With a growing number of electric vehicles (EVs) on the road and charging infrastructure investments lagging, occupation of installed charging stations is growing and available charging points for EV drivers are becoming scarce. Installing more charging infrastructure is problematic from both a public(tax payers money, parking availability) and private (business case) perspective. Increasing the utilization of available charging stations is one of the solutions to satisfy the growing charging need of EV drivers and managing other stakeholders interests. Currently, in the Netherlands only 15-25% of the time connected to a public charging station is actually used for charging. The longest 4% of all sessions account for over 20% of all time connected while barely using this time for actually charging. The behaviour in which EV users stay connected to a charging station longer than necessary to charge their car is called “charging station hogging”. Using a large dataset (1.3 million sessions) on publiccharging infrastructure usage, this paper analyses the inefficient use of charging stations along three axes: where the hogging takes place (spatial), by whom (the characteristics of the user) and during which time frames (day, week and year). Using the results potential solutions are evaluated and assessed including their potential and pitfalls.
This paper presents data-driven insights from a case study that was carried out in an University EV charging plaza where EV charging demand is met with the combination of the University campus grid and installed solar capacity. First, we assessed the plaza dependency on the grid for meeting EV charging demand and intake of excess solar energy using the available dataset. By modifying the plaza network to accommodate a small approx. 50 kWh battery storage can significantly reduce the grid dependency of the plaza by approx. 30% compared to the present situation and can also increase the green energy utility for EV charging by 10-20%. Having an battery storage could also help overcome the limitations due to the campus grid capacity during EV charging peak demand by means of scheduling algorithms. Second, we assessed the utility rate of the plaza which indicated that the average utility of charging infrastructure is about 30% which has an increasing trend over the analysed period. The low utility and EV charging peak demand may be the result of current EV user behavior where the average idle time during charging sessions is found to be approx. 90 minutes. Reduction in idle time by one third may increase the capacity and utility of plaza by two to two and half times the forecasted daily demand. By having the campus grid capacity and user information may further help with effect EV demand forecasting and scheduling.
In the Interreg Smart Shared Green Mobility Hubs project, electric shared mobility is offered through eHUBs in the city. eHUBs are physical places inneighbourhoods where shared mobility is offered, with the intention of changing citizens’ travel behaviour by creating attractive alternatives to private car use.In this research, we aimed to gain insight into psychological factors that influence car owners’ intentions to try out shared electric vehicles from an eHUB in order to ascertain:1. The psychological factors that determine whether car owners are willing to try out shared electric modalities in the eHUBs and whether these factors are identical for cities with different mobility contexts.2. How these insights into psychological determinants can be applied to entice car owners to try out shared electric modalities in the eHUBs.Research was conducted in two cities: Amsterdam (the Netherlands) and Leuven (Belgium). An onlinesurvey was distributed to car owners in both cities inSeptember 2020 and, additionally, interviews wereheld with 12 car owners in each city.In general, car owners from Amsterdam and Leuven seem positive about the prospect of having eHUBs in their cities. However, they show less interest inusing the eHUBs themselves, as they are satisfied with their private car, which suits their mobility needs. Car owners mentioned the following reasons for notbeing interested in trying out the eHUBs: they simply do not see a need to do so, the costs involved with usage, the need to plan ahead, the expected hasslewith registration and ‘figuring out how it works’, having other travel needs, safety concerns, having to travel a distance to get to the vehicle, and a preferencefor ownership. Car owners who indicated that they felt neutral, or that they were likely to try out an eHUB, mentioned the following reasons for doing so:curiosity, attractive pricing, convenience, not owning a vehicle like those offered in an eHUB, environmental concerns, availability nearby, and necessity when theirown vehicle is unavailable.In both cities, the most important predictor determining car owners’ intention to try out an eHUB is the perceived usefulness of trying out an eHUB.In Amsterdam, experience with shared mobility and familiarity with the concept were the second and third factors determining car owners’ interest in tryingout shared mobility. In Leuven, pro-environmental attitude was the second factor determining car owners’ openness to trying out the eHUBs, and agewas the third factor, with older car owners being less likely to try one out.Having established that perceived usefulness was the most important determinant for car owners to try out shared electric vehicles from an eHUB, weconducted additional research, which showed that, in both cities, three factors contribute to perceived usefulness, in order of relevance: (1) injunctive norms(e.g., perceiving that society views trying out eHUBs as correct behaviour); (2) trust in shared electric mobility as a solution to problems in the city (e.g., expecting private car owners’ uptake of eHUBs to contributeto cleaner air, reduce traffic jams in city, and combat climate change); and (3) trust in the quality and safety of the vehicles, including the protection of users’privacy. In Amsterdam specifically, two additional factors contributed to perceived usefulness of eHUBs: drivers’ confidence in their capacity to try out anunfamiliar vehicle from the eHUB and experience of travelling in various modes of transport.Drawing on the relevant literature, the results of our research, and our behavioural expertise, we make the following recommendations to increase car users’ uptake of shared e-mobility:1. Address car owners’ attentional bias, which filters out messages on alternative transport modes.2. Emphasise benefits of (trying out) shared mobility from different perspectives so that multiple goals can be addressed.3. Change the environment and the infrastructure, as infrastructure determines choice of transport.4. For Leuven specifically: target younger car owners and car owners with high pro-environmental attitudes.5. For Amsterdam specifically: provide information on eHUBs and opportunities for trying out eHUBs.
MULTIFILE
Dutch Cycling Intelligence (DCI) embodies all Dutch cycling knowledge to enhances customer-oriented cycling policy. Based on the data-driven cycle policy enhancement tools and knowledge of the Breda University of Applied Sciences, DCI is the next step in creating a learning community between road authorities, consultants, cycling industry, and knowledge institutes with their students. The DCI consists of three pilars:- Connecting- Accelerating knowledge- Developing knowledgeConnecting There are many stakeholders and specialists in the cycling domain. Specialists with additional knowledge about socio-cultural impacts, geo-special knowledge, and technical traffic solutions. All of these specialists need each other to ensure a perfect balance between the (electric) bicycle, the cyclist and the cycle path in its environment. DCI connects and brings together all kind of different specialists.Accelerating knowledge Many bicycle innovations take place in so-called living labs. Within the living lab, the triple helix collaboration between road authorities the industry and knowledge institutes is key. Being actively involved in state-of-the-art innovations creates an inspiring work and learning environment for students and staff. A practical example of a successful living lab is the cycle superhighway F261 between Tilburg and Waalwijk, where BUAS tested new cycle route signage. Next, the Cycling Lab F58 is created, where the road authorities Breda and Tilburg opened up physical cycling infrastructure for entrepreneurs in the bicycle domain and knowledge institutes to develop e-cycling innovation. The living labs are test environments where pilots can be carried out in practice and an excellent environment for students to conduct scientifically applied research.Developing knowledge Ultimately, data and information must be translated into knowledge. With a team of specialists and partners Breda University of applied sciences developed knowledge and tools to monitor and evaluate cycling behavior. By participating in (inter)national research programs BUAS has become one of the frontrunners in data-driven cycle policy enhancement. In close collaboration with road authorities, knowledge institutes as well as consultants, new insights and answers are developed in an international context. By an active knowledge contribution to the network of the Dutch Cycling Embassy, BUAS aims to strengthen its position and add to the global sustainability challenges. Partners: Province Noord-Brabant, Province Utrecht, Vervoerregio Amsterdam, Dutch Cycling Embassy, Tour de Force, University of Amsterdam, Technical University Eindhoven, Technical University Delft, Utrecht University, DTV Capacity building, Dat.mobility, Goudappel Coffeng, Argaleo, Stratopo, Move.Mobility Clients:Province Noord-Brabant, Province Utrecht, Province Zuid-Holland, Tilburg, Breda, Tour de Force
Despite increasing efforts regarding knowledge valorisation, a significant gap between knowledge development and policy practice remains. Urban Intelligence bridges this gap by bringing cutting edge knowledge to the table, developing new policy concepts and by promoting smart data use.The professorship of Urban Intelligence takes a multimodal and integrated approach by connecting knowledge of transport engineering, urban planning and urban design. Research output encompasses data-driven projects, such as ‘Multimodal Brabant’ and ‘Measurement Weeks Breda‘, which translate big data into knowledge for policy development.Furthermore, data analysis tool and data dashboards for cycling, such as ‘CyclePRINT’ have been developed. To enhance the integration of built environment and transportation, we developed the Bicycle-Oriented Development (BOD) concept. This is currently being integrated into an overarching development philosophy, ‘Multimodal Urban Development’, which integrates the optimisation of multimodal networks, location choices for new urban developments and the provision of shared mobility via mobility hubs.
