Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam University of Applied Sciences, in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht, and the Metropolitan Region of Amsterdam Electric, developed both the back- and front-end of a charging infrastructure assessment platform that processes and represents real-life charging data. Charging infrastructure planning and design methods described in the literature use geographic information system data, traffic flow data of non-EV vehicles, or geographical distributions of, for example, refueling stations for combustion engine vehicles. Only limited methods apply real-life charging data. Rolling out public charging infrastructure is a balancing act between stimulating the transition to zero-emission transport by enabling (candidate) EV drivers to charge, and limiting costly investments in public charging infrastructure. Five key performance indicators for charging infrastructure utilization are derived from literature, workshops, and discussions with practitioners. The paper describes the Data Warehouse architecture designed for processing large amounts of charging data, and the web-based assessment platform by which practitioners get access to relevant knowledge and information about the current performance of existing charging infrastructure represented by the key performance indicators developed. The platform allows stakeholders in the decision-making process of charging point installation to make informed decisions on where and how to expand the already existing charging infrastructure. The results are generalizable beyond the case study regions in the Netherlands and can serve the roll-out of charging infrastructure, both public and semi-public, all over the world.
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Many cities in Europe have ambitious goals when it comes to making their public transport buses emission free. This article outlines the reasoning behind the choices made in the city of Amsterdam with regards to charging infrastructure for electric buses. Emphasising the importance of operational demands, and taking into consideration relevant context factor for this city in particular, the article provides pointers for cities, public transport operators (PTOs) and original equipment manufacturers (OEMs) that are considering to introduce emission free public transport by bus.
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It has been suggested that physical education (PE) and active transport can make a meaningful contribution to children's physical activity (PA) levels. However, data on the contribution these activities to total PA is scarce, and PE's contribution to total physical activity energy expenditure (PAEE) has to our knowledge never been determined. This is probably explained by the methodological complexity of determining PAEE (Welk, 2002). In this paper, we present the first data of an ongoing study using combined heart rate monitoring and accelerometry, together with activity diaries. Over the six measurement days, PE contributed 5% to total PAEE, and 16% to school-related PAEE, whereas active transportation had a much larger contribution.
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Since the European Union wants to reduce the oil dependence of the transportation system, the uptake of alternative vehicle technologies are stimulated in the member states. In the Netherlands, stimulation is already largely implemented in the form of a comprehensive charging infrastructure. This infrastructure is widely used by the electric vehicle drivers and thus there may occur a form of competition for the charging points. In this paper we address this problem by predicting the short-term availability of charging points at a given location and time by using the historical charging data in a space-time series model. The model shows better accuracy with respect to a naive method for short term predictions up to one day. This will allow charging point operators to provide customers with the service of looking up estimated charging point availability in the nearby future.
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Background: The purpose of this study is to increase our understanding of environmental correlates that are associated with route choice during active transportation to school (ATS) by comparing characteristics of actual walking and cycling routes between home and school with the shortest possible route to school. Methods: Children (n = 184; 86 boys, 98 girls; age range: 8–12 years) from seven schools in suburban municipalities in the Netherlands participated in the study. Actual walking and cycling routes to school were measured with a GPS-device that children wore during an entire school week. Measurements were conducted in the period April–June 2014. Route characteristics for both actual and shortest routes between home and school were determined for a buffer of 25 m from the routes and divided into four categories: Land use (residential, commercial, recreational, traffic areas), Aesthetics (presence of greenery/natural water ways along route), Traffic (safety measures such as traffic lights, zebra crossings, speed bumps) and Type of street (pedestrian, cycling, residential streets, arterial roads). Comparison of characteristics of shortest and actual routes was performed with conditional logistic regression models. Results: Median distance of the actual walking routes was 390.1 m, whereas median distance of actual cycling routes was 673.9 m. Actual walking and cycling routes were not significantly longer than the shortest possible routes. Children mainly traveled through residential areas on their way to school (>80 % of the route). Traffic lights were found to be positively associated with route choice during ATS. Zebra crossings were less often present along the actual routes (walking: OR = 0.17, 95 % CI = 0.05–0.58; cycling: OR = 0.31, 95 % CI = 0.14–0.67), and streets with a high occurrence of accidents were less often used during cycling to school (OR = 0.57, 95 % CI = 0.43–0.76). Moreover, percentage of visible surface water along the actual route was higher compared to the shortest routes (walking: OR = 1.04, 95 % CI = 1.01–1.07; cycling: OR = 1.03, 95 % CI = 1.01–1.05). Discussion: This study showed a novel approach to examine built environmental exposure during active transport to school. Most of the results of the study suggest that children avoid to walk or cycle along busy roads on their way to school. https://doi.org/10.1186/s12966-016-0373-y
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Charging infrastructure deployment has taken off in many cities with the rise of the number of electric vehicles on the road. Expansion of infrastructure is a matter of prioritisation of resources to optimise the infrastructure. This paper explores how to measure charging station performance, to address the challenges that policy makers face. These performance indicators are used in a regression model, based upon current utilisation of the network, to predict which charging stations perform best. The results show that a model based on available geographical data and performance metrics of the current network are best combined to predict infrastructure performance. The variability between public charging stations is however big, as frequent user characteristics do determine the performance to a large extent.
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In many regions, governments are motivating increased bicycle ridership by designing new and improving existing bicycle infrastructure. Cycle highways are an effective and cost-efficient type of bicycle-specific infrastructure that are designed to provide a functional connection between places where people work, go to school and live. One important element of developing high quality cycle highways is the development of an effective wayfinding system which allows current, potential, and new users to clearly identify and navigate a bicycle network. The wayfinding design standards used for conventional bicycle infrastructure may not be compatible for cycle highways, which encourage cyclists to travel at relatively higher speeds. This may warrant introducing specific wayfinding signage compatible for this new type of bicycle infrastructure. This study uses qualitative analysis including field observations, ride-along videos, and semi-structured interviews, to assess electrically assisted pedal bicycle (e-bike) users' opinions and experiences with wayfinding signage along a pilot cycle highway route located between Tilburg and Waalwijk in the Netherlands. In the summer of 2018, base-line observations and interviews were administered with twelve e-bike users who were unfamiliar with the route to assess their experiences with conventional signage for cyclists before changes were made to the wayfinding system. Follow-up observations were held in the fall, after the installation of two new pilot wayfinding systems that were specifically designed to accommodate cycle highway users. Initial findings suggest that the changes made to the location, size and clarity of the signage improve cyclists' overall experiences, and that cyclists' perceptions of the built environment are important. Specifically, it became easier for users to navigate the route, their overall travel related stress decreased, and several participants perceived shorter travel times. Policy makers and transportation planners are likely to be interested in the results of this study as they reveal how specific improvements to wayfinding along cycle highways not only help improve navigation, but also positively influence cyclists' overall comfort and stress.
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This paper describes an agent-based software infrastructure for agile industrial production. This production is done on special devices called equiplets. A grid of these equiplets connected by a fast network is capable of producing a variety of different products in parallel. The multi-agent-based underlying systems uses two kinds of agents: an agent representing the product and an agent representing the equiplet.
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This systematic review examined the effect of built environment infrastructural changes (BEICs) on physical activity (PA), active transportation (AT) and sedentary behavior (SB). A literature search resulted in nineteen eligible articles. On- and off-road bicycling and/or walking trails resulted in inconsistent effects on overall PA and walking, and in predominantly positive effects on bicycling. More extensive BEICs led to mixed results, with mainly non-significant effects. However, positive effects on bicycling were found for people living closer to BEICs. None of the studies assessed SB. Improved understanding of the potential of BEICs to increase PA levels and decrease SB at population level asks for more high-quality, in-depth research, that takes into account the broader system.
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As Vehicle-to-Everything (V2X) communication technologies gain prominence, ensuring human safety from radiofrequency (RF) electromagnetic fields (EMF) becomes paramount. This study critically examines human RF exposure in the context of ITS-5.9 GHz V2X connectivity, employing a combination of numerical dosimetry simulations and targeted experimental measurements. The focus extends across Road-Side Units (RSUs), On-Board Units (OBUs), and, notably, the advanced vehicular technologies within a Tesla Model S, which includes Bluetooth, Long Term Evolution (LTE) modules, and millimeter-wave (mmWave) radar systems. Key findings indicate that RF exposure levels for RSUs and OBUs, as well as from Tesla’s integrated technologies, consistently remain below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) exposure guidelines by a significant margin. Specifically, the maximum exposure level around RSUs was observed to be 10 times lower than ICNIRP reference level, and Tesla’s mmWave radar exposure did not exceed 0.29 W/m2, well below the threshold of 10 W/m2 set for the general public. This comprehensive analysis not only corroborates the effectiveness of numerical dosimetry in accurately predicting RF exposure but also underscores the compliance of current V2X communication technologies with exposure guidelines, thereby facilitating the protective advancement of intelligent transportation systems against potential health risks.
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