Sistema de geolocalización y análisis vehicular para motociclistas

Currently there are a number of applications functioning through internet connections aimed at assisting motorcyclists. However, most of these applications wither do not function or require the route maps to be downloaded prior to the trip. This paper proposes a vehicular analysis system where the motorcyclists have access to an application developed for Android devices, without relying on an internet connection. This will done either through data of the routes stored on the mobile device or through data hosted on a server through the implementation of a web service when there is a connection. Additional, variables are tracked and plotted, such as instant geographical position, percentage of necessary fuel, and speed, obtained through the design and implementation of an electronic circuit that acquires the signals of the motorcycle sensors and submit such information via Bluetooth to the mobile device. From the tests carried out it is observed that the system works efficiently with an absolute error up to 2 meters from the destination point. However, the routes from actual location of the motorcyclist to the intermediate position, the precision is even better with an error possibility of only centimeters. In general, for some distance, the system presents a standard deviation of 15,19 meters. The storage of the data and the user orientation are in real time, and the system can be implemented on any kind of vehicle.Actualmente existen aplicaciones dedicadas a la orientación de motociclistas que funcionan soportadas en una conexión a internet, pero cuando se carece de ella la mayoría no funcionan y otras permiten el funcionamiento solo si anteriormente se descargaron los mapas de los trayectos a realizar. Por lo anterior, este artículo propone un sistema de análisis vehicular en donde los motociclistas tienen acceso a una aplicación desarrollada para dispositivos con sistema operativo Android que les mostrará una metodología de orientación sin depender exclusivamente de una conexión a internet; esta orientación –en cambio- se realiza con base en los datos de recorridos almacenados en el dispositivo móvil, o en los datos alojados en un servidor mediante la implementación de un servicio web cuando hay conexión. Adicionalmente, se realiza seguimiento y graficación de las variables: posición geográfica instantánea, porcentaje de nivel de gasolina, y velocidad, obtenidas mediante el diseño e implementación de un circuito electrónico encargado de capturar las señales de los sensores de la motocicleta y enviar dicha información vía Bluetooth al dispositivo móvil. De las pruebas realizadas se observa que el sistema funciona eficientemente con un error absoluto menor a 2 metros hasta el punto de destino; sin embargo, para el recorrido desde el punto actual del usuario hasta uno intermedio la precisión es del orden de centímetros

Methodology and Algorithms for Pedestrian Network Construction

With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database which represents a network (e.g., road networks in car navigation systems) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activities studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there are no techniques to automatically construct pedestrian networks, impeding research and development of applications requiring pedestrian data. This coupled with the increased demand for pedestrian networks is the prime motivation for this dissertation which is focused on development of a methodology and algorithms that can construct pedestrian networks automatically. A methodology, which involves three independent approaches, network buffering (using existing road networks), collaborative mapping (using GPS traces collected by volunteers), and image processing (using high-resolution satellite and laser imageries) was developed. Experiments were conducted to evaluate the pedestrian networks constructed by these approaches with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatically constructing pedestrian networks

GPS CaPPture: a System for GPS Trajectory Collection, Processing, and Destination Prediction

In the United States, smartphone ownership surpassed 69.5 million in February 2011 with a large portion of those users (20%) downloading applications (apps) that enhance the usability of a device by adding additional functionality. a large percentage of apps are written specifically to utilize the geographical position of a mobile device. One of the prime factors in developing location prediction models is the use of historical data to train such a model. with larger sets of training data, prediction algorithms become more accurate; however, the use of historical data can quickly become a downfall if the GPS stream is not collected or processed correctly. Inaccurate or incomplete or even improperly interpreted historical data can lead to the inability to develop accurately performing prediction algorithms. As GPS chipsets become the standard in the ever increasing number of mobile devices, the opportunity for the collection of GPS data increases remarkably. the goal of this study is to build a comprehensive system that addresses the following challenges: (1) collection of GPS data streams in a manner such that the data is highly usable and has a reduction in errors; (2) processing and reduction of the collected data in order to prepare it and make it highly usable for the creation of prediction algorithms; (3) creation of prediction/labeling algorithms at such a level that they are viable for commercial use. This study identifies the key research problems toward building the CaPPture (collection, processing, prediction) system

Semantic Trajectories:Computing and Understanding Mobility Data

Thanks to the rapid development of mobile sensing technologies (like GPS, GSM, RFID, accelerometer, gyroscope, sound and other sensors in smartphones), the large-scale capture of evolving positioning data (called mobility data or trajectories) generated by moving objects with embedded sensors has become easily feasible, both technically and economically. We have already entered a world full of trajectories. The state-of-the-art on trajectory, either from the moving object database area or in the statistical analysis viewpoint, has built a bunch of sophisticated techniques for trajectory data ad-hoc storage, indexing, querying and mining etc. However, most of these existing methods mainly focus on a spatio-temporal viewpoint of mobility data, which means they analyze only the geometric movement of trajectories (e.g., the raw ‹x, y, t› sequential data) without enough consideration on the high-level semantics that can better understand the underlying meaningful movement behaviors. Addressing this challenging issue for better understanding movement behaviors from the raw mobility data, this doctoral work aims at providing a high-level modeling and computing methodology for semantically abstracting the rapidly increasing mobility data. Therefore, we bring top-down semantic modeling and bottom-up data computing together and establish a new concept called "semantic trajectories" for mobility data representation and understanding. As the main novelty contribution, this thesis provides a rich, holistic, heterogeneous and application-independent methodology for computing semantic trajectories to better understand mobility data at different levels. In details, this methodology is composed of five main parts with dedicated contributions. Semantic Trajectory Modeling. By investigating trajectory modeling requirements to better understand mobility data, this thesis first designs a hybrid spatio-semantic trajectory model that represents mobility with rich data abstraction at different levels, i.e., from the low-level spatio-temporal trajectory to the intermediate-level structured trajectory, and finally to the high-level semantic trajectory. In addition, a semantic based ontological framework has also been designed and applied for querying and reasoning on trajectories. Offline Trajectory Computing. To utilize the hybrid model, the thesis complementarily designs a holistic trajectory computing platform with dedicated algorithms for reconstructing trajectories at different levels. The platform can preprocess collected mobility data (i.e., raw movement tracks like GPS feeds) in terms of data cleaning/compression etc., identify individual trajectories, and segment them into structurally meaningful trajectory episodes. Therefore, this trajectory computing platform can construct spatio-temporal trajectories and structured trajectories from the raw mobility data. Such computing platform is initially designed as an offline solution which is supposed to analyze past trajectories via a batch procedure. Trajectory Semantic Annotation. To achieve the final semantic level for better understanding mobility data, this thesis additionally designs a semantic annotation platform that can enrich trajectories with third party sources that are composed of geographic background information and application domain knowledge, to further infer more meaningful semantic trajectories. Such annotation platform is application-independent that can annotate various trajectories (e.g., mobility data of people, vehicle and animals) with heterogeneous data sources of semantic knowledge (e.g., third party sources in any kind of geometric shapes like point, line and region) that can help trajectory enrichment. Online Trajectory Computing. In addition to the offline trajectory computing for analyzing past trajectories, this thesis also contributes to dealing with ongoing trajectories in terms of real-time trajectory computing from movement data streams. The online trajectory computing platform is capable of providing real-life trajectory data cleaning, compression, and segmentation over streaming movement data. In addition, the online platform explores the functionality of online tagging to achieve fully semantic-aware trajectories and further evaluate trajectory computing in a real-time setting. Mining Trajectories from Multi-Sensors. Previously, the focus is on computing semantic trajectories using single-sensory data (i.e., GPS feeds), where most datasets are from moving objects with wearable GPS-embedded sensors (e.g., mobility data of animal, vehicle and people tracking). In addition, we explore the problem of mining people trajectories using multi-sensory feeds from smartphones (GPS, gyroscope, accelerometer etc). The research results reveal that the combination of two sensors (GPS+accelerometer) can significantly infer a complete life-cycle semantic trajectories of people's daily behaviors, both outdoor movement via GPS and indoor activities via accelerometer

When Whereabouts is No Longer Thereabouts:Location Privacy in Wireless Networks

Modern mobile devices are fast, programmable and feature localization and wireless capabilities. These technological advances notably facilitate mobile access to Internet, development of mobile applications and sharing of personal information, such as location information. Cell phone users can for example share their whereabouts with friends on online social networks. Following this trend, the field of ubiquitous computing foresees communication networks composed of increasingly inter-connected wireless devices offering new ways to collect and share information in the future. It also becomes harder to control the spread of personal information. Privacy is a critical challenge of ubiquitous computing as sharing personal information exposes users' private lives. Traditional techniques to protect privacy in wired networks may be inadequate in mobile networks because users are mobile, have short-lived encounters and their communications can be easily eavesdropped upon. These characteristics introduce new privacy threats related to location information: a malicious entity can track users' whereabouts and learn aspects of users' private lives that may not be apparent at first. In this dissertation, we focus on three important aspects of location privacy: location privacy threats, location-privacy preserving mechanisms, and privacy-preservation in pervasive social networks. Considering the recent surge of mobile applications, we begin by investigating location privacy threats of location-based services. We push further the understanding of the privacy risk by identifying the type and quantity of location information that statistically reveals users' identities and points of interest to third parties. Our results indicate that users are at risk even if they access location-based services episodically. This highlights the need to design privacy into location-based services. In the second part of this thesis, we delve into the subject of privacy-preserving mechanisms for mobile ad hoc networks. First, we evaluate a privacy architecture that relies on the concept of mix zones to engineer anonymity sets. Second, we identify the need for protocols to coordinate the establishment of mix zones and design centralized and distributed approaches. Because individuals may have different privacy requirements, we craft a game-theoretic model of location privacy to analyze distributed protocols. This model predicts strategic behavior of rational devices that protects their privacy at a minimum cost. This prediction leads to the design of efficient privacy-preserving protocols. Finally, we develop a dynamic model of interactions between mobile devices in order to analytically evaluate the level of privacy provided by mix zones. Our results indicate the feasibility and limitations of privacy protection based on mix zones. In the third part, we extend the communication model of mobile ad hoc networks to explore social aspects: users form groups called "communities" based on interests, proximity, or social relations and rely on these communities to communicate and discover their context. We analyze using challenge-response methodology the privacy implications of this new communication primitive. Our results indicate that, although repeated interactions between members of the same community leak community memberships, it is possible to design efficient schemes to preserve privacy in this setting. This work is part of the recent trend of designing privacy protocols to protect individuals. In this context, the author hopes that the results obtained, with both their limitations and their promises, will inspire future work on the preservation of privacy

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

INTRODUCING A GRAPH-BASED NEURAL NETWORK FOR NETWORKWIDE TRAFFIC VOLUME ESTIMATION

Traffic volumes are an essential input to many highway planning and design models; however, collecting this data for all the roads in a network is not practical nor cost-effective. Accordingly, transportation agencies must find ways to leverage limited ground truth count data to obtain reasonable estimates at scale on all the network segments. One of the challenges that complicate this estimation is the complex spatial dependency of the links’ traffic state in a transportation network. A graph-based model is proposed to estimate networkwide traffic volumes to address this challenge. This model aims to consider the graph structure of the network to extract its spatial correlations while estimating link volumes. In the first step, a proof-of-concept methodology is presented to indicate how adding the simple spatial correlation between the links in the Euclidian space improves the performance of a state-of-the-art volume estimation model. This methodology is applied to the New Hampshire road network to estimate statewide hourly traffic volumes. In the next step, a Graph Neural Network model is introduced to consider the complex interdependency of the road network in a non-Euclidean domain. This model is called Fine-tuned Spatio-Temporal Graph Neural Network (FSTGCN) and applied to various Maryland State networks to estimate 15-minute traffic volumes. The results illustrate significant improvement over the existing state-of-the-art models used for networkwide traffic volume estimation, namely ANN and XGBoost

Profiling and Grouping Space-time Activity Patterns of Urban Individuals

No abstract