34 research outputs found
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
Mobility Data Science (Dagstuhl Seminar 22021)
This report documents the program and the outcomes of Dagstuhl Seminar 22021 "Mobility Data Science". This seminar was held January 9-14, 2022, including 47 participants from industry and academia. The goal of this Dagstuhl Seminar was to create a new research community of mobility data science in which the whole is greater than the sum of its parts by bringing together established leaders as well as promising young researchers from all fields related to mobility data science. Specifically, this report summarizes the main results of the seminar by (1) defining Mobility Data Science as a research domain, (2) by sketching its agenda in the coming years, and by (3) building a mobility data science community. (1) Mobility data science is defined as spatiotemporal data that additionally captures the behavior of moving entities (human, vehicle, animal, etc.). To understand, explain, and predict behavior, we note that a strong collaboration with research in behavioral and social sciences is needed. (2) Future research directions for mobility data science described in this report include a) mobility data acquisition and privacy, b) mobility data management and analysis, and c) applications of mobility data science. (3) We identify opportunities towards building a mobility data science community, towards collaborations between academic and industry, and towards a mobility data science curriculum
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