Martí Gimeno
The transportation of people and goods is both a complex problem and an essential service in modern society. Among the various modes of transportation, road transport offers unique advantages and challenges, thanks to its flexibility and operation in both urban and interurban areas. The growing social concern for the environment also affects road transportation, as motor vehicles are a major source of greenhouse gas emissions. However, the digitalisation of society and the emergence of new transport models indicate the potential for improvement in transportation, which could be better adapted to its users while operating in a more sustainable way.
In this thesis, we address the improvement of road transportation by means of computational techniques and artificial intelligence. This includes the modelling of transportation through multi-agent systems and their subsequent simulation. The operation of transportation fleets is determined by the distribution of tasks, the planning of the actions of each vehicle and their subsequent coordination. We explore different techniques and develop proposals that improve the operation of different transportation systems by considering three points of view: that of the operator, that of the user and, finally, that of sustainability. In other words, we aim to obtain systems with higher economic performance and quality of service while reducing their environmental impact.
The objective of improving road transportation is pursued on three fronts. First, a framework for the effective modelling and simulation of transportation systems is proposed. This contribution serves as a tool for the experimentation of the rest of the research. Next, the research focuses on urban transportation, a use case for which we model the city as a shared resource scenario. We propose the use of decentralised vehicle fleets for greater reactivity of the system. Through self-interested modelling, vehicles are incentivised to provide a better service to users while avoiding resource congestion. Finally, with the intention of bringing innovative solutions also to rural areas, our previous proposals are adapted to the use case of rural interurban transportation. In this case, we note the need for flexible and user-friendly public transportation, with special emphasis on its economic sustainability. Our system proposals follow these principles following the demand-responsive transportation paradigm.
The results of this thesis provide practical solutions for the enhancement of different road transportation systems, contributing to a future of more sustainable and user-tailored flexible mobility. As a contribution to the field of artificial intelligence the developed techniques have the potential to be adapted to fields beyond transportation, providing general solutions for the task allocation and the coordination of distributed elements.
