Semi-Automated Location Planning for Urban Bike-Sharing Systems

Bike-sharing has developed into an established part of many urban transportation systems. However, new bikesharing systems (BSS) are still built and existing ones are extended. Particularly for large BSS, location planning is complex since factors determining potential usage are manifold. We propose a semi-automatic approach for creating or extending real-world sized BSS during general planning. Our approach optimizes locations such that the number of trips is maximized for a given budget respecting construction as well as operation costs. The approach consists of four steps: (1) collecting and preprocessing required data, (2) estimating a demand model, (3) calculating optimized locations considering estimated redistribution costs, and (4) presenting the solution to the planner in a visualization and planning front end. The full approach was implemented and evaluated positively with BSS and planning experts

Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications

Data-driven machine learning is playing a crucial role in the advancements of Industry 4.0, specifically in enhancing predictive maintenance and quality inspection. Federated learning (FL) enables multiple participants to develop a machine learning model without compromising the privacy and confidentiality of their data. In this paper, we evaluate the performance of different FL aggregation methods and compare them to central and local training approaches. Our study is based on four datasets with varying data distributions. The results indicate that the performance of FL is highly dependent on the data and its distribution among clients. In some scenarios, FL can be an effective alternative to traditional central or local training methods. Additionally, we introduce a new federated learning dataset from a real-world quality inspection setting

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Integrating Open Spaces into OpenStreetMap Routing Graphs for Realistic Crossing Behaviour in Pedestrian Navigation. GI_Forum|GI_Forum 2016, Volume 1 – open:spatial:interfaces|

Map data for pedestrian routing and navigation provided by OpenStreetMap is getting more and more detailed, but current approaches often fail to take advantage of available information. This paper addresses the issue of integrating open spaces, such as squares and plazas, into pedestrian routing graphs to support realistic crossing behaviour. We evaluate different approaches to solving this issue, including skeletonization algorithms as well as approaches for wayfinding in digital worlds, and recommend that – for pedestrian navigation applications – the visibility graph approach should be preferred over the commonly-used medial axis or straight skeleton approaches, since it provides direct routes, which are more realistic and better suited for pedestrian routing applications

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

Integrating Open Spaces into OpenStreetMap Routing Graphs for Realistic Crossing Behaviour in Pedestrian Navigation. GI_Forum|GI_Forum 2016, Volume 1 – open:spatial:interfaces|

Map data for pedestrian routing and navigation provided by OpenStreetMap is getting more and more detailed, but current approaches often fail to take advantage of available information. This paper addresses the issue of integrating open spaces, such as squares and plazas, into pedestrian routing graphs to support realistic crossing behaviour. We evaluate different approaches to solving this issue, including skeletonization algorithms as well as approaches for wayfinding in digital worlds, and recommend that – for pedestrian navigation applications – the visibility graph approach should be preferred over the commonly-used medial axis or straight skeleton approaches, since it provides direct routes, which are more realistic and better suited for pedestrian routing applications

Where are we going? Spatial and Mobility Aspects of Twitter Streams

<p>Social media and micro blogging plattforms such as Twitter are becoming an ever bigger part of our life. At the same time, cities and regions all over the world face growing transit problems which can only be handled by developing better transport systems. This work shows the potential of tweets as a data source for intelligent transport systems. It presents examples analyzing both spatial and textual tweet content and shows how to bring it all together.</p