In recent years electric mobility has gained a great deal of attention, leading to electric vehicles on the market and development of necessary charging infrastructure. Charging infrastructure is mostly enabled through subsidies by local or national governments to overcome the chicken and egg problem, while the business case for charge stations in this early stage of development is not yet sufficient. The municipality of Amsterdam is a forerunner in the development of charge infrastructure, with over 500 public charge points available. The municipality and service providers struggle how to optimize the roll out of further charge points and how to optimize the use of the charge points. This paper gives a detailed analysis of the actual usage patterns of the public charging infrastructure in the city of Amsterdam, based on more than 109.000 charge sessions collected at the existing local charge points in 2012/2013. The conclusions from this analysis can be used to gain insight in the actual usage patterns of public charging infrastructure and may lead to recommendations concerning further roll out of charge stations, increasing effectiveness and improving the business case for charge points. The conclusions and recommendations may have implications for, and may support municipalities in the effective development of charging infrastructure.
On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term.
Deployment and management of environmental infrastructures, such as charging infrastructure for Electric Vehicles (EV), is a challenging task. For policy makers, it is particularly difficult to estimate the capacity of current deployed public charging infrastructure for a given EV user population. While data analysis of charging data has shown added value for monitoring EV systems, it is not valid to linearly extrapolate charging infrastructure performance when increasing population size.We developed a data-driven agent-based model that can explore future scenarios to identify non-trivial dynamics that may be caused by EV user interaction, such as competition or collaboration, and that may affect performance metrics. We validated the model by comparing EV user activity patterns in time and space.We performed stress tests on the 4 largest cities the Netherlands to explore the capacity of the existing charging network. Our results demonstrate that (i) a non-linear relation exists between system utilization and inconvenience even at the base case; (ii) from 2.5x current population, the occupancy of non-habitual charging increases at the expense of habitual users, leading to an expected decline of occupancy for habitual users; and (iii) from a ratio of 0.6 non-habitual users to habitual users competition effects intensify. For the infrastructure to which the stress test is applied, a ratio of approximately 0.6 may indicate a maximum allowed ratio that balances performance with inconvenience. For policy makers, this implies that when they see diminishing marginal performance of KPIs in their monitoring reports, they should be aware of potential exponential increase of inconvenience for EV users.
The focus of this project is on improving the resilience of hospitality Small and Medium Enterprises (SMEs) by enabling them to take advantage of digitalization tools and data analytics in particular. Hospitality SMEs play an important role in their local community but are vulnerable to shifts in demand. Due to a lack of resources (time, finance, and sometimes knowledge), they do not have sufficient access to data analytics tools that are typically available to larger organizations. The purpose of this project is therefore to develop a prototype infrastructure or ecosystem showcasing how Dutch hospitality SMEs can develop their data analytic capability in such a way that they increase their resilience to shifts in demand. The one year exploration period will be used to assess the feasibility of such an infrastructure and will address technological aspects (e.g. kind of technological platform), process aspects (e.g. prerequisites for collaboration such as confidentiality and safety of data), knowledge aspects (e.g. what knowledge of data analytics do SMEs need and through what medium), and organizational aspects (what kind of cooperation form is necessary and how should it be financed).
Digital transformation has been recognized for its potential to contribute to sustainability goals. It requires companies to develop their Data Analytic Capability (DAC), defined as their ability to collect, manage and analyze data effectively. Despite the governmental efforts to promote digitalization, there seems to be a knowledge gap on how to proceed, with 37% of Dutch SMEs reporting a lack of knowledge, and 33% reporting a lack of support in developing DAC. Participants in the interviews that we organized preparing this proposal indicated a need for guidance on how to develop DAC within their organization given their unique context (e.g. age and experience of the workforce, presence of legacy systems, high daily workload, lack of knowledge of digitalization). While a lot of attention has been given to the technological aspects of DAC, the people, process, and organizational culture aspects are as important, requiring a comprehensive approach and thus a bundling of knowledge from different expertise. Therefore, the objective of this KIEM proposal is to identify organizational enablers and inhibitors of DAC through a series of interviews and case studies, and use these to formulate a preliminary roadmap to DAC. From a structure perspective, the objective of the KIEM proposal will be to explore and solidify the partnership between Breda University of Applied Sciences (BUas), Avans University of Applied Sciences (Avans), Logistics Community Brabant (LCB), van Berkel Logistics BV, Smink Group BV, and iValueImprovement BV. This partnership will be used to develop the preliminary roadmap and pre-test it using action methodology. The action research protocol and preliminary roadmap thereby developed in this KIEM project will form the basis for a subsequent RAAK proposal.
Snelheid is één van de belangrijkste basisrisicofactoren in het verkeer. Hoe sneller er gereden wordt in een auto hoe groter de kans op (zware) ongevallen2 en hoe hoger de uitstoot. Veel verkeersveiligheidsbeleid spitst zich daarom toe op het voorkomen van te hoge snelheden en het voorkomen van te grote snelheidsverschillen. ISA, Intelligente Snelheid Adaptatie, is een van de technologische oplossingen die kan bijdragen aan het voorkomen van te hoge snelheden in auto’s. ISA kent vele verschijningsvormen, van informerend (via slimme technologie wordt de bestuurder geïnformeerd over de geldende maximumsnelheid) tot dwingend (de auto wordt fysiek beperkt om harder te rijden dan de maximumsnelheid). Inmiddels bestaat voldoende bewijs dat de acceptatiegraad van ISA hoog kan zijn, wanneer het systeem perfect werkt. De praktijk is echter weerbarstig, doordat systemen (soms) technisch kunnen falen of onvoldoende correcte informatie doorgeven aan de bestuurder. Dit staat de acceptatie van ISA in de weg; niet in de laatste plaats omdat onderzoek heeft aangetoond dat bestuurders hogere normen hanteren voor het accepteren van technisch falen in zelfrijdende voertuigen5. Een (rijtaakondersteunend)systeem moet ten alle tijden beter functioneren dan de mens. In ACTI-I wordt dit spanningsveld onderzocht. De vraag luidt: Welke impact heeft technisch falen op de acceptatie van ISA? Deze vraag wordt beantwoord middels 1) literatuuronderzoek naar falen en acceptatie van technologische systemen; 2) rijsimulator/deelnemersonderzoek naar de waardering voor ISA en of, en zo ja hoe, de waardering verandert al naar gelang het falen van het systeem toeneemt. We werken hiervoor samen met drie MKB’s die ISA systemen ontwikkelen en verkopen aan particulieren en de overheid. De resultaten van ACTI-I zullen worden gepubliceerd en vormen de basis voor een RAAK-MKB onderzoek naar de relatie tussen technisch falen en de bestuurdersacceptatie van ISA en andere geavanceerde rijhulpsystemen
