An improved method for mobility prediction using a Markov model and density estimation

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe prediction of an individual's future locations is a significant part of scientific researches. While a variety of solutions have been investigated for the prediction of future locations, predicting departure and arrival times at predicted locations is a task with higher complexity and less attention. While the challenges of combining spatial and temporal information have been stated in various works, the proposed solutions lack accuracy and robustness. This paper proposes a simple yet effective way to predict not only an individual's future location, but also most probable departure and arrival times as well as the most probable route from origin to destination

A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data

Tourism is an important application domain for recommender systems. In this domain, recommender systems are for example tasked with providing personalized recommendations for transportation, accommodation, points-of-interest (POIs), or tourism services. Among these tasks, in particular the problem of recommending POIs that are of likely interest to individual tourists has gained growing attention in recent years. Providing POI recommendations to tourists \emph{during their trip} can however be especially challenging due to the variability of the users' context. With the rapid development of the Web and today's multitude of online services, vast amounts of data from various sources have become available, and these heterogeneous data sources represent a huge potential to better address the challenges of in-trip POI recommendation problems. In this work, we provide a comprehensive survey of published research on POI recommendation between 2017 and 2022 from the perspective of heterogeneous data sources. Specifically, we investigate which types of data are used in the literature and which technical approaches and evaluation methods are predominant. Among other aspects, we find that today's research works often focus on a narrow range of data sources, leaving great potential for future works that better utilize heterogeneous data sources and diverse data types for improved in-trip recommendations.Comment: 35 pages, 19 figure

Location Embedding and Deep Convolutional Neural Networks for Next Location Prediction

International audienc

Geolocation Android Mobile Phones Using GSM/UMTS

The proliferation of cellular network enabled users through various positioning tools to track locations, location information is being continuously captured from mobile phones, created a prototype that enables detected location based on using the two invariant models for Global Systems for Mobile (GSM) and Universal Mobile Telecommunications System (UMTS). The smartphone application on an Android platform applies the location sensing run as a background process and the localization method is based on cell phones. The proposed application is associated with remote server and used to track a smartphone without permissions and internet. Mobile stored data location information in the database (SQLite), then transfer it into location API to obtain locations result implemented in Google Maps. Track a smartphone with fixed identifiers mostly SSN (SIM (Subscriber Identity Module) Serial Number) and IMEI (International Mobile Equipment Identity) derived from an identifying string unique to the user's device. The result located place is Moderate correct according to the (GSM) and (UMTS) cellular networks which is used for obtaining location information

Modeling Human Mobility Entropy as a Function of Spatial and Temporal Quantizations

The knowledge of human mobility is an integral component of several different branches of research and planning, including delay tolerant network routing, cellular network planning, disease prevention, and urban planning. The uncertainty associated with a person's movement plays a central role in movement predictability studies. The uncertainty can be quantified in a succinct manner using entropy rate, which is based on the information theoretic entropy. The entropy rate is usually calculated from past mobility traces. While the uncertainty, and therefore, the entropy rate depend on the human behavior, the entropy rate is not invariant to spatial resolution and sampling interval employed to collect mobility traces. The entropy rate of a person is a manifestation of the observable features in the person's mobility traces. Like entropy rate, these features are also dependent on spatio-temporal quantization. Different mobility studies are carried out using different spatio-temporal quantization, which can obscure the behavioral differences of the study populations. But these behavioral differences are important for population-specific planning. The goal of dissertation is to develop a theoretical model that will address this shortcoming of mobility studies by separating parameters pertaining to human behavior from the spatial and temporal parameters

Location Embedding and Deep Convolutional Neural Networks for Next Location Prediction

International audienc

Vers des services Internet basés sur les profils de mobilité des utilisateurs

Nowadays, mobility prediction models play an important role in many locationbased services, such as food delivery, transportation planning, and advertisement posting. Most previous studies on predicting mobility have worked on computer generated data and focused on mathematical modeling principally due to the lack of a real mobility data. Such studies have limited ability to capture human mobility accurately. However, with the democratization of mobility data and the availability of large data sets, numerous research activities turned toward predicting mobility based on examining real mobility data traces with the aim of building realistic models that can capture and understand human’s mobility behaviors as well as making accurate mobility prediction. In this thesis, we present the methods we proposed to predict spatial and temporal behaviors of mobile users. Our first work focuses on predicting the next location of mobile users by analyzing large data sets of the history of their movements. We make use of past location sequences, also called location history, to train a classification model that will be used to predict future locations. Contrary to traditional mobility prediction techniques based on Markovian models, we investigate the use of modern deep learning techniques such as the use of Convolutional Neural Networks (CNNs). Inspired by the word2vec embedding technique used for the next word prediction, we present a new method called loc2vec in which each location is encoded as a vector whereby the more often two locations cooccur in the location sequences, the closer their vectors will be. Using the vector representation, we divide long mobility sequences into several sub-sequences and use them to form Mobility Subsequence Matrices on which we run CNN classification which will be used later for the prediction. We run extensive testing and experimentation on a subset of a large real mobility trace database made publicly available through the CRAWDAD project. Our results show that loc2vec embedding and CNN-based prediction provide significant improvement in the next location prediction accuracy compared to state-of-the-art methods. We also show that transfer learning on existing pre-trained CNN models provides further improvement over CNN models build from scratch on mobility data. We also show that our loc2vec-CNN model enhanced with transfer learning achieves better results than other variants including our iother proposal onehot-CNN and existing Markovian models. In the second work, we focus on predicting the temporal behavior, particularly the residence time, of mobile users at their relevant locations. In this work, we explored the joint use of location history, arrival time, and the previous residence time to accurately predict the residence time at the current location. We developed a model that integrates all these parameters and uses our modified Moving-Average and CDF time-aided algorithms that include the arrival time in the model. We run performance evaluation experiments on a subset of the same mobility trace collected by Dartmouth College. Our results show that adding high-granularity temporal information to the mobility model allows to significantly improve the residence time prediction compared to state-of-theart methods. The prediction accuracy improvement for the dataset we work on has been consistent and of about 20% on the average. We also presented two linear mobility models for residence time prediction, namely Linear Regression (LR), and Auto-Regression (AR). We run performance evaluation experiments on two different WiFi mobility traces datasets made available through the CRAWDAD project. Our results show that using linear regression-based learning algorithms significantly improve the residence time prediction accuracy compared to stateof-the-art methods, and achieve prediction errors in the order of seconds and minutes for a large number of users

Understanding and supporting mobile application usage

In recent years mobile phones have evolved significantly. While the very first cellular phones only provided functionality for conducting phone calls, smartphones nowadays provide a rich variety of functionalities. Additional hardware capabilities like new sensors (e.g.~for location) and touch screens as new input devices gave rise to new use cases for mobile phones, such as navigation support, taking pictures or making payments. Mobile phones not only evolved with regard to technology, they also became ubiquitous and pervasive in people\u27s daily lives by becoming capable of supporting them in various tasks. Eventually, the advent of mobile application stores for the distribution of mobile software enabled the end-users themselves to functionally customize their mobile phones for their personal purposes and needs. So far, little is known about how people make use of the large variety of applications that are available. Thus, little support exists for end-users to make effective and efficient use of their smartphones given the huge numbers of applications that are available. This dissertation is motivated by the evolution of mobile phones from mere communication devices to multi-functional tool sets, and the challenges that have arisen as a result. The goal of this thesis is to contribute systems that support the use of mobile applications and to ground these systems\u27 designs in an understanding of user behavior gained through empirical observations. The contribution of this dissertation is twofold: First, this work aims to understand how people make use of, organize, discover and multitask between the various functionalities that are available for their smartphones. Findings are based on observations of user behavior by conducting studies in the wild. Second, this work aims to assist people in leveraging their smartphones and the functionality that is available in a more effective and efficient way. This results in tools and improved user interfaces for end-users. Given that the number of available applications for smartphones is rapidly increasing, it is crucial to understand how people make use of such applications to support smartphone use in everyday life with better designs for smartphone user interfaces.Mobiltelefone haben sich innerhalb der letzten Jahre signifikant weiterentwickelt. Während erste Modelle lediglich Sprachtelefonie zur Verfügung stellten, ermöglichen heutige Smartphones vielseitige Dienste. Technologische Fortschritte, wie beispielsweise GPS-Lokalisierung und berührungsempfindliche Displays, haben neue Einsatzbereiche für Mobiltelefone eröffnet, wie solche als Navigationsgerät oder als Fotoapparat. Doch nicht nur in Bezug auf die Technologie haben sich Mobiltelefone weiterentwickelt, sondern auch in der Verbreitung ist die Anzahl der Geräte enorm gestiegen. Sie werden allgegenwärtig im täglichen Leben genutzt, da sie ihre Anwender bei verschiedensten Aufgaben unterstützen können. Das Aufkommen von Vetriebsplattformen für die Verbreitung mobiler Software erlaubt es dem Anwender selbstständig Modifikationen an der Funktionalität seines Geräts vorzunehmen und dieses an persönliche Zwecke und Ansprüche anzupassen. Bisher ist wenig darüber bekannt, wie sich Anwender die Vielfalt zu Verfügung stehender Applikationen zu Nutze machen. Als Folge daraus gibt es bisher nur rudimentäre Unterstützung für Anwender, die Vielfalt von Applikationen effektiv und effizient einzusetzen. Diese Dissertation ist durch den Wandel des Mobiltelefons vom reinen Kommunikationsgerät hin zum multifunktionalen Werkzeug motiviert. Das Ziel dieser Arbeit ist es, Systeme für die Unterstützung einer besseren mobilen Applikationsnutzung zu entwickeln, deren Design auf dem neuen Verständnis von Benutzerverhalten beruht, das durch empirische Studien gewonnen wird. Diese Dissertation hat einen zweiteiligen Beitrag: Zum einen werden theoretische Erkenntnisse dazu erarbeitet, wie Anwender die Applikationsvielfalt nutzen, installierte Applikationen auf ihren Geräten organisieren, neue Applikationen entdecken und zwischen diesen in der Ausführung wechseln. Die Erkenntnisse hierzu beruhen auf der empirischen Beobachtung von Nutzungsverhalten. Zum anderen hat diese Arbeit ingenieurwissenschaftliche Ziele dahingehend, die Anwender von Applikationen dabei zu unterstützen, ihre Smartphones sowie deren Funktionsvielfalt effektiver und effizienter einzusetzen. Dieser Beitrag resultiert in der Beschreibung implementierter Systeme und verbesserter Benutzerschnittstellen für Anwender. Angesichts der rapide wachsenden Zahl zur Verfügung stehender mobiler Applikationen ist es wichtig, zu verstehen wie Endanwender diese nutzen, denn nur so kann die Nutzung von Smartphones gebrauchstauglicher und einfacher gestaltet werden