Survey of Machine Learning Methods Applied to Urban Mobility

To increase the sustainability in urban mobility, it is necessary to optimally combine public and shared vehicles throughout a passenger's trip. In this work, we present a survey on urban mobility based on passengers' data and machine learning methods. We focus on four applications for urban mobility: public datasets, passenger localization, detection of the transport mode and pattern recognition and generation of mobility models. Public datasets lack data of multimodal trips and are in need of guidelines to facilitate the data collection and documentation processes. Passenger localization is predominantly done through fingerprinting in indoor environments; and fingerprinting relies on unsupervised learning to survey access points. The most common mean of transport detected is the bus, followed by walking and biking, while e-scooters are not included within the detected transport modes. The existing works focus on predicting the travel time of the passenger's trajectory and no machine learning method stands out to estimate the travel time. There is still a need for works that analyze how passengers make use of the urban infrastructure, which will support municipalities and transport mode operators in resource planning and service design

A taxonomy for planning and designing smart mobility services

The development of smart mobility initiatives requires specialized and contextualized policies addressing the needs and interests of many stakeholders involved. Since the development of such policies is challenging, there is a need to learn from the experience of many cities around the world offering efficient and successfully adopted smart mobility services. However, in practice, the information provided about such initiatives is shallow and unstructured. To address this issue, we study the state of the art in mobility services, reviewing scientific publications and 42 smart mobility services delivered by nine smart cities around the world, and we propose a taxonomy for planning and designing smart mobility services. The taxonomy provides a common vocabulary to discuss and share information about such services. It comprises eight dimensions: type of services, maturity level, users, applied technologies, delivery channels, benefits, beneficiaries, and common functionality. The contribution of the proposed taxonomy is to serve as a tool for guiding policy makers by identifying a spectrum of mobility services that can be provided, to whom, what technologies can be used to deliver them, and what is the delivered public value so to justify their implementation. In addition, the taxonomy can also assist researchers in further developing the domain. By identifying common functionality, it could also help Information Technology (IT) teams in building and maintaining smart mobility services. Finally, we further discuss usage scenarios of the taxonomy by policy makers, IT staff and researchers.NORTE-01-0145-FEDER-000037, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (EFDR). The first author is also supported by the Portuguese funding agency, FCT, under grant PD/BD/52238/201

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd

Enhancing Mobility Applications Through Bluetooth Communications

In the world of short and medium-range wireless technologies, Bluetooth has recently come to the forefront of innovation. Within the next five years its market presence, especially in its Low Energy variation, is expected to nearly double across all market segments. The technology is quickly and steadily gaining importance for a wide range of applications with a specific focus on Internet of Things (IoT) devices. The growing availability and variety of such devices constitute an untapped potential that we plan on exploiting. Our focus in this thesis is to understand Bluetooth’s capabilities and explore its potential in mobile contexts. One specific field where this technology remains unexplored is Vehicular Ad Hoc Networks (VANETs). Because of the need to implement and moderate vehicular communications, the topic of Intelligent Transportation Systems (ITSs) is now trending more than ever. In this thesis we propose two ways we can benefit from Bluetooth in a mobile environment. Firstly, we consider the technology as a communication medium to investigate how di↵erent mobilities a↵ect the link performance between two devices. To do this, we define a set of communication experiments, in our case between two vehicles, to analyse how Bluetooth Low Energy (BLE) is a↵ected by varying speed, distance and traffic conditions. We find that the maximum communication range between two devices can go beyond 100m and that a robust connection, capable of handling sudden signal losses or interference, can be achieved up to a distance of 50m. The experiments were conducted using a proof-of-concept mobile application for o↵-the-shelf smartphones that can be used to transmit data over multiple hops in various Vehicle-to-Everything (V2X) scenarios. Secondly, we consider Bluetooth discovery capabilities as an information medium by using a connectionless approach to analyse di↵erent mobility frameworks. As there is an increasing need for vehicles and objects to become aware of their context, we implement Bluetooth as a sensing system to provide contextual information about its surroundings. Our challenge is to find out to what extent we can exploit the Bluetooth discovery and beaconing scheme for this purpose. We collect and analyse a dataset of Bluetooth Classic and BLE discoveries and evaluate their respective characteristics and ability to provide context-aware information from a vehicular perspective. By examining data recorded about encountered devices, such as quantity, quality of signal and device class information, we infer distinctive Bluetooth behaviours related to context and application. For this purpose, we propose a set a features to train a classification model to recognize di↵erent driving environments (i.e. road classes). Investigating the performance of our classifier, we were able to predict up to three classes (highway, city, extra-urban) by using only Bluetooth discovery data and no geographical information. This outcome gives promising results targeted at low energy and privacy-friendly applications and can open up a wide range of research directions. In conclusion, in this thesis we present two ways of applying Bluetooth to mobile contexts for deploying novel human mobility applications

User mobility prediction and management using machine learning

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility

Inferring the transportation mode from sparse GPS data

Understanding travel behaviour and travel demand is of constant importance to transportation communities and agencies in every country. Nowadays, attempts have been made to automatically infer the modes of transport from positional data (such as GPS data) to significantly reduce the cost in time and budget of conventional travel diary surveys. Some limitations, however, exist in the literature, in aspects of data collection (spatio-temporal sample distribution, duration of study, granularity of data, device type), data pre-processing (managing GPS errors, choice of modes, trip information generalisation, data labelling strategy), the classification method used and the choice of variables used for classification, track segmentation methods used (clustering techniques), and using transport network datasets. Therefore, this research attempts to fully understand these aspects and their effect on the process of inference of mode of transport. Furthermore, this research aims to solve a classification problem of sparse GPS data into different transportation modes (car, walk, cycle, underground, train and bus). To address the data collection issues, we conduct studies that aim to identify a representative sample distribution, study duration, and data collection rate that best suits the purpose of this study. As for the data pre-processing issues, we standardise guidelines for managing GPS errors and the required level of detail of the collected trip information. We also develop an online WebGIS-based travel diary that allows users to view, edit, and validate their track information to assure obtaining high quality information. After addressing the validation issues, we develop an inference framework to detect the mode of transport from the collected data. We first study the variables that could contribute positively to this classification, and statistically quantify their discriminatory power using ANOVA analysis. We then introduce a novel approach to carry out this inference using a framework based on Support Vector Machines (SVMs) classification. The classification process is followed by a segmentation phase that identifies stops, change points and indoor activity in GPS tracks using an innovative trajectory clustering technique developed for this purpose. The final phase of the framework develops a network matching technique that verifies the classification and segmentation results by testing their obedience to rules and restrictions of different transport networks. The framework is tested using coarse-grained GPS data, which has been avoided in previous studies, achieving almost 90% accuracy with a Kappa statistic reflecting almost perfect agreement

Exploring urban visitors' mobilities. A multi-method approach

Aquesta tesi doctoral sorgeix de la necessitat d’aprofundir en el coneixement de les mobilitats dels visitants, entendre les decisions que configuren el seu comportament espacio-temporal i identificar i explorar els efectes que les seves mobilitats tenen sobre les destinacions urbanes. La tesi es desenvolupa entorn a quatre objectius específics que s’emmarquen en l’àmbit de recerca relacionat amb el seguiment de l’activitat dels visitants en destinacions turístiques urbanes. Cadascun d’aquests objectius es desenvolupa en cadascun dels articles científics que conformen aquesta tesi doctoral, publicats tots ells en revistes de revisió per parells. El primer article es proposa com a objectiu identificar els factors, relacionats amb el perfil socioeconòmic dels turistes i amb les característiques de la seva estada, que determinen la selecció d’opcions de transport i mobilitat sostenible per moure’s per la destinació urbana. El segon article pretén analitzar i comprendre com afecta el comportament espacio-temporal dels turistes en els seus patrons de consum econòmic i, per tant, en la generació d’ingressos per a l’economia local. El tercer article es proposa analitzar la influència de l’espai urbà sobre la forma en què els visitants es desplacen per la destinació. I finalment, el quart article té per objectiu reconstruir trajectòries i/o fluxos espacio-temporals a partir de dades geolocalitzades de les xarxes socials per tal de detectar patrons de mobilitat dels visitants de destinacions urbanes. Les fonts de dades i els mètodes utilitzats per complir amb els objectius de partida són diverses. En aquest sentit, la tesi aporta també una àmplia radiografia dels pros i les contres de les diferents fonts de dades disponibles per a l’anàlisi de les mobilitats dels visitants en destinacions turístiques.Esta tesis doctoral surge de la necesidad de profundizar en el conocimiento de las movilidades de los visitantes,entender las decisiones que configuran su comportamiento espaciotemporal e identificar y explorar los efectos que sus movilidades tienen sobre los destinos urbanos. La tesis se desarrolla en torno a cuatro objetivos específicos que se enmarcan en el ámbito de investigación de seguimiento de visitantes, y que se desarrollan en cada uno de los artículos científicos, publicados todos ellos en revistas de revisión por pares, que conforman esta tesis. El primer artículo se propone como objetivo identificar los factores, relacionados con el perfil socioeconómicos de los turistas y con las características de su estancia, que determinan la selección de opciones de transporte y movilidad sostenible para moverse por el destino urbano. El segundo artículo pretende analizar y comprender cómo afecta el comportamiento espaciotemporal de los turistas en sus patrones de consumo económico y, por tanto, en la generación de ingresos para la economía local. El tercer artículo se propone analizar la influencia del espacio urbano sobre la forma en que los visitantes se desplazan por el destino. Y finalmente, el cuarto artículo tiene por objetivo reconstruir trayectorias y / o flujos espaciotemporales a partir de datos geolocalizados de las redes sociales para detectar patrones de movilidad de los visitantes de destinos urbanos. Las fuentes de datos y los métodos utilizados para cumplir con los objetivos de partida son diversos. En este sentido, la tesis aporta también una amplia radiografía de los pros y contras de las diferentes fuentes de datos disponibles para el análisis de las movilidades de los visitantes en destinos turísticos.This dissertation arises from the need to deepen the knowledge of the mobility of visitors, understand the decisions that shape their spatiotemporal behaviour and identify and explore the effects that their mobility has on urban destinations. The thesis is developed around four specific objectives that fall within the scope of visitor tracking research, and that are developed in each of the scientific articles, all of them published in peer-reviewed journals, that make up this thesis. The first article aims to identify the factors, related to the socioeconomic profile of tourists and the characteristics of their stay, that determine the selection of sustainable transport and mobility options to move within the urban destination. The second article aims to analyse and understand how the visitors’ spatiotemporal behaviour affects their patterns of economic consumption and, therefore, the generation of income for the local economy. The third article aims to analyse the influence of the built environment on the visitors’ mobilities at destination. And finally, the fourth article aims to reconstruct trajectories and / or spatiotemporal flows from geolocated data obtained from social networks in order to detect visitors’ mobility patterns at urban destinations. The data sources and methods used to meet the objectives are multiple. In this sense, the thesis also provides an extensive x-ray of the pros and cons of the different data sources available for the analysis of visitors’ mobilities in tourist destinations

Data Collection and Analysis in Urban Scenarios

The United Nations estimates that the world population will continue to grow, with a projection indicating a world population of up to approximately 8.5 billion people in 2030, 9.7 billion in 2050 and 10.9 billion in 2100. In addition to the phenomenon of population growth, the United Nations also estimates that in 2050 about 70% of the total world population will live in cities. These conditions increase the complexity of the services that public administrations and private companies must provide to citizens with the aim of optimising resources and increasing the level of quality of life. For an adequate design, implementation and management of these services, an extensive effort is required towards the design of effective solutions for data collection and analysis, applying Data Science and Artificial Intelligence techniques. Several approaches were addressed during the development of this research thesis. Furthermore, different real-world use cases are introduced where the presented work was tested and validated. The first thesis part focuses on data analysis on data collected using crowdsourcing. A real case study used for the analyses was a study conducted in Sheffield in which the goal was to understand people’s interaction with green areas and their wellbeing. In this study, an app with a chatbot was used to ask questions targeted to the study and collected not only the subjective answers but also objective data like users’ location. Through the analysis of this data, it was possible to extract insights that otherwise would not be easily reachable in other ways. Some limitations have arisen for less frequented areas, in fact, not enough information has been collected to have a statistical significance of the insights found. Conversely, more information than necessary was collected in the most frequented areas. For this reason, a framework that analyses the amount of information and its statistical significance in real-time has been developed. It increases the efficiency of the study and reduces intrusiveness towards the study participants. The limit that this approach presents is certainly the low sample of data that can be acquired. In the second part of this thesis, a move on to passive data collection is done, where the user does not have to interact in any way. Any data acquired is pseudonymised upon capture so that the dictates of the privacy legislation are respected. A system is then presented that collects probe requests generated by Wi-Fi devices while scanning radio channels to detect Access Points. The system processes the collected data to extract key information on people’s mobility, such as crowd density by area of interest, people flow, permanence time, return time, heat maps, origin-destination matrix and estimate of the locations of the people. The main novelty with respect to the state of the art is related to new powerful indicators necessary for some key services of the city, such as safety management and passenger transport services, and to experimental activities carried out in real scenarios. Furthermore, a de-randomisation algorithm to solve the problem of MAC address randomisation is presented.N/

Road safety investigation of the interaction between driver and cyclist

With growing global concern to reduce CO2 emissions, the transportation modal shift from car to bicycle is an encouraging alternative, which is getting more popular in Europe and North America, thanks to very low impact on the environment. On the other hand, the infrastructure for cyclist should be improved, since cyclists are vulnerable road users and with an increase in the number of cyclists the concern for their safety also gets increased. In this thesis, the analysis of accidents in which cyclists have been involved and understanding the reason for these accidents have been discussed, then the necessary requirements to design and implement a safe bicycle network is introduced. The study focuses on the drivers’ behavior in terms of interaction with cyclists when there is a presence of a cyclist crossing. Therefore the road safety investigation on cyclist infrastructure was made with observing drivers’interaction with cyclists. Then the time-based surrogacy measures used to investigate the safety level of the cylist, in particular PET (Post Encroachment Time) and TTC (Time to Collision) between driver and bicyclist were determing keeping in mind the right-angle collision. Furthermore we tried to find the reaction time of the drivers especially on signals and also with the presence of cyclist on the crossing to understand the time which is needed for the driver to stop the car. All of this data could be later useful for the reconstruction of the accidents. Understanding the instants at which driver applies the brakes was made possible by installing a V-Box device inside our test vehicle which also used to determine measures such as speed, distance and other important. Finally using mobile eye tracker the driver visual behavior when arriving the crossing point where observed and results showed that at number of situations driver’s gaze was distracted and only cyclist became an important focus only when he was at a considerable length from the crossing