A large share of urban freight in cities is related to construction works. Construction is required to create attractive, sustainable and economically viable cities. When activities at and around construction sites are not managed effectively, they can have a negative impact on the cities liveability. Construction companies implementing logistics concepts show a reduction of logistic costs, less congestion around the sites and improved productivity and safety. The client initially sets the ‘ground rules’ for construction in the tendering process. This paper explores how tendering for construction projects can support sustainable urban construction logistics. We explore the potential for tendering construction projects, by both public and private clients, for sustainable urban construction logistics and we present a conceptual framework for specifying ‘logistics quality’ as a quality criterion for EMAT (Economically Most Advantageous Tender). Our exploration results in questions for further research in tendering for sustainable urban construction logistics.
Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators.
The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
Designing with the Sun is a KIEM-GoCI explorative research project on the theme Energy Transition and Sustainability. The project is aimed at network and agenda building and design research that explores new (cultural) practices of renewable energy consumption, based on a shift from ‘energy blindness’ to ‘energy awareness’. Up until now the solar industry has been propelled forward by technical innovations, offering mostly pragmatic, economic benefits to consumers. Innovation in this field mostly concerns making solar panels more efficient and less costly. However, to succeed, the energy transition also needs new cultural practices. These practices should reflect the ways renewables are different from fossil fuels. For solar, this means using more direct solar energy, when the sun is there, and being able to adapt to periods of low energy. Currently, consumers are mostly ‘blind’ to the infrastructure behind fossil-based energy. However, for energy sources such as solar and wind ‘awareness’ of their availability becomes more important. What could such an awareness look or feel like? How can it be enacted? And how can a change in practice that is more attuned to availability be experienced positively? Solar companies see opportunities in using design to help build motivating practices and narratives within the solar field, enabling awareness through personal relationships between consumer and solar energy. However, the knowledge of how to get there is lacking. In a research-through-design trajectory, and together with partners from the Creative Industries, Designing with the Sun aims to explore new ways of relating citizens to solar energy. Ultimately, these insights should enable the newly emerging field of solar design to contribute to the emergence of more sustainable and rewarding energy awareness and practices.
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low. As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers. To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is: How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems? HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases: I. Highway: non-professional drivers; II. Distribution Centre: professional drivers.
