Mobility is central to living an independent life, to participating in society, and to maintaining well-being in later life. The point of departure in this thesis is that retirement implies changes in time-space use and interruption in routines, which influence demands and preconditions for mobility in different ways.  The aim of this thesis is to explore mobility strategies and changes in mobility upon retirement and how mobility develops during the first years of retirement. A further aim is to provide knowledge of the extent to which newly retired people maintain a desired mobility based on their needs and preconditions. The thesis is empirically based on travel diaries kept by newly retired people, and qualitative interviews with the same persons, and follow-up interviews three and a half years later. The results show that mobility is a way of forming a structure in the new everyday life as retirees by getting out of the house, either just for a walk or to do errands. Many patterns of everyday life remain the same upon retirement, but the informants also merge new responsibilities and seek new social arenas and activities. As a result, the importance of the car have not changed, but it is used for other reasons than before. After leaving paid work, new space-time constraints are created which influences demands for mobility. The study further shows that “third places” become important, especially among those who live alone, as they give an opportunity to being part of a social context and a reason for getting out of the house. The follow-up interviews revealed that declining health changes the preconditions for mobility. Daily walks had to be made shorter, and the car had to be used for most errands to where they previously could walk or cycle. However, mobility can also be maintained despite a serious illness and a long period of rehabilitation. / Mobilitet är en förutsättning för oberoende, delaktighet och välbefinnande när man åldras. Utgångspunkten i avhandlingen är att pensioneringen innebär tidsrumsliga förändringar och brott i rutiner som på olika sätt påverkar människors behov av att resa och deras förutsättningar för mobilitet. Syftet med avhandlingen är att utforska mobilitetsstrategier och förändringar i mobilitet i samband med pensioneringen samt hur mobiliteten utvecklas under de första åren som pensionär. Ambitionen är att öka kunskapen om i vilken utsträckning nya pensionärer upprätthåller en önskad mobilitet utifrån deras egna behov och förutsättningar. Avhandlingen baseras empiriskt på resedagböcker som nyblivna pensionärer har fört och kvalitativa intervjuer med samma personer, samt uppföljningsintervjuer tre och ett halvt år senare. Resultaten visar att mobiliteten är en strategi för att skapa en struktur i vardagen som pensionär genom att komma hemifrån, t.ex. för att ta en promenad eller för att uträtta ärenden. Många vardagsmönster behålls vid pensioneringen men informanterna finner också nya åtaganden och söker nya sociala arenor och aktiviteter. Betydelsen av bilen har inte förändrats men den används av andra anledningar än tidigare. Vid pensioneringen skapas andra tidsrumsliga begränsningar vilka inverkar på efterfrågan på mobilitet. Resultaten visa också att "tredje platser" blir viktiga, särskilt bland dem som lever ensamma, eftersom de ger en möjlighet att vara en del av ett socialt sammanhang och en anledning att komma hemifrån. Uppföljningsintervjuerna visade att förutsättningarna för mobilitet förändras när hälsan försämras. Promenaderna blir kortare och bilen används i högre utsträckning för de ärenden dit de tidigare kunde gå eller cykla. Men trots allvarliga sjukdomar och långa perioder av rehabilitering kan mobiliteten upprätthållas. / ERA-NET 2007 "Keep moving: improving the mobility of older persons" / Sentrip - Senior life transition points

This study utilized both quantitative and qualitative methods to investigate different actors and layers of policy, implementation, and reception of pro-environmental Travel Demand Management policy and measures in Umeå. One initiative by Upab (Umeå Parkering AB) and Umeå municipality, Grönt parkeringsköp, which means moving parking spaces from the central area of Umeå and replacing them with facilities that promote sustainable travel, was investigated more thoroughly. The data was collected through a manually distributed survey in three properties that have implemented Grönt parkeringsköp, as well as through interviews with property owners. Utilizing discourse analysis, thematic analysis as well as OLS-regressions, the results have shown that the comprehensive plan of Umeå puts emphasis on sustainable growth to 200 000 inhabitants, as well as minimising car traffic in the central areas of town, mainly through densification of already built-up areas. The property owners stated several motives to implement such policies, including ecological, financial as well as brandstrengthening benefits. Attitudes amongst survey respondents are generally positive towards measures that improve conditions for bicycle users, and more negative towards push-measures. There are different predictors for attitudes and perceived importance of Travel Demand Management measures, including altruism and self-interest. The use of the installed measures through Grönt parkeringsköp however appear to be limited, possibly due to a lack of information.

It is now widely accepted that human behaviour accounts for a large portion of total global emissions, and thus influences climate change to a large extent. Changing human behaviour when it comes to mode of transportation is one component which could make a difference in the long term. In order to achieve behavioural change, we investigate the use of a persuasive multiplayer game. Transportation mode recognition is used within the game to provide bonuses and penalties to users based on their daily choices regarding transportation. To easily identify modes of transportation, an approach to transport recognition based on accelerometer and gyroscope data is analysed and extended. Preliminary results from the machine learning tests show that the classification true-positive rate for recognizing 10 different classes can reach up to 95% when using a history set (66% without). Preliminary results from testers of the game indicate that using games may be successful in causing positive change in user behaviour. / <p>Del av Erasmus Mundus PERCCOM. Redovisning skedde på anordnad summer school av partner-universitet där hela konsortiet närvarade.</p>

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19851号 / 情博第602号 / 新制||情||105(附属図書館) / 32887 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 美濃 導彦, 教授 石田 亨, 教授 岡部 寿男 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

A wide range of contemporary technologies leveraging ubiquitous mobile phones have addressed the challenge of transportation mode recognition, which involves identifying how users move about, such as walking, cycling, driving a car, or taking a bus. This problem has found applications in various areas, including smart city transportation, carbon footprint calculation, and context-aware mobile assistants. Previous research has primarily focused on recognizing mobility modes using GPS and motion sensor data from smartphones. However, these approaches often necessitate the installation of specialized mobile applications on users’ devices to collect sensor data, resulting in power inefficiency and privacy concerns. In this study, we tackle these issues by presenting a user-independent system capable of distinguishing four forms of locomotion—walking, bus, car, and train—solely based on mobile data (4G) from smartphones. Our system was developed using data collected in three diverse locations (Mekkah, Jeddah, KAUST) in the Kingdom of Saudi Arabia. The underlying concept is to correlate phone speed with features extracted from Channel State Information (CSI), which includes information about Physical Cell ID, received signal strength, and other relevant data. The feature extraction process involves utilizing sliding windows over both the time and frequency domains. By employing statistical classification and boosting techniques, we achieved remarkable F-scores of 85%, 95%, 88%, and 70% for the car, bus, walking, and train modes, respectively. Moreover, we conducted an analysis of the handover rate in a one-tier network and compared the analytical results with real data. This investigation provided novel insights into the influence of transportation modes on handover rate, revealing the correlation between different modes of mobility and network connectivity. This work sets the stage for the development of more efficient and privacy-friendly solutions in transportation mode recognition and network optimization.

In this report we attempt to provide insights to how a resource efficient solution for transportation mode recognition can be implemented on a smartphone using the accelerometer and magnetometer as sensors for data collection. The proposed system uses a hierarchical classification process where instances are first classified as vehicles or non-vehicles, then as wheel or rail vehicles, and lastly as belonging to one of the transportation modes: bus, car, motorcycle, subway, or train. A virtual gyroscope is implemented as a low-power source of simulated gyroscope data. Features are extracted from the accelerometer, magnetometer and virtual gyroscope readings that are sampled at 30 Hz, before they are classified using machine learning algorithms from the WEKA machine learning library. An Android application was developed to classify real-time data, and the resource consumption of the application was measured using the Trepn profiler application. The proposed system achieves an overall accuracy of 82.7% and a vehicular accuracy of 84.9% using a 5 second window with 75% overlap while having an average power consumption of 8.5 mW. / I denna rapport försöker vi ge insikter om hur en resurseffektiv lösning för transportlägesigenkänning kan implementeras på en smartphone genom att använda accelerometern och magnetometern som sensorer för datainsamling. Det föreslagna systemet använder en hierarkisk klassificeringsprocess där instanser först klassificeras som fordon eller icke-fordon, sedan som hjul- eller järnvägsfordon, och slutligen som tillhörande ett av transportsätten: buss, bil, motorcykel, tunnelbana eller tåg. Ett virtuellt gyroskop implementeras som en lågenergi källa till simulerad gyroskopdata. Olika särdrag extraheras från accelerometer, magnetometer och virtuella gyroskopläsningar som samlas in vid 30 Hz, innan de klassificeras med hjälp av maskininlärningsalgoritmer från WEKA-maskinlärningsbiblioteket. En Android-applikation har utvecklats för att klassificera realtidsdata, och programmets resursförbrukning mättes med hjälp av Trepn profiler-applikationen. Det föreslagna systemet uppnår en övergripande noggrannhet av 82.7% och en fordonsnoggrannhet av 84.9% genom att använda ett 5 sekunders fönster med 75% överlappning med en genomsnittlig energiförbrukning av 8.5 mW.

The focus of this dissertation is on safety improvement at intersections and presenting how Vehicle/Bicycle-to-Infrastructure Communications can be a potential countermeasure for crashes resulting from drivers' and cyclists' violations at intersections. The characteristics (e.g., acceleration capabilities, etc.) of transportation modes affect the violation behavior. Therefore, the first building block is to identify the users' transportation mode. Consequently, having the mode information, the second building block is to predict whether or not the user is going to violate. This step focuses on two different modes (i.e., driver violation prediction and cyclist violation prediction). Warnings can then be issued for users in potential danger to react or for the infrastructure and vehicles so they can take appropriate actions to avoid or mitigate crashes. A smartphone application was developed to collect sensor data used to conduct the transportation mode recognition task. Driver violation prediction task at signalized intersections was conducted using observational and simulator data. Also, a naturalistic cycling experiment was designed for cyclist violation prediction task. Subsequently, cyclist violation behavior was investigated at both signalized and stop-controlled intersections. To build the prediction models in all the aforementioned tasks, various Artificial Intelligence techniques were adopted. K-fold Cross-Validation as well as Out-of-Bag error was used for model selection and validation. Transportation mode recognition models contributed to high classification accuracies (e.g., up to 98%). Thus, data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. Driver violation (i.e., red light running) prediction models were resulted in high accuracies (i.e., up to 99.9%). Time to intersection (TTI), distance to intersection (DTI), the required deceleration parameter (RDP), and velocity at the onset of a yellow light were among the most important factors in violation prediction. Based on logistic regression analysis, movement type and presence of other users were found as significant factors affecting the probability of red light violations by cyclists at signalized intersections. Also, presence of other road users and age were the significant factors affecting violations at stop-controlled intersections. In case of stop-controlled intersections, violation prediction models resulted in error rates of 0 to 10 percent depending on how far from the intersection the prediction task is conducted. / Ph. D.

Traffic congestion has become one of the major challenging problems of modern life in many urban areas. This growing problem leads to negative environmental impacts, wasted fuel, lost productivity, and increased travel time. In big cities, trains and buses bring riders to transit stations near shopping and employment centers, but riders then need another transportation mode to reach their final destination, which is known as the last mile problem. A smart bike-sharing system (BSS) can help address this problem and encourage more people to ride public transportation, thus relieving traffic congestion. At the strategic level, we start with proposing a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms. In the transportation sector, researchers can use smartphones to track and obtain information of multi-mode trips. These data can be used to recognize the user's transportation mode, which can be then utilized in several different applications; such as planning new BSS instead of using costly surveys. Next, a new method is proposed to quantify the effect of several factors such as weather conditions on the prediction of bike counts at each station. The proposed approach is promising to quantify the effect of various features on BSSs in cases of large networks with big data. Third, these resulted significant features were used to develop state-of-the-art toolbox algorithms to operate BSSs efficiently at two levels: network and station. Finally, we proposed a quality-of-service (QoS) measurement, namely Optimal Occupancy, which considers the impact of inhomogeneity in a BSS. We used one of toolbox algorithms modeled earlier to estimate the proposed QoS. Results revealed that the Optimal Occupancy is beneficial and outperforms the traditionally-known QoS measurement. / PHD / A growing population, with more people living in cities, has led to increased pollution, noise, congestion, and greenhouse gas emissions. One possible approach to mitigating these problems is encouraging the use of bike-sharing systems (BSSs). BSSs are an integral part of urban mobility in many cities and are sustainable and environmentally friendly. As urban density increases, it is likely that more BSSs will appear due to their relatively low capital and operational costs, ease of installation, pedal assistance for people who are physically unable to pedal for long distances or on difficult terrain, and the ability to track bikes in some cases. This dissertation is a building block for a smart BSS in the strategic level, which could be used in real and different applications. The main aims of the dissertation are to boost the redistribution operation, to gain new insights into and correlations between bike demand and other factors, and to support policy makers and operators in making good decisions regarding planning new or existing BSS. This dissertation makes many significant contributions. These contributions include novel methods, measurements, and applications using machine learning and statistical learning techniques in order to design a smart BSS. We start with proposing a novel framework that increases the accuracy of traditional transportation mode classification algorithms. In the transportation sector, researchers can use smartphones to track and obtain information of multi-mode trips. These data can be used to recognize the user’s transportation mode, which can be then used in planning new BSS. Next, a new method is proposed to quantify the effect of several factors such as weather conditions on the prediction of bike station counts. Third, we use state-of-the-art data analytics to develop a toolbox to operate BSSs efficiently at two levels: network and station. Finally, we propose a quality-of-service (QoS) measurement, which considers the impact of inhomogeneity of BSS properties.

In this study, carbon dioxide emissions resulting from transportation are assessed, carbon emission reduction opportunities in the current service supply chain design of Cisco Systems, Inc. are explored. Among these opportunities, changing transport mode from a high-carbon transport mode to a low-carbon transport mode is found to be the most promising option and is scrutinized. The effect of transportation mode change on carbon emission and expected total cost are scrutinized by developing a mathematical model that minimizes expected total cost subject to aggregate fill rate constraint. Furthermore, a second model that minimizes the expected total cost under aggregate expected fill rate and carbon emission constraints is developed. In this model transportation mode choice decisions are integrated into inventory decisions. Since it is difficult to make transportation mode selection for each individual item, the items are clustered and transportation mode selection is made for each cluster. Therefore we propose two clustering methods that are k-means clustering and an adopted ABC analysis. In addition, a greedy algorithm based on second model is developed. Since currently there are no regulations on carbon emissions, in order to examine possible regulation scenarios computational studies are carried out. In these studies, efficient solutions are generated and the most preferred solutions that have less carbon emission and lower total cost among all efficient solutions are examined.

Aujourd'hui, la plupart des internautes ont leurs données dispersées dans plusieurs appareils, applications et services. La gestion et le contrôle de ses données sont de plus en plus difficiles. Dans cette thèse, nous adoptons le point de vue selon lequel l'utilisateur devrait se voir donner les moyens de récupérer et d'intégrer ses données, sous son contrôle total. À ce titre, nous avons conçu un système logiciel qui intègre et enrichit les données d'un utilisateur à partir de plusieurs sources hétérogènes de données personnelles dans une base de connaissances RDF. Le logiciel est libre, et son architecture innovante facilite l'intégration de nouvelles sources de données et le développement de nouveaux modules pour inférer de nouvelles connaissances. Nous montrons tout d'abord comment l'activité de l'utilisateur peut être déduite des données des capteurs de son téléphone intelligent. Nous présentons un algorithme pour retrouver les points de séjour d'un utilisateur à partir de son historique de localisation. À l'aide de ces données et de données provenant d'autres capteurs de son téléphone, d'informations géographiques provenant d'OpenStreetMap, et des horaires de transports en commun, nous présentons un algorithme de reconnaissance du mode de transport capable de retrouver les différents modes et lignes empruntés par un utilisateur lors de ses déplacements. L'algorithme reconnaît l'itinéraire pris par l'utilisateur en retrouvant la séquence la plus probable dans un champ aléatoire conditionnel dont les probabilités se basent sur la sortie d'un réseau de neurones artificiels. Nous montrons également comment le système peut intégrer les données du courrier électronique, des calendriers, des carnets d'adresses, des réseaux sociaux et de l'historique de localisation de l'utilisateur dans un ensemble cohérent. Pour ce faire, le système utilise un algorithme de résolution d'entité pour retrouver l'ensemble des différents comptes utilisés par chaque contact de l'utilisateur, et effectue un alignement spatio-temporel pour relier chaque point de séjour à l'événement auquel il correspond dans le calendrier de l'utilisateur. Enfin, nous montrons qu'un tel système peut également être employé pour faire de la synchronisation multi-système/multi-appareil et pour pousser de nouvelles connaissances vers les sources. Les résultats d'expériences approfondies sont présentés. / Typical Internet users today have their data scattered over several devices, applications, and services. Managing and controlling one's data is increasingly difficult. In this thesis, we adopt the viewpoint that the user should be given the means to gather and integrate her data, under her full control. In that direction, we designed a system that integrates and enriches the data of a user from multiple heterogeneous sources of personal information into an RDF knowledge base. The system is open-source and implements a novel, extensible framework that facilitates the integration of new data sources and the development of new modules for deriving knowledge. We first show how user activity can be inferred from smartphone sensor data. We introduce a time-based clustering algorithm to extract stay points from location history data. Using data from additional mobile phone sensors, geographic information from OpenStreetMap, and public transportation schedules, we introduce a transportation mode recognition algorithm to derive the different modes and routes taken by the user when traveling. The algorithm derives the itinerary followed by the user by finding the most likely sequence in a linear-chain conditional random field whose feature functions are based on the output of a neural network. We also show how the system can integrate information from the user's email messages, calendars, address books, social network services, and location history into a coherent whole. To do so, it uses entity resolution to find the set of avatars used by each real-world contact and performs spatiotemporal alignment to connect each stay point with the event it corresponds to in the user's calendar. Finally, we show that such a system can also be used for multi-device and multi-system synchronization and allow knowledge to be pushed to the sources. We present extensive experiments.