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In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%.

With rising electricity demand through digitization and innovation, the urgency of climate change mitigation, and the recent geopolitical crisis, stakeholders in developing countries face the complex task to build reliable, affordable, and low-emission energy systems. Information inaccessibility, data unavailability, and scarce local expertise are major challenges for planning and transitioning to decentralized solutions. Motivated by the calls for more solution-oriented research regarding sustainability, we design, develop, and evaluate the web-based decision support system NESSI4Dweb+ that is tailored to the needs and capabilities of various stakeholders in developing countries. NESSI4Dweb+ is open access and considers location-specific circumstances to facilitate multi-energy planning. Its applicability is demonstrated with a case study of a representative rural village in southern Madagascar and evaluated through seven interviews with experts and stakeholders. We show that NESSI4Dweb+ can support the achievement of the United Nations Sustainable Development Goals and enable the very prerequisite of digitization: reliable electrification.

Voice commerce creates unprecedented opportuni­ties for consumers and vendor firms to interact, engage, and relate. With artificial intelligence–pow­ered voice assistants, consumers can make technol­ogy-mediated purchases without using their tactile senses, which represents a new space for commer­cial interactions. Drawing on the customer journey as an organizing framework, this article proposes a structured research agenda, in an attempt to shed light on the bright side effects of voice commerce while also acknowledging concerns for consumer protection and society in general. Voice assistants can enhance every stage of the purchase journey, yet their use might have negative consequences for customer relationships. In the prepurchase stage, voice assistants can act as information curators or manipulators. In the purchase stage, voice assis­tants can adopt roles as shopping concierges or impediments. In the postpurchase and usage stage, voice assistants can become trusted relationship partners or hostile intruders in consumers’ lives.

For the investigation of major traffic accidents, larger vessels are obliged to install a voyage data recorder (VDR). However, not every vessel is equipped with a VDR, and the readout is often a manual process that is costly. In addition, not only ship-related information can be relevant for reconstructing traffic accidents, but also information from other entities such as meteorological services or port operators. Moreover, another major challenge is that entities tend to trust only their records, and not those of others as these could be manipulated in favor of the particular recording entity (e.g., to disguise any damage caused). This paper presents an approach to documenting arbitrary data from different entities in a trustworthy, decentralized, and tamper-proof manner to support the conflict resolution process. For this purpose, all involved entities in a traffic situation can contribute to the documentation by persisting their available data. Since maritime stakeholders are equipped with various sensors, a diverse and meaningful data foundation can be aggregated. The data is then signed by a mutually agreed upon timestamping authority (TSA). In this way, everyone can cryptographically verify whether the data has been subsequently changed. This approach was successfully applied in practice by documenting a vessel’s mooring maneuver.

The term ”Agriculture 4.0” emerged from the term “Industry 4.0” like amany other “4.0” terms. However, are Industry 4.0 technologies and concepts really applicable to agriculture? Are the benefits that Industry 4.0 brings to industrial use cases transferable to livestock farming? This paper tries to answer this question for the three dominant sectors of livestock farming in Central Europe and Germany: Poultry, pig fattening, and dairy farming. These sectors are analyzed along with the eight most relevant Industry 4.0 benefits. The results show that only part of the Industry 4.0 benefits are relevant for livestock farming in a similar manner as in industrial production. Due to basic differences between industrial and livestock farming use cases, some of the benefits must be adapted. The presence of individual living animals and the strong environmental impact of livestock farming affect the role of digital individualization and demand orientation. The position of livestock farming within the value chain minimizes the need for flexibilization. The introduction and adoption of Industry 4.0 concepts and technologies may contribute significantly to transforming agriculture into something that may be called Agriculture 4.0. Technologies are indispensable for this development step, but vocational education and open-mindedness of farmers towards Industry 4.0 is essential as well.