A perception-based emotion contagion model in crowd emergent evacuation simulation

With the increasing number of emergencies, the crowd simulation technology has attracted wide attention in recent years. Existing emergencies have shown that individuals are easy to be influenced by other’s emotion during the evacuation. This will make it easier for people to aggregate together and increase security risks. Some of the existing evacuation models without considering emotion are therefore not suitable for describing crowd behaviors in emergencies. We propose a perception-based emotion contagion model and use multi-agent technology to simulate the crowd behaviors. Navigation points are introduced to guide the movement of the agents. Based on the proposed model, a prototype simulation system for crowd emotion contagion is developed. The comparative simulation experiments verify that the model can effectively deduct the evacuation time and crowd emotion contagion. The proposed model could be an assistant analysis method for crowd management in emergencies

A roadmap for the future of crowd safety research and practice: Introducing the Swiss Cheese Model of Crowd Safety and the imperative of a Vision Zero target

Crowds can be subject to intrinsic and extrinsic sources of risk, and previous records have shown that, in the absence of adequate safety measures, these sources of risk can jeopardise human lives. To mitigate these risks, we propose that implementation of multiple layers of safety measures for crowds—what we label The Swiss Cheese Model of Crowd Safety—should become the norm for crowd safety practice. Such system incorporates a multitude of safety protection layers including regulations and policymaking, planning and risk assessment, operational control, community preparedness, and incident response. The underlying premise of such model is that when one (or multiple) layer(s) of safety protection fail(s), the other layer(s) can still prevent an accident. In practice, such model requires a more effective implementation of technology, which can enable provision of real-time data, improved communication and coordination, and efficient incident response. Moreover, implementation of this model necessitates more attention to the overlooked role of public education, awareness raising, and promoting crowd safety culture at broad community levels, as one of last lines of defence against catastrophic outcomes for crowds. Widespread safety culture and awareness has the potential to empower individuals with the knowledge and skills that can prevent such outcomes or mitigate their impacts, when all other (exogenous) layers of protection (such as planning and operational control) fail. This requires safety campaigns and development of widespread educational programs. We conclude that, there is no panacea solution to the crowd safety problem, but a holistic multi-layered safety system that utilises active participation of all potential stakeholders can significantly reduce the likelihood of disastrous accidents. At a global level, we need to target a Vision Zero of Crowd Safety, i.e., set a global initiative of bringing deaths and severe injuries in crowded spaces to zero by a set year

the herding effect": evidence from chinese stock markets

In this work we examined the investment behavior of market agents within the chinese stock market, in particular in relation to their tendency to conform towards the market consensus called by the scholars herding behavior. the testing methodology is based on the approach of chang and khorana(2000) the herding phenomenon moreover is studied across different scenarios but it is not shown everywhere in the same amount or with the same importance in each of the frameworks analyse

Detecting intentional herding: what lies beneath intraday data in the Spanish stock market

This is a post-peer-review, pre-copyedit version of an article published in Journal of Operational Research Society. The definitive publisher-authenticated version [Blasco, N., Corredor, P. and Ferreruela, S. (2011): Detecting intentional herding: what lies beneath intraday data in the Spanish stock market. Journal of the Operational Research Society 62, 1056–1066. doi:10.1057/jors.2010.34] is available online at: http://www.palgrave-journals.com/jors/journal/v62/n6/pdf/jors201034a.pdfThis paper examines the intentional herd behaviour of market participants, using Li´s test to compare the probability distributions of the scaled cross-sectional deviation in returns in the intraday market with the cross-sectional deviation in returns in an “artificially created” market free of intentional herding effects. The analysis is carried out for both the overall market and a sample of the most representative stocks. Additionally, a bootstrap procedure is applied in order to gain a deeper understanding of the differences across the distributions under study. The results show that the Spanish market exhibits a significant intraday herding effect that is not detected using other traditional herding measures when familiar and heavily traded stocks are analysed. Furthermore, it is suggested that intentional herding is likely to be better revealed using intraday data, and that the use of a lower frequency data may obscure results revealing imitative behaviour in the market.N. Blasco and S. Ferreruela wish to acknowledge the financial support of the Spanish Ministry of Education and Science (SEJ2006-14809-C03- 03/ECON), the Spanish Ministry of Science and Innovation (ECO2009- 12819-C03-02), ERDF funds, the Caja de Ahorros de la Inmaculada (Europe XXI Programme) and the Government of Aragon. P. Corredor is grateful for the financial support of the Spanish Ministry of Education and Science (SEJ2006-14809-C03-01), the Spanish Ministry of Science and Innovation (ECO2009-12819-C03-01), ERDF funds and the Government of Navarra

Learning and Controlling Network Diffusion in Dependent Cascade Models

Abstract—Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion dynamics parameters and (b) taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved at each network node. As a case study, we address learning visitor mobility pattern at a theme park based on observed historical wait times at individual attractions, and use the learned model to plan management actions that reduce wait time at attractions. We test on real-world data from a theme park in Singapore and show that our learning approach can achieve an accuracy close to 80 % for popular attractions, and the decision support algorithm can provide about 10-20 % reduction in wait time. I

Flood-pedestrian simulator: an agent-based modelling framework for urban evacuation planning

Agent-Based Modelling (ABM) is an increasingly used approach for characterisation of human behaviour in evacuation simulation modelling. ABM-based evacuation models used in flood emergency are developed mostly for vehicular scenarios at regional scale. Only a few models exist for simulating evacuations of on-foot pedestrians responding to floods in small and congested urban areas. These models do not include the heterogeneity and variability of individuals’ behaviour influenced by their dynamic interactions with the floodwater properties. This limitation is due to the modelling restrictions pertaining to the computational complexity and the modelling flexibility for agent characterisation. This PhD research has aimed to develop a new ABM-based pedestrian evacuation model that overcomes these challenges through an ABM platform called Flexible Large-scale Agent Modelling Environment for the Graphics Processing Units (FLAME GPU). To achieve this aim, a hydrodynamic model has been integrated into a pedestrian model within the FLAME GPU framework. The dynamic interactions between the flood and pedestrians have been formulated based on a number of behavioural rules driving the mobility states and way-finding decisions of individuals in and around the floodwaters as well as the local changes in the floodwater properties as a result of pedestrians’ crowding. These rules have been progressively improved and their added value has been explored systematically by diagnostically comparing the simulation results obtained from the base setup and the augmented version of the model applied to a synthetic test case. A real-world case study has been further used to specifically evaluate the added value of rules relating the individuals’ way-finding mechanism to various levels of flood-risk perception. The findings from this research have shown that increasing the level of pedestrians’ heterogeneity and the effect of pedestrians’ crowding on the floodwater hydrodynamics yield to a considerably different prediction of flood risk and evacuation time. Besides, accounting for pedestrians’ various levels of flood-risk perception has been found to be one determinant factor in the analysis of flood risk and evacuation time when there are multiple destinations. Finally, the sensitivity analysis on the simulation results have shown that the deviations in the simulation outcomes increases in line with the increase in the sophistication of human behavioural rules