Seit einigen Jahren wird spekuliert, dass eine neue Generation heranwächst, die das eigene Auto nicht mehr unhinterfragt als Grundausstattung des modernen Menschen versteht, sondern ihre Mobilität effektiver und nachhaltiger gestaltet. Steuern wir also auf eine schöne neue Mobilitätswelt zu? Diese Schlussfolgerung zu ziehen wäre angesichts der Vorläufigkeit der Befunde und der unsicheren empirischen Basis der Aussagen voreilig. Um die Debatten zur Mobilität junger Menschen zu versachlichen und Einblick in die Komplexität der Wirkungszusammenhänge zu gewinnen, führte das InnoZ im Jahr 2012 eine empirisch-qualitative Explorationsstudie durch. Die Ergebnisse dieser Studie sind in Form eines Werkstattberichts im vorliegenden Baustein dokumentiert. Trotz vieler offener Fragen belegen die Ergebnisse der Studie, dass ein Wandel von Mobilitätsmustern junger Menschen möglich ist, aber kein Selbstläufer sein wird. Die Schüler nutzen die zur Verfügung stehenden Verkehrsmittel bedürfnisorientiert und sind dabei offen für die Kombination unterschiedlicher Verkehrsmittel. Eines ist jedoch auch deutlich geworden: Junge Menschen kennen die neuen Alternativen noch zu wenig. In den Gesprächen rund um neue Angebotsformen wie Carsharing und Elektromobilität wurde deutlich, dass sich junge Menschen erst dann mit den Optionen auseinandersetzen, wenn diese für die eigene Mobilitätsbewältigung in Betracht kommen. Zwei Ergebnisse der Explorationsstudie sollten zukünftig stärker beachtet werden. Erstens existieren bei den meisten Befragten weder Vorbehalte gegenüber Alternativen zum privaten Pkw noch monomodale Fixierungen. Wichtig ist den jungen Menschen nicht der eigene Pkw, sondern vielmehr Unabhängigkeit und Flexibilität. Wenn dies mittels geteilter Nutzung von Pkw oder anderer alternativer Verkehrsmittel realisiert werden kann, so spielt das Auto in den Augen nahezu aller Teilnehmer keine exklusive Rolle mehr. Wenn es keine entsprechenden Alternativen gibt, ist das eigene Auto jedoch nach wie vor das Maß aller Dinge. Junge Menschen sind grundsätzlich offen für alle zur Verfügung stehenden Angebote − dies zeigt die Explorationsstudie deutlich. Sie müssen nur bekannt, einfach zu nutzen, schnell, flexibel und kostengünstig sein. Das Smartphone scheint dabei eine wichtige Funktion zur Organisation der eigenen Mobilität einzunehmen. Zweitens wurde deutlich, dass junge Menschen in der Begründung ihrer Verkehrsmittelwahl primär auf begrenzte Budgets verweisen. Ein Verkehrsmittel sollte folglich nicht nur Unabhängigkeit und Flexibilität gewährleisten, sondern es sollte in den Augen der jungen Menschen vor allem nicht allzu viel Kosten verursachen. Es sollten folglich nicht nur neue Mobilitätsangebote entwickelt werden, sondern auch attraktive Tarifsysteme, die es erlauben, die Zahlungsbereitschaft der jungen Menschen möglichst optimal zu adressieren. / Young people’s mobility patterns are changing. They no longer regard the private car as a basic need of the modern human being. This hypothesis has been the subject of debate for several years. Following this argument, it seems that the young generation is moving along a more sustainable path. Are we heading for a brave new world of transport? Drawing this conclusion would be too hastily. The empirical basis is too shallow and findings are still preliminary. In 2012, the InnoZ conducted an explorative study on youth mobility to display the complexity of the topic and channel the debate. Results of this study are documented in this working paper. Despite many unanswered questions, the study shows that although a change of mobility patterns of young people is possible it will be anything but self-propelled. Young people use available transport modes according to their needs and are open towards an efficient combination of different means of transport. However, the study additionally showed that young people are still not sufficiently aware of new alternatives. Discussions with teenagers uncovered that new mobility services like carsharing and electric vehicles only become a relevant issue if regarded as practical options for their everyday mobility. Two results of the explorative study should be considered in future research. Firstly, most interviewees neither expressed reservations about alternatives to the private car nor demonstrated a fixed orientation towards a single mode type. It isn’t the private car that is important to young people but rather independence and flexibility. If alternative mobility services provide those attributes, the private car and its alternatives are not mutually exclusive. Basically, young people are open to all accessible options – this can be derived quite clearly from our study. Nevertheless, car ownership still constitutes the measure of all things if there’s a lack of alternatives. Our study also revealed that the smartphone seems to play an important role for the organisation of young people’s individual mobility. Secondly, young people tend to decide on their means of transport in context of their − usually limited − budgets. Consequently, new mobility services should ensure not only independence and flexibility, but above all have to be affordable. When developing new mobility services, young people’s willingness to pay should be addressed by implementing attractive fares and charging systems.

Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to operate properly and therefore access it from everywhere and at any time. Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. There is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications support its users by leveraging mobility predictions in a distinct application scenario. Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile application code. In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic. First, we address the selection of human mobility prediction models. We report on an extensive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR – a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR – an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called LOTUS, is designed and evaluated. Second, we present EBC – a novel algorithm for prefetching mobile application content. EBC’s goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption. Developing human mobility and application usage prediction algorithms requires access to human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the lack of ground-truth mobility data and (2) the lack of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR, which was used to collect our data sets. In summary, the contributions of this thesis provide a step further towards supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain.

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