Incremental data acquisition from gps-traces

GPS traces can track actual time and coordinates of regular vehicles going their own business, and it is easy to scale to the entire area with an accuracy of 6 to 10 meters in normal condition. As a result, extracting road map from GPS traces could be an alternative way to traditional way of road map generation. The basic idea of this paper is to describe a process which incrementally improves existing road data with incoming new information in terms of GPS traces. In this way we consider the GPS traces as measurements which represent a "digitization" of the true road. Although the accuracy of the traces is not too high, due to the high number of measurements an improvement of the quality of the road information can be achieved. Thus, this paper presents a method for integrating GPS traces and an existing road map towards a more accurate, up-to-data and detailed road map. First we profile the existing road by a sequence of perpendicular profiles and get the road's candidate sampling traces which intersect with the profile. Then we match the potential traces with the road and finally estimate the new road centerline from its corresponding traces. In addition to the geometry of roads we also mine attribute information from GPS traces, such as number of lanes. Furthermore, we explore the benefit of an incremental acquisition by a temporal analysis of the data

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

Enrichment of OpenStreetMap data completeness with sidewalk geometries using data mining techniques

Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset ("ground truth dataset"). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions

Unveiling E-bike potential for commuting trips from GPS traces

Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips

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

Ghana airborne geophysics project in the Volta and Keta Basin : BGS final report

This report describes the work undertaken by BGS between November 2006 and March 2009 in collaboration with Fugro Airborne Surveys Pty Ltd on an airborne geophysical survey and ground reconnaissance mapping of the Volta River and Keta Basins, Ghana. The project was supported by the EU as part of the Mining Sector Support Programme, Project Number 8ACP GH 027/13. The initial contract duration was three years, but this was extended by five months to account for acquisition of gravity data by another project. Some parts of Ghana have been airborne surveyed as part of the Mining Sector Development and Environmental Project, co-funded by the World Bank and the Nordic Development Fund, but no work was carried out on the Volta River and Keta basins, which together form a major portion of the Ghanaian territory. The approximate areas covered by the surveys are estimated at 98,000 km² for the satellite imagery and the airborne geophysics, except for the Time Domain Electromagnetic (TDEM) survey which was limited to 60,000 km². The main beneficiary of this project is the Geological Survey Department, GSD. The work enhanced its geological infrastructure and its personnel received hands-on training on modern geological mapping technology. Indirect beneficiaries were the mining and exploration companies that can follow up the reconnaissance work with detailed exploration work. The project was conducted in five phases, and this document reports on the BGS input to Phase 1, 4 and 5, with no inputs required in Phases 2 and 3: • Phase1: geological outline through Radar and optical satellite imageries. • Phase 2: airborne geophysical survey over the two basins for magnetics and Gamma Ray spectrometry (Fugro survey). • Phase 3: airborne electromagnetic and magnetic geophysical survey of specific areas, following the completion and interpretation of phase 2, using fixed wing time domain technology (Fugro survey). • Phase 4: interpretation of the combined geology and geophysics. • Phase 5: production of factual and interpretation maps. The full list of BGS products is outlined in Table 1 below, while Jordan et al. (2006) describe the products delivered on schedule in Phase 1

Automatic extraction of relevant road infrastructure using connected vehicle data and deep learning model

This thesis presents a novel approach for extracting road infrastructure information from connected vehicle trajectory data, employing geohashing and image classification techniques. The methodology involves segmenting trajectories using geohash boxes and generating image representations of road segments. These images are then processed using YOLOv5 to accurately classify straight roads and intersections. Experimental results demonstrate a high level of accuracy, with an overall classification accuracy of 95%. Straight roads achieve a 97% F1 score, while intersections achieve a F1 score of 90%. These results validate the effectiveness of the proposed approach in accurately identifying and classifying road segments. The integration of geohashing and image classification techniques offers numerous benefits for road network analysis, traffic management, and autonomous vehicle navigation systems. By extracting road infrastructure information from connected vehicle data, a comprehensive understanding of road networks is achieved, facilitating optimization of traffic flow and infrastructure maintenance. The scalability and adaptability of the approach make it well-suited for large-scale datasets and urban areas. The combination of geohashing and image classification provides a robust framework for extracting valuable insights from connected vehicle data, thereby contributing to the advancement of smart transportation systems. The results emphasize the potential of the proposed approach in enhancing road network analysis, traffic management, and autonomous vehicle navigation, thereby expanding the knowledge in this field and inspiring further research