Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training.

Most transportation publications to date lack sufficient transparency in terms of open source and data, in order to enable reproducibility. This work sets out to highlight the need for reproducibility of computational simulation studies, and to develop the level of professionality in the transportation domain when it comes to open source. Moreover, this work aims to equip the readers with a guide on best practices inspired by the computer science domain, templates for documentation of software and data, and guidelines on repository management and open source. The empirical analysis of more than 11,879 simulation studies and 672 repositories in the field reveals that only few works (less than 2%) share supplementary material alongside the scientific publication, on a mostly mediocre repository quality, but with a strong trend towards more transparency in the last years. Besides, simulation studies with enhanced transparency are found to not receive significantly more citations when compared with studies that do not provide additional repositories. What’s more, a survey of more than 87 transportation researchers conveys that reproducibility is perceived as a significant issue by the community, and that time and legal constraints are the largest impediments to more reproducible research.

Green innovation and technology diffusion must be financially and commercially attractive to convince corporate decision makers. This paper focuses on the factors that determine the financial outcome of successful green innovation activities conducted by large, listed companies. We employ a cross-industry dataset including more than 97,954 reports on corporate environmentalism from 286 international listed companies. Our results indicate that economic, political, cultural, firm-specific, investor-related, and governance factors significantly determine the financial performance of green innovation, measured by abnormal returns. Moreover, we can show that factors that reduce the competition in green innovation markets benefit the financial success of firms operating via them. Finally, we find an opposing influence for several factors that benefit earlier stages of innovation (e.g., research output) while harming the later stages (e.g., market introduction and financial performance). These findings imply that a spatial separation strategy for different stages of innovation supports corporate environmentalism activities. Moreover, physical property rights, the governments’ willingness to support green technologies, and economic framework conditions such as oil price, GDP, or public R&D budget need to be balanced by policymakers to address and stimulate green innovation.

City road infrastructure can be considered a public good, and congestion is the intersection of demand for mobility with the limited available transportation supply. Congestion pricing is effective in reducing demand to sustainable levels below network capacity limits, but also controversial, as it introduces equity issues and systematically discriminates against lower-income groups. Karma is a non-monetary, fair, and efficient resource allocation mechanism that employs an artificial currency different from money, incentivises cooperation amongst selfish individuals, and achieves a balance between giving and taking. Where money does not do its job, Karma achieves socially more desirable allocations by being aligned with consumers’ needs rather than their financial power. This work highlights the value proposition of Karma, provides guidance on Karma mechanism design, and equips the reader with a useful software framework to model Karma economies and predict consumers’ behaviour. A case study of road pricing demonstrates Karma’s potential.

Vehicle trajectories offer valuable insights for a wide range of road transportation applications. Due to the rise of drone technology, a growing branch of literature explores optical vehicle trajectory extraction from aerial videos, where object detection using neural networks is an important component. Horizontal bounding box object detection struggles to differentiate well between rotated vehicles, especially when dealing with complex backgrounds or densely packed vehicles. This work proposes a generalizable computation pipeline that leverages angular information to extract high-quality trajectories starting from video recordings and ending in trajectories in Cartesian and lane coordinates. A trajectory reconstruction algorithm is designed to be vehicle- and driver-informed and to maximize the physical consistency of the reconstructed trajectories both on the individual vehicles’ and platoon levels. A comprehensive benchmark of 18 object detection models on a real-world video dataset demonstrates how oriented object detection and the use of angular information can be used to significantly improve the consistency of extracted trajectories (15% better internal, and 20% better platoon consistency), and that orientation-informed trajectories can be reconstructed to lane coordinates of higher quality. The reconstructed vehicle trajectories better capture car-following and traffic dynamics, thereby improving their usability for traffic flow studies.