Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair

Understanding Human Mobility with Emerging Data Sources: Validation, spatiotemporal patterns, and transport modal disparity

Human mobility refers to the geographic displacement of human beings, seen as individuals or groups, in space and time. The understanding of mobility has broad relevance, e.g., how fast epidemics spread globally. After 2030, transport is likely to become the sector with the highest emissions in the 2\ub0C\ua0scenario. Better informed policy-making requires up-to-date empirical mobility data with good quality. However, the conventional methods are limited when dealing with new challenges. The prevalence of digital technologies enables a large-scale collection of human mobility traces, through social media data and GPS-enabled devices etc, which contribute significantly to the understanding of human mobility. However, their potentials for the further application are not fully exploited.This thesis uses emerging data sources, particularly Twitter data, to enhance the understanding of mobility and apply the obtained knowledge in the field of transport. The thesis answers three questions: Is Twitter a feasible data source to represent individual and population mobility? How are Twitter data used to reveal the spatiotemporal dynamics of mobility? How do Twitter data contribute to depicting the modal disparity of travel time by car vs public transit? In answering these questions, the methodological contribution of this thesis lies in the applied side of data science.Using geotagged Twitter data, mobility is firstly described by abstract metrics and physical models; in Paper A to reveal the population heterogeneity of mobility patterns using data mining techniques; and in Paper B to estimate travel demand with a novel approach to address the sparsity issue of Twitter data. In Paper C, GIS techniques are applied to combine the travel demand as revealed by Twitter data and the transportation network to give a more realistic picture of the modal disparity in travel time between car and public transit in four cities in different countries at a high spatial and temporal granularity. The validation of using Twitter data in mobility study contributes to better utilisation of this low-cost mobility data source. Compared with a static picture obtained by conventional data sources, the dynamics introduced by social media data among others contribute to better-informed policymaking and transport planning

Crowdsourcing Real-Time Traveler Information Systems

In the last decade, the concept of collecting traffic data using location aware and data enabled smartphones in place of traditional sensor networks has received much attention. With a steady market growth for smartphones enabled with GPS chipsets, the potential of this technology is enormous. This combined with the pervasion of participatory paradigms such as crowdsourcing wherein individuals with portable sensors instead of physical networks serve as sensors providing information. Crowd sensed data overcome a number of issues with traditional physical sensor networks by providing wider coverage, real-time data, data redundancy and cost effectiveness to name a few. While there has been a lot of work on actual implementations of crowd sensed traffic monitoring programs, there is limited work on assessing the quality, and validity of crowd sensed data. A systematic analysis of quality and validity is needed before this paradigm can be more commonly adopted for traffic monitoring applications. To this end, research is underway to deploy a crowdsourced platform for monitoring and providing real-time transit information for shuttles that serve the University of Connecticut. The thesis develops a framework and an open-source prototype system that is able to produce real-time traveler information based on crowdsourced data. In order to build the prototype, first it implements a robust Hidden Markov Model based map-matching algorithm to position the crowdsourced data on the underlying road network and retrieve the likely path. The accuracy of the map-matching algorithm has been found satisfactory for the current usage even when the GPS points are sampled at low frequency. Next, to predict the travel condition across the network from the crowdsourced data, a travel time prediction algorithm, based on Regularized Least Square Regression, has been implemented as well. This travel time prediction algorithm, together with the map-matching algorithm, has been applied in a simulated crowdsourcing environment. The travel time prediction results of the simulation show that the prototype system is quite capable of predicting travel time even when the crowdsourced real-time data is sparse. The simulation tests the performance of the travel time prediction algorithm in different scenarios. From the demonstrated predictive performance of the implemented prototype system, this approach to providing real-time traveler information is found promising. It is also possible to apply the prototype to all regions and all modes of transportation, exploiting its generalized approach of providing real-time traveler information from crowdsourced data

The impact of urban road network morphology on pedestrian wayfinding behavior

During wayfinding pedestrians do not always choose the shortest available route. Instead, route choices are guided by several well-known wayfinding strategies or heuristics. These heuristics minimize cognitive effort and usually lead to satisfactory route choices. Our previous study evaluated the costs of four well-known pedestrian wayfinding heuristics and their variation across nine network morphologies. It was observed that the variation in the cost of these wayfinding heuristics increased with an increase in the irregularity of the network, indicating that people may opt for more diverse heuristics while walking through relatively regular networks, and may prefer specific heuristics in the relatively irregular ones. The study presented here aims to investigate this claim by comparing simulated routes with observed pedestrian trajectories in Beijing and Melbourne, two cities at opposite ends of the regularity spectrum. We found that the values of mean route length and mean Network Hausdorff Distance for walking trips made in Melbourne were consistently lesser than the corresponding values obtained in Beijing. Also, across both the cities, we found that while there was minimal variation in the popularity of heuristics overall, in cases where different heuristics produced dissimilar routes, the shortest leg first strategy and the least angle strategy were more popular

A survey on Human Mobility and its applications

Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

The impact of urban road network morphology on pedestrian wayfinding behaviour

During wayfinding pedestrians do not always choose the shortest available route. Instead, route choices are guided by several well-known wayfinding strategies or heuristics. These heuristics minimize cognitive effort and usually lead to satisfactory route choices. Our previous study evaluated the costs of four well-known pedestrian wayfinding heuristics and their variation across nine network morphologies. It was observed that the variation in the cost of these wayfinding heuristics increased with an increase in the irregularity of the network, indicating that people may opt for more diverse heuristics while walking through relatively regular networks, and may prefer specific heuristics in the relatively irregular ones. The study presented here aims to investigate this claim by comparing simulated routes with observed pedestrian trajectories in Beijing and Melbourne, two cities at opposite ends of the regularity spectrum. We found that the values of mean route length and mean Network Hausdorff Distance for walking trips made in Melbourne were consistently lesser than the corresponding values obtained in Beijing. Also, across both the cities, we found that while there was minimal variation in the popularity of heuristics overall, in cases where different heuristics produced dissimilar routes, the shortest leg first strategy and the least angle strategy were more popular

User behaviour identification based on location data

Over the years there has been an almost exponential increase in the use of new technologies in various sectors. These technologies have as their main objective, to improve or facilitate our daily life. This study will focus on one of these technologies used within a theme that has been widely talked about over the last few years, the use of personal data of various people to identify certain types of behavior. More specifically, this study aims primarily to use the GPS data stored in the respective Google accounts of nine volunteers in order to identify the places they frequent most, also known as Points of Interest. This same data will also be used to identify the trajectories covered more often by each of the same volunteers. A study was carried out with a sample of 9 participants, sending them their maps with POI and trajectories, thus obtaining their validation. It was thus possible to conclude that the best way to identify POI is to use daily clusters using DBSCAN. In the case of trajectories, the Snap-to-Road method was the one that gave the best results. It was found that it was possible to respond to the initial problem, and thus a method was found that identifies most of the POI successfully and also some trajectories.Based on this work, there is a great opportunity to improve some of the algorithms and processes that have some limitations in the future, and with this in mind it's possible to develop more effective solutions.Ao longo dos anos tem-se verificado um aumento quase exponencial no que toca à utilização de novas tecnologias em vários sectores. Estas tecnologias têm como objetivo principal, melhorar ou facilitar o quotidiano. O presente estudo vai incidir sobre uma destas tecnologias utilizada dentro de um tema que tem sido muito falado nos últimos anos, a utilização de dados pessoais de um grupo de indvíduos para identificar certos tipos de comportamentos. Mais concretamente, tem como objetivo utilizar os dados de GPS, guardados nas respectivas contas Google de nove voluntários, de modo a identificar os locais que estes mais frequentam - Pontos de Interesse. Os dados são utilizados também para identificar as trajectórias percorridas mais vezes por cada um dos voluntários. Foi realizado um estudo com uma amostra de 9 participantes, enviando-lhes os respectivos mapas com POI e trajectórias obtendo assim a validação dos mesmos. Desta forma foi possível concluir que que a melhor forma de identificar POI tem como base a utilização de clusters diários utilizando DBSCAN. Para o caso das trajectórias, o método Snap-to-Road foi o que originou melhores resultados. Verificou-se que foi possível responder ao problema inicial, desta forma, foi encontrado um método que identifica a maior parte dos POI com sucesso, bem como algumas trajetórias. Com base neste trabalho, existe uma oportunidade para futuramente melhorar alguns dos algoritmos e processos que possuem algumas limitações de modo a desenvolver soluções mais eficazes