COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference

The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.Comment: BuildSys 201

Detecting road intersections from GPS traces using longest common subsequence algorithm

Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users’ turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods in terms of the F-score

Road intersection detection through finding common sub-tracks between pairwise GNSS traces

This paper proposes a novel approach to detect road intersections from GNSS traces. Different from the existing methods of detecting intersections directly from the road users’ turning behaviors, the proposed method detects intersections indirectly from common sub-tracks shared by different traces. We first compute the local distance matrix for each pair of traces. Second, we apply image processing techniques to find all “sub-paths” in the matrix, which represents good alignment between local common sub-tracks. Lastly, we identify the intersections from the endpoints of the common sub-tracks through Kernel Density Estimation (KDE). Experimental results show that the proposed method outperforms the traditional turning point-based methods in terms of the F-score, and our previous connecting point-based method in terms of computational efficiency

Why GPS makes distances bigger than they are

Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), are among the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation error. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS: the distance between two points recorded with a GPS is -- on average -- bigger than the true distance between these points. This systematic `overestimation of distance' becomes relevant if the influence of interpolation error can be neglected, which is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and we illustrate that it functionally depends on the autocorrelation of GPS measurement error ($C$). We argue that $C$ can be interpreted as a quality measure for movement data recorded with a GPS. If there is strong autocorrelation any two consecutive position estimates have very similar error. This error cancels out when average speed, distance or direction are calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine $C$ in real-world GPS movement data sampled at high frequencies. We apply our approach to a set of pedestrian and a set of car trajectories. We find that the measurement error in the data is strongly spatially and temporally autocorrelated and give a quality estimate of the data. Finally, we want to emphasize that all our findings are not limited to GPS alone. The systematic bias and all its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.Comment: 17 pages, 8 figures, submitted to IJGI

Mapping Rural Road Networks from Global Positioning System (GPS) Trajectories of Motorcycle Taxis in Sigomre Area, Siaya County, Kenya

Effective transport infrastructure is an essential component of economic integration, accessibility to vital social services and a means of mitigation in times of emergency. Rural areas in Africa are largely characterized by poor transport infrastructure. This poor state of rural road networks contributes to the vulnerability of communities in developing countries by hampering access to vital social services and opportunities. In addition, maps of road networks are incomplete, and not up-to-date. Lack of accurate maps of village-level road networks hinders determination of access to social services and timely response to emergencies in remote locations. In some countries in sub-Saharan Africa, communities in rural areas and some in urban areas have devised an alternative mode of public transport system that is reliant on motorcycle taxis. This new mode of transport has improved local mobility and has created a vibrant economy that depends on the motorcycle taxi business. The taxi system also offers an opportunity for understanding local-level mobility and the characterization of the underlying transport infrastructure. By capturing the spatial and temporal characteristics of the taxis, we could design detailed maps of rural infrastructure and reveal the human mobility patterns that are associated with the motorcycle taxi system. In this study, we tracked motorcycle taxis in a rural area in Kenya by tagging volunteer riders with Global Positioning System (GPS) data loggers. A semi-automatic method was applied on the resulting trajectories to map rural-level road networks. The results showed that GPS trajectories from motorcycle taxis could potentially improve the maps of rural roads and augment other mapping initiatives like OpenStreetMap(VLID)286170

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

Inferring directed road networks from GPS traces by track alignment

This paper proposes a method to infer road networks from GPS traces. These networks include intersections between roads, the connectivity between the intersections and the possible traffic directions between directly-connected intersections. These intersections are localized by detecting and clustering turning points, which are locations where the moving direction changes on GPS traces. We infer the structure of road networks by segmenting all of the GPS traces to identify these intersections. We can then form both a connectivity matrix of the intersections and a small representative GPS track for each road segment. The road segment between each pair of directly-connected intersections is represented using a series of geographical locations, which are averaged from all of the tracks on this road segment by aligning them using the dynamic time warping (DTW) algorithm. Our contribution is two-fold. First, we detect potential intersections by clustering the turning points on the GPS traces. Second, we infer the geometry of the road segments between intersections by aligning GPS tracks point by point using a stretch and then compress strategy based on the DTW algorithm. This approach not only allows road estimation by averaging the aligned tracks, but also a deeper statistical analysis based on the individual track's time alignment, for example the variance of speed along a road segment

Extraction of routing relevant geodata using telemetry sensor data of agricultural vehicles

The mechanization of processes in agriculture is growing within the last hundred years. Within the last decades, the information technology in this sector constantly grew and data is one of the key factors for process optimization. For planning and execution of logistic processes and harvest campaigns, road maps and field geometries are essential to guide the large vehicles to their work places and to optimize harvest chains. Through nowadays available telemetry data of these vehicles, a new data source generates new opportunities. Mining geographic data from these movement data, that can improve agricultural work processes is one of the main objectives of this thesis. As a first step, data cleaning processes, and further preprocessing steps are shown. With classification algorithms, the continuous movement data will be separated into different work processes. Based on this data, algorithms to generate geographic geographic features, such as field boundaries have been analyzed and improved. As quality metric to compare the results, the Jaccard-Distance has been established. With the classified road representing measurements, the rural road networks were created and the results of different algorithmic approaches have been compared. The usability of volunteered geographic information to route the heterogeneous set of agricultural vehicles is shown in a third step. Due to the fact, that routes for e.g. harvesters are not ending at the field boundary, solutions for infield route graph generation have been given. The presented components provide the content and the services within a framework structure. The concluding prototype, a web based routing system demonstrates the interaction of all components and provides a consecutive routing from farm to field and within the field