Relaxing door-to-door matching reduces passenger waiting times: a workflow for the analysis of driver GPS traces in a stochastic carpooling service

Carpooling has the potential to transform itself into a mass transportation mode by abandoning its adherence to deterministic passenger-driver matching for door-to-door journeys, and by adopting instead stochastic matching on a network of fixed meeting points. Stochastic matching is where a passenger sends out a carpooling request at a meeting point, and then waits for the arrival of a self-selected driver who is already travelling to the requested meeting point. Crucially there is no centrally dispatched driver. Moreover, the carpooling is assured only between the meeting points, so the onus is on the passengers to travel to/from them by their own means. Thus the success of a stochastic carpooling service relies on the convergence, with minimal perturbation to their existing travel patterns, to the meeting points which are highly frequented by both passengers and drivers. Due to the innovative nature of stochastic carpooling, existing off-the-shelf workflows are largely insufficient for this purpose. To fill the gap in the market, we introduce a novel workflow, comprising of a combination of data science and GIS (Geographic Information Systems), to analyse driver GPS traces. We implement it for an operational stochastic carpooling service in south-eastern France, and we demonstrate that relaxing door-to-door matching reduces passenger waiting times. Our workflow provides additional key operational indicators, namely the driver flow maps, the driver flow temporal profiles and the driver participation rates

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

Review and classification of trajectory summarisation algorithms: From compression to segmentation

With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-

Knowledge Discovery through Mobility Data Integration

In the era of Big Data a huge amount of information are available from every sin- gle citizen of our hyper-connected world. A simple smartphone can collect data with different kinds of information: a big part of these are related to mobility. A smartphone is connected to networks, such as GSM, GPS, Internet (and then social networks): each of them can provide us information about where, how and why the user is moving across space and time. Data integration has a key role in this understanding process: the combination of different data sources increases the value of the extracted knowledge, even though such integration task is often not trivial. This thesis aim to represent a step toward a reliable Mobility Analysis framework, capable to exploit the richness of the spatio-temporal data nowadays available. The work done is an exploration of meaningful open challenges, from an efficient Map Matching of low sampling GPS data to Inferring Human Activities from GPS tracks. A further experimentation has been performed over GSM and Twitter data, in order to detect and recognize significant events in terms of people presence and related tweets. Another promising perspective is the use of such extracted knowledge to enrich actual geospatial Datasets with a ’Wisdom of the crowd’ dimension to derive, for instance, routing policies over road networks: most chosen paths among usual drivers are more meaningful than simple shortest paths

Fundamental structures of dynamic social networks

Social systems are in a constant state of flux with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding spreading of influence or diseases, formation of friendships, and the productivity of teams. While there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the micro-dynamics of social networks. Here we explore the dynamic social network of a densely-connected population of approximately 1000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geo-location, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-minute time slices we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores are preceded by coordination behavior in the communication networks, and demonstrating that social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39 pages, 34 figure

Combining landscape genetics and movement ecology to assess functional connectivity for red deer (Cervus elaphus) in Schleswig-Holstein, Germany

Die anthropogen bedingte Zerschneidung der Landschaft stellt eine wichtige Herausforderung für den Natur- und Artenschutz dar. Große Säugetiere, wie zum Beispiel der Rothirsch (Cervus elaphus) sind durch die Fragmentierung einer Verkleinerung und zunehmenden Isolierung der Lebensräume ausgesetzt. Dies kann weitreichende Folgen wie einen verringerten Austausch an Individuen und damit langfristig an Genen mit sich ziehen. Um diesen Folgen entgegenzuwirken und den genetischen Austausch zu verbessern sind objektive Beurteilungsverfahren über die Konnektivität der Landschaft notwendig. Die Erfassung und Modellierung der funktionellen Landschaftskonnektivität für eine Zielart basiert häufig auf Grundlagen wie Expertenwissen, Habitatmodellen oder Bewegungsdaten. Allerdings werden diese Methoden hinsichtlich ihrer Repräsentativität für tatsächliche Abwanderungen oder effektivem Genfluss diskutiert. Im Rahmen von landschaftsgenetischen Analysen werden Informationen über den genetischen Austausch zwischen Populationen oder einzelnen Individuen mit entsprechenden Ausprägungen der Landschaft korreliert. Genetische Daten haben dabei den Vorteil, dass sie sowohl eine erfolgreiche Wanderung zwischen Verbreitungsgebieten als auch die anschließende Reproduktion mit anderen Individuen, widerspiegeln können. Daher stellt die Landschaftsgenetik eine innovative Ansatzmöglichkeit zur Beurteilung der funktionellen Landschaftskonnektivität dar. Ziel der Dissertation ist die Konzipierung und Evaluierung von artspezifischen Modellen der Landschaftskonnektivität mit Hilfe von Gendaten und Telemetrie-Ergebnissen. Der Rothirsch in Schleswig-Holstein dient dabei als Beispielart, mit der die Unterschiede bezüglich der methodischen und konzeptionellen Herangehensweisen demonstriert werden sollen. Insbesondere für die naturschutzfachliche Praxis und Korridorplanung ist dies von grundlegender Bedeutung. 8 Im ersten Kapitel wird zunächst eine generelle Einleitung in die Problematik der Landschaftszerschneidung gegeben und anhand des Rothirschs in Schleswig-Holstein verdeutlicht. Anschließend werden die verschiedenen Ansatzmöglichkeiten der Landschaftsgenetik als auch der Bewegungsökologie zur Beurteilung der Landschaftskonnektivität dargestellt. Die Bewegungsökologie setzt sich unter anderem damit auseinander, welche Faktoren die Bewegungen von Organismen in ihrem Lebensraum beeinflussen. Durch die Verknüpfung von Bewegungsdaten mit Landschaftsvariablen lassen sich so wichtige Erkenntnisse über die Lebensraumansprüche einer Zielart gewinnen. Dabei können unter anderem die Habitatpräferenzen während unterschiedlicher Bewegungsmuster, wie zum Beispiel der Abwanderung in neue Gebiete, differenziert betrachtet werden. Das zweite Kapitel befasst sich mit der genetischen Diversität und Differenzierung der lokalen Rothirschvorkommen in Schleswig-Holstein. Anhand der genetischen Daten wird dabei verdeutlicht, dass die regionalen Managementeinheiten (Hegeringe) nicht immer in sich geschlossene Populationen darstellen. Die Rothirschpopulationen weisen vielmehr eine hierarchische Struktur auf. Zum Beispiel ist der Genfluss, je nach Dichte der benachbarten Populationen, unterschiedlich stark ausgeprägt. Insgesamt konnte für mehrere Populationen eine im europäischen Vergleich geringe genetische Diversität festgestellt werden. Dies unterstreicht, dass ein besseres Verständnis über die Auswirkungen der Landschaftszerschneidung sowie eine Bewertung der Landschaftskonnektivität aus Sicht des Rothirschs notwendig ist, um dem Verlust an genetischer Vielfalt entgegenzuwirken. Eine Möglichkeit die Landschaftskonnektivität zu bewerten stellt die Analyse von Telemetrie-Daten dar. Für die Auswertung von solchen Bewegungsdaten stehen eine Vielzahl an Methoden zur Verfügung. Im dritten Kapitel werden die verschiedenen Ansätze zur Differenzierung unterschiedlicher Bewegungsmuster aus Telemetrie-Daten zusammengestellt. Durch eine umfangreiche Methodenübersicht werden Entscheidungshilfen für die Anwendung solcher Pfad-Segmentierungen zur Beantwortung bestimmter Fragestellungen in der Bewegungsökologie gegeben. Das vierte Kapitel greift unter anderem auf eine solche Methode der Pfad-Segmentierung zurück, um potentielle Ausbreitungsbewegungen innerhalb der Telemetrie-Daten von besenderten Rothirschen zu ermitteln. Diese Bewegungsdaten 9 werden anschließend mit Landschaftsvariablen verknüpft und ein Modell abgeleitet, welches den Widerstand für Wanderbewegungen darstellt (Widerstandsmodell). Darüber hinaus werden in dieser Studie weitere methodische Ansätze zur Modellierung der funktionellen Landschaftskonnektivität verglichen. Diese basieren unter anderem auf Expertenwissen und Habitatmodellen sowie weiteren Auswertungsansätzen der Bewegungsdaten. Für den Vergleich der resultierenden Widerstandsmodelle wird die Landschaftsgenetik hinzugezogen. Dabei werden effektive Distanzen basierend auf den jeweiligen Modellen den genetischen Distanzmaßen gegenübergestellt. Die Modelle mit der höchsten Übereinstimmung werden ferner genutzt, um methodische Unterschiede in der Ausweisung von Korridoren darzustellen. Es zeigte sich, dass für weitreichende Abwanderungen die Rothirsche auf geeignete Habitatverhältnisse innerhalb der Landschaftsmatrix angewiesen sind. Die Auswertung der Bewegungsdaten ergab hingegen, dass für kürzere Distanzen auch suboptimale Gebiete durchquert werden können. Abschließend werden im fünften Kapitel die Ergebnisse zusammengefasst und diskutiert. Besonderer Schwerpunkt liegt dabei auf dem Beitrag der Anwendung von Landschaftsgenetik und Bewegungsökologie im angewandten Naturschutz und welche Erkenntnisse für die Ausweisung und Effektivität von Korridoren gewonnen werden können.Human-caused restrictions like the fragmentation of the landscape poses a major challenge to wildlife conservation. Large and mobile species such as red deer (Cervus elaphus) are subject to increasing effects of isolation and a decrease of primary habitats. This can result in a reduction of the exchange of individuals or even a long-term loss of gene flow. In order to counteract these negative effects and to promote genetic exchange, suitable approaches for estimating functional connectivity of the landscape are necessary. In most cases, landscape models of functional connectivity for a given study species are based on expert knowledge, habitat suitability, or movement data. However, there is an ongoing debate whether these methods are representative of actual dispersal or effective gene flow. Landscape genetic analyses correlate estimates of genetic differentiation between populations or individuals with landscape composition. The advantage of genetic data is that it reflects both successful dispersal between populations, as well as subsequent reproduction with other individuals. Therefore, landscape genetics represent an innovative approach for assessing functional connectivity of the landscape matrix. The aim of this dissertation is to compare different species-specific models of functional connectivity utilizing genetic and movement data. Using red deer in Northern Germany as an example, the methodological and conceptual differences of multiple approaches are demonstrated. Overall, the presented thesis provides important insights for applied conservation of wildlife and planning of corridors. The first chapter provides a general introduction to the issue of landscape fragmentation and illustrates the effects on red deer in the study area of Schleswig-Holstein. Furthermore, the potential applications of landscape genetics and movement ecology to assess landscape connectivity are presented. For example, movement ecology provides an integral framework to explore the potential factors shaping the movements of organisms and the ecological consequences of these movements such as gene flow. The second chapter comprises a study on the genetic diversity and structure of red deer populations in Northern Germany. The results indicate that local populations are best described as an hierarchical network of subpopulations with different levels of gene flow. Overall, genetic diversity of red deer from the study area is quite low compared to other populations from Central Europe. This underlines that a better understanding of the isolation effects caused by landscape fragmentation and species-specific assessment of landscape connectivity for red deer are needed to address the observed loss of genetic diversity. One possible approach for estimating functional connectivity is by linking telemetry data with landscape variables in order to gain insights into the habitat requirements of a target species. However, habitat preferences are very likely to change with different movement behaviors. This represents an important point to consider when studying the effects of landscape composition on actual dispersal movements. The third chapter of this thesis presents an extensive overview on different methods for identifying behavioral patterns from movement data. Furthermore, it provides guidelines for deciding among the available methods of path-segmentation and shows how they can be applied to answer research questions within the movement ecology paradigm. The study described in the fourth chapter utilizes such a path-segmentation method to detect potential dispersal movements from telemetry data of multiple red deer individuals. The observed movements are then linked to landscape variables in order to model functional connectivity based on landscape resistance towards dispersal of red deer throughout the study area. In addition, the study applies and compares different methodological approaches for modeling functional connectivity based on expert knowledge, habitat models and other analyses of movement data. A landscape genetic approach is used as a means to compare the resulting resistance models. Effective distances derived from the models are compared with estimates on genetic distance. The highest ranked models are further used to illustrate methodological differences in the designation of conservation corridors. The results show that for large scale dispersal red deer rely on primary habitat conditions within the landscape matrix. However, connectivity based on the identified dispersal movements showed that areas of poor habitat quality can be traversed by red deer at shorter distances. Finally, in the fifth chapter, the results of the presented studies are summarized and discussed. In particular, the contribution of landscape genetics and movement ecology to applied conservation and landscape planning are elaborated. The results of this thesis could ultimately increase the effectiveness of conservation measures such as the placement of corridors.2021-06-2