During epidemics, decision-making regarding intervention measures faces complex trade-offs. Interventions targeting indoor venues can mitigate disease spread, since they are associated with higher infection risk for respiratory pathogens. However, as experienced during the COVID-19 pandemic, these measures can lead to economic losses, especially in the hospitality sector. In this study, we propose a hybrid modeling and simulation framework to provide decision support for reducing the infection risk in indoor venues while maintaining viable economic activity. Our framework integrates (i) a microscopic pedestrian model for human movement, (ii) a hybrid simulation model for virus spread and transmission, and (iii) a multi-criteria decision-making approach to identify the best service options. The framework is demonstrated for the SARS-CoV-2 infection risk. The restaurant case study results illustrate that maximizing the distance between seating groups can have a limited effect on the infection risk. Service duration and service capacity are key determinants of expected economic activity, but they constitute significant trade-offs: the former has a substantial impact on the infection risk, and the latter drives the probability of infectious introductions. Our analysis demonstrates the need for multi-criteria approaches during an outbreak and consideration of the epidemiological context for operational decision-making, even at an individual venue.

Computational simulation models serve as powerful instruments for analysing complex systems, but individually they are often limited in representing systems of an interdisciplinary nature. This paper presents a multi-modelling project that takes the first steps towards an open generic solution of multi-model infrastructure (MMI). It aims to loosely couple individual independent (often already existing) models, facilitate model reuse and meaningful model interoperation in order to support integral decision-making in the Dutch energy transition.
The MMI is a minimal viable product collaboratively designed and developed by a diverse group of modellers and energy experts. It includes facilitating services such as software and methods that enable multi-modelling but not the individual independent models themselves. We share the vision, approach and initial outcomes of the project, in particular, give an overview of the multi-model platform architecture design and the three use cases (and multi-models) of marco, meso and micro scales developed to demonstrate the potential of MMI. We also discuss the lessons learnt and future work, with the intension to invite more research, debate and collaboration on topics of multi-modelling, in particular simulation model reuse and interoperation, and to form an even stronger and broader interdisciplinary community of multi-modellers.

It is widely considered that the energy transition should be just, yet achieving this goal is a complex socio-technical process. Models serve as valuable tools to support decision-making in navigating these complexities. However, they are not adequately equipped to address justice considerations that are becoming central to energy transition planning. They are unable to provide support in decision spaces that are rich in normative uncertainties, with stakeholders holding differing interpretations of what a just energy transition is. While the importance of integrating justice into computational models is recognized, a significant gap remains in understanding how justice is and can be defined, interpreted, and implemented within these models or, in short, how justice can be operationalized. This paper addresses the gap by examining studies that use computational models for decision-support through the lens of the three tenets of energy justice: procedural, recognition, and distributive justice. We argue that operationalizing justice in energy transition modelling can take place both in the modeling process and with the enrichment of model logic. This paper emphasizes that discussions of justice in relation to models cannot be separated from the design of effective participatory modelling settings that stem from a careful evaluation of the justice requirements of stakeholders in the decision space. We propose a framework that enables modellers and model users to be more explicit about their normative interpretations of justice and derive modelling processes and model requirements that represent diverse justice perceptions in the decision space. By doing this, models can refrain from propagating only dominant ideas of justice and instead actively incorporate otherwise neglected perceptions, to ensure that the decision-support facilitates a just energy transition.

Cities consume almost 80 percent of world’s energy and account for 60 percent of all the emissions of carbon dioxide and significant amounts of other greenhouse gases (GHG). The ongoing rapid urbanization will further increase GHG emissions of cities. The quantification of the environmental impact generated in cities is an important step to curb the impact. In fact, quantifying the consumption activities taking place inside a city, if differentiated by socioeconomic and demographic groups, can provide important insights for sustainable-consumption policies. However, the lack of high-resolution data related to these activities makes it difficult to quantify urban GHG emissions (as well as other impacts). This paper presents a methodology that can quantify the carbon footprint of households in cities using consumption data from a national or European level, where the resource consumption is linked to socioeconomic attributes of a population. The methodology is applied to analyzing the environmental impact by household resource consumption in the city of The Hague in the Netherlands. The key insights reveal potential intervention areas regarding resource consumption categories and demographic groups that can be targeted to reduce GHG emissions due to consumption-driven activities in the city.

The COVID-19 pandemic in Indonesia has led to a significant change in human mobility. It is also considered the most serious threat to the inter-island trade network since the economic crisis in 1998. Leveraging two-year historical port call data (covering 6,000 records in total) of Indonesian domestic cargo vessels from the Automatic Identification System (AIS), this study examines the spatiotemporal changes of maritime freight transport network connectivity and cargo shipping capacity throughout the COVID-19 outbreaks period. We constructed two directed graphs, one in 2019 and another in the 2020 period, based on 1,283 Indonesian domestic cargo ship trajectories that connect 25 main Indonesian ports through 370 links. This study calculated and compared the four metrics of complex network analysis, including assortative coefficient, average degree, betweenness centrality, and clustering coefficient, to figure out the shipping network pattern changes. The result shows that the network connectivity and its shipping capacity changed in 2020, although the national port call trend is not significantly different from 2019. Based on our observation, we notice that the network is shifting from a "main hub-and-spoke connection," which dominantly involves western Indonesia hub-ports structure, towards a "multi hub-and-spoke connection," which increases the ports centrality position in eastern Indonesia. We also analyzed the change of cargo shipping capacity in each link to reflect how shipping liner companies respond to the pandemics. The insights generated in this study are hoping to contribute toward more rapid, effective, and comprehensive responses to this unprecedented disruption.

As higher education digitalises, institutions increasingly outsource the development and management of their digital infrastructure including server hardware and services such as email, shared storage, and video conferencing, to private companies. This outsourcing trend is a change in paradigm, since universities have historically been pioneers in deploying and maintaining their own digital infrastructure, a practice also known as self-hosting. Digital infrastructure has a key role in all of a university’s functions: administration, research, and education. While outsourcing infrastructure has benefits in the form of convenience and lower costs, it also erodes institutional independence, centralises points of failure, and dele- gates highly relevant value choices about privacy, data ownership and environmental impact to external actors. In this article, we provide a first quantification of a potential return to self-hosting, emphasising its effect in energy reduction and avoided e-waste. We then outline some policy actions that could enable higher education institutions to re-take control over their digital infrastructure by building local services. This mode of operation reduces waste, and has the added benefit of increased resilience to scenarios of resource scarcity and collapse of external infrastructure. As an example of what could be achieved leveraging these policies, we detail the architecture of a low-impact data centre made of upcycled hardware and resource-aware software. By exploring our main structural choices we aim to showcase how, even starting from a generally heavy-weight software stack such as Kubernetes, there is significant space to reduce digital infrastructure’s overall resource footprint.

Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the particle filter (PF) method is often a good option for stochastic simulation models of discrete systems. The probability distributions of model runs, however, make it computationally intensive. The experimental conditions therein are understudied. This paper studied three critical conditions of PF-based DA in a discrete event model: (1) the time interval between two consecutive DA iterations, (2) the number of particles, and (3) the actual level and perceived level of measurement errors (or noises). The study conducted identical-twin experiments of an M/M/1 single server queuing system. The ground truth is imitated in a stand-alone simulation model. The measurement errors are superimposed so that the effect of the three conditions can be quantitatively evaluated in a controlled manner. The results show that the estimation accuracy of such a system using PF is more constrained by the choice of time intervals than the number of particles. An under estimation of measurement errors produces worse state estimates than an over estimation of errors. A correct perception of the measurement errors does not guarantee better state estimates. Moreover, a slight over estimation of errors results in better state estimates, and it is more responsive to abrupt system changes than an accurate perception of measurement errors.

In this study, we use a complex network analysis approach to investigate the topological structure of container shipping networks in the Indonesia archipelago to understand the network topology. Containerized cargo is responsible for more than half of the inter-island trade volume, making it one critical freight transport mode in the Indonesia archipelago. We summarize the network topological structure by measures such as degree distribution, average path length, and average clustering coefficient. Based on the initial result, we find that the degree distribution of the container shipping network in archipelago fits a hybrid distribution. The distribution proves the studied network is not scale-free. With regards to the network structure, the archipelago’s shipping network exhibits a short path length and a low value of the clustering coefficient, potentially rejecting the small-world structure hypothesis. These initial findings provide evidence that the maritime shipping network in a large-scale archipelago shows a distinctive pattern compared to other maritime shipping networks in the existing literature.

Dynamic data-driven simulation (DDDS) incorporates real-time measurement data to improve simulation models during model run-time. Data assimilation (DA) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical conditions of DA using PFs: (1) the time interval of iterations, (2) the number of particles and (3) the level of actual and perceived measurement errors (or noises), and provide recommendations on how to strategically use data assimilation for DDDS considering these conditions. The results show that the estimation accuracy in DA is more constrained by the choice of time intervals than the number of particles. Good accuracy can be achieved without many particles if the time interval is sufficiently short. An over estimation of the level of measurement errors has advantages over an under estimation. Moreover, a slight over estimation has better estimation accuracy and is more responsive to system changes than an accurate perceived level of measurement errors.

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the road infrastructure requirements that would lead to safe operation of automated vehicles. In this context, a field test with Lane Departure Warning and Lane Keeping Systems-enabled vehicles was conducted in the province of North Holland, The Netherlands. The performance of these automated systems was evaluated using performance indicators such as Mean Lateral Position and Standard Deviation of Lane Position. In this study, the Systems Theoretic Accident Modelling and Processes (STAMP) model was adopted to understand the relationships between the various components of the “Road System”, which in this study include the road authority, the automated vehicle system, elements of the road infrastructure, and weather conditions. Empirical data from the experiment is used to estimate the relationships between the different components, followed by the assessment of their impact on the performance of the automated vehicles. It was found that visibility conditions have a significant effect on detection performance, which worsens in rainy conditions especially under streetlights. It has been also observed that there is a significant difference in Lane Position between Left Curves and Straight sections, and between lane widths less than 250 cms and those that have larger widths. These findings are combined with the results from the STAMP analysis to formulate a set of road infrastructure requirements that would lead to safe performance of Lane Assistance Systems.

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the driving environment characteristics that affect the performance of automated vehicles. In this context, a field test with Lane Departure Warning (LDW) and Lane Keeping Systems (LKS)-enabled vehicles was conducted in the Netherlands. Empirical data from the experiment was used to estimate the impact of driving environment components such as weather condition and lane width on the performance of the automated vehicles. Driving at night in the presence of streetlights with rain resulted in least detection performance for both the vehicles as compared to other visibility conditions. As for lane-keeping performance, the LKS positioned the vehicle significantly more to the left of the lane on left-curves than on straight sections. The LKS also positioned the vehicle more left on lanes with a width less than 250 cm than on wider lanes. These findings were translated into levels of service of the Operational Design Domain (ODD). Each level of service corresponded to a performance level of the lane assistance systems, classified as “High”, “Medium”, and “Low”, and defined using indicators.

New developments in the automotive world have the power to change mobility, but because of high uncertainties, municipalities are adopting a wait-and-see attitude. Nonetheless, autonomous, connected and shared vehicle technologies are in a far stage of development and it is only a matter of time before shared autonomous vehicles (SAVs) enter urban traffic. This research aims to provide insights into the congestion effects of SAVs on urban traffic, focusing on the differences in microscopic behaviour from conventional cars, and to investigate which easy-to-implement solutions a municipality could apply to facilitate the new mix of traffic. This was researched by performing a simulation study, using the traffic simulation package Vissim and a case study of a network in the city of The Hague during the morning peak in 2040. Several SAV market penetration scenarios were tested: 0%, 3%, 25%, 50% and 100% SAV usage by travellers. Additionally, two network designs were implemented: dedicated lanes for SAVs and kiss & ride (K&R)-facilities. From the results, it was clear that while the autonomous driving capabilities of SAVs help reduce traffic congestion, they also have a negative effect by stopping on the curbside to drop off passengers, forming bottlenecks for other road users, and by circulating on the network using low capacity links. Below the line, this adds up to an overall negative effect on urban traffic congestion according to our results. The dedicated lanes design was unsuccessful at reducing this congestion caused by SAVs. The K&R design, however, was successful at reducing delays, but only for SAV penetration rates higher than 25%. These exact effects are not generalizable due to limitations in network size and simulation software. However, the results can be seen as indicative for planning purposes. Similar effects could be expected in cities where transport network companies (TNCs) such as Uber become exceptionally popular with non-autonomous cars. The advice for municipalities is to closely monitor the situation and to account for SAVs (and TNCs) in each new infrastructural project.

China is undergoing large changes to tackle carbon dioxide emissions and air pollution. While the top-down governance allows for clear setting of emission reduction targets for industrial sectors and major cities, reducing emissions in residential sectors in smaller (the so-called low-tier) cities remain challenging and often unaddressed. This paper studies policy options to reduce emissions in residential sectors in low-tier Chinese cities. We conducted interviews and surveys in the city of Jingmen in the Hubei province and developed simulation models with feasible policy options and realistic consumption choice preferences. The simulation provided insights to the policies on reducing household coal consumption and ensuing emissions. Our research found that top-down restrictive policies such as coal ban and coal tax are effective in reducing emissions. They, however, restrict access to affordable energy for heating and cooking, especially within rural areas. They hence need to be combined with supportive policies such as electricity subsidy to yield long-term positive impact.

The attitude-behaviour gap in energy consumption refers to the imparity between people's environmental values (and attitudes) and their actual behavior in consumption. This paper calls for the facilitation of the behaviour change process that is implementable in the context of one's everyday life to address the attitude-behaviour gap in household energy consumption. Two interrelated intervention design constructs are proposed based on the results of literature review, namely (1) providing consumers accurate information about actionable suggestions in the specific context of their everyday life, and (2) fostering consumers' motivation to engage in the behavior change process towards energy conservation. Smart Grid technologies are instrumental in both intervention types.

This paper presents YouPower, an open source platform designed to make people more aware of their energy consumption and encourage sustainable consumption with local communities. The platform is designed iteratively in collaboration with users in the Swedish and Italian test sites of the project to improve the design and increase active user participation. The community-oriented design is composed of parts that link energy data to energy actions, provide comparisons at different levels, generate dynamic time-of-use signals, offer energy conservation suggestions, and support social sharing. The goal is to bridge people's attitude-behavior gap in energy consumption and to facilitate the behavior change process towards sustainable energy consumption that is implementable in people's daily life. Preliminary results show that community-oriented energy intervention has the potential to improve user engagement significantly.