Migration is among the most uncertain and contested topics for policymaking. The increasing number of migrants and refugees globally necessitates effective planning and management, particularly in addressing infrastructure needs such as access to healthcare. While efforts to accom- modate a surge of refugees prioritise primary needs, improving structural access to essential infrastructure becomes imperative over time. However, the path-dependent nature of the expansion of refugee settlements poses challenges for infrastructure development. Existing facility location models for infrastructure planning overlook the interplay of infrastructure growth and human behaviour. This chapter presents a study on the interplay between the settling preferences of refugees (behaviour) and the location of healthcare facilities as essential infrastructure. We develop a data-based approach that combines an agent-based model representing decision beha- viour with facility location optimisation models for infrastructure planning. Through a case study of Cox's Bazar, Bangladesh, home to over 1 million Rohingya refugees, we demonstrate the implications of different optimisa- tion approaches and thereby explore how and in how far digital tools influence policymaking on one of the most contested and uncertain topics in the current policy landscape. Our findings underscore the importance of integrating uncertainty about human behaviour in infrastructure decisions.

Scenario planning has become a common approach within transportation research to understand the varying impacts of transportation planning. By examining a range of uncertainties, scenarios can be developed that enable an exploration of alternative future visions of the world. Whilst there has been growing concern over the equity impacts of transport investments, particularly in relation to accessibility of opportunities, equity of access considerations remain an underdeveloped area within transportation scenarios research. This has tremendous consequences for realising socially just mobility futures. Utilising the case study of Cape Town, in South Africa several transport scenarios are collectively developed through stakeholder engagement by analysing a number of parameters that have been identified as significant operational factors and policy levers. We develop representative urban network models for each scenario and evaluate equity of access to places of employment using a comparative equity framework. We find that a continuation of past trends leads to greater inequities, whereas alternative visions focused on the adoption of integrated transport and cycling indicate potential to decrease inequities. Overall the study highlights how the adoption of accessibility focused planning is not only an engineering problem, but a societal problem related to institutional capacity, trust, community agency and political vision

Evaluating accessibility based on multiple notions of justice allows for a multi-perspective analysis of the trade-offs between the benefits and burdens associated with the provision of infrastructure. This presents a challenge due to a lack of metrics which operationalise multiple notions of justice for comparative purposes. It is further complicated by the reliance on General Transit Feed Specification (GTFS) data to do many kinds of accessibility analyses, which is often not freely available and accessible, especially in data scarce regions. This paper presents the MAP open-source software package that allows for the incorporation of multiple notions of justice in accessibility analysis. Firstly, MAP supports the development of an Urban Network Model based on open-access data. Secondly, using this model it enables the calculation of Neighbourhood Reach Centrality, a cumulative accessibility metric. Finally, it allows for the evaluation of accessibility based on three comparative metrics of spatial justice visualised through maps. For illustrative purposes, data sets from the City of Cape Town in South Africa are provided as a ready-to-use data-product. This software package offers an efficient method for incorporating spatial justice considerations into accessibility analysis offering the potential to be used as a boundary object within interdisciplinary teams of researchers, policy-analysts, transport engineers, and other stakeholders.

Effective enforcement of laws and regulations hinges heavily on robust inspection policies. While data-driven approaches to testing the effectiveness of these policies are gaining popularity, they suffer significant drawbacks, particularly a lack of explainability and generalizability. This paper proposes an approach to crafting inspection policies that combines data-driven insights with behavioral theories to create an agent-based simulation model that we call a theory-infused phenomenological agent-based model (TIP-ABM). Moreover, this approach outlines a systematic process for combining theories and data to construct a phenomenological ABM, beginning with defining macro-level empirical phenomena. Illustrated through a case study of the Dutch inland shipping sector, the proposed methodology enhances explainability by illuminating inspectors' tacit knowledge while iterating between statistical data and underlying theories. The broader generalizability of the proposed approach beyond the inland shipping context requires further research.

Scenario planning has become a common approach within transportation research to understand the varying impacts of transportation planning. By examining a range of uncertainties, scenarios can be developed that enable an exploration of alternative future visions of the world. Whilst there has been growing concern over the equity impacts of public transport investments, particularly in relation to accessibility of social and economic opportunities, equity of access considerations remain an underdeveloped area within transportation scenarios research. This has tremendous consequences for realising socially just mobility futures. Utilising the case study of Cape Town, in South Africa several transport scenarios are collectively developed through stakeholder engagement by analysing a number of parameters that have been identified as significant operational factors and policy levers. We develop representative urban network models for each scenario and evaluate equity of access to places of employment using a comparative equity framework. We find that a continuation of past trends leads to greater inequities, whereas alternative participatory future visions focused on the adoption of integrated transport and cycling indicate potential to decrease inequities. Overall the study highlights how the adoption of transportation solutions towards greater accessibility is not only an engineering problem, but a human problem related to institutional capacity, trust, coordination, community agency and political vision.

Agent-based modeling proliferates across applications and scientific disciplines. The downsides of this success are the plurality of code implementations and redundant solutions to recurring modeling tasks. It is especially critical for simulations concerned with modeling human behavior and social institutions. Reusable building blocks (RBBs) are seen as a solution due to their potential to foster standardization grounded in best practices, integration of domain knowledge (including qualitative social sciences) in code, and efficient model design. RBBs are compact code components representing mechanisms or processes useful across models and applications. RBBs have been extensively discussed in the agent-based community, with little progress in implementation. Here, we present an open-access online community platform – AGENTBLOCKS – designed to facilitate the sharing, comparison, review, reuse, and improvement of RBBs. As an international community effort, AGENTBLOCKS leverages lessons from past RBBs discussions and principles from other modeling communities that successfully apply modular, reusable code practices. The paper introduces the interface and structure of this repository, presents templates for RBBs documentation, provides tips to support aspiring users, and first examples. We highlight the need for alternative RBB implementations that share the same generic description. We also acknowledge that RBBs might represent different levels of interactions, starting from decisions concerning a single agent to interactions between multiple agents or agents and their environment. While initially designed to assist agent-based community, the platform can be utilized by other modelers (e.g. system dynamics, integrated assessment, equilibrium) who seek to improve the representation of human behavior, micro-level processes, heterogeneity, interactions, learning, and other complex dynamics. Naturally, the platform is only one element in the chain towards a successful adoption of best software development practices like RBBs. Future work should focus on populating the repository, refining review processes, and systematizing the variety of RBBs’ implementations including engagement with domain experts. Following this initial phase, we hope to further support technical improvements of the platform and widen its impact in and beyond the agent-based community.

Changes in policy over the last thirty years, particularly within advanced economies, have allowed for increased financialization, deregulation and globalisation of housing. What differentiates real-estate from other financial markets is that it possesses a salient socio-spatial geography. Housing inequalities are often framed as an outcome of macro-economic structural changes or as a product of local socio-spatial conditions, but the interactions between the two are less understood. To address this gap, we develop a descriptive methodology to connect the analysis of national housing policy trends in the Netherlands with local socio-spatial trajectories of neighbourhood change using nearly 20 years of historical data across a range of socio-spatial dimensions from the City of Rotterdam. Whilst nationally there has been an increasing policy preference for home ownership associated with a narrative of social upliftment, the spatial-temporal analysis reveals that the wealthiest neighbourhoods have benefitted significantly more from capital gains and increased rates of home ownership over time. Through descriptive analysis, the results highlight the role of divergent neighbourhood characteristics and path dependencies, suggesting that housing policies could benefit from the adoption of a more localised approach. Overall, the study sheds light on housing inequalities by integrating macro socio-economic factors with micro-level neighbourhood conditions.

Large Language Models (LLMs) are expected to significantly impact various socio-technical systems, offering transformative possibilities for improved interaction between humans and technology. However, their integration poses complex challenges due to the intricate interplay between societal structures, human behaviour, and technological innovation. This research explores these multifaceted challenges, emphasising the need for a human-centered approach in integrating LLMs to ensure that technological advancements are aligned with ethical standards and societal needs. Utilizing a structured methodology comprising a workshop, literature analysis, and expert collaborations, the study uses a multi-dimensional human-centered AI framework to guide the responsible integration of LLMs. Key insights include the importance of inclusive data, considering unintended consequences, maintaining privacy, and respecting intellectual property rights. The paper identifies and advocates for principles like human-in-the-loop, continuous longitudinal studies, proactive awareness campaigns, and regular audits to develop LLMs that are ethically sound, adaptable, and effectively integrated into various socio-technical systems, thus addressing user needs and broader societal impacts. The paper also underlines the importance of collaboration among academia, industry, and policymakers to develop LLMs that are ethically aligned, socially beneficial, and adaptable to future societal needs. The findings offer valuable insights into the strategic integration of LLMs, advocating for a broader research perspective beyond industrial motivations to fully understand and leverage LLMs in socio-technical landscapes.

Over the past decade, there has been growing interest in using human behavioral and physiological data to detect Social Anxiety Disorder (SAD). Machine learning and deep learning techniques that use multimodal sensing have emerged as promising tools for detecting SAD characteristics. Additionally, extensive research on technology-assisted psychological interventions for SAD aims to enhance treatment efficacy and address the shortcomings of existing treatments by exploring how these interventions can be tailored to individual anxiety levels, symptom severity, and personal preferences. This review provides an overview of approaches for generalised SAD, covering advancements in both sensing and interventions while highlighting the potential of affective computing. It synthesises key insights on current emerging trends, identifies research gaps, and outlines directions for future research.

The United Nations World Social Report (2020) reveals that more than two thirds of the world's population live in countries where urban inequalities have increased in the last three decades. While urban inequalities are traditionally characterized as an economic issue, scholars are increasingly applying methods from geospatial analysis to study them. In the context of these advancements, it remains unclear what underlying perspectives are guiding decisions to concentrate on certain aspects of urban inequalities, while potentially ignoring others. We address this gap by reviewing the literature centered on the geospatial analysis of urban inequalities and identify three predominant research lenses from accessibility, distribution, and policy and stakeholder perspectives. As a primary contribution of this article, we connect the perspectives with ideas drawn from complexity theory to develop an overarching socio-technical framework for how urban inequalities emerge over space and time. While traditional scientific frameworks seek to increase knowledge through causality, complexity science acknowledges the inherent challenges in defining, understanding and solving complex problems such as urban inequalities, which has profound implications for their representation, modeling and interpretation. We critically reflect on the framework through key relational themes and insights drawn from the literature and close with considerations for future research.

Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, potentially reaching up to 20 Terabytes. This data size is influenced by factors such as the duration of driving, the data rate, and the sensor specifications. Consequently, this substantial amount of data can lead to significant power consumption on the vehicle. Similarly, a substantial amount of data will be exchanged between infrastructure sensors and vehicles for collaborative vehicle applications or fully connected autonomous vehicles. This communication process generates an additional surge of energy consumption. Although the autonomous vehicle domain has seen advancements in sensory technologies, wireless communication, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate these technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights from this survey can benefit the collaborative driving service development on low-power and memory-constrained systems and the energy optimization of autonomous vehicles.

As the use of distributed energy resources increases, peer-to-peer (P2P) energy trading is becoming a promising way to harmonize the decarbonization and decentralization transformations in the energy sector. P2P markets give households the autonomy to make individual decisions and thus they may cooperate with each other to obtain economic benefits. However, existing studies on cooperative behaviors in P2P markets focus mostly on the electricity sector and P2P multi-energy markets are rarely studied. In fact, other energy carriers not only constitute a large part of the total energy demand, but their coupling can potentially benefit the system as well as the end-users. In this paper, we propose a P2P multi-energy market mechanism that allows peers to trade both electricity and heat. Two trading coalitions, i.e., an electricity-only trading coalition and an electricity–heat trading coalition, are predefined. The peers will join one of the coalitions based on their potential benefits and will trade energy inside the coalition. The energy markets are cleared separately per coalition and per energy carrier and hence, multi-energy markets are modeled. The proposed mechanism is a first-of-its-kind that explores the integrated effects of the multi-energy coupling and the cooperative behaviors in the P2P market. It is illustrated by a case study on a neighborhood in the Netherlands using realistic data. Results show that the mechanism is prosumer-centric as peers choose to join different coalitions at different time steps which benefit them the most. Compared to the reference scenario where there is no P2P trading, the P2P multi-energy market leads to higher economic benefits for all the peers altogether and benefits most individuals. The case study also demonstrates a benefit transfer from service-sector peers to residential peers.

Governments look at explainable artificial intelligence's (XAI) potential to tackle the criticisms of the opaqueness of algorithmic decision-making with AI. Although XAI is appealing as a solution for automated decisions, the wicked nature of the challenges governments face complicates the use of XAI. Wickedness means that the facts that define a problem are ambiguous and that there is no consensus on the normative criteria for solving this problem. In such a situation, the use of algorithms can result in distrust. Whereas there is much research advancing XAI technology, the focus of this paper is on strategies for explainability. Three illustrative cases are used to show that explainable, data-driven decisions are often not perceived as objective by the public. The context might raise strong incentives to contest and distrust the explanation of AI, and as a consequence, fierce resistance from society is encountered. To overcome the inherent problems of XAI, decisions-specific strategies are proposed to lead to societal acceptance of AI-based decisions. We suggest strategies to embrace explainable decisions and processes, co-create decisions with societal actors, move away from an instrumental to an institutional approach, use competing and value-sensitive algorithms, and mobilize the tacit knowledge of professionals

This paper addresses the challenge of establishing a resilient disaster communication system that transitions seamlessly from a phone-based ad hoc network to any portable infrastructure and back. For this purpose, this paper presents a value-based design of an autonomous and self-organized protocol (SOS-hybrid). This design ensures seamless integration between various communication networks taking local context into account to increase inclusion and continuity of connectivity. SOS-hybrid has two benefits. First, local self-organization can adapt to the local situation in a disaster area. Second, context-awareness can fill in the spatial gaps of coverage associated with top-down approaches. An agent-based modelling approach was used to develop the simulation of the proposed communication network to evaluate the impact of introducing SOS-hybrid in the aftermath of a disaster. SOS-hybrid allows phones to simultaneously provide the benefits of (i) ad hoc mobile networking, allowing hard-to-reach people to connect, and (ii) infrastructure-based communication, allowing phones to more efficiently send messages over long distances. Benefits include two-way communication between community and rescue operators, inclusion and continued connectivity for immobile citizens stuck in isolated out of coverage areas, and seamless transition without loss of messages.

Participatory resilience of disaster-struck communities requires reliable communication for self-organized rescue, as conventional communication infrastructure is damaged. Disasters often lead to blackouts preventing citizens from charging their phones, leading to disparity in battery charges and a digital divide in communication opportunities. We propose a value-based emergency communication system based on participatory fairness, ensuring equal communication opportunities for all, regardless of inequality in battery charge. The proposed infrastructure-less emergency communication network automatically and dynamically (i) assigns high-battery phones as hubs, (ii) adapts the topology to changing battery charges, and (iii) self-organizes to remain robust and reliable when links fail or phones leave the network. The novelty of the proposed mobile protocol compared to mesh communication networks is demonstrated by comparative agent-based simulations. An evaluation using the Gini coefficient demonstrates that our network design results in fairer participation of all devices and a longer network lifetime, benefiting the community and its participants.

High penetration of renewable energy sources brings both opportunities and challenges for Smart Grid operation. Due to their high contribution to energy consumption, aggregated load flexibility of small residential and service sector consumers has a potential to address the intermittency challenge of distributed generation. Predicting aggregated load flexibility of this consumer sector involves access to sensitive smart meter data, raising data collection and sharing concerns. Federated Learning, a decentralized machine learning technique that uses data distributed on user devices to construct an aggregated, global model, offers potential solutions to tackling this challenge. This paper explores the potential of using Federated Learning for flexibility prediction in Smart Grids through an analysis of its opportunities and implications for different stakeholders involved, as well as the challenges faced. The analysis shows that Federated Learning is a promising approach for building privacy-preserving energy portfolios of aggregated demand data.

The development of metropolitan public transport networks often involves choosing between investing in extending radial lines or constructing ring connections. While the former enlarges network coverage the latter enhances network connectivity and reduces the need to perform detours. Moreover, investments might be better directed at increasing the capacity of already existing infrastructure. In this study we address the following question: how do transport networks in metropolitan areas evolve over time and how can we effectively model this growth as function of demand and cost function? The goal of this study is to determine the fundamental relations between population distribution, modal costs on the prevailing network structure and its evolution. The approach taken in this study offers a theoretical contribution to the field of transport network growth by combining principles from several research streams: transport geography, economics of network growth and network science. We propose an iterative investment model network analysis framework. The results of the network growth experiments manifest an overall trend in network growth with an early phase of expansion of the network, followed by a period of intensification manifested in capacity increments and finally adding some links that contribute to its densification. Furthermore, our findings suggest that bus networks include more ring-radial connections than Light Rail Train and Metro networks which are more concentrated on radial connections.

When physical communication network infrastructures fail, infrastructure-less communication networks such as mobile ad-hoc networks (MANET), can provide an alternative. This, however, requires MANETs to be adaptable to dynamic contexts characterized by the changing density and mobility of devices and availability of energy sources. To address this challenge, this paper proposes a decentralized context-adaptive topology control protocol. The protocol consists of three algorithms and uses preferential attachment based on the energy availability of devices to form a loop-free scale-free adaptive topology for an ad-hoc communication network. The proposed protocol has a number of advantages. First, it is adaptive to the environment, hence applicable in scenarios where the number of participating mobile devices and their availability of energy resources is always changing. Second, it is energy-efficient through changes in the topology. This means it can be flexibly combined with different routing protocols. Third, the protocol requires no changes on the hardware level. This means it can be implemented on all current phones, without any recalls or investments in hardware changes. The evaluation of the protocol in a simulated environment confirms the feasibility of creating and maintaining a self-adaptive ad-hoc communication network, consisting of multitudes of mobile devices for reliable communication in a dynamic context.

In the immediate aftermath of a disaster, local and international aid organisations deploy to deliver life-saving aid to the affected population. Yet pre-disaster road maps and road transportation models do not capture disruptions to the transportation network caused by the disaster or the dynamic changes of the situation, resulting in uncertainty and inefficiency in planning and decision-making. The integration of data in near real time on the status of the road infrastructure in the affected region can help aid organisations to keep track of the rapidly shifting conditions on the ground and to assess the implications for their logistics planning and operations. In this paper, we present a rapid graph-theoretical reachability information system based on a combination of OpenStreetMap and open humanitarian data. The system supports logistics planning in determining road access to affected communities. We demonstrate the results of our approach in a case study on the 2018 earthquake in Papua New Guinea. Our findings show the reachability of affected communities depending on the actual status of the road network, allowing for the prioritization of targeted locations and the identification of alternative routes to get there.

Resilient critical airport infrastructures affected by a disaster need to sustain minimal functionality and quickly resume full operation, while at the same time coping with the increased operational demands imposed by the unfolding disaster response. In this paper, we develop a resilience framework and model-driven approach that focuses on the ability of the infrastructure to rapidly adapt to a new steady state under these conditions. This requires both the extension of capacity as well as the adaptation of key processes. Through discrete event simulations, we study the implications of different policies to improve airport resilience under different disaster impact scenarios for a stylized case. Our results show that although decision-makers may be tempted to focus on short-term measures that can be implemented immediately, resilience is improved most by a combination of rapid process changes and longer-term measures that structurally increase airport capacity.