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Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth dataset is missing so far. This work aims at closing this gap by evaluating four different methods for validating localization accuracies of a vehicle’s position trajectory to different ground truths: (1) a static driving-path, (2) the lane-centerline of a high-definition (HD) map with validated accuracy, (3) localized vehicle body overlaps of the lane-boundaries of a HD map, and (4) longitudinal accuracy at stop points. The methods are evaluated using two localization test datasets, one acquired by an automated vehicle following a static driving path, being additionally equipped with roof-mounted localization systems, and a second dataset acquired from manually-driven connected vehicles. Results show the broad applicability of the approach for evaluating localization accuracy and reveal the pros and cons of the different methods and ground truths. Results also show the feasibility of achieving localization accuracies below 0.1 m at confidence levels up to 99.9% for high-quality localization systems, while at the same time demonstrate that such accuracies are still challenging to achieve.
Karl Rehrl; Simon Gröchenig. Evaluating Localization Accuracy of Automated Driving Systems. Sensors 2021, 21, 5855 .
AMA StyleKarl Rehrl, Simon Gröchenig. Evaluating Localization Accuracy of Automated Driving Systems. Sensors. 2021; 21 (17):5855.
Chicago/Turabian StyleKarl Rehrl; Simon Gröchenig. 2021. "Evaluating Localization Accuracy of Automated Driving Systems." Sensors 21, no. 17: 5855.
In the context of intelligent transport systems, dynamic route planning is an eagerly researched topic. While the research community during the last decade has focused on performance and scalability of dynamic route planning algorithms, the question concerning route qualities has been widely neglected. The current work contributes to this question by proposing a methodology to assess the quality of arbitrary routes based on seven spatio‐temporal route quality metrics (five spatial and two temporal ones). The methodology is evaluated by calculating quality metrics for 45 routes being derived from three dynamic route planning systems for three selected origin–destination pairs during one day. For objectively assessing route qualities, the approach matches the route planning results to a reference digital road network and uses reference travel times for calculating quality metrics. A spatio‐temporal cross‐evaluation illustrates different quality aspects in the context of both quality dimensions and demonstrates the usefulness of the proposed approach.
Karl Rehrl; Stefan Kranzinger; Simon Gröchenig. Which quality is a route? A methodology for assessing route quality using spatio‐temporal metrics. Transactions in GIS 2020, 25, 869 -896.
AMA StyleKarl Rehrl, Stefan Kranzinger, Simon Gröchenig. Which quality is a route? A methodology for assessing route quality using spatio‐temporal metrics. Transactions in GIS. 2020; 25 (2):869-896.
Chicago/Turabian StyleKarl Rehrl; Stefan Kranzinger; Simon Gröchenig. 2020. "Which quality is a route? A methodology for assessing route quality using spatio‐temporal metrics." Transactions in GIS 25, no. 2: 869-896.
Trajectory data mining is a lively research field in the domain of spatio-temporal data mining. Trajectory pattern mining comprises a set of specific pattern mining methods, which are applied as consecutive steps on a trajectory with the goal to extract and classify re-occurring spatio-temporal patterns. Despite the common nature and frequent usage of such methods by the GIScience community, a methodological approach is missing so far, especially when it comes to the use of machine learning-based classification methods. The current work closes this gap by proposing and evaluating a machine learning-based 3-steps trajectory data mining methodology using the detection and classification of stop points in vehicle trajectories as example. The work describes in detail the applied methodologies with respect to the three mining steps ‘stop detection’, ‘feature extraction’ and ‘classification in traffic-relevant and non-traffic-relevant stops’ and evaluates six machine learning-based classification algorithms using a real-world dataset of 15,498 vehicle trajectories with 5,899 detected stops (thereof 2,032 manually classified). Due to its exemplary nature, the presented methodology is suited to act as blueprint for similar trajectory data mining problems.
Karl Rehrl; Simon Gröchenig; Stefan Kranzinger. Why did a vehicle stop? A methodology for detection and classification of stops in vehicle trajectories. International Journal of Geographical Information Science 2020, 34, 1953 -1979.
AMA StyleKarl Rehrl, Simon Gröchenig, Stefan Kranzinger. Why did a vehicle stop? A methodology for detection and classification of stops in vehicle trajectories. International Journal of Geographical Information Science. 2020; 34 (10):1953-1979.
Chicago/Turabian StyleKarl Rehrl; Simon Gröchenig; Stefan Kranzinger. 2020. "Why did a vehicle stop? A methodology for detection and classification of stops in vehicle trajectories." International Journal of Geographical Information Science 34, no. 10: 1953-1979.
Map matching, i.e. matching a moving entity’s position trajectory to an underlying transport network, is a crucial functionality of many location-based services. During the last decade, numerous map-matching algorithms have been proposed, tackling challenging aspects like sparse trajectory data or online matching. This work describes GraphiumMM, an open-source map-matching implementation combining and optimizing geometrical and topological matching concepts from previous works. The implementation aims at highly accurate and performant map matching in online and offline mode taking trajectories with average sampling intervals between 1 and 120 s as input. For evaluating its runtime performance and matching quality, results are compared to results from the open-source map-matcher Barefoot. Results indicate better matching quality and runtime performance especially for sampling intervals from 1 to 15 s in offline and online mode.
Karl Rehrl; Simon Gröchenig; Michael Wimmer. Optimization and Evaluation of a High-Performance Open-Source Map-Matching Implementation. Lecture Notes in Geoinformation and Cartography 2018, 251 -270.
AMA StyleKarl Rehrl, Simon Gröchenig, Michael Wimmer. Optimization and Evaluation of a High-Performance Open-Source Map-Matching Implementation. Lecture Notes in Geoinformation and Cartography. 2018; ():251-270.
Chicago/Turabian StyleKarl Rehrl; Simon Gröchenig; Michael Wimmer. 2018. "Optimization and Evaluation of a High-Performance Open-Source Map-Matching Implementation." Lecture Notes in Geoinformation and Cartography , no. : 251-270.
In recent years, sensors of mobile devices are increasingly used in the research field of Active and Assisted Living (AAL), in particular, for movement analysis. Questions, such as where users typically stay (and for how long), where they have been or where they will most likely be going to, are of utmost importance for implementing smart AAL services. Due to the plethora of application scenarios and varying requirements, the challenge is the identification of an appropriate stay detection approach. Thus, this paper presents a comprehensive framework covering the entire process from data acquisition, pre-processing, parameterization to evaluation so that it can be applied to evaluate various stay detection methods. Additionally, ground truth data as well as application field data are used within the framework. The framework has been validated with three different spatio-temporal clustering approaches (time-based/incremental clustering, extended density based clustering, and a mixed method approach). Using the framework with ground truth data and data from the AAL field, it can be concluded that the time-based/incremental clustering approach is most suitable for this type of AAL applications. Furthermore, using two different datasets has proven successful as it provides additional data for selecting the appropriate method. Finally, the way the framework is designed it might be applied to other domains such as transportation, mobility, or tourism by adapting the pre-selection criteria.
Cornelia Schneider; Simon Gröchenig; Verena Venek; Michael Leitner; Siegfried Reich. A Framework for Evaluating Stay Detection Approaches. ISPRS International Journal of Geo-Information 2017, 6, 315 .
AMA StyleCornelia Schneider, Simon Gröchenig, Verena Venek, Michael Leitner, Siegfried Reich. A Framework for Evaluating Stay Detection Approaches. ISPRS International Journal of Geo-Information. 2017; 6 (10):315.
Chicago/Turabian StyleCornelia Schneider; Simon Gröchenig; Verena Venek; Michael Leitner; Siegfried Reich. 2017. "A Framework for Evaluating Stay Detection Approaches." ISPRS International Journal of Geo-Information 6, no. 10: 315.
Cornelia Schneider; Sebastian Zutz; Karl Rehrl; Richard Brunauer; Simon Gröchenig. Evaluating GPS sampling rates for pedestrian assistant systems. Journal of Location Based Services 2016, 10, 1 -28.
AMA StyleCornelia Schneider, Sebastian Zutz, Karl Rehrl, Richard Brunauer, Simon Gröchenig. Evaluating GPS sampling rates for pedestrian assistant systems. Journal of Location Based Services. 2016; 10 (3):1-28.
Chicago/Turabian StyleCornelia Schneider; Sebastian Zutz; Karl Rehrl; Richard Brunauer; Simon Gröchenig. 2016. "Evaluating GPS sampling rates for pedestrian assistant systems." Journal of Location Based Services 10, no. 3: 1-28.
Over the last decade, volunteered geographic information (VGI) has become established as one of the most relevant geographic data sources in terms of worldwide coverage, representation of local knowledge and open data policies. Beside the data itself, data about community activity provides valuable insights into the mapping progress which can be useful for estimating data quality, understanding the activity of VGI communities or predicting future developments. This work proposes a conceptual as well as technical framework for structuring and analyzing mapping activity building on the concepts of activity theory. Taking OpenStreetMap as an example, the work outlines the necessary steps for converting database changes into user- and feature-centered operations and higher-level actions acting as a universal scheme for arbitrary spatio-temporal analyses of mapping activities. Different examples from continent to region and city-scale analyses demonstrate the practicability of the approach. Instead of focusing on the interpretation of specific analysis results, the work contributes on a meta-level by addressing several conceptual and technical questions with respect to the overall process of analyzing VGI community activity.
Karl Rehrl; Simon Gröchenig. A Framework for Data-Centric Analysis of Mapping Activity in the Context of Volunteered Geographic Information. ISPRS International Journal of Geo-Information 2016, 5, 37 .
AMA StyleKarl Rehrl, Simon Gröchenig. A Framework for Data-Centric Analysis of Mapping Activity in the Context of Volunteered Geographic Information. ISPRS International Journal of Geo-Information. 2016; 5 (3):37.
Chicago/Turabian StyleKarl Rehrl; Simon Gröchenig. 2016. "A Framework for Data-Centric Analysis of Mapping Activity in the Context of Volunteered Geographic Information." ISPRS International Journal of Geo-Information 5, no. 3: 37.
This work reports on results from a field trial regarding the collection of floating car data with smartphones in Austria. The field trial has been conducted within Austria’s National Floating Car Data Testbed pursuing the goal to test different aspects of floating car data technology for traffic data collection, traffic state estimation and traffic prediction. The test bed collects, processes and analyses FCD from several thousand vehicles. The field trial for smartphone-based data collection has been conducted within the Federal State of Salzburg covering 1500 kilometres of major road network. Between the launch of the Android-based smartphone application in March 2014 and the end of the field trial in February 2015, the application has been downloaded by more than 2100 users. One year after launch the app is still installed on 650 devices and attracts around 15 users daily. The work gives insights into the application’s concepts and discusses app usage statistics, usage patterns and user feedback in the context of community-driven traffic data collection. On the one hand, results from the field trial confirm that community-driven traffic data collection is still not a phenomenon of the masses due to various challenges discussed throughout the work. On the other hand, results contribute to a deeper understanding of community-driven data collection in the traffic domain and help to learn for future trials.
Karl Rehrl; Richard Brunauer; Simon Gröchenig. Collecting floating car data with smartphones: results from a field trial in Austria. Journal of Location Based Services 2016, 10, 16 -30.
AMA StyleKarl Rehrl, Richard Brunauer, Simon Gröchenig. Collecting floating car data with smartphones: results from a field trial in Austria. Journal of Location Based Services. 2016; 10 (1):16-30.
Chicago/Turabian StyleKarl Rehrl; Richard Brunauer; Simon Gröchenig. 2016. "Collecting floating car data with smartphones: results from a field trial in Austria." Journal of Location Based Services 10, no. 1: 16-30.
Simon Gröchenig; Cornelia Schneider. A Cookie-Cutter Approach for Determining Places and Stays from Movement Data. GI_Forum 2016, 4, 53 -64.
AMA StyleSimon Gröchenig, Cornelia Schneider. A Cookie-Cutter Approach for Determining Places and Stays from Movement Data. GI_Forum. 2016; 4 (1):53-64.
Chicago/Turabian StyleSimon Gröchenig; Cornelia Schneider. 2016. "A Cookie-Cutter Approach for Determining Places and Stays from Movement Data." GI_Forum 4, no. 1: 53-64.
Changes are immanent to digital geographic vector datasets. While the majority of changes nowadays are quantitatively detectable by the use of geographic information systems their classification and impact assessment on a qualitative level with respect to specific data usage scenarios is often neglected. To close this gap, this work proposes a classification approach consisting of three parts: (1) a taxonomy for classifying quantitatively detectable edits in digital feature datasets (e.g. attribute or geometry changes), (2) a taxonomy for classifying edits into qualitative and therefore meaningful change types (e.g. feature revision or identity change) and (3) a mapping scheme providing the link from quantitative to qualitative classifications. In the context of a case study with OpenStreetMap history data the proposed classification approach is used to automatically detect and classify feature changes with respect to two feature types, namely streets and buildings, leading to a refined mapping scheme for two selected data usage scenarios, namely vehicle routing and map rendering. Results show the applicability of the approach, especially for assessing the impact of feature changes on different data usage scenarios, and provide a useful foundation for any change detection task in the context of geographic vector datasets.
Karl Rehrl; Richard Brunauer; Simon Gröchenig. Towards a Qualitative Assessment of Changes in Geographic Vector Datasets. Lecture Notes in Geoinformation and Cartography 2015, 181 -197.
AMA StyleKarl Rehrl, Richard Brunauer, Simon Gröchenig. Towards a Qualitative Assessment of Changes in Geographic Vector Datasets. Lecture Notes in Geoinformation and Cartography. 2015; ():181-197.
Chicago/Turabian StyleKarl Rehrl; Richard Brunauer; Simon Gröchenig. 2015. "Towards a Qualitative Assessment of Changes in Geographic Vector Datasets." Lecture Notes in Geoinformation and Cartography , no. : 181-197.
Volunteered geographic information (VGI) data-sets are characterised by heterogeneity due to influences from technical, social, environmental or economic factors. As a result, mapping progress does neither follow a spatially nor a temporally equal distribution, and thus can be hardly measured or predicted. Positively stated, heterogeneity leads to interesting VGI data-sets revealing regional peculiarities such as diverse community activities. This work proposes an approach for identifying regionally and temporally different developments with respect to mapping progress. Regional mapping progress is measured with a modified version of a previously proposed model for classifying activity stages, which has been used as foundation for a massive spatial and temporal analysis of the worldwide OpenStreetMap contributions between the years 2006 and 2013. It also allows the evaluation of rural and unpopulated areas. Results reveal that regional mapping progress heavily depends on a number of distinct influences such as geographical or legal borders, data imports, unexpected events or diverse community developments. The work highlights regions with distinct results by revealing individual mapping stories.
Simon Gröchenig; Richard Brunauer; Karl Rehrl. Digging into the history of VGI data-sets: results from a worldwide study on OpenStreetMap mapping activity. Journal of Location Based Services 2014, 8, 198 -210.
AMA StyleSimon Gröchenig, Richard Brunauer, Karl Rehrl. Digging into the history of VGI data-sets: results from a worldwide study on OpenStreetMap mapping activity. Journal of Location Based Services. 2014; 8 (3):198-210.
Chicago/Turabian StyleSimon Gröchenig; Richard Brunauer; Karl Rehrl. 2014. "Digging into the history of VGI data-sets: results from a worldwide study on OpenStreetMap mapping activity." Journal of Location Based Services 8, no. 3: 198-210.
Due to the dynamic nature and heterogeneity of Volunteered Geographic Information (VGI) datasets a crucial question isu concerned with geographic data quality. Among others, one of the main quality categories addresses data completeness. Most of the previous work tackles this question by comparing VGI datasets to external reference datasets. Although such comparisons give valuable insights, questions about the quality of the external dataset and syntactic as well as semantic differences arise. This work proposes a novel approach for internal estimation of regional data completeness of VGI datasets by analyzing the changes in community activity over time periods. It builds on empirical evidence that completeness of selected feature classes in distinct geographical regions may only be achieved when community activity in the selected region runs through a well-defined sequence of activity stages beginning at the start stage, continuing with some years of growth and finally reaching saturation. For the retrospective calculation of activity stages, the annual shares of new features in combination with empirically founded heuristic rules for stage transitions are used. As a proof-of-concept the approach is applied to the OpenStreetMap History dataset by analyzing activity stages for 12 representative metropolitan areas. Results give empirical evidence that reaching the saturation stage is an adequate indication for a certain degree of data completeness in the selected regions. Results also show similarities and differences of community activity in the different cities, revealing that community activity stages follow similar rules but with significant temporal variances.
Simon Gröchenig; Richard Brunauer; Karl Rehrl. Estimating Completeness of VGI Datasets by Analyzing Community Activity Over Time Periods. Lecture Notes in Geoinformation and Cartography 2014, 3 -18.
AMA StyleSimon Gröchenig, Richard Brunauer, Karl Rehrl. Estimating Completeness of VGI Datasets by Analyzing Community Activity Over Time Periods. Lecture Notes in Geoinformation and Cartography. 2014; ():3-18.
Chicago/Turabian StyleSimon Gröchenig; Richard Brunauer; Karl Rehrl. 2014. "Estimating Completeness of VGI Datasets by Analyzing Community Activity Over Time Periods." Lecture Notes in Geoinformation and Cartography , no. : 3-18.