Incorporating intelligence into exit choice model for typical evacuation

Integrating an exit choice model into a microscopic crowd dynamics model is an essential approach for obtaining more efficient evacuation model. We describe various aspects of decision-making capability of an existing rule-based exit choice model for evacuation processes. In simulations, however, the simulated evacuees clogging at exits have behaved non-intelligently, namely they do not give up their exits for better ones for safer egress. We refine the model to endow the individuals with the ability to leave their exits due to dynamic changes by modifying the model of their excitement resulted from the source of panic. This facilitates the approximately equal crowd size at exits for being until the end of the evacuation process, and thereby the model accomplishes more optimal evacuation. For further intelligence, we introduce the prediction factor that enables higher probability of equally distributing evacuees at exits. A simulation to validate the contribution is performed, and the results are analyzed and compared with the original model

A Comprehensive Framework for Fire Evacuation Modelling

Strategic planning for the evacuation of occupants from buildings becomes crucial during disaster response operations, especially in the context of fire emergencies that pose a direct threat to human lives. This study addresses the specific challenges associated with fire evacuation in smart buildings within the Internet of Things environment. Despite their enhanced connectivity and accessibility, these buildings are still vulnerable to crises; therefore, efficient and swift occupant evacuation planning is necessary. Developing a successful evacuation plan for these situations calls for in-depth knowledge of smart building characteristics, evacuation factors, and skilful modelling. In order to overcome this problem, the research proposed a metamodel approach that serves as a modelling grammar and syntax for systematic design. The metamodel is constructed based on common evacuation model terminology and fire emergency variables. Through the use of a graphical editor and model transformation, the metamodel undergoes validation using a model-checking technique. In abstract modelling, this validation technique provides crucial insights into the accuracy and completeness of the metamodel in enhancing the resilience of smart building evacuation systems

Multi-agent Spatiotemporal Simulation of Autonomous Vehicle Fleet Operation

Autonomous vehicle fleets, consisting of self-driving vehicles, are at the forefront of transportation innovation. The appearance of autonomous vehicles (AVs) provides a new solution for traffic problems and a new market for transportation network companies such as DiDi and Uber. Conducting simulations in the present is indeed crucial to prepare for the eventual operation of autonomous vehicles, as their widespread adoption is expected to occur in the near future. This research adopts an Agent-Based Modelling (ABM) approach to understand and optimize the performance of autonomous vehicle systems. Moreover, Geographic Information System (GIS) technology also plays a crucial role in enhancing the effectiveness and accuracy of the simulation process. GIS enables the representation and manipulation of geospatial data, such as road networks, land-use patterns, and population distribution. The combination of ABM and GIS allows for the incorporation of real-world geographic data, providing a realistic and geographically accurate environment for the agents in the virtual environment. In this thesis, the multi-agent spatiotemporal simulation is conducted by the GAMA platform. The model simulates the behaviour and interactions of individual agents, which are fleet agents and commuters, to observe the emergent behaviour of the entire system. Within the experiment, different scenarios are considered for both people and fleets to explore a range of approaches and strategies. These scenarios aim to evaluate the effectiveness of various approaches in meeting dynamic commute needs and optimizing fleet operations. By simulating these different scenarios and analyzing their outcomes, the study aims to provide insights into the improvement of fleet size and deployment in autonomous vehicle systems. The ultimate goal is to identify effective strategies that lead to optimized fleet size in different scenarios, reduced idling time and emission, improved traffic management, and overall more efficient and sustainable autonomous vehicle systems

Performance-Based Design in Structural Fire Engineering

The performance-based design of structures in fire is gaining growing interest as a rational alternative to the traditionally adopted prescriptive code approach. This interest has led to its introduction in different codes and standards around the world. Although engineers widely use performance-based methods to design structural components in earthquake engineering, the adoption of such methods in fire engineering is still very limited. This Special Issue addresses this shortcoming by providing engineers with the needed knowledge and recent research activities addressing performance-based design in structural fire engineering, including the use of hotspot analysis to estimate the magnitude of risk to people and property in urban areas; simulations of the evacuation of large crowds; and the identification of fire effects on concrete, steel, and special structures