Clustering Trajectories for Map Construction

We propose a new approach for constructing the underlying map from trajectory data. Our algorithm is based on the idea that road segments can be identified as stable subtrajectory clusters in the data. For this, we consider how subtrajectory clusters evolve for varying distance values, and choose stable values for these. In doing so we avoid a global proximity parameter. Within trajectory clusters, we choose representatives, which are combined to form the map. We experimentally evaluate our algorithm on vehicle and hiking tracking data. These experiments demonstrate that our approach can naturally separate roads that run close to each other and can deal with outliers in the data, two issues that are notoriously difficult in road network reconstruction

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Mobility Data Science (Dagstuhl Seminar 22021)

This report documents the program and the outcomes of Dagstuhl Seminar 22021 "Mobility Data Science". This seminar was held January 9-14, 2022, including 47 participants from industry and academia. The goal of this Dagstuhl Seminar was to create a new research community of mobility data science in which the whole is greater than the sum of its parts by bringing together established leaders as well as promising young researchers from all fields related to mobility data science. Specifically, this report summarizes the main results of the seminar by (1) defining Mobility Data Science as a research domain, (2) by sketching its agenda in the coming years, and by (3) building a mobility data science community. (1) Mobility data science is defined as spatiotemporal data that additionally captures the behavior of moving entities (human, vehicle, animal, etc.). To understand, explain, and predict behavior, we note that a strong collaboration with research in behavioral and social sciences is needed. (2) Future research directions for mobility data science described in this report include a) mobility data acquisition and privacy, b) mobility data management and analysis, and c) applications of mobility data science. (3) We identify opportunities towards building a mobility data science community, towards collaborations between academic and industry, and towards a mobility data science curriculum

Personalization and Location-based Technologies for E-Commerce Applications

Tailoring web-pages to different user characteristics such as location, preferences and previous history (page-hits, products bought) have been shown to be effective tools for personalizing web-content. In this paper, we briefly summarize the techniques in these state-of-the-art personalization technologies. We first describe personalization using user preferences or history and then describe personalization based on user's current location. Whereas the former is applicable for deployment in web-sites, the latter is useful in providing location-based content to mobile users and wireless applications

QACHE: Query Caching in Location-Based Services

Abstract. Many emerging applications of location-based services continuously monitor a set of moving objects and answer queries pertaining to their locations. Query processing in such services is critical to ensure high performance of the system. Observing that one predominant cost in query processing is the frequent accesses to the database, in this paper we describe how to reduce the number of moving object to database server round-trips by caching query information on the application server tier. We propose a novel caching framework, named QACHE, which stores and organizes spatially-relevant queries for selected moving objects. QACHE leverages the spatial indices and other algorithms in the database server for organizing and refreshing relevant cache entries within a configurable area of interest, referred to as the cache-footprint, around a moving object. QACHE contains appropriate refresh policies and prefetching algorithms for efficient cache-based evaluation of queries on moving objects. In experiments comparing QACHE to other proposed mechanisms, QACHE achieves a significant reduction (from 63 % to 99%) in database accesses thereby improving the throughput of an LBS system. Key words: location-based services, query processing, caching