Pedestrians will increasingly have to share their space with autonomous vehicles (AVs), at pedestrian crossings, and in urban shared spaces where segregation between pedestrians and vehicles is minimized. This article proposes an integrative framework to analyze pedestrian behavior in shared spaces with AVs. Following the "perception-cognition-action" cycle, the proposed framework breaks down pedestrian behavior into 3 parts: 1. Sensation and Perception; 2. Emotion and Cognition; 3. Action and Communication. The framework is used to review and synthesize current knowledge on pedestrian behavior in urban shared spaces. Studies involving pedestrians in shared environments with AVs are limited. Since an AV can be seen as a hybrid of a conventional car and a mobile robot, this review includes studies on pedestrian behavior with conventional cars and with mobile robots as well as with AVs. Examining pedestrian behavior in these three situations of interaction allows us to make assumptions about how humans will behave in sharing their urban spaces with AVs. The reviewed interactions reveal that pedestrians have diverse and imperfect behaviors. AVs must consider this variety of behaviors and follow socially compliant rules in order to be understood and accepted by pedestrians. Perspectives for AVs in shared spaces and research directions are also identified.

The Internet of Behaviors (IoB) puts human behavior at the core of engineering intelligent connected systems. IoB links the digital world to human behavior to integrate human-driven design, development, and adaptation processes. This paper defines the novel IoB concept with a constructed model based on a collective effort interacting with software engineers, human-computer interaction scientists, social scientists, and cognitive science communities. The model for IoB is created based on an exploratory study that synthesizes state-of-the-art analysis and experts interviews. The architecture of a real industry 4.0 manufacturing infrastructure helps to explain the IoB model and its application. The conceptual model was used to successfully implement a socio-technical infrastructure for a crowd monitoring and queue management system for the Uffizi Galleries, Florence, Italy. The experiment, which started in the fall of 2016 and was operational in the fall of 2018, used a data-driven approach to feed the system with real-time sensory data. It also incorporated prediction models on visitors’ mobility behavior. The system’s main objective was to capture human behavior, model it, and build a mechanism that considers changes, adapts in real-time, and continuously learns from repetitive behaviors. In addition to the conceptual model and the real-life evaluation, this paper provides recommendations from experts and gives future directions for IoB to become a significant technological advancement in the coming years.

As the reallocator of liquidity from banks with excess to banks with a deficit, the interbank money market (IMM) plays a fundamental role in the proper functioning of the banking system and the economy as a whole. The aggregate uncertainty derived from stochasticity of the overall level of the demand for short‐term liquidity and the likelihood of domino failures of tightly connected competitors who lend themselves vast amounts of liquidity explains the complexity of decisions in this environment. To identify the most significant factors influencing actors’ strategies, we first present the five underlying patterns discovered through a bibliometric analysis of 609 scientific documents in this field: contagion and systemic risk, stability, market structure, relationship and trust, and default and failure. Then, our detailed study findings on 160 recent works indicate elements that affect central banks’ strategies in reducing systemic risk and preventing financial contagion, as well as managing the interbank network in a way that makes it more stable and resilient to shocks to conserve market confidence. Furthermore, they address factors that influence banks’ strategies to maintain their lending relationships and mitigate default risk. In addition to summarizing potential research directions, this paper provides market participants with a strategy fact‐sheet.

Each summer in Australia, bushfires burn many hectares of forest, causing deaths, injuries, and destroying property. Agent-based simulation is a powerful tool for decisionmakers to explore different strategies for managing such crisis, testing them on a simulated population; but valid results require realistic underlying models. It is therefore essential to be able to compare models using different architectures to represent the human behaviour, on objective and subjective criteria. In this paper we describe two simulations of the Australian population's behaviour in bushfires: one with a finite-state machine architecture; one with a BDI architecture. We then compare these two models with respect to a number of criteria.

This paper introduces an integrated framework for sustainability and urban security socio-technical systems. The focus is to design and develop a hardware/software system based on human expected and real behaviour. The paper explains the steps taken through developing the Uffizi Museum crowd monitoring and queue management system. The goal of implementing such system was to remove queues outside the Museum which is in line with urban security and visitors comfort. We took advantage of a data-driven approach mapped on sustainability framework. Such approach which was fed with both real-time sensory data and prediction models, successfully eliminated long queues to access the museum. We took into consideration performance of software system as well to reduce the response time to a threshold that is compliant with real-time requirements. We started our experiments from fall 2016 and operationalized it in October, 2018. During this experimentation period, we learned a lot of lessons that we report in this paper.

Human behaviour during crisis is social in nature. Affiliation, activated by threat, results in individuals seeking the proximity of attachment figures (i.e. family members, friends, colleagues, strangers, social groups, familiar places, and objects). This affects the actions, directions, and speeds adopted by individuals seeking safe areas. This paper presents SOLACE, a multi-agent model of human behaviour during seismic crisis based on social attachment theory. SOLACE is an attempt to show the effect of social attachment on the number of victims, and the time that it takes, to reach a safe area; this increases the realism in evacuation modelling. Real-geographic data are used to define the spatial context of the crisis environment, delimit mobility with barriers (e.g. buildings, debris), and constrain movement to freespace. A belief, desire, and intention (BDI) approach was adopted to integrate social bonds in human agent interaction and mobility. Initial results include realism in: (1) the synthetic crisis environment indicated by a power law distribution of earthquake effects: (a) intensities on buildings and shaking felt by human agents, and (b) evacuation delay, (2) agent movement and interaction (e.g. parent with child) influencing speed of evacuation.