Traffic light policies for low penetration rate of monitoring devices

La gestione del traffico è una delle principali problematiche delle città moderne, e porta alla definizione di nuove sfide per quanto riguarda l’ottimizzazione del flusso veicolare. Il controllo semaforico è uno degli elementi fondamentali per ottimizzare la gestione del traffico. Attualmente la rilevazione del traffico viene effettuata tramite sensori, tra i quali vengono maggiormente utilizzate le spire magnetiche, la cui installazione e gestione implica costi elevati. In questo contesto, il progetto europeo COLOMBO si pone come obiettivo l’ideazione di nuovi sistemi di regolazione semaforica in grado di rilevare il traffico veicolare mediante sensori più economici da installare e mantenere, e capaci, sulla base di tali rilevazioni, di auto organizzarsi, traendo ispirazione dal campo dell’intelligenza artificiale noto come swarm intelligence. Alla base di questa auto organizzazione semaforica di COLOMBO vi sono due diversi livelli di politiche: macroscopico e microscopico. Nel primo caso le politiche macroscopiche, utilizzando il feromone come astrazione dell’attuale livello del traffico, scelgono la politica di gestione in base alla quantità di feromone presente nelle corsie di entrata e di uscita. Per quanto riguarda invece le politiche microscopiche, il loro compito è quello di deci- dere la durata dei periodi di rosso o verde modificando una sequenza di fasi, chiamata in COLOMBO catena. Le catene possono essere scelte dal sistema in base al valore corrente della soglia di desiderabilità e ad ogni catena corrisponde una soglia di desiderabilità. Lo scopo di questo elaborato è quello di suggerire metodi alternativi all’attuale conteggio di questa soglia di desiderabilità in scenari di bassa presenza di dispositivi per la rilevazione dei veicoli. Ogni algoritmo complesso ha bisogno di essere ottimizzato per migliorarne le performance. Anche in questo caso, gli algoritmi proposti hanno subito un processo di parameter tuning per ottimizzarne le prestazioni in scenari di bassa presenza di dispositivi per la rilevazione dei veicoli. Sulla base del lavoro di parameter tuning, infine, sono state eseguite delle simulazioni per valutare quale degli approcci suggeriti sia il migliore.

Traffic Signal Optimization of Urban Arterial Network by Vehicle-to-Infrastructure

Urbanization has occasionally been linked to negative consequences. Traffic light system in urban arterial networks plays an essential role to the operation of transport systems. The availability of new Intelligent Transportation System innovations paved the way for connecting vehicles and road infrastructure. GLOSA, or the Green Light Optimal Speed Advisory, is a recent integration of vehicle-to-everything (v2x) technology. This thesis emphasized GLOSA system's potential as a tool for addressing traffic signal optimization. GLOSA serves as an advisory to drivers, informing them of the speed they must maintain to reduce waiting time. The considered study area in this thesis is the Via Aurelio Saffi – Via Emilia Ponente corridor in the Metropolitan City of Bologna which has several signalized intersections. Several simulation runs were performed in SUMOPy software on each peak-hour period (morning and afternoon) using recent actual traffic count data. GLOSA devices were placed on a 300m GLOSA distance. Considering the morning peak-hour, GLOSA outperformed the actuated traffic signal control, which is the baseline scenario, in terms of average waiting time, average speed, average fuel consumption per vehicle and average CO2 emissions. A remarkable 97% reduction on both fuel consumption and CO2 emissions were obtained. The average speed of vehicles running through the simulation was increased as well by 7% and a time saved of 25%. Same results were obtained for the afternoon peak hour with a decrease of 98% on both fuel consumption and CO2 emissions, 20% decrease on average waiting time, and an increase of 2% in average speed. In addition to previously mentioned benefits of GLOSA, a 15% and 13% decrease in time loss were obtained during morning and afternoon peak-hour, respectively. Towards the goal of sustainability, GLOSA shows a promising result of significantly lowering fuel consumption and CO2 emissions per vehicle.

Simulation of visible light communications in vehicular networks

The rapid development in the field of lighting and illumination allows low energy consumption and a rapid growth in the use, and development of solid-state sources. As the efficiency of these devices increases and their cost decreases there are predictions that they will become the dominant source for general illumination in the short term. The objective of this thesis is to study, through extensive simulations in realistic scenarios, the feasibility and exploitation of visible light communication (VLC) for vehicular ad hoc networks (VANETs) applications. A brief introduction will introduce the new scenario of smart cities in which visible light communication will become a fundamental enabling technology for the future communication systems. Specifically, this thesis focus on the acquisition of several, frequent, and small data packets from vehicles, exploited as sensors of the environment. The use of vehicles as sensors is a new paradigm to enable an efficient environment monitoring and an improved traffic management. In most cases, the sensed information must be collected at a remote control centre and one of the most challenging aspects is the uplink acquisition of data from vehicles. My thesis discusses the opportunity to take advantage of short range vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communications to offload the cellular networks. More specifically, it discusses the system design and assesses the obtainable cellular resource saving, by considering the impact of the percentage of vehicles equipped with short range communication devices, of the number of deployed road side units, and of the adopted routing protocol. When short range communications are concerned, WAVE/IEEE 802.11p is considered as standard for VANETs. Its use together with VLC will be considered in urban vehicular scenarios to let vehicles communicate without involving the cellular network. The study is conducted by simulation, considering both a simulation platform (SHINE, simulation platform for heterogeneous interworking networks) developed within the Wireless communication Laboratory (Wilab) of the University of Bologna and CNR, and network simulator (NS3). trying to realistically represent all the wireless network communication aspects. Specifically, simulation of vehicular system was performed and introduced in ns-3, creating a new module for the simulator. This module will help to study VLC applications in VANETs. Final observations would enhance and encourage potential research in the area and optimize performance of VLC systems applications in the future.

Copy-paste data augmentation for domain transfer on traffic signs

City streets carry a lot of information that can be exploited to improve the quality of the services the citizens receive. For example, autonomous vehicles need to act accordingly to all the element that are nearby the vehicle itself, like pedestrians, traffic signs and other vehicles. It is also possible to use such information for smart city applications, for example to predict and analyze the traffic or pedestrian flows. Among all the objects that it is possible to find in a street, traffic signs are very important because of the information they carry. This information can in fact be exploited both for autonomous driving and for smart city applications. Deep learning and, more generally, machine learning models however need huge quantities to learn. Even though modern models are very good at gener- alizing, the more samples the model has, the better it can generalize between different samples. Creating these datasets organically, namely with real pictures, is a very tedious task because of the wide variety of signs available in the whole world and especially because of all the possible light, orientation conditions and con- ditions in general in which they can appear. In addition to that, it may not be easy to collect enough samples for all the possible traffic signs available, cause some of them may be very rare to find. Instead of collecting pictures manually, it is possible to exploit data aug- mentation techniques to create synthetic datasets containing the signs that are needed. Creating this data synthetically allows to control the distribution and the conditions of the signs in the datasets, improving the quality and quantity of training data that is going to be used. This thesis work is about using copy-paste data augmentation to create synthetic data for the traffic sign recognition task.