This paper presents the results of an experimental field study, in which the effects were studied of personalized travel feedback on car owners’ car habits, awareness of the environmental impact of their travel choices, and the intention to switch modes. For a period of six weeks, 349 car owners living in Amsterdam used a smart mobility app that automatically registered all their travel movements. Participants in the experiment group received information about travel distance, time, and CO2 emission. Results show that the feedback did not influence self-reported car habits, intention, and awareness, suggesting that personalized feedback may not be a one-size-fits-all solution to change travel habits.
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This paper analyses the effect of two new developments: electrification and ‘free floating’ car sharing and their impact on public space. Contrary to station based shared cars, free floating cars do not have dedicated parking or charging stations. They therefore park at public parking spots and utilize public charging stations. A proper network of public charging stations is therefore required in order to keep the free floating fleet up and running. As more municipalities are considering the introduction of an electric free floating car sharing system, the outline of such a public charging network becomes a critical piece of information. The objective of this paper is to create insights that can optimize charging infrastructure for free floating shared cars, by presenting three analyses. First, a business area analysis shows an insight into which business areas are of interest to such a system. Secondly, the parking and charging behaviour of the vehicles is further examined. The third option looks deeper into the locations and their success factors. Finally, the results of the analysis of the city of Amsterdam are used to model the city of The Hague and the impact that a free floating electric car sharing system might have on the city and which areas are the white spots that need to be filled in.
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As society has to adapt to changing energy sources and consumption, it is driving away from fossil energy. One particular area of interest is electrical driving and the increasing demand for (public) charging facilities. For municipalities, it is essential to adapt to this changing demand and provide more public charging facilities.In order to accommodate on roll-out strategies in metropolitan areas a data driven simulation model, SEVA1, has been developed The SEVA base model used in this paper is an Agent-Based model that incorporate past sessions to predict future charging behaviour. Most EV users are habitual users and tend to use a small subset of the available charge facilities, by that obtaining a pattern is within the range possibilities. Yet, for non-habitual users, for example, car sharing users, obtaining a pattern is much harder as the cars use a significantly higher amount of charge points.The focus of this research is to explore different model implementations to assess the potential of predicting free-floating cars from the non-habitual user population. Most important result is that we now can simulate effects of deployement of car sharing users in the system, and with that the effect on convenience for habitual users. Results show that the interaction between habitual and non habitual EV users affect the unsuccessful connection attempts based increased based on the size of the car-sharing fleet up to approximately 10 percent. From these results implications for policy makers could be drawn.
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Fast charging is usually seen as a means to facilitate long distance driving for electric vehicles and roll-out therefore often happens with corridors in mind. Due to limited charging speeds, EV drivers usually tend to charge at home or work when the car is parked for a longer period to avoid unnecessarily time loss. However with increasing charging speeds and different modes (taxi, car sharing) also switching to electric vehicles, a different approach to fast charging is needed. This research investigates the different intentions of EV drivers at fast charging stations in inner-cities and along highways to see how usage at such stations differs to inform policy makers and charging point operators to accommodate an efficient roll-out strategy.
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The transition towards electric mobility is expected to take off the coming years, as more EV car models access the market and charging infrastructure is being expanded. The expansion of charging infrastructure will have to accelerate to keep pace with the fast-growing need for charging. The coming years will be marked by uncertainty regarding technological developments (batteries, range), charging technologies (e.g. fast charging, inductive), growth of car sharing and autonomous driving and impact on user preferences and charging behaviour Data management is key to the EV market and public parties involved: to be able to adapt quickly to changes and to reduce risks and costs. This paper describes the five most important preconditions for effective data management that allows stakeholders to monitor the performance of their charging infrastructure and to take informed decisions on rollout strategies based on data science research results.
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Introduction: The Netherlands has been known as one of the pioneers in the sharing economy. At the beginning of the 2010s, many local initiatives such as Peerby (borrow tools and other things from your neighbours), SnappCar (p2p car-sharing), and Thuisafgehaald (cook for your neighbours) launched that enabled consumers to share underused resources or provide services to each other. This was accompanied by a wide interest from the Dutch media, zooming in on the perceived social and environmental benefits of these platforms. Commercial platforms such as Uber, UberPop and Airbnb followed soon after. After their entrance to the market, the societal debate about the impact of these platforms also started to include the negative consequences. Early on, universities and national research and policy institutes took part in these discussions by providing definitions, frameworks, and analyses. In the last few years, the attention has shifted from the sharing economy to the much broader defined platform economy.
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De wereld verandert in een razend tempo. Technologische ontwikkelingen hebben een grote impact op mens en maatschappij. Het verandert niet alleen onze manier van werken maar ook onze manier van leven. Steeds meer disciplines hanteren technologie als basis om in een professionele omgeving het werk kwalitatief beter, sneller en effi ciënter uit te voeren. Digitalisering, globalisering en informatisering maakt het mogelijk om plaats- en tijdonafhankelijk te studeren en te werken. Fontys Hogescholen speelt hier op in door tal van initiatieven te ondersteunen die gericht zijn op het volgen van deze en opkomende trends rondom technologische ontwikkelingen en de impact voor het onderwijs. Met het Fontys Objexlab zetten we deze beweging door. Opkomende technologieën zoals 3D printing en Robotica maken we graag toegankelijk voor collega’s. Andere instituten kunnen hiervan gebruik maken zodat zij hun onderwijs nog aantrekkelijker en actueler kunnen maken. In het najaar van 2014 zijn we gestart met het samenbrengen van collega’s van verschillende instituten en opleidingen om enerzijds deze nieuwe technologieen te leren en te ervaren, om daarna een stap te maken in het initiëren van ideeën en plannen om met deze kennis en vaardigheden onderwijsvernieuwing gezamenlijk vorm- en inhoud te geven.
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This study is the first to systematically and quantitatively explore the factors that determine the length of charging sessions at public charging stations for electric vehicles in urban areas, with particular emphasis placed on the combined parking- and charging-related determinants of connection times. We use a unique and large data set – containing information concerning 3.7 million charging sessions of 84,000 (i.e., 70% of) Dutch EV-users – in which both private users and taxi and car sharing vehicles are included; thus representing a large variation in charging duration behavior. Using multinomial logistic regression techniques, we identify key factors explaining heterogeneity in charging duration behavior across charging stations. We show how these explanatory variables can be used to predict EV-charging behavior in urban areas and we derive preliminary implications for policy-makers and planners who aim to optimize types and size of charging infrastructure.
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We need mental and physical reference points. We need physical reference points such as signposts to show us which way to go, for example to the airport or the hospital, and we need reference points to show us where we are. Why? If you don’t know where you are, it’s quite a difficult job to find your way, thus landmarks and “lieux de memoire” play an important role in our lives.
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