The development of the World Wide Web, the emergence of social media and Big Data have led to a rising amount of data. Infor¬mation and Communication Technol¬ogies (ICTs) affect the environment in various ways. Their energy consumption is growing exponentially, with and without the use of ‘green’ energy. Increasing envi¬ronmental aware¬ness has led to discussions on sustainable development. The data deluge makes it not only necessary to pay attention to the hard‑ and software di¬mensions of ICTs but also to the ‘value’ of the data stored. In this paper, we study the possibility to methodically reduce the amount of stored data and records in organizations based on the ‘value’ of informa¬tion, using the Green Archiving Model we have developed. Reducing the amount of data and records in organizations helps in allowing organizations to fight the data deluge and to realize the objectives of both Digital Archiving and Green IT. At the same time, methodi¬cally deleting data and records should reduce the con¬sumption of electricity for data storage. As a consequencs, the organizational cost for electricity use should be reduced. Our research showed that the model can be used to reduce [1] the amount of data (45 percent, using Archival Retention Levels and Retention Schedules) and [2] the electricity con¬sumption for data storage (resulting in a cost reduction of 35 percent). Our research indicates that the Green Ar¬chiving Model is a viable model to reduce the amount of stored data and records and to curb electricity use for storage in organi¬zations. This paper is the result of the first stage of a research project that is aimed at devel¬oping low power ICTs that will automa¬tically appraise, select, preserve or permanently delete data based on their ‘value’. Such an ICT will automatically reduce storage capacity and reduce electricity con¬sumption used for data storage. At the same time, data dispos¬al will reduce overload caused by storing the sa¬me data in different for¬mats, it will lower costs and it reduces the po¬tential for liability.
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.
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.
Logistics represents around 10-11% of global CO2 emissions, around 75% of which come from road freight transport. ‘The European Green Deal’ is calling for drastic CO2 reduction in this sector. This requires advanced and very expensive technological innovations; i.e. re-design of vehicle units, hybridization of powertrains and automatic vehicle technology. Another promising way to reach these environmental ambitions, without excessive technological investments, is the deployment of SUPER ECO COMBI’s (SEC). SEC is the umbrella name for multiple permutations of 32 meter, 70 tons, road-train combinations that can carry the payload-equivalent of 2 normal tractor-semitrailer combinations and even 3 rigid trucks. To fully deploy a SEC into the transport system the compliance with the existing infrastructure network and safety needs to be guaranteed; i.e. to deploy a specific SEC we should be able to determine which SEC-permutation is most optimal on specific routes with respect to regulations (a.o. damage to the pavement/bridges), the dimensions of specific infrastructures (roundabouts, slopes) and safety. The complexity of a SEC compared to a regular truck (double articulation, length) means that traditional optimisation methods are not applicable. The aim of this project is therefore to develop a first methodology enabling the deployment of the optimal SEC permutation. This will help transport companies (KIEM: Ewals) and trailer manufactures (KIEM: Emons) to invest in the most suitable designs for future SEC use. Additionally the methodology will help governments to be able to admit specific SEC’s to specific routes. The knowledge gained in this project will be combined with the knowledge of the broader project ENVELOPE (NWA-IDG). This will be the start of broader research into an overall methodology of deploying optimal vehicle combinations and a new regulatory framework. The knowledge will be used in master courses on vehicle dynamics.
Climate change is increasing the challenges for water management worldwide. Extreme weather conditions, such as droughts and heavy rainfall, are increasingly limiting the availability of water, especially for agriculture. Nature-Based Solutions (NBS) offer potential solutions. They help to collect and infiltrate rainwater and thus play an important role in climate adaptation.Green infrastructure, such as rain gardens (sunken plant beds) and wadis (sunken grass fields for temporary storage of rainwater), help to restore the urban water balance. They reduce rainwater runoff, stabilize groundwater levels and solve problems with soil moisture and temperature. Despite these advantages, there is still much ignorance in practice about the possibilities of NBS. To remedy this, freely accessible knowledge modules are being developed that can help governments and future employees to better understand the application of these solutions. This research, called GINA (Green Infrastructure in Urban Areas), aims to create more sustainable and climate-resilient cities by developing and sharing knowledge about NBS, and supports local governments and students in effectively deploying these green infrastructures.
Intelligent technology in automotive has a disrupting impact on the way modern automobiles are being developed. New technology not only has brought complexity to already existing information in the car (digitization of driver instruments) but also brings new external information to the driver on how to optimize the driving style amongst others from the perspective of communicating with infrastructures (Vehicle to Infrastructure communication (V2I)). The amount of information that a driver has to process in modern vehicles is increasing rapidly due to the introduction of multiple displays and new external information sources. An information overload lies awaiting, yet current Human Machine Interface (HMI) designs and the corresponding legal frameworks lag behind. Currently, many initiatives (Pratijkproef Amsterdam, Concorda) are being developed with respect to V2I, amongst others with Rijkswaterstaat, North Holland and Brabant. In these initiatives, SME’s, like V-Tron, focus on the development of specific V2I hardware. Yet in the field of HMI’s these SME’s need universities (HAN University of Applied Science, Rhine Waal University of Applied Science) and industrial designers (Yellow Chess) to help them with design guidelines and concept HMI’s. We propose to develop first guidelines on possible new human-machine interfaces. Additionally, we will show the advantages of HMI’s that go further than current legal requirements. Therefore, this research will focus on design guidelines averting the information overload. We show two HMI’s that combine regular driver information with V2I information of a Green Light Optimized Speed Advise (GLOSA) use case. The HMI’s will be evaluated on a high level (focus groups and a small simulator study). The KIEM results in two publications. In a plenary meeting with experts, the guidelines and the limitations of current legal requirements will be discussed. The KIEM will lead to a new consortium to extend the research.
