DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision

Nowadays, mobile devices enable constant tracking of the user's position and location trajectories can be used to infer personal points of interest (POIs) like homes, workplaces, or stores. A common way to extract POIs is to first identify spatio-temporal regions where a user spends a significant amount of time, known as stay regions (SRs). Common approaches to SR extraction are evaluated either solely unsupervised or on a small-scale private dataset, as popular public datasets are unlabeled. Most of these methods rely on hand-crafted features or thresholds and do not learn beyond hyperparameter optimization. Therefore, we propose a weakly and self-supervised transformer-based model called DeepStay, which is trained on location trajectories to predict stay regions. To the best of our knowledge, this is the first approach based on deep learning and the first approach that is evaluated on a public, labeled dataset. Our SR extraction method outperforms state-of-the-art methods. In addition, we conducted a limited experiment on the task of transportation mode detection from GPS trajectories using the same architecture and achieved significantly higher scores than the state-of-the-art. Our code is available at https://github.com/christianll9/deepstay.Comment: Paper under peer revie

Robust and Hierarchical Stop Discovery in Sparse and Diverse Trajectories

The advance of GPS tracking technique brings a large amount of trajectory data. To better understand such mobility data, semantic models like “stop/move” (or inferring “activity”, “transportation mode”) recently become a hot topic for trajectory data analysis. Stops are important parts of tra- jectories, such as “working at office”, “shopping in a mall”, “waiting for the bus”. There are several methods such as velocity, clustering, density algorithms being designed to discover stops. However, existing works focus on well-defined trajectories like movement of vehicle and taxi, not working well for heterogeneous cases like diverse and sparse trajectories. On the contrary, our paper addresses three main challenges: (1) provide a robust clustering-based method to discover stops; (2) discover both shared stops and personalized stops, where shared stops are the common places where many trajectories pass and stay for a while (e.g. shopping mall), whilst personalized stops are individual places where user stays for his/her own purpose (e.g. home, office); (3) further build stop hierarchy (e.g. a big stop like EPFL campus and a small stop like an office building). We evaluate our approach with several diverse and spare real-life GPS data, compare it with other methods, and show its better data abstraction on trajectory

Recommending places blased on the wisdom-of-the-crowd

The collective opinion of a great number of users, popularly known as wisdom of the crowd, has been seen as powerful tool for solving problems. As suggested by Surowiecki in his books [134], large groups of people are now considered smarter than an elite few, regardless of how brilliant at solving problems or coming to wise decisions they are. This phenomenon together with the availability of a huge amount of data on the Web has propitiated the development of solutions which employ the wisdom-of-the-crowd to solve a variety of problems in different domains, such as recommender systems [128], social networks [100] and combinatorial problems [152, 151]. The vast majority of data on the Web has been generated in the last few years by billions of users around the globe using their mobile devices and web applications, mainly on social networks. This information carries astonishing details of daily activities ranging from urban mobility and tourism behavior, to emotions and interests. The largest social network nowadays is Facebook, which in December 2015 had incredible 1.31 billion mobile active users, 4.5 billion “likes” generated daily. In addition, every 60 seconds 510 comments are posted, 293, 000 statuses are updated, and 136,000 photos are uploaded1. This flood of data has brought great opportunities to discover individual and collective preferences, and use this information to offer services to meet people’s needs, such as recommending relevant and interesting items (e.g. news, places, movies). Furthermore, it is now possible to exploit the experiences of groups of people as a collective behavior so as to augment the experience of other. This latter illustrates the important scenario where the discovery of collective behavioral patterns, the wisdom-of-the-crowd, may enrich the experience of individual users. In this light, this thesis has the objective of taking advantage of the wisdom of the crowd in order to better understand human mobility behavior so as to achieve the final purpose of supporting users (e.g. people) by providing intelligent and effective recommendations. We accomplish this objective by following three main lines of investigation as discussed below. In the first line of investigation we conduct a study of human mobility using the wisdom-of- the-crowd, culminating in the development of an analytical framework that offers a methodology to understand how the points of interest (PoIs) in a city are related to each other on the basis of the displacement of people. We experimented our methodology by using the PoI network topology to identify new classes of points of interest based on visiting patterns, spatial displacement from one PoI to another as well as popularity of the PoIs. Important relationships between PoIs are mined by discovering communities (groups) of PoIs that are closely related to each other based on user movements, where different analytical metrics are proposed to better understand such a perspective. The second line of investigation exploits the wisdom-of-the-crowd collected through user-generated content to recommend itineraries in tourist cities. To this end, we propose an unsupervised framework, called TripBuilder, that leverages large collections of Flickr photos, as the wisdom-of- the-crowd, and points of interest from Wikipedia in order to support tourists in planning their visits to the cities. We extensively experimented our framework using real data, thus demonstrating the effectiveness and efficiency of the proposal. Based on the theoretical framework, we designed and developed a platform encompassing the main features required to create personalized sightseeing tours. This platform has received significant interest within the research community, since it is recognized as crucial to understand the needs of tourists when they are planning a visit to a new city. Consequently this led to outstanding scientific results. In the third line of investigation, we exploit the wisdom-of-the-crowd to leverage recommendations of groups of people (e.g. friends) who can enjoy an item (e.g. restaurant) together. We propose GroupFinder to address the novel user-item group formation problem aimed at recommending the best group of friends for a pair. The proposal combines user-item relevance information with the user’s social network (ego network), while trying to balance the satisfaction of all the members of the group for the item with the intra-group relationships. Algorithmic solutions are proposed and experimented in the location-based recommendation domain by using four publicly available Location-Based Social Network (LBSN) datasets, showing that our solution is effective and outperforms strong baselines

Using Volunteer Tracking Information for Activity-Based Travel Demand Modeling and Finding Dynamic Interaction-Based Joint-Activity Opportunities

Technology used for real-time locating is being used to identify and track the movements of individuals in real time. With the increased use of mobile technology by individuals, we are now able to explore more potential interactions between people and their living environment using real-time tracking and communication technologies. One of the potentials that has hardly been taken advantage of is to use cell phone tracking information for activity-based transportation study. Using GPS-embedded smart phones, it is convenient to continuously record our trajectories in a day with little information loss. As smart phones get cheaper and hence attract more users, the potential information source for self-tracking data is pervasive. This study provides a cell phone plus web method that collects volunteer cell phone tracking data and uses an algorithm to identify the allocation of activities and traveling in space and time. It also provides a step that incorporates user-participated prompted recall attribute identification (travel modes and activity types) which supplements the data preparation for activity-based travel demand modeling. Besides volunteered geospatial information collection, cell phone users’ real-time locations are often collected by service providers such as Apple, AT&T and many other third-party companies. This location data has been used in turn to boost new location-based services. However, few applications have been seen to address dynamic human interactions and spatio-temporal constraints of activities. This study sets up a framework for a new kind of location-based service that finds joint-activity opportunities for multiple individuals, and demonstrates its feasibility using a spatio-temporal GIS approach

A Survey and Experimental Study on Privacy-Preserving Trajectory Data Publishing

Trajectory data has become ubiquitous nowadays, which can benefit various real-world applications such as traffic management and location-based services. However, trajectories may disclose highly sensitive information of an individual including mobility patterns, personal profiles and gazetteers, social relationships, etc, making it indispensable to consider privacy protection when releasing trajectory data. Ensuring privacy on trajectories demands more than hiding single locations, since trajectories are intrinsically sparse and high-dimensional, and require to protect multi-scale correlations. To this end, extensive research has been conducted to design effective techniques for privacy-preserving trajectory data publishing. Furthermore, protecting privacy requires carefully balance two metrics: privacy and utility. In other words, it needs to protect as much privacy as possible and meanwhile guarantee the usefulness of the released trajectories for data analysis. In this survey, we provide a comprehensive study and a systematic summarization of existing protection models, privacy and utility metrics for trajectories developed in the literature. We also conduct extensive experiments on two real-life public trajectory datasets to evaluate the performance of several representative privacy protection models, demonstrate the trade-off between privacy and utility, and guide the choice of the right privacy model for trajectory publishing given certain privacy and utility desiderata

Understanding Chinese tourist mobility and consumption-related behaviours in London using Sina Weibo check-ins

In this paper, we detail an individual-level analysis of under-exploited location-based social network (LBSN) data extracted from Sina Weibo, a comprehensive source for data-driven research focused on Chinese populations. The richness of the Sina Weibo data, coupled with high-quality venue and attraction information from Foursquare, enables us to track Chinese tourists visiting London and understand behaviours and mobility patterns revealed by their activities and venue-based ‘check-ins’. We use these check-ins to derive a series of indicators of mobility which reveal aggregate and individual-level behaviours associated with Chinese tourists in London, and which act as a tool to segment tourists based on those behaviours. Our data-driven tourist segmentation indicates that different groups of Chinese tourists have distinctive activity preferences and travel patterns. Our primary interest is in tourists’ consumption behaviours, and we reveal that tourists with similar activity preferences still exhibit individualised behaviours with regards to the nature and location of key consumption activities such as shopping and dining out. We aim to understand more about Chinese tourist shopping behaviours as a secondary activity associated with multi-purpose trips, demonstrating that these data could permit insights into tourist behaviours and mobility patterns which are not well captured by official tourism statistics, especially at a localised level. This analysis could be up-scaled to incorporate additional LBSN data sources and broader population subgroups in order to support data-driven urban analytics related to tourist mobilities and consumption behaviours