Anita Graser

Processing: A Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS

Processing is an object-oriented Python framework for the popular open source Geographic Information System QGIS, which provides a seamless integration of geoprocessing tools from a variety of different software libraries. In this paper, we present the development history, software architecture and features of the Processing framework, which make it a versatile tool for the development of geoprocessing algorithms and workflows, as well as an efficient integration platform for algorithms from different sources. Using real-world application examples, we furthermore illustrate how the Processing architecture enables typical geoprocessing use cases in research and development, such as automating and documenting workflows, combining algorithms from different software libraries, as well as developing and integrating custom algorithms. Finally, we discuss how Processing can facilitate reproducible research and provide an outlook towards future development goals.

Fast Hidden Markov Model Map-Matching for Sparse and Noisy Trajectories

The problem of map-matching sparse and noisy GPS trajectories to road networks has gained increasing importance in recent years. A common state-of-the-art solution to this problem relies on a Hidden Markov Model (HMM) to identify the most plausible road sequence for a given trajectory. While this approach has been shown to work well on sparse and noisy data, the algorithm has a high computational complexity and becomes slow when working with large trajectories and extended search radii. We propose an optimization to the original approach which significantly reduces the number of state transitions that need to be evaluated in order to identify the correct solution. In experiments with publicly available benchmark data, the proposed optimization yields nearly identical map-matching results as the original algorithm, but reduces the algorithm runtime by up to 45%. We demonstrate that the effects of our optimization become more pronounced when dealing with larger problem spaces and indicate how our approach can be combined with other recent optimizations to further reduce the overall algorithm runtime.

The elevation factor: Digital elevation model quality and sampling impacts on electric vehicle energy estimation errors

Energy used to overcome elevation is a significant factor in estimating energy consumption of moving objects and (electric) vehicles in particular. A common source of elevation data for electric vehicle energy estimations are digital elevation models (DEMs). These DEMs are available from multiple providers and with varying quality as free or paid data. This paper presents an evaluation of the impacts of DEM quality and methods used to sample DEM values for elevation profiles on energy estimations for electric vehicle routes. The evaluation is carried out for two different study areas: an urban mostly flat area, and a rural alpine area. An overview of the error obtained with different DEMs and sampling methods in these two areas is provided. These results can serve as a reference for estimating the magnitude of the energy estimation error in case high resolution elevation data is not available in a study area.

Is OSM Good Enough for Vehicle Routing? A Study Comparing Street Networks in Vienna

As a result of OpenStreetMap’s (OSM) openness and wide availability, there is increasing interest in using OSM street network data in routing applications. But due to the heterogeneous nature of Volunteered Geographic Information (VGI) in general and OSM in particular, there is no universally valid answer to questions about the quality of these data sources. In this paper we address the lack of systematic analyses of the quality of the OSM street network for vehicle routing and the effects of using OSM rather than proprietary street networks in vehicle routing applications. We propose a method to evaluate the quality of street networks for vehicle routing purposes which compares relevant street network features as well as computed route lengths and geometries using the Hausdorff distance. The results of our case study comparing OSM and the official Austrian reference graph in the city of Vienna show close agreement of one-way street and turn restriction information. Comparisons of 99,000 route pairs with an average length of 6,812 m show promising results for vehicle routing applications with OSM, especially for route length computation where we found median absolute length differences of 1.0 %.

Assessing traffic performance using position density of sparse FCD

We present an approach for evaluating traffic performance along corridors and its variation based on floating car data (FCD). In contrast to existing work, our approach can cope with long and irregular FCD reporting intervals. Resampling of sparse FCD in time and interpolation increases spatial resolution of FCD positions along the corridors. FCD position density is computed with a uniform kernel, which leads to traffic performance expressed as average travel time per meter and average speed. Experimental results based on real-world FCD for a freeway section and arterial roads in Vienna illustrate the plausibility of the approach, and an example illustrating our approach before and after a traffic influencing measure shows its advantage over using dedicated probe vehicle runs, temporary sensor installations or human observers. A sensitivity analysis provides guidelines for the important parameters.

Combining spatial information and optimization for locating emergency medical service stations: A case study for Lower Austria

OBJECTIVES Emergency medical services have been established in many countries all over the world. Good first care improves the outcome of patients in terms of hospital stay duration, chances of full recovery and of treatment costs. In this paper, we present an integrated approach combining spatial information and integer optimization for emergency medical service location planning. The research is motivated by a recent call for bids to restructure the location of emergency medical services in the Austrian federal state of Lower Austria by the local state government. METHODS Our framework allows for constraints on the places where an emergency care physician is stationed, accounting for the fact that - for economical reasons - it might not be feasible to arbitrarily place emergency care physicians. We use maximum coverage linear programs to get accurate solutions for the problem instances (depending on the maximum allowed number of emergency care physicians and the constraints of their placement). We optimize for the maximum number of covered residents given certain parameters. The travelling distances are calculated by means of a digital road graph. Moreover we analyze the coverage of the day population as there are significant shifts in the number of persons present at daytime. For every problem instance we have calculated the ten best solutions and examined the variance among them. For the demand point aggregation we have used a cell grid. RESULTS Using our method we can show that with less emergency care physicians more residents can be covered. This is highly applicable to low populated areas where the coverage becomes better. There is little variance from the best to the second best solution: There are only small changes (usually only one cell is shifted) between the best and the second best solution. The coverage of the day population - except for a few problem instances - is always better than the coverage of the residents (reflecting the fact that many residents commute to more densely populated areas). CONCLUSIONS In our study, we show that our solutions provide better coverage of residents with fewer emergency care physicians than the current status quo.

Improving vehicle speed prediction transferability with network centrality

Even though we are currently witnessing an unprecedented growth in the collection of movement data, practitioners in many fields still struggle with gaining access to reusable mobility data, such as traffic flows and speeds. Data availability varies considerably between different cities and regions. While some publish comprehensive open datasets, others either do not provide their data or do not even posses any traffic data. This paper proposes a solution to the problem of missing vehicle speed data. Our approach is to train a prediction model in an area where data is available and then transfer this model to areas where data is lacking. The proposed method requires only readily available static road network data in the target area. We improve upon previously published prediction models by incorporating local network centrality measures. Our approach reduces errors in vehicle speed prediction by as much as 24%.

Untangling origin-destination flows in geographic information systems

Origin–destination flow maps are a popular option to visualize connections between different spatial locations, where specific routes between the origin and destination are unknown or irrelevant. Visualizing origin–destination flows is challenging mainly due to visual clutter which appears quickly as data sets grow. Clutter reduction techniques are intensively explored in the information visualization and cartography domains. However, current automatic techniques for origin–destination flow visualization, such as edge bundling, are not available in geographic information systems which are widely used to visualize spatial data, such as origin–destination flows. In this article, we explore the applicability of edge bundling to spatial data sets and necessary adaptations under the constraints inherent to platform-independent geographic information system scripting environments. We propose (1) a new clustering technique for origin–destination flows that provides within-cluster consistency to speed up computations, (2) an edge bundling approach based on force-directed edge bundling employing matrix computations, (3) a new technique to determine the local strength of a bundle leveraging spatial indexes, and (4) a geographic information system–based technique to spatially offset bundles describing different flow directions. Finally, we evaluate our method by applying it to origin–destination flow data sets with a wide variety of different data characteristics.

Improving vehicle speed estimates using street network centrality

ABSTRACT This paper describes a novel approach to improve prediction models which estimate vehicle speeds and their diurnal variation for road network links in urban street networks using only static map attributes. The presented approach takes into account previously neglected spatial information by integrating network centrality measures for closeness (indicating how central a link is) and betweenness (indicating how important a road link is) into the prediction model. The model is calibrated with a real-world dataset of 100 million individual speed measurements from a fleet of 3500 taxi probe vehicles in Vienna, Austria. Given that centrality can be derived directly from readily available street network data, the experimental results demonstrate that integrating centrality measures considerably improves the predictions without the need for adding a supplementary data source. Improvements for vehicle speed estimates are particularly prevalent on important street network links in the city center as well as rural streets in the periphery.