Planning Processes for Transport Solutions An Application To the City of Bologna

Mathematical models and the involved methods applied to real contexts are essential tools for designing and evaluating solutions concerning physical elements and/or organizational components of transportation systems. To deal with this, the systems engineering approach is used, which considers the relationships among the transportation system elements and their performances. This approach allows quantifying the effects of transportation projects by taking into account the intrinsic complexity of the transportation system and then assessing the effects of solutions to solve – or mitigate – transportation problems. This thesis focuses on the application of the transport system engineering approach to a real city – Bologna, in northern Italy – in order to: 1. simulate the current transportation system conditions (status quo); 2. compare and assess the results obtained by two different approaches for simulating the link traffic flows on the road transportation network and their related impacts (externalities) 3. identify potential solutions to solve critical aspects, particularly in terms of traffic flow congestion and related environmental impacts (findings)

Decrescita sostenibile applicata alla mobilità nelle aree urbane.

La mobilità nelle aree urbane di medie e grandi dimensioni risente di molte criticità e spesso è causa di numerose discussioni. L'utilizzo sempre crescente del mezzo di trasporto privato ha prodotto conseguenze negative: l'aumento del traffico e degli incidenti stradali, dell'inquinamento atmosferico e del rumore sono accompagnati da un forte spreco energetico. Dall'altra parte, il trasporto pubblico locale (TPL) non è riuscito a costruirsi canali preferenziali all'interno dell'immaginario cittadino, poiché sottomesso da un modello economico e un mercato fortemente dipendenti dall'automobile. Una via d'uscita dalla mobilità non sostenibile basata sull'utilizzo di combustibili fossili porta a concepire un trasporto pubblico locale gratuito, fruibile dal cittadino in qualsiasi momento della giornata. Attraverso un'analisi dell'azienda del trasporto pubblico bolognese (ATC) e dei dati provenienti dalla sanità regionale, questa tesi intende mostrare che vi sarà un miglioramento della qualità di vita in aree urbane nel momento in cui la teoria della decrescita venga condivisa da tutti i cittadini. Così sarà possibile liberare i centri urbani dai mezzi privati e quindi dagli alti livelli di inquinamento acustico e atmosferico, e dare ai cittadini la vera libertà di movimento. Mobility in medium and large sized urban areas is critical and often a cause for numerous debates. The use of private transport is in constant increase and has generated negative consequences: congestion and road accidents, air and noise pollutio as well as a considerable waste of energy. On the other hand, the local public transport (LPT) has not succeeded in representing the preferred choice by citizens in the urban imaginary. Its potential has been subdued by economic models and markets that are largely dependant on the production of vehicles. An alternative to the current non sustainable mobility based on the combustion of fossil fuels could be the provision of a free local transport network available to the citizen from anywhere at any time. This dissertation's objective is to show how an improvement of the quality of life in urban areas is connected to a collective awareness on the degrowth theory. I intend to achieve this by analysing thoroughly the system of the public transportation agency in Bologna (ATC) and considering data from the local health department. Only then we will be able to limit private vehicles from city centres and as a result of that drastically decrease air and noise pollution whilst providing a true service for a free moving citizen.

Stationary states in random walks on networks

In this thesis we dealt with the problem of describing a transportation network in which the objects in movement were subject to both finite transportation capacity and finite accomodation capacity. The movements across such a system are realistically of a simultaneous nature which poses some challenges when formulating a mathematical description. We tried to derive such a general modellization from one posed on a simplified problem based on asyncronicity in particle transitions. We did so considering one-step processes based on the assumption that the system could be describable through discrete time Markov processes with finite state space. After describing the pre-established dynamics in terms of master equations we determined stationary states for the considered processes. Numerical simulations then led to the conclusion that a general system naturally evolves toward a congestion state when its particle transition simultaneously and we consider one single constraint in the form of network node capacity. Moreover the congested nodes of a system tend to be located in adjacent spots in the network, thus forming local clusters of congested nodes.

Trajectory Planning in UAV-Aided Wireless Networks Via Reinforcement Learning

This thesis is focused on the design of a flexible, dynamic and innovative telecommunication's system for future 6G applications on vehicular communications. The system is based on the development of drones acting as mobile base stations in an urban scenario to cope with the increasing traffic demand and avoid network's congestion conditions. In particular, the exploitation of Reinforcement Learning algorithms is used to let the drone learn autonomously how to behave in a scenario full of obstacles with the goal of tracking and serve the maximum number of moving vehicles, by at the same time, minimizing the energy consumed to perform its tasks. This project is an extraordinary opportunity to open the doors to a new way of applying and develop telecommunications in an urban scenario by mixing it to the rising world of the Artificial Intelligence.

Neural Network based Non Orthogonal Random Access for 6G NTN-IoT

Pervasive and distributed Internet of Things (IoT) devices demand ubiquitous coverage beyond No-man’s land. To satisfy plethora of IoT devices with resilient connectivity, Non-Terrestrial Networks (NTN) will be pivotal to assist and complement terrestrial systems. In a massiveMTC scenario over NTN, characterized by sporadic uplink data reports, all the terminals within a satellite beam shall be served during the short visibility window of the flying platform, thus generating congestion due to simultaneous access attempts of IoT devices on the same radio resource. The more terminals collide, the more average-time it takes to complete an access which is due to the decreased number of successful attempts caused by Back-off commands of legacy methods. A possible countermeasure is represented by Non-Orthogonal Multiple Access scheme, which requires the knowledge of the number of superimposed NPRACH preambles. This work addresses this problem by proposing a Neural Network (NN) algorithm to cope with the uncoordinated random access performed by a prodigious number of Narrowband-IoT devices. Our proposed method classifies the number of colliding users, and for each estimates the Time of Arrival (ToA). The performance assessment, under Line of Sight (LoS) and Non-LoS conditions in sub-urban environments with two different satellite configurations, shows significant benefits of the proposed NN algorithm with respect to traditional methods for the ToA estimation.