CellEVAC: An adaptive guidance system for crowd evacuation through behavioral optimization

The emergency sign system provides evacuation information and route guidance for people and plays an important role in crowd evacuation in case of emergency. This paper investigates crowd dynamics with emergency signs and mainly focuses on the influence of signs on crowd evacuation dynamics under the visual field-limited environment. A modified social force model is proposed by taking into account the evacuation guidance of signs and interaction force between the pedestrians and signs. The effects of quantity and locations of emergency signs and self-organization behavior on evacuation efficiency are explored by simulations. According to the simulation results, the crowd evacuation with emergency signs shows an orderly evacuation process and the evacuation time is reduced significantly compared with that without signs in a smoke-filled room scenario. Moreover, emergency signs with appropriate locations and quantity are more conducive to crowd evacuation. Besides, the herding behavior can also improve the crowd evacuation efficiency.

With the continuous expansion of the city scale and urbanization, urban road networks are becoming increasingly complex. Moreover, severe and extreme weather events, earthquakes, and other natural disasters occur frequently. Therefore, how to effectively and quickly evacuate urban crowd in dynamic environments is an urgent issue. To carry out the above objective, an unmanned system-guided crowd evacuation method is proposed in the current study. In the proposed method, the robot can perceive the environment in a timely and accurate manner to generate the evacuation map via advanced information technologies such as the Internet of Things or urban brain. Subsequently, an improved elliptic tangent graph approach based on global and local information (ETG-GLI) is utilized to plan a feasible and short evacuation path in large-scale scenarios. Finally, a novel crowd evacuation model based on the social force model is proposed to simulate the actual crowd evacuation process in complex and large-scale environments. To test the performance of the proposed path planning method, 25 different scenarios are proposed to simulate complex urban crowd evacuation environments. The experimental results show that the proposed algorithm outperforms other competitors in terms of path planning ability and computational time. Three actual evacuation cases with 324 pedestrians are modeled to further test the performance of the proposed algorithm. The simulation results demonstrate that the unmanned system-guided crowd evacuation method can find a shorter evacuation path for reducing the evacuation time in three complex and large-scale environments when compared with three other methods. Therefore, the proposed algorithm is a highly effective and promising approach to provide useful decision support and guidance for actual urban planning and urban emergence management. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In modern cities, the population density is high and the road network is complex. To evacuate the crowd in a timely and safe manner, planning feasible and short paths in large-scale and complex environments is a critical and challenging task. Therefore, the present study aims to provide a novel method to plan high-quality evacuation routes to guide the pedestrian flow. The performance of the proposed approach is validated in 25 test scenarios and 3 real-world instances. Experimental results demonstrate that the proposed algorithm performs well in terms of path length and computation time. Moreover, the proposed crowd evacuation model can simulate the actual process of crowd evacuation.

An agent-based modeling approach to collaborative classrooms evacuation process

The crowd evacuation strategy seeks to arrange crowd evacuation in an orderly manner to protect people’s lives and reduce property damage in case of sudden emergencies in crowded and complex places. In recent years, there have been several works to apply deep learning to crowd evacuation to make evacuation strategies more intelligent. However, existing researches rarely consider the integration of scene perception and crowd evacuation, which leads to evacuation methods that are detached from the scene and also ignore the crowd collaboration in the evacuation process. To this end, we propose Intelligent Crowd Evacuation Architecture based on Visual features using Multi-Agent Reinforcement Learning (ICEA-VMARL). Subsequently, we present modeling analysis on the crowd grouping and group evacuation modules of the architecture. First, we propose the Population Grouping algorithm based on Continuous Spatiotemporal individual Similarity (PGCSS), which combines crowd features to group crowds. Then, we propose a Group Collaborative Evacuation algorithm based on Multi-Agent Reinforcement Learning (GCE-MARL), which considers group collaboration while evacuating to achieve global optimal evacuation. Finally, we build an experimental crowd simulation system, and the results demonstrate that the crowd grouping algorithm and group evacuation algorithm proposed have better performance compared with other methods.

This study focuses on the safe evacuation strategy and evacuation process in the subway train under the fires. The subway station evacuation mode should be adopted if the power system of a subway train is normal on fire. While, the tunnel evacuation mode should be adopted if the power system of the train fails because of the effects of fire. Under the tunnel evacuation mode, the direction of tunnel smoke should be opposite to that of most passengers, and passengers should be evacuated toward the fresh wind. By using the numerical simulation software Pathfinder and PyroSim, the passenger evacuation time under different conditions is calculated, and the safety of the evacuation process is evaluated. The results show that the evacuation time of the station evacuation mode is obviously shorter than that of the tunnel evacuation mode. With the same conditions, the evacuation time of the tunnel evacuation mode is 2193 s, which is about four times as much as the evacuation time of the station evacuation mode (526 s). The total evacuation time increases with the total number of passengers and the proportion of older people and children. Under an oil pool fire, which is an extreme fire condition, the fire environment inside the train may reach a level threatening the passengers’ safety before the evacuation is complete, even before the door opens; therefore, special attention should be paid to the safety issues in stage from the fire begins to the evacuation complete.

Existing crowd evacuation simulation methods commonly face challenges of low efficiency in path planning and insufficient realism in pedestrian movement during the evacuation process. In this study, we propose a novel crowd evacuation path planning approach based on the learning curve–deep deterministic policy gradient (LC‐DDPG) algorithm. The algorithm incorporates dynamic experience pool and a priority experience sampling strategy, enhancing convergence speed and achieving higher average rewards, thus efficiently enabling global path planning. Building upon this foundation, we introduce a double‐layer method for crowd evacuation using deep reinforcement learning. Specifically, within each group, individuals are categorized into leaders and followers. At the top layer, we employ the LC‐DDPG algorithm to perform global path planning for the leaders. Simultaneously, at the bottom layer, an enhanced social force model guides the followers to avoid obstacles and follow the leaders during evacuation. We implemented a crowd evacuation simulation platform. Experimental results show that our proposed method has high path planning efficiency and can generate more realistic pedestrian trajectories in different scenarios and crowd sizes.

&lt;p&gt;A comprehensive traffic monitoring system can assist authorities in identifying parts of a road transportation network that exhibit poor performance. In addition to monitoring, it is essential to develop a localized and efficient analytical transportation model that reflects various network scenarios and conditions. A comprehensive transportation model must consider various components such as vehicles and their different mechanical characteristics, human and their diverse behaviours, urban layouts and structures, and communication and transportation infrastructure and their limitations. Development of such a system requires a bringing together of ideas, tools, and techniques from multiple overlapping disciplines such as traffic and computer engineers, statistics, urban planning, and behavioural modelling. In addition to modelling of the urban traffic for a typical day, development of a large-scale emergency evacuation modelling is a critical task for an urban area as this assists traffic operation teams and local authorities to identify the limitations of traffic infrastructure during an evacuation process through examining various parameters such as evacuation time. In an evacuation, there may be severe and unpredictable damage to the infrastructure of a city such as the loss of power, telecommunications and transportation links. Traffic modelling of a large-scale evacuation is more challenging than modelling the traffic for a typical day as historical data is usually available for typical days, whereas each disaster and evacuation are typically one-off or rare events. Damage due to a disaster, combined with a sudden increase of demand due to the evacuation of people will likely result in increased pressure on the remaining, potentially fragmented, infrastructure. The lessons learnt from evacuation modelling can assist traffic operation teams and local authorities to provide safer and more efficient planning. The development of pervasive personal digital devices such as phones, watches, and headphones which can be interconnected with technologies such as Bluetooth, has led to a disruptive change in the ways in which local governments can monitor traffic flows within their cities. Moreover, modern vehicles and navigation systems can interconnect to the personal devices of drivers and passengers primarily via Bluetooth technologies. By continuously monitoring such devices when they are discoverable and in range, traffic patterns can be estimated based on, not only the volume of detection, but also other characteristics of the devices that can be used to give more refined estimates of the real underlying traffic flows. This thesis examines Bluetooth traffic data collected from Bluetooth Traffic Monitoring Systems (BTMS) for modelling and monitoring the urban traffic. BTMS can monitor and track individual detected vehicles through a city. Installation, processing, data transmission, and maintenance of BTMS are easier, quicker and cheaper than existing standard monitoring systems such as CCTV cameras and inductive loops. Inductive loops are typically point-wise traffic monitoring systems that are installed in the roads and can measure the traffic flow. However, the use of BTMS devices presents several challenges: not every vehicle has a detectable device, some have many, and there are devices carried by pedestrians and non-motor vehicles as well as stationary devices. This thesis enumerates and investigates these challenges through statistical modelling, various protocols for cleaning and data preparation, dynamic estimation of the detection rate, and simulation through the case study of the city of Wellington, New Zealand. The city of Wellington experienced damage from the 2016 Kaikoura earthquake (a magnitude 7.8 earthquake), which led to road closures and other infrastructure damage. As part of modelling, performance evaluation, and identifying impacted routes by the 2016 Kaikoura earthquake, this thesis analyses three weeks of BTMS data from the periods before and after the earthquake. Furthermore, this thesis proposes a multi-disciplinary dynamic traffic modelling (TFDA2M) framework and evaluates the performance of TFDA2M on various large-scale evacuation scenarios. These scenarios cover a wide range of real-world use cases which may occur during a disaster such as power failure, an abrupt increase in demand, and damage to the main transportation infrastructure. The findings of this thesis highlight an immediate need for preparations of a large-scale evacuation planning for Wellington to mitigate the consequences of a large-scale evacuation due to a future disaster. Moreover, TFDA2M can assist traffic operation managers and authorities in making smarter decisions (both quantitative and spatially) through the simulation process. Since TFDA2M has a flexible schema, it can be set to monitor, assess, and manage the traffic flow on a daily basis and disaster occasions.&lt;/p&gt;

The Whale-Bat Chaotic Algorithm (WOABCM) is a revolutionary tool for optimizing pathfinding navigation in crowd evacuation scenarios. Conventional evacuation plans rely on static routes, causing traffic jams and decreased safety. WOABCM uses the erratic behavior of whales and bats to optimize escape routes in real time, making it particularly useful in large crowd evacuations. The algorithm can adapt to different scenarios, ensuring its continued efficacy across real-world conditions. Iteratively iterating through the evacuation environment, WOABCM iteratively finds the most effective ways for agents to reach exits. In this experiment, the results show notable improvements in evacuation times, with WOABCM determining and directing agents through the shortest and least crowded paths in a room setting and assigning agents to exits in more complicated scenarios. The algorithm's flexibility also allows it to swiftly recompute pathways for agents, avoiding obstructions or congestion points. This groundbreaking approach to crowd evacuation simulation demonstrates how chaos theory-inspired algorithms can be used to solve practical problems

Emergency signage systems provide effective route guidance and evacuation information for pedestrians in case of emergencies such as fire, blackout, and explosion. This paper proposes a modified social force (SF) model to investigate crowd evacuation dynamics taking into account the influence of emergency signs. The perceiving probability model is formulated for the quantitative description of the probability that pedestrians can successfully notice the sign and clearly perceive the guidance information. Simulation experiments and controlled experiments are designed to calibrate the parameters of the proposed model. The effectiveness of the modified SF model is preliminarily verified by comparing the simulation results with experimental data and/or empirical results such as fundamental diagrams and self-organization phenomena. A case study of crowd evacuation simulations at a typical Beijing subway station is conducted to evaluate evacuation performance of three signage distribution schemes, i.e., Maximal Covering (MaxCover), Uniform, and Random, which are proposed by the maximal covering location and empirical approaches, as well as contrasted scheme without emergency signs. The effects of the quantity and distribution of emergency signs on crowd evacuation efficiency are studied quantitatively by simulations. The results show that installing emergency signs can improve evacuation efficiency no matter what distribution scheme is adopted. By choosing an appropriate distribution scheme i.e., MaxCover, the evacuation performance can be further improved and the evacuation time can be significantly reduced.

Integration of cumulative prospect theory in cellular automata model for building evacuation

The microscopic simulation model based on artificial potential field and Agent was proposed from the individual perspective to evacuate in fire environment.First,various factors including fire,doors,obstacles,etc.which can affect peoples' behavior were quantified to construct artificial potential field system.Second,taking the individual as the object,movement direction could be determined through stress analysis.Finally through comparing whether to open the security passage and whether to involve personnel persuasion,crowd evacuation was simulated reasonably.The evacuation system was realized based on VC + + and can more really simulate the crowd evacuation process from the experimental results.

The crowd evacuation simulation is essential to provide important results for occupants, especially in the large capacity building compared to the human fire drill exercise. The strategy of evacuation such as the use of obstacles may need to be adapted by many organizations as an aid to help in visualizing and estimating the evacuation time during an emergency. During certain crowd events, they may consider the various setting of the object to ensure smoothness and effective crowd evacuation flow. In this paper, it aims to provide the simulation with 100-1000 agents and testing with obstacle using Anylogic tool and analysis of evacuation time validated using SPSS. The results show that the placement of obstacles near the exit way indeed can reduce the evacuation time and complies with the anti-arching phenomenon during evacuation.

Aim: The main aim of the study was to investigate the possibility to use the Pathfinder simulation software to determine the duration of an evacuation and to plan its course for various scenarios of evacuation organisation, for specific profiles and behaviours of passengers and on-board personnel, and for a specific geometrical arrangement of the passenger cabin of a selected passenger aircraft. In addition, the paper discusses selected factors that affect the evacuation from the passenger aircraft. Project and methods: The Pathfinder software used in the research has the graphical interface to create an evacuation simulation model (2D and 3D) as well as tools to visualise the results. Pathfinder is based on artificial intelligence algorithms, in which each passenger has a number of individual features that can influence his/her movements and decisions during the simulation. The simulation of people's movement is determined by their profiles and behaviours, the parameters of which can be entered by means of probability distributions, which makes it possible to take into account the stochastic nature of the evacuation process. Results: The Boeing 787 Dreamliner was selected for the study, for which six options for simulating the evacuation of 252 passengers and eight members of on-board personnel were conducted. The shortest evacuation time was achieved by changing the even distribution of the number of passengers to the individual emergency exits, thus avoiding congestions in sensitive areas of the passenger cabin. The increase in passengers' maximum speed has paradoxically increased evacuation times, as it has increased the intensity of passenger collisions. It was found that one of the key issues affecting the timing of an evacuation is the proper organisation of the evacuation by on-board personnel, who, by guiding passengers through the geometrically most advantageous passageways, results in the fastest possible evacuation. The simulations in variants five and six have achieved satisfactory evacuation times, which are within the emergency aircraft evacuation time limit required in a certification process. Conclusions: The presented simulation models, the results obtained, and the wide range of possibilities of three-dimensional visualisation of research results give a rational basis for the use of Pathfinder software for testing the evacuation process and thus: for the usage in the aircraft design process, for preparing aircrafts for tests, for shaping evacuation procedures, for on-board personnel training and for air accident investigation.

Temporal and spatial distribution of pedestrians in subway evacuation under node failure by multi-hazards

Computer simulations on the evacuation process in a story are launched with cellular automaton in this article. The story is composed of five rooms and one corridor. Influence of various parameters on the evacuation process is investigated. It shows that the width of the door of rooms has little influence but the width of the corridor and the maximum velocity of the pedestrian have great influence on the time for evacuation. The relation between evacuation time and the width of corridor is found as tc ∝ W−0.84. It is also found that appropriate shape of the room is helpful to evacuation.