Beyond Mobility Patterns

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2013

Developing a model of the needs of a mobile traveler is critical to good personalization. Transportation planners have been modeling these needs for years, but these models have not been used to date due to two outstanding questions: 1) are they applicable to individual travelers 2) are they useful beyond the studied region. This study demonstrates these studies can directly enhance the model of mobile users, and be done in a practical way through the transference of activity patterns across cities. This work then demonstrates how these studies can be combined with patterns of an individual mobile user successfully.

Pervasive and Mobile Computing, 2010

Introduction to the special issue on ''Human Behavior in Ubiquitous Environments: Modeling of Human Mobility Patterns'' Ubiquitous computing requires seamless access to media, information sharing and communication through heterogeneous systems, which are often distributed for example deployed on mobile devices or deeply embedded in the physical environment. Recent advances in computing technology allow researchers and practitioners to realize at least in part this vision of ubiquitous computing and build large-scale systems thanks to the widespread availability of more and more powerful mobile computing platforms. These devices have become an essential part of the everyday experience for billions of people. For this reason, a key aspect is the design and implementation of systems built around the life of individuals. Thus, the understanding of human behavior-and, in particular, its impact on technological issues relevant to these systems-has emerged as a fundamental research area in ubiquitous computing. In this first of two special issues of Pervasive and Mobile Computing on human behavior in ubiquitous environments we present state-of-the-art research contributions in this fundamental and exciting field. In particular, the work presented in this issue relates to models of human behavior with special focus on human mobility and the inference of particular activities. Smart-phones are emerging as a de-facto standard computing platform for ubiquitous context-aware systems, since they are able to continuously sense the dynamic context of users. In ''Predicting mobility events on personal devices'' Peddemors et al. propose a method for the prediction in time of the next occurrence of a context event of interest. More specifically, the authors focus on the prediction of network visibility events as observed through the wireless network interfaces of mobile devices. The approach is based on a predictor that analyses the event stream for forecasting context changes. Using two real world data sets, the authors found that including predictors of infrequently occurring events results in better predictions. They also prove that cross-sensor and cross-interface information in most cases improves the prediction performance. Mobility is also the key theme of the following paper, ''The co-evolution of taxi drivers and their in-car navigation systems''. In this article Girardin and Blat study how the adoption of in-car navigation systems changed the practice of the community of taxi drivers of Barcelona, Spain, from an ethnographical perspective. The results show co-evolution: taxi drivers adapt to their in-car navigation systems and, at the same time, they adapt them to their needs. In particular, the authors found evidence of an alteration of the learning processes to reduce stress rather than to improve efficiency. These results can be applied to the design of the next generation of this class of systems. Location information plays a key role in many mobile applications, from context-based search and advertising to sensing systems. In ''Mobility Profiler: A Framework for Discovering Mobility Profiles of Cell Phone Users'', Ali Bayr et al. present Mobility Profiler, a framework for the discovery of mobile cellular phone user profiles starting from cell based location data. To validate their results, the authors use real-world cell phone log data and report results about frequent mobility patterns and profiles, showing that user residence time follows a long tail distribution. The following paper by Kaltenbrunner et al. is also about the analysis of human movement, again from measurements in Barcelona, Spain. In ''Urban cycles and mobility patterns-Exploring and predicting trends in a bicycle-based public transport system'' the authors provide study human mobility in an urban area by analyzing the amount of available bikes in the stations of the community bicycle program ''Bicing'' in Barcelona. In fact, by means of data sampled from the operator's website, they are able to detect temporal and geographic mobility patterns within the city. These patterns are applied to predict the number of available bikes for any station in advance. In ''Utilization of User Feedback in Indoor Positioning System'' Hossain et al. propose an interpolation-based fingerprinting technique exploiting user feedback which does not require the typical exhaustive training phase of the existing indoor localization solutions. The authors show that users' feedback can be used for fine-tuning an under-trained positioning system with filtering. The experimental results also demonstrate that the participation of end-users can actually assist in the incremental construction of a positioning system. The authors show that the proposed user feedback-based positioning system can dynamically adapt to context changes.

Transportation Research Procedia

Retrieving exhaustive information about individual mobility patterns is an essential step in order to implement effective mobility solutions. Despite their popularity, digital travel surveys still require a significant amount of inputs from the respondent. Consequently, they require great efforts from both respondents and analysts, and are limited to a relatively short period of timebetween a few weeks and a year. Driven by these motivations, the approach proposed in this paper uses mobile phone location history to automatically detect activity location without any interaction with the respondent. The proposed methodology uses raw location data together with a special indexing technique to calculate the probability of performing a certain activity in a certain location. It uses a heuristic rule to improve this estimation by considering the value of information over time. Finally, GIS data about the number of facilities located in a certain area is downloaded in real-time to further improve the overall estimation. Results of this exploratory study support the idea that the proposed approach can reconstruct complex mobility patterns while minimizing the number of active inputs from the respondent.

Sustainability, 2021

Within the Mobility Choices (MC) project we have developed an app that allows users to record their travel behavior and encourages them to try out new means of transportation that may better fit their preferences. Tracks explicitly released by the users are anonymized and can be analyzed by authorized institutions. For recorded tracks, the freely available app automatically determines the segments with their transportation mode; analyzes the track according to the criteria environment, health, costs, and time; and indicates alternative connections that better fit the criteria, which can individually be configured by the user. In the second step, the users can edit their tracks and release them for further analysis by authorized institutions. The system is complemented by a Web-based analysis program that helps authorized institutions carry out specific evaluations of traffic flows based on the released tracks of the app users. The automatic transportation mode detection of the syste...

2015

World of Wireless, Mobile …, 2009

Lecture Notes in Computer Science, 2015

This paper proposes an innovative methodology for extracting and learning personal mobility patterns. The objective is to award daily commuters in a city with personalized and proactive recommendations, related with their mobility habits on a daily basis. In currently approaches, users have to explicitly provide their routes (origin, destination and date/time) to a routing engine in order to be notified about traffic events. The proposed approach goes beyond and learns daily mobility habits from the users, without the need to provide any information. The work presented here, is currently being addressed under the EU OPTIMUM project. Results achieved establish the basis for the formalization of the OPTIMUM domain knowledge on personal mobility patterns.

National Academies of Sciences, Engineering, and Medicine, 2018

Cell Phone Location Data for Travel Behavior Analysis presents guidelines for transportation planners and travel modelers on how to evaluate the extent to which cell phone location data and associated products accurately depict travel. The report identifies whether and how these extensive data resources can be used to improve understanding of travel characteristics and the ability to model travel patterns and behavior more effectively. It also supports the evaluation of the strengths and weaknesses of anonymized call detail record locations from cell phone data. The report includes guidelines for transportation practitioners and agency staff with a vested interest in developing and applying new methods of capturing travel data from cell phones to enhance travel models.

Studies in Computational Intelligence, 2013

Mobility is intrinsic to human behavior and influences the dynamics of all social phenomena. As such, technology has not remained indifferent to the imprint of mobility. Today we are seeing a shift in tides as the focus is turning towards portability, as well as performance; mobile devices and wireless technologies have become ubiquitous in order to fulfil the needs of modern society. Today the need for mobility management is gradually becoming one of the most important and challenging problems in pervasive computing. In this chapter, we present an analysis of research activities targeting mobility. We present the challenges of analyzing and understanding the mobility (is mobility something that is inherently predictable? are humans socially inclined to follow certain paths?), to techniques that use mobility results to facilitate the interaction between peers in mobile networks, or detect the popularity of certain locations. Our studies are based on the analysis of real user traces extracted from volunteers. We emphasize the entire process of studying the dynamics of mobile users, from collecting the user data, to modelling mobility and interactions, and finally to exploring the predictability of human behavior. We point out the challenges and the limitations of such an endeavour. Furthermore, we propose techniques and methodologies to study the mobility and synergy of mobile users and we show their applicability on two case studies.

GeoInformatica

With the continuing advances in wireless communications, geo-positioning, and portable electronics, an infrastructure is emerging that enables the delivery of on-line, location-enabled services to very large numbers of mobile users. A typical usage situation for mobile services is one characterized by a small screen and no keyboard, and by the service being only a secondary focus of the user. Under such circumstances, it is particularly important to deliver the “right” information and service at the right time, with as little user interaction as possible. This may be achieved by making services context aware. Mobile users frequently follow the same route to a destination as they did during previous trips to the destination, and the route and destination constitute important aspects of the context for a range of services. This paper presents key concepts underlying a software component that identifies and accumulates the routes of a user along with their usage patterns and that makes...