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Carsharing is an integral part of the transformation toward flexible and sustainable mobility. New carsharing programs are entering the market to challenge large operators by offering innovative services. This study investigates the use of generative machine learning models for creating synthetic data to support carsharing decision–making when data access is limited. To this end, it explores the evaluation, selection, and implementation of leading-edge methods, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), to generate synthetic tabular transaction data of carsharing trips. The study analyzes usage data of an emerging carsharing program that is expanding its services to include free-floating electric vehicles (EVs). The results show that augmenting real training data with synthetic samples improves predictive modeling of upcoming trips by up to 4.63%. These results support carsharing researchers and practitioners in generating and leveraging synthetic mobility data to develop solutions to real-world decision support problems in carsharing.
We study the action of the nonlinear mapping G[z] between real or complex Banach spaces in the vicinity of a given curve with respect to possible linearization, emerging patterns of level sets, as well as existing solutions of G[z]=0. The results represent local generalizations of the standard implicit or inverse function theorem and of Newton's Lemma, considering the order of approximation needed to obtain solutions of G[z]=0.
The main technical tool is given by Jordan chains with increasing rank, used to obtain an Ansatz, appropriate for transformation of the nonlinear system to its linear part. The family of linear mappings is restricted to the case of an isolated singularity.
Geometrically, the Jordan chains define a generalized cone around the given curve, composed of approximate solutions of order 2k with k denoting the maximal rank of Jordan chains needed to ensure k-surjectivity of the linear family. Along these lines, the zero set of G[z] in the cone is calculated immediately, agreeing up to the order of k−1 with the given approximation. Hence, the results may also be interpreted as a version of Tougeron's implicit function theorem or Hensel's Lemma in Banach spaces, essentially restricted to the arc case of a single variable.
Finally, by considering a left shift of the Jordan chains, the Ansatz can be modified in a systematic way to obtain a sequence of refined versions of linearization theorems and Newton Lemmas in Banach spaces.
Intensive research in the field over the past decades highlighted the complexity of aroma partition. Still, no general model for predicting aroma matrix interactions could be described. The vision outlined here is to discover the blueprint for the prediction of aroma partitioning behavior in complex foods by using machine learning techniques. Therefore, known physical relationships governing aroma release are combined with machine learning to predict the 𝐾𝑚𝑔 value of aroma compounds in foods of different compositions. The approach will be optimized on a data set of a specific food product. Afterward, the model should be transferred using explainable artificial intelligence (XAI) to a different food category to validate its applicability. Furthermore, we can transfer our approach to other relevant questions in the food field such as aroma quantification, extraction processes, or food spoilage.
Many modern automated vehicle sensor systems use light detection and ranging (LiDAR) sensors. The prevailing technology is scanning LiDAR, where a collimated laser beam illuminates objects sequentially point-by-point to capture 3D range data. In current systems, the point clouds from the LiDAR sensors are mainly used for object detection. To estimate the velocity of an object of interest (OoI) in the point cloud, the tracking of the object or sensor data fusion is needed. Scanning LiDAR sensors show the motion distortion effect, which occurs when objects have a relative velocity to the sensor. Often, this effect is filtered, by using sensor data fusion, to use an undistorted point cloud for object detection. In this study, we developed a method using an artificial neural network to estimate an object’s velocity and direction of motion in the sensor’s field of view (FoV) based on the motion distortion effect without any sensor data fusion. This network was trained and evaluated with a synthetic dataset featuring the motion distortion effect. With the method presented in this paper, one can estimate the velocity and direction of an OoI that moves independently from the sensor from a single point cloud using only one single sensor. The method achieves a root mean squared error (RMSE) of 0.1187 m s−1 and a two-sigma confidence interval of [−0.0008 m s−1, 0.0017 m s−1] for the axis-wise estimation of an object’s relative velocity, and an RMSE of 0.0815 m s−1 and a two-sigma confidence interval of [0.0138 m s−1, 0.0170 m s−1] for the estimation of the resultant velocity. The extracted velocity information (4D-LiDAR) is available for motion prediction and object tracking and can lead to more reliable velocity data due to more redundancy for sensor data fusion.
Automated vehicles use light detection and ranging (LiDAR) sensors for environmental scanning. However, the relative motion between the scanning LiDAR sensor and objects leads to a distortion of the point cloud. This phenomenon is known as the motion distortion effect, significantly degrading the sensor’s object detection capabilities and generating false negative or false positive errors. In this work, we have introduced ray tracing-based deterministic and analytical approaches to model the motion distortion effect on the scanning LiDAR sensor’s performance for simulation-based testing. In addition, we have performed dynamic test drives at a proving ground to compare real LiDAR data with the motion distortion effect simulation data. The real-world scenarios, the environmental conditions, the digital twin of the scenery, and the object of interest (OOI) are replicated in the virtual environment of commercial software to obtain the synthetic LiDAR data. The real and the virtual test drives are compared frame by frame to validate the motion distortion effect modeling. The mean absolute percentage error (MAPE), the occupied cell ratio (OCR), and the Barons cross-correlation coefficient (BCC) are used to quantify the correlation between the virtual and the real LiDAR point cloud data. The results show that the deterministic approach matches the real measurements better than the analytical approach for the scenarios in which the yaw rate of the ego vehicle changes rapidly.
The effect of ionomer to carbon (I/C) weight ratio and relative humidity (RH) on cathode catalyst degradation was investigated by comprehensive in situ characterization. Membrane electrode assemblies (MEA) with I/C ratios of 0.5, 0.8 and 1.2 were subjected to an accelerated stress test performed at 40, 70 and 100% RH. The results show an increasing loss in electrochemical active surface area (ECSA) for both higher I/C ratios and RH during voltage cycling. To differentiate between ionomer and water connected ECSA, carbon monoxide stripping measurements were performed at varying RH. Before degradation, all MEAs show comparable total ECSA values, while higher I/C ratios lead to a larger fraction of ionomer connected ECSA. After degradation, ECSA measurements of the lowest I/C ratio showed a relatively higher loss of Pt in contact with ionomer than Pt in contact with water, while an opposite trend was observed for higher I/C ratios. H2 /N2 impedance measurements showed drastically increasing protonic catalyst layer resistances for decreasing RH especially at low I/C ratios, which might hinder Pt 2+ ion diffusion towards the membrane, hence decreasing the ECSA loss. Limiting current measurements show increasing molecular O 2 diffusion resistances at end of test for samples with higher I/C ratios and higher ECSA loss.
Die Corona-Pandemie hat tiefe Spuren auch in der empirischen Sozialforschung hinterlassen. Insbesondere qualitativ Forschende sehen die Pandemie als Einschnitt und Zensur. Dieser Artikel versucht dagegen, die Herausforderungen der Pandemie mit Blick auf die arbeitssoziologische Fallstudienforschung als Beschleunigung von Wandlung wie Heterogenität arbeitssoziologischer Forschung zu begreifen. Dies wird anhand der theoretisch-konzeptionellen, methodologischen wie empirischen Ebenen aufgezeigt. Die Pandemie und ihre Folgen führen demnach weniger zu einer substanziellen Krise insbesondere der qualitativen Forschung, als vielmehr zur Reflexion eigener Grundannahmen und Vorgehensweisen auf den genannten Ebenen: Empirisch wie forschungspolitisch wird das eigene Erwartungsmanagement herausgefordert, methodisch wie konzeptionell verlangt der heterogenere Zugang zu Interviewees, Betrieben und die damit verbundenen Veränderungen in den Fallrekonstruktionen Antworten.
The development of Automated Driving Systems (ADS) has the potential to revolutionize the transportation industry, but it also presents significant safety challenges. One of the key challenges is ensuring that the ADS is safe in the event of Foreseeable Misuse (FM) by the human driver. To address this challenge, a case study on simulation-based testing to mitigate FM by the driver using the driving simulator is presented. FM by the human driver refers to potential driving scenarios where the driver misinterprets the intended functionality of ADS, leading to hazardous behavior. Safety of the Intended Functionality (SOTIF) focuses on ensuring the absence of unreasonable risk resulting from hazardous behaviors related to functional insufficiencies caused by FM and performance limitations of sensors and machine learning-based algorithms for ADS. The simulation-based application of SOTIF to mitigate FM in ADS entails determining potential misuse scenarios, conducting simulation-based testing, and evaluating the effectiveness of measures dedicated to preventing or mitigating FM. The major contribution includes defining (i) test requirements for performing simulation-based testing of a potential misuse scenario, (ii) evaluation criteria in accordance with SOTIF requirements for implementing measures dedicated to preventing or mitigating FM, and (iii) approach to evaluate the effectiveness of the measures dedicated to preventing or mitigating FM. In conclusion, an exemplary case study incorporating driver-vehicle interface and driver interactions with ADS forming the basis for understanding the factors and causes contributing to FM is investigated. Furthermore, the test procedure for evaluating the effectiveness of the measures dedicated to preventing or mitigating FM by the driver is developed in this work.
As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.
Hintergrund:
Nicht nur in stationären pflegerischen Kontexten, sondern auch im Privatbereich wird digitalen Technologien ein hohes Potenzial zugeschrieben. Somit geraten pflegende An- und Zugehörige als Zielgruppe für digitale Angebote in den Fokus. Obwohl pflegende Angehörige heterogene Bedarfslagen und Lebenssituationen aufweisen, sind diese ebenso häufig mit ähnlichen Herausforderungen konfrontiert: unübersichtliche Angebotslandschaften zu Leistungs- und Beratungszugängen sowie ein hoher organisatorischer und zeitlicher Aufwand zur Koordination der Pflege. Um diesen Herausforderungen zu begegnen, stehen auch in Deutschland vermehrt digitale Angebote zur Verfügung, welche bei diesen Tätigkeiten Unterstützung bieten sollen.
Fragestellung:
Im Rahmen der Studie „DiVa – Digitale Versorgungsanwendungen“ wurde der Frage nachgegangen, welche Anforderungen und Wünsche pflegende Angehörige an für sie konzipierte Apps haben. Anhand einer marktreifen Smartphone-App wurde untersucht, in welchem Zusammenhang die App in realen Pflegesettings genutzt wird und ob, und unter welchen Bedingungen, sie von pflegenden Angehörigen als Unterstützung im Pflegealltag wahrgenommen wird.
Methode:
Im Zeitraum 09/2022 – 08/2023 wurde eine frei verfügbare Smartphone-App von 16pflegenden Angehörigen über mehrere Monate genutzt. Mit allen Teilnehmenden wurden jeweils zwei qualitative, leitfadengestützte Interviews durchgeführt (vor und nach der Nutzung der Anwendung). Die Auswertung erfolgte mithilfe der Inhaltsanalyse nach Kuckartz.
Ergebnisse:
Die Ergebnisse verdeutlichen eine Diskrepanz zwischen einer allgemein positiven Einschätzung der App und deren Funktionen einerseits und einem geringen Nutzungsverhalten bzw. einer kaum wahrgenommenen Erleichterung andererseits. In diesem Zusammenhang kommt dem Zeitpunkt innerhalb der Pflegebiografie, zu welchem die Teilnehmenden die App nutzen, eine hohe Bedeutung zu. So erleben die informell Pflegenden, die ihre Angehörigen bereits über einen längeren Zeitraum pflegen, durch die Nutzung der App – im Vergleich zu den bereits eingespielten Abläufen und bekannten Informationskanälen im Pflegealltag – kaum Erleichterung. Ein potenzieller Mehrwert und eine Entlastung durch die App werden zu Beginn einer Pflegeübernahme gesehen. Insgesamt wurde die App punktuell genutzt, die Zufriedenheit mit den darin aufbereiteten Informationen war u. a. von der individuellen Pflegesituation abhängig. Die Teilnehmenden weisen auf bisher nicht integrierte, aber wünschenswerte Anpassungen hin, z. B. regionale Informationen.
Schlussfolgerung:
Die Ergebnisse zeigen die Bedeutung des frühzeitigen Wissens um digitale Technologien für die Angehörigenpflege: Anwendungen, welche auf Informationen, Koordination und Organisation abzielen, entfalten ihr erhofftes Unterstützungspotenzial vor allem, wenn sie von Beginn an in den Alltag integriert werden können. Die Befunde belegen zudem, dass es eine zentrale Herausforderung ist, informell Pflegende frühzeitig über die Existenz und Möglichkeiten digitaler Versorgungsanwendungen zu informieren. Als weitere Voraussetzungen für eine breite Akzeptanz von Pflege-Apps wurden u. a. eine vertrauenswürdige Darstellung der Inhalte, deren Individualisierbarkeit und Aktualität genannt. Die Ergebnisse können wertvolle Einblicke in die Gestaltung und Umsetzung digitaler Lösungen für die häusliche Pflege liefern, insbesondere wenn es darum geht, die Bedürfnisse und Erwartungen informell Pflegender zu berücksichtigen.
Scenario-based testing is essential for Highly Automated Driving (HAD) vehicles to determine the safety-related input parameters and their boundaries. The increasing complexity, vehicle functions, and operational design pose new challenges for scenario-based testing, as the number of scenarios is enormous. Therefore, an efficient and systematic process is required in the various stages of scenario-based testing. The contribution of this study is to provide sensitivity information of safety related parameters and support logical scenario reduction. This paper presents an approach that supports to optimize the safety-related parameters boundary towards logical scenario reduction. Additionally, sensitivity analysis is applied by computing Variance- Based Sensitivity Analysis (VBSA) indices and prioritize the input parameters. Two datasets are investigated by VBSA based on the input parameters. One dataset is based on the samples from realworld scenarios and other dataset is derived from the samples considering statistic distributions with a specific parameter range. Moreover, the proposed approach is applied to an exemplary use case and the outcomes are demonstrated.
Sustainability is a critical challenge in modern tourism, exacerbated by climate change and globalization. Thanks to digitization, data-driven approaches constitute a key technology for addressing related issues, such as overtourism. However, the overarching complexity of the touristic data landscape, amplified by the interplay of diverse digital platform ecosystems, poses considerable challenges to both data owners and consumers. To mitigate such issues, knowledge graphs (KGs) have received significant attention. KGs focus on data quality by employing unified data models and continuous data refinements, making them well-suited for data-driven applications. Although promising, many challenges must be addressed to make KGs useful in practice. This paper overviews the state of the art of the field and identifies avenues for future research, explicitly focusing on touristic value and sustainability. Following our results, future research should focus on different areas, notably real-time knowledge graph population, distributed and parallelized processes, and ontologies for dynamic data types.
This paper reviews the recent literature on the “social ranking problem”, that is, the problem of converting group rankings into individual rankings. We introduce and categorize existing social ranking methods and we briefly explain their attributes. Three main categories of social ranking methods are identified: lexicographic social rankings, methods based on voting mechanisms, and those inspired by the theory of coalitional games. An open-source R package called socialranking for computing the majority of the existing social rankings is also presented and discussed.
Computing Shapley values for large cooperative games is an NP-hard problem. For practical applications, stochastic approximation via permutation sampling is widely used. In the context of machine learning applications of the Shapley value, the concept of antithetic sampling has become popular. The idea is to employ the reverse permutation of a sample in order to reduce variance and accelerate convergence of the algorithm. We study this approach for the Shapley and Banzhaf values, as well as for the Owen value which is a solution concept for games with precoalitions. We combine antithetic samples with established stratified sampling algorithms. Finally, we evaluate the performance of these algorithms on four different types of cooperative games.
Motion sickness research has always been shaped by current events. With the advent of highly automated vehicles (HAVs), the topic is currently being revisited as 60% of users of HAV functions are expected to suffer from motion sickness. Failure to address this condition will jeopardize user acceptance of HAV functions. We investigated the vestibular mechanisms of motion misinterpretation and hypothesized that cross-coupled stimuli induce more sensory conflict and lead to higher motion sickness incidence compared to the non-coupled control condition. We conducted an experiment on a dynamic driving simulator with realistic motion profiles and analyzed the influence of cross-coupled motion on motion sickness incidence. Results show no significant difference in motion sickness incidence between cross-coupled and non-coupled motion profiles. Further research is needed to investigate the thresholds of the Coriolis effect and should include the measurement of compensatory or inertial head motion of participants.
Im November/Dezember 2022 führte das Bayerische Zentrum für Tourismus das dritte Jahr in Folge eine Online-Befragung unter Erstsemesterstudierenden an acht bayerischen Universitäten und Hochschulen durch. Die Umfrageergebnisse bieten einen exemplarischen Einblick in berücksichtigte Aspekte bei der Wahl der Hochschule und des Studiengangs, Vorbildfunktionen, Berufs- und Lebensziele, aktuelle Sorgen und Ängste, das Reiseverhalten und Assoziationen zum Thema Reisen der Erstsemesterstudierenden in Bayern.
Im Fokus der Befragung steht insbesondere die Generation Z, allerdings erheben die gewonnenen Daten keinen Repräsentativitätsanspruch für Erstsemesterstudierende in Bayern. Dennoch vermittelt die Gegenüberstellung der Umfrageergebnisse der drei Erhebungsreihen einen Eindruck von den jüngeren Studierendengenerationen sowie von möglichen zukünftigen Entwicklungstendenzen in den untersuchten Lebens- und Themenbereichen.
The Asymmetric Public Goods Game (APGG) C++ framework offers an easy to use environment to study game theoretical questions. Specifically, it is designed to address questions in the domain of asymmetric public goods games.
We hereby publish version 1.1.2 of our software APGG (Release for DOI via Zenodo for paper in JOSS, the Journal of Open Source Software).
The Asymmetric Public Goods Game (APGG) C++ framework offers an easy to use environment to study game theoretical questions. Specifically, it is designed to address questions in the domain of asymmetric public goods games. The modular architecture allows for a vast amount of scenarios and setups for experimenting with different public goods games, using easy to change parameters. Users can experiment with well mixed and structured populations as well as with symmetric and asymmetric payoffs. APGG also features group level payoffs and individual payoffs, and different evolutionary selection mechanisms (Miller et al., 1995) and replication schemes. Results are automatically saved in semantic and descriptive structures and can be easily visualized with the included Python scripts. This paper aims to explain the functionality and the structure of the framework, to show the workflow that APGG follows, to present the different modules that are available, and to show how APGG can be used to run experiments with public goods games on example scenarios.
This paper has the goal to propose an approach to enable reliable planning of BEBs in a traffic simulation which acts as the virtual environment. It was shown that the defaults for the vehicle trajectories from the traffic simulation SUMO, introduce significant errors. With some fine tuning and optimization of the vehicle behavior, reasonable results can be obtained from the traffic simulation. This can be used for planning of new bus networks considering many aspects, like energy demand, impact of traffic on delays and the resulting charging times at terminal stations.