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This study presents an approach to collect and classify usage data of public charging infrastructure in order to predict usage based on socio-demographic data within a city. The approach comprises data acquisition and a two-step machine learning approach, classifying and predicting usage behavior. Data is acquired by gathering information on charging points from publicly available sources. The first machine learning step identifies four relevant usage patterns from the gathered data using an agglomerative clustering approach. The second step utilizes a Random Forest Classification to predict usage patterns from socio-demographic factors in a spatial context. This approach allows to predict usage behavior at locations for potential new charging points. Applying the presented approach to Munich, a large city in Germany, results confirm the adaptability in complex urban environments. Visualizing the spatial distribution of the predicted usage patterns shows the prevalence of different patterns throughout the city. The presented approach helps municipalities and charging infrastructure operators to identify areas with certain usage patterns and, hence different technical requirements, to optimize the charging infrastructure in order to help meeting the increasing demand of electric mobility.
Driving forest machines on wet soils causes irreversible soil compaction, often associated with intensive rut formation and inaccessibility of wheeled forest machines for future forest operations. The German forestry equipment manufacturer FHS, Forsttechnik Handel & Service GmbH, engineered a forwarder, the Trac 81/11, equipped with conventional, well-proved bogie-axles embraced by a closed rubber track. At the center of the bogie-axle, four additional supportive rollers are placed to increase the load-carrying section between the tires of the bogie-axle. The study aimed to characterize the principle concept and the trafficability of the forwarder by analyzing the footprint area, the contact pressure, the rut formation on forest sites and the slippage during driving. Therefore, the effective contact area was measured on steel plates and rut formation was analyzed on a case study basis. Results showed that the supportive rollers increase the contact surface area by about 1/3. By this, a decrease of peak loads below the wheels and a more homogenous load distribution were observed. However, the contact surface area is still clearly divided into three parts; the area between the supportive rollers and the wheels does not take any load. Results of the rut formation were diverse: After 20 passes with 26,700 kg total mass, rut depth varied between 12.6 and 212.5 mm. Overall, the new undercarriage concept of FHS demonstrated a generally positive performance. The engineered forwarder contributes to reduce the environmental impact associated with log extraction.
Purpose
Due to the growing percentage share of urban dwellers, the physical distribution of products faces altering conditions. This research explores the effects that urbanization has on the performance of a fast-moving consumer goods distribution network. A focus is set on changes in distribution cost, the cost-minimal network design, and greenhouse gas emissions.
Design/methodology/approach
The analyses are based on a quantitative distribution network model of an existing manufacturer of consumer goods.
Findings
The results indicate that the foreseen population shift will affect the network's economic and environmental performance. Effects are, among others, due to differences in the efficiency of supplying urban and nonurban regions. The combined effects of urbanization and the development of the population size will even more affect the network's performance.
Originality/value
Research dealing with distribution logistics and urbanization primarily focuses on city logistics. In this paper, the object of analysis is the entire distribution system.
Considering climate change, recent political debates often focus on measures to reduce CO2 emissions. One key component is the reduction of emissions produced by motorized vehicles. Since the amount of emission directly correlates to the velocity of a vehicle via energy consumption factors, a general speed limit is often proposed. This article presents a methodology to combine openly available topology data of road networks from OpenStreetMap (OSM) with pay-per-use API traffic data from TomTom to evaluate such measures transparently by analyzing historical real-world circumstances. From our exemplary case study of the German motorway network, we derive that most parts of the motorway network on average do not reach their maximum allowed speed throughout the day due to traffic, construction sites and general road utilization by network participants. Nonetheless our findings prove that the introduction of a speed limit of 120 km per hour on the German autobahn would restrict 50.74% of network flow kilometers for a CO2 reduction of 7.43% compared to the unrestricted state.
Environmental regulations force car manufacturers to renew the powertrain technology portfolio offered to the customer to comply with greenhouse gas (GHG) emission targets. In turn, automotive companies face the task of identifying the “right” powertrain technology portfolio consisting of, for example, internal combustion engines and electric vehicles, because the selection of a particular powertrain technology portfolio affects different company targets simultaneously. What makes this decision even more challenging is that future market shares of the different technologies are uncertain. Our research presents a new decision-support approach for assembling optimal powertrain technology portfolios while making decision-makers aware of the trade-offs between the achievable profit, the achievable market share, the market share risk, and the GHG emissions generated by the selected vehicle fleet. The proposed approach combines “a posteriori” decision-making with multi-objective optimization. In an application case, we feed the outlooks of selected market studies into the proposed decision-support system. The result is a visualization and analysis of the current real-world decision-making problem faced by many automotive companies. Our findings indicate that for the proposed GHG restriction at work in 2030 in the European Union, no optimal powertrain technology portfolio with less than 35% of vehicles equipped with an electric motor exists.
Natural Language Processing, such as speech-to-text technology, is increasingly implemented in collaboration software that is used by global virtual teams (GVT). GVT collaboration has become ubiquitous and has additionally accelerated during the COVID-19 pandemic. The main issues of global virtual teams are technology difficulties, language and time zone differences, and lower levels of psychological safety. Advances in collaboration technology aim at improving collaboration for GVT. But we know little about the acceptance of these technologies. Therefore, the objective of this study is to explore how Millennial and Gen Z members of GVT accept speech-to-text technology; namely, automated captions in virtual conferences and automated meetings transcripts. Particularly, we are comparing antecedents of acceptance across levels of language proficiency and psychological safety. We surveyed 530 users of speech-to-text technology in GVT both before and after they used the technology. The pre-survey was administered before the COVID-19 pandemic hit; when participants completed the post-survey all were under some degree of lockdown. Results suggest that use of the technology reduces anxiety and effort, but decreases performance expectation and hedonic motivation. Non-native speakers rate the technology more positively. The impact of psychological safety is limited to self-efficacy and anxiety.