Fast flow algorithm prioritizing the most time-consuming source point by using a multi-source evacuation model

In many public spaces (e.g. colleges and shopping malls), people are frequently distributed discretely, and thus, single-source evacuation, which means there’s only one point of origin, is not always a feasible solution. Hence, this paper discusses a multi-source evacuation model and algorithm, which are intended to evacuate all the people that are trapped within the minimum possible time. This study presents a fast flow algorithm to prioritize the most time-consuming source point under the constraint of route and exit capacity to reduce the evacuation time. This fast flow algorithm overcomes the deficiencies in the existing global optimization fast flow algorithm and capacity constrained route planner (CCRP) algorithm. For the fast flow algorithm, the first step is to determine the optimal solution to single-source evacuation and use the evacuation time of the most time-consuming source and exit gate set as the initial solution. The second step is to determine a multi-source evacuation solution by updating the lower limit of the current evacuation time and the exit gate set continually. The final step is to verify the effectiveness and feasibility of the algorithm through comparison.

Modeling and Simulation of Exit Selection Behavior in Pedestrian Evacuation Based on Information Perception and Transmission

This paper aims to present an improved evacuation model, which is capable of simulating individual exit selection behavior based on the acquisition and processing of information, especially in dangerous and unfamiliar environments. Firstly, an evacuation model was improved by the introduction of a floor field of gas concentration and an exit selection model, considering the congestion avoidance and danger avoidance behavior. Secondly, the process of information perception and transmission was studied and introduced into the model with a set of rules. Finally, real experiments in a simple double-exit room were conducted for model validation and parameter setting, and simulation experiments in scenarios with an unknown hazard or unknown exits were conducted to confirm the necessity and rationality of introducing information perception and transmission. The simulation results show that, with the increase in perception distance or trust extent, the pedestrian safety increases. The critical values of perception distance or trust extent, below which some people cannot acquire any new information, vary depending on the pedestrian density. When the density is high, the influence of perception distance or trust extent reduces, and the probability of reselecting an exit increases, which causes the safety of pedestrians to decrease.

A Novel Emergency Evacuation Model of Subway Station Passengers Considering Personality Traits

Subway station emergencies have caused serious casualties in recent years, so the aim of this research was to develop and establish an evacuation model that considers the OCEAN personality psychological traits to improve the credibility of the emergency pedestrian evacuation simulation. Firstly, the relationship between the personality and psychological stress was established based on the reconstruction of a passenger’s personality traits. Secondly, the relationship between the expected speed and a passenger’s personality traits was modified based on the social force model. Finally, the simulation was carried out using the Anylogic software. The results show that as the value of the personality increases, the evacuation time of personalities ψA and ψC gradually increases, but the opposite effect is observed for personalities ψN and ψE. Similarly, as the value of personality traits increases, the speed of personalities ψA and ψC gradually decreases, but the opposite effect is observed for personalities ψN and ψE. Only during peak periods, as the value of personality traits increases, the density of the connecting area of passengers with personality traits ψA and ψC gradually increases; on the contrary, that of passengers with personality traits ψN and ψE gradually decreases. The conclusion of this study is that different personality traits have different effects on evacuation behavior, which enriches the model of pedestrian evacuation further.

Network Modeling of Hurricane Evacuation Using Data-Driven Demand and Incident-Induced Capacity Loss Models

The development of a hurricane evacuation simulation model is a crucial task in emergency management and planning. Two major issues affect the reliability of an evacuation model: one is estimations of evacuation traffic based on socioeconomic characteristics, and the other is capacity change and its influence on evacuation outcome due to traffic incidents in the context of hurricanes. Both issues can impact the effectiveness of emergency planning in terms of evacuation order issuance, and evacuation route planning. The proposed research aims to investigate the demand and supply modeling in the context of hurricane evacuations. This methodology created three scenarios for the New York City (NYC) metropolitan area, including one base and two evacuation scenarios with different levels of traffic demand and capacity uncertainty. Observed volume data prior to Hurricane Sandy is collected to model the response curve of the model, and the empirical incident data under actual evacuation conditions are analyzed and modeled. Then, the modeled incidents are incorporated into the planning model modified for evacuation. Simulation results are sampled and compared with observed sensor-based travel times as well as O-D-based trip times of NYC taxi data. The results show that the introduction of incident frequency and duration models can significantly improve the performance of the evacuation model. The results of this approach imply the importance of traffic incident consideration for hurricane evacuation simulation.

Multifactor Variance Assessment for Determining the Number of Repeat Simulation Runs in Evacuation Modelling

Evacuation models commonly employ pseudorandom sampling from distributions to represent the variability of human behaviour in the evacuation process, otherwise referred to as ‘behavioural uncertainty’. This paper presents a method based on functional analysis and inferential statistics to study the convergence of probabilistic evacuation model results to inform deciding how many repeat simulation runs are required for a given scenario. Compared to existing approaches which typically focus on measuring variance in evacuation times, the proposed method utilises multifactor variance to assess the convergence of a range of different evacuation model outputs, referred to as factors. The factors include crowd density, flowrates, occupant locations, exit usage, and queuing times. These factors were selected as they represent a range of means to assess variance in evacuation dynamics between repeat simulation runs and can be found in most evacuation models. The application of the method (along with a tool developed for its implementation) is demonstrated through two case studies. The first case study consists of an analysis of convergence in evacuation simulation results for a building including 1855 occupants. The second case study is a simple verification test aimed at demonstrating the capabilities of the method. Results from the case studies suggest that multifactor variance assessment provides a more holistic assessment of the variance in evacuation dynamics and results provided by an evacuation model compared to existing methods which adopt single factor analysis. This provides increased confidence in determining an appropriate number of repeat simulation runs to ensure key evacuation dynamics and results which may be influenced by pseudorandom sampling are represented.