Intelligent Evacuation Management Systems: A Review
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paper_title: Collecting Data on Crowds and Rallies: A New Method of Stationary Sampling

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This paper proposes a field procedure for collecting data at stationary mass rallies. Noting the research gap in adequate data about ongoing gatherings, we present a set of techniques summarized under the name zone-sector strategies. We emphasize area sampling, dividing the crowd into zones and sectors, and collecting data during stationary phases of assemblages. We also employ two-member interview teams, in an effort to collect reliable information about attitudinal and nonvisible characteristics of participants during the demonstration itself. Methods of testing reliability and validity of information are presented. Data from four political rallies exemplify the techniques and basically support the validity of the zone-sector approach. We conclude by stating possible uses and advantages of the methods. Social researchers have shown interest in the difficult problem of collecting data at sporadic demonstrations and other gatherings (e.g., Evans; Milgram and Toch). Some have noted the general lack of good data on crowd behavior (Berk: a, b). Others have pointed out the difficulties inherent in research on crowds and the pitfalls of the techniques most commonly used, such as sideline observation and retrospective questioning (e.g., Couch; Fisher; McPhail, a). Recent attempts to gather data have stressed primarily the stationary phase of crowds or assemblages. They have paid attention to improving observation. Fisher suggested a dramaturgical framework for such observation (see Ponting). Others (McPhail, b; McPhail and Pickens) have been developing a photographic method for observing crowd processes, with emphasis on at least momentary participant alignment. Jacobs has figured out how to calculate the size of a relatively stationary crowd, again using photographic observation. While these observational techniques and recent general guidelines for research (e.g., Lofland; Quarantelli and Dynes; Schatzman and Strauss) have advanced the methods of gathering data as collective episodes occur, new techniques could still be profitable. The lack of data about crowds themselves, mentioned by Berk (b, 15) persists. In particular, the field of collective behavior needs a *This research was supported in part by funds made available through a National Science Foundation Science Development grant to the University of North Carolina at Chapel Hill. We are very grateful to Glen H. Elder, N. David Milder, E. L. Quarantelli, Timothy J. Curry, and John S. Reed for helpful comments on earlier drafts. We also express our appreciation to the following interviewers: Edward Arroyo, Greg Bird, Habibullah Dada, Suzann Frey, Marg Gainer, Carol Goldsmith, Linda Harris, Marsha Hill, Evon Kruse, Roxi Ann Liming, Ken Long, Jane McCaskey, Frank McIntyre, Christine Nelms, Steven Rinehart, Anne Sykes, Darlene Walker, and Barbara Werner.
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paper_title: Stereo Vision Tracking of Multiple Objects in Complex Indoor Environments

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This paper presents a novel system capable of solving the problem of tracking multiple targets in a crowded, complex and dynamic indoor environment, like those typical of mobile robot applications. The proposed solution is based on a stereo vision set in the acquisition step and a probabilistic algorithm in the obstacles position estimation process. The system obtains 3D position and speed information related to each object in the robot’s environment; then it achieves a classification between building elements (ceiling, walls, columns and so on) and the rest of items in robot surroundings. All objects in robot surroundings, both dynamic and static, are considered to be obstacles but the structure of the environment itself. A combination of a Bayesian algorithm and a deterministic clustering process is used in order to obtain a multimodal representation of speed and position of detected obstacles. Performance of the final system has been tested against state of the art proposals; test results validate the authors’ proposal. The designed algorithms and procedures provide a solution to those applications where similar multimodal data structures are found.
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paper_title: Urban Computing: Concepts, Methodologies, and Applications

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Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.
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paper_title: A Novel Wireless Sensor Network Architecture for Crowd Disaster Mitigation

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Disasters aroused due to dynamic movement of large, uncontrollable crowds are ever increasing. The inherent real-time dynamics of crowd need to be tightly monitored and alerted to avoid such disasters. Most of the existing crowd monitoring systems is difficult to deploy, maintain, and dependent on single component failure. This research work proposes novel network architecture based on the key technologies of wireless sensor network and mobile computing for the effective prediction of causes of crowd disaster particularly stampedes in the crowd and thereby alerting the crowd controlling station to take appropriate actions in time. In the current implemented version of the proposed architecture, the smart phones act as wireless sensor nodes to estimate the probability of occurrence of stampede using data fusion and analysis of embedded sensors such as tri-axial accelerometers, gyroscopes, GPS, light sensors etc. The implementation of the proposed architecture in smart phones provides light weight, easy to deploy, context aware wireless services for effective crowd disaster mitigation.
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paper_title: Capturing crowd dynamics at large scale events using participatory GPS-localization

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Large-scale festivals with a multitude of stages, food stands, and attractions require a complex perimeter design and program planning in order to manage the mobility of crowds as a controlled process. Errors in the planning phase can cause unexpected crowd dynamics and lead to stampedes with lethal consequences. We deployed an official app for Zuri Fascht 2013 — the largest Swiss event — over a period of three days. The app offered information about the festival and featured a background localization allowing us to collect continuously the visitor position. With 56,000 app downloads and 28,000 users contributing 25M location updates in total, we obtained a large scale dataset. By aggregation of location points complex crowd dynamics can be captured during the entire festival. In this paper we present the data collection for Zuri Fascht 2013 and best practices to acquire as many contributing users as possible for such an event. Furthermore, we show the potential of aggregated location data and visualize relevant parameters that can serve as tool for analysis and planning of program and perimeter design.
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paper_title: The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: a case study of the Ghent Festivities.

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Abstract In this paper, proximity-based Bluetooth tracking is postulated as an efficient and effective methodology for analysing the complex spatiotemporal dynamics of visitor movements at mass events. A case study of the Ghent Festivities event (1.5 million visitors over 10 days) is described in detail and preliminary results are shown to give an indication of the added value of the methodology for stakeholders of the event. By covering 22 locations in the study area with Bluetooth scanners, we were able to extract 152,487 trajectories generated by 80,828 detected visitors. Apart from generating clear statistics such as visitor counts, the share of returning visitors, and visitor flow maps, the analyses also reveal the complex nature of this event by hinting at the existence of several mutually different visitor profiles. We conclude by arguing why Bluetooth tracking offers significant advantages for tracking mass event visitors with respect to other and more prominent technologies, and outline some of its remaining deficiencies.
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paper_title: Mobile Mapping of Sporting Event Spectators Using Bluetooth Sensors: Tour of Flanders 2011

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Accurate spatiotemporal information on crowds is a necessity for a better management in general and for the mitigation of potential security risks. The large numbers of individuals involved and their mobility, however, make generation of this information non-trivial. This paper proposes a novel methodology to estimate and map crowd sizes using mobile Bluetooth sensors and examines to what extent this methodology represents a valuable alternative to existing traditional crowd density estimation methods. The proposed methodology is applied in a unique case study that uses Bluetooth technology for the mobile mapping of spectators of the Tour of Flanders 2011 road cycling race. The locations of nearly 16,000 cell phones of spectators along the race course were registered and detailed views of the spatiotemporal distribution of the crowd were generated. Comparison with visual head counts from camera footage delivered a detection ratio of 13.0 ± 2.3%, making it possible to estimate the crowd size. To our knowledge, this is the first study that uses mobile Bluetooth sensors to count and map a crowd over space and time.
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paper_title: Bluetooth based collaborative crowd density estimation with mobile phones

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We present a technique for estimating crowd density by using a mobile phone to scan the environment for Bluetooth devices. The paper builds on previous work directed to use Bluetooth scans to analyze social context and extends it with more advanced features, leveraging collaboration between close by devices, and the use of relative features that do not directly depend on the absolute number of devices in the environment. The method is evaluated on a data set from an experiment at the public viewing event in Kaiserslautern during the European soccer championship showing over 75% recognition accuracy on seven discrete classes.
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paper_title: An Efficient Sequential Approach to Tracking Multiple Objects Through Crowds for Real-Time Intelligent CCTV Systems

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Efficiency and robustness are the two most important issues for multiobject tracking algorithms in real-time intelligent video surveillance systems. We propose a novel 2.5-D approach to real-time multiobject tracking in crowds, which is formulated as a maximum a posteriori estimation problem and is approximated through an assignment step and a location step. Observing that the occluding object is usually less affected by the occluded objects, sequential solutions for the assignment and the location are derived. A novel dominant color histogram (DCH) is proposed as an efficient object model. The DCH can be regarded as a generalized color histogram, where dominant colors are selected based on a given distance measure. Comparing with conventional color histograms, the DCH only requires a few color components (31 on average). Furthermore, our theoretical analysis and evaluation on real data have shown that DCHs are robust to illumination changes. Using the DCH, efficient implementations of sequential solutions for the assignment and location steps are proposed. The assignment step includes the estimation of the depth order for the objects in a dispersing group, one-by-one assignment, and feature exclusion from the group representation. The location step includes the depth-order estimation for the objects in a new group, the two-phase mean-shift location, and the exclusion of tracked objects from the new position in the group. Multiobject tracking results and evaluation from public data sets are presented. Experiments on image sequences captured from crowded public environments have shown good tracking results, where about 90% of the objects have been successfully tracked with the correct identification numbers by the proposed method. Our results and evaluation have indicated that the method is efficient and robust for tracking multiple objects ( ges 3) in complex occlusion for real-world surveillance scenarios.
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paper_title: Floor Fields for Tracking in High Density Crowd Scenes

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This paper presents an algorithm for tracking individual targets in high density crowd scenes containing hundreds of people. Tracking in such a scene is extremely challenging due to the small number of pixels on the target, appearance ambiguity resulting from the dense packing, and severe inter-object occlusions. The novel tracking algorithm, which is outlined in this paper, will overcome these challenges using a scene structure based force model. In this force model an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in the scene. The key ingredients of the force model are three floor fields, which are inspired by the research in the field of evacuation dynamics, namely Static Floor Field (SFF), Dynamic Floor Field (DFF), and Boundary Floor Field (BFF). These fields determine the probability of move from one location to another by converting the long-range forces into local ones. The SFF specifies regions of the scene which are attractive in nature (e.g. an exit location). The DFF specifies the immediate behavior of the crowd in the vicinity of the individual being tracked. The BFF specifies influences exhibited by the barriers in the scene (e.g. walls, no-go areas). By combining cues from all three fields with the available appearance information, we track individual targets in high density crowds.
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paper_title: Histograms of oriented gradients for human detection

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We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
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paper_title: Smart Objects Identification System for Robotic Surveillance

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Video surveillance is an active research topic in computer vision. In this paper, humans and cars identification technique suitable for real time video surveillance systems is presented. The technique we proposed includes background subtraction, foreground segmentation, shadow removal, feature extraction and classification. The feature extraction of the extracted foreground objects is done via a new set of affine moment invariants based on statistics method and these were used to identify human or car. When the partial occlusion occurs, although features of full body cannot be extracted, our proposed technique extracts the features of head shoulder. Our proposed technique can identify human by extracting the human head-shoulder up to 60%---70% occlusion. Thus, it has a better classification to solve the issue of the loss of property arising from human occluded easily in practical applications. The whole system works at approximately 16---29 fps and thus it is suitable for real-time applications. The accuracy for our proposed technique in identifying human is very good, which is 98.33%, while for cars' identification, the accuracy is also good, which is 94.41%. The overall accuracy for our proposed technique in identifying human and car is at 98.04%. The experiment results show that this method is effective and has strong robustness.
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paper_title: Human identification system based on moment invariant features

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Video surveillance is an active research topic in computer vision. Recent research in video surveillance system has shown an increasing focus on creating reliable systems utilizing non-computationally expensive technique for detecting and observing humans' appearance, movements and activities. In this paper, we present a human identification technique suitable for video surveillance. The technique we propose includes background subtraction, foreground segmentation, feature extraction and classification. First of all, we extract all foreground objects from the background. Then, we perform a morphological reconstruction algorithm to recover the distorted foreground objects. The feature extraction is done using affine moment invariants of full body and head-shoulder of the extracted foreground objects and these were used to identify human. When the partial occlusion occurs, although feature of full body cannot be extracted, still the features of head shoulder can be extracted. Thus, it has a better classification on solving the issue of the loss of property arising from human occluded easily in practical applications. The experiment results show that this method is effective, and it has strong robustness.
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paper_title: Dynamics of crowd disasters: An empirical study

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Many observations of the dynamics of pedestrian crowds, including various self-organization phenomena, have been successfully described by simple many-particle models. For ethical reasons, however, there is a serious lack of experimental data regarding crowd panic. Therefore, we have analyzed video recordings of the crowd disaster in Mina/Makkah during the Hajj in 1426H on 12 January 2006. They reveal two subsequent, sudden transitions from laminar to stop-and-go and "turbulent" flows, which question many previous simulation models. While the transition from laminar to stop-and-go flows supports a recent model of bottleneck flows [D. Helbing, Phys. Rev. Lett. 97, 168001 (2006)], the subsequent transition to turbulent flow is not yet well understood. It is responsible for sudden eruptions of pressure release comparable to earthquakes, which cause sudden displacements and the falling and trampling of people. The insights of this study into the reasons for critical crowd conditions are important for the organization of safer mass events. In particular, they allow one to understand where and when crowd accidents tend to occur. They have also led to organizational changes, which have ensured a safe Hajj in 1427H.
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paper_title: Hajj crowd management and navigation system: People tracking and location based services via integrated mobile and RFID systems

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Yearly there's an influx of over three million Muslims to Makkah., Saudi Arabia to perform Hajj. As this large group of pilgrims move between the different religious sites safety and security becomes an issue of main concern. This research looks into the integration of different mobile technologies to serve the purpose of crowd management., people tracking and location based services. It explores the solution to track the movement of pilgrims via RFID technology. A location aware mobile solution will also be integrated into this. This will be made available to pilgrims with smartphones to enhance the accuracy and tracking time of the pilgrims and provide them with location based services for Hajj.
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paper_title: Pilgrims Tracking and Identification Using RFID Technology

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Now a days there were so many problems regarding the crowd control and security issues in the holy areas.Especially during pilgrimage, the pilgrimage authority is facing so many problems regarding crowd control,security issues,identification of the pilgrims and the tracking of the pilgrims.India is a multi-religious country, in which so many holy areas are thereEvery year,the respective authorities of the holy areas werefacing many problems. But they are unable to provide those facilities in a full fledge manner.However,providing a solution to solve the problems completely is impossible.But we can reduce these problems upto some extent.There are so many technologies which were implemented in Saudi Arabia during Hajj pilgrimage for to reduce these types of problems.Here,we are proposing one architecture in which we are using RFID technology. In our proposed paper,we are dealing the Tirupati pilgrims case.Details about every pilgrim will be taken at the entrance, and each pilgrim will be given an active RFID tag with a specific UID numbers.Here readers were used to read the UID numbers of each tag and these UID numbers are given to the host server where it updates the location of the particular pilgrim.In this we are providing these tags as wrist band tags.Here we are using LAN network between the readers and the host servers,Intranet between the PML servers and Internet between the Gateway PML servers.A mobile sensor node is placed in every segmented area which is an additional feature in our paper
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paper_title: Tracking human mobility at mass gathering events using WISP

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Mass gathering events, where thousands of people meet in a confined area for a defined period of time, pose a severe strain on the safety and security of the gathering crowds. The process of managing and controlling crowd movements has been a challenge. Lack of good management and planning of high crowd density with restricted points of access may end up with tragic disasters and mortalities. With the significant advancement of wireless networks, mobile technologies, and communication protocols, many host organizers are embracing the use of modern technologies to assist in managing, controlling, and tracking crowds to ensure their security and safety. In this paper, we propose a novel framework based on Wireless Identification and Sensing Platform (WISP) for tracking, monitoring, and managing human movement at mass gathering events. Individuals are offered WISP tags that store their basic identity and contact information. WISP readers and writers are positioned at various locations during a mass gathering event to monitor and control crowd movement. A central management system utilizes the collected data to provide assistance to crowd managers and trigger potential alerts.
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paper_title: Crowd Management with RFID and Wireless Technologies

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The recent spread of communicable diseases like swine flu, disasters like stampedes and ongoing security issues have made management of large crowded events more critical than ever before. Managing large crowds is a very complex, challenging and costly exercise. Many of the problems encountered in crowd management can be minimized by the use of RFID and other wireless technologies. These technologies are already being used in managing and administering many activities of daily life. However, the effectiveness of these technologies is yet to be tested for managing dense crowds and poses a challenge to the industry. The aim of this paper is to provide a management framework for large & dense crowds. The analysis of the technological framework is done with help of Hajj & Kumbh case studies. The research is industrial in nature and we hope that it would help the organizers of crowded events and law and order enforcement agencies.
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paper_title: RFID Technology and Crowded Event Management

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Efficient management of large crowded events is always a challenge. Successful management of such events largely depends on the use of technologies. There are many business cases where the use of latest technology can vastly improve their management. In recent times, many types of identification and sensor devices, including RFID tags, have been developed. Such technologies, combined with appropriate backend database systems, can be used to improve the crowd and event management. Hajj, an annual pilgrimage to Mecca, is a very large and unique gathering, which attracts millions of pilgrims for two or more weeks. Despite tremendous advancement of technology and its availability, Hajj continues to be managed manually. There are many aspects of Hajj which are worth researching. The aim of this paper is to identify appropriate technologies which can be used to improve the management of large gatherings such as those of Hajj and Kumbh gatherings in India.
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paper_title: CROWD CONTROL SYSTEM USING IR TRANSMITTER AND RECEIVER

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An efficient crowd control system is needed for safety of lives, property, time and economy. Crowd control system presents a design and implementation of low cost, low power consummated and more reliable and an infrared based intelligent crowd control system. The system contains Infrared transmitters and receiver. The basic concept of IR (Infrared) obstacle detection is to transmit the IR signal (radiation) in a direction and signal is received at the IR receiver when the IR radiation bounces back from a surface of the object. The system can response rapidly with violation of crowd limit. System describes highly accurate crowd control system using infrared communication. Proposed system achieves high accuracy and more efficiency at four way terminals. In every direction the road will consist of an IR transmitter-receiver pair at a certain distance. When crowd will be heavy in one particular direction during emergency situation it will indicate the administrator by sending message. So the heavy crowd can be routed to other route by preventing the stampede.
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paper_title: Inferring Crowd Conditions from Pedestrians' Location Traces for Real-Time Crowd Monitoring during City-Scale Mass Gatherings

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There is a need for event organizers and emergency response personnel to detect emerging, potentially critical crowd situations at an early stage during city-wide mass gatherings. In this work, we introduce and describe mathematical methods based on pedestrian-behavior models to infer and visualize crowd conditions from pedestrians' GPS location traces. We tested our approach during the 2011 Lord Mayor's Show in London by deploying a system able to infer and visualize in real-time crowd density, crowd turbulence, crowd velocity and crowd pressure. To collection location updates from festival visitors, a mobile phone app that supplies the user with event-related information and periodically logs the device's location was distributed. We collected around four million location updates from over 800 visitors. The City of London Police consulted the crowd condition visualization to monitor the event. As an evaluation of the usefulness of our approach, we learned through interviews with police officers that our approach helps to assess occurring crowd conditions and to spot critical situations faster compared to the traditional video-based methods. With that, appropriate measure can be deployed quickly helping to resolve a critical situation at an early stage.
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paper_title: Probing crowd density through smartphones in city-scale mass gatherings

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City-scale mass gatherings attract hundreds of thousands of pedestrians. These pedestrians need to be monitored constantly to detect critical crowd situations at an early stage and to mitigate the risk that situations evolve towards dangerous incidents. Hereby, the crowd density is an important characteristic to assess the criticality of crowd situations.
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paper_title: Exploring Trampling and Crushing in a Crowd

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Using published field data on accidents in crowds involving either the trampling or crushing of pedestrians, a theory has been developed to model similar dangerous situations. Whether a particular situation is fatal is statistical, but general rules may be established. In cases involving trampling, which occurs when pedestrians are moving, it was found that the density of pedestrians is an important isotropic quantity that appears to determine the probability of a fatal accident. Previous studies have also identified the density of a crowd as determining the probability of crushing within a stationary crowd, although different densities are involved in the initiation of these two types of accidents. In a moving crowd, trampling will occur before crushing, but trampling does not occur in a static crowd. The cross-sectional size of pedestrians within the crowd is important in determining whether a particular density is dangerous in both types of accidents. Consequently, comparisons of accidents involving different groups of pedestrians, possibly in different countries, need to be standardized for the characteristics of the group.
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paper_title: Density, velocity and flow relationships for closely packed crowds

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Abstract Work undertaken to quantify the relationships between crowd velocities, flow rates and densities for uni-directional motion is reviewed. Most of the available data has been generated for underground stations in the UK; similar work for commuter stations in Japan is introduced and developed. Maximum observed flow rates from this work are compared with those suggested in the ‘Green Guide’ for the evacuation of sports grounds. The ‘Green Guide’ figures are higher than the maximum values obtained from the work reviewed.
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paper_title: Crowd disasters as systemic failures: analysis of the Love Parade disaster

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Each year, crowd disasters happen in different areas of the world. How and why do such disasters happen? Are the fatalities caused by relentless behavior of people or a psychological state of panic that makes the crowd ‘go mad’? Or are they a tragic consequence of a breakdown of coordination? These and other questions are addressed, based on a qualitative analysis of publicly available videos and materials, which document the planning and organization of the Love Parade in Duisburg, Germany, and the crowd disaster on July 24, 2010. Our analysis reveals a number of misunderstandings that have widely spread. We also provide a new perspective on concepts such as ‘intentional pushing’, ‘mass panic’, ‘stampede’, and ‘crowd crushes’. The focus of our analysis is on the contributing causal factors and their mutual interdependencies, not on legal issues or the judgment of personal or institutional responsibilities. Video recordings show that people stumbled and piled up due to a ‘domino effect’, resulting from a phenomenon called ‘crowd turbulence’ or ‘crowd quake’. Crowd quakes are a typical reason for crowd disasters, to be distinguished from crowd disasters resulting from ‘mass panic’ or ‘crowd crushes’. In Duisburg, crowd turbulence was the consequence of amplifying feedback and cascading effects, which are typical for systemic instabilities. Accordingly, things can go terribly wrong in spite of no bad intentions from anyone. Comparing the incident in Duisburg with others, we give recommendations to help prevent future crowd disasters. In particular, we introduce a new scale to assess the criticality of conditions in the crowd. This may allow preventative measures to be taken earlier on. Furthermore, we discuss the merits and limitations of citizen science for public investigation, considering that today, almost every event is recorded and reflected in the World Wide Web.
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paper_title: The investigation of the Hillsborough disaster by the Health and Safety Executive

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This paper describes the work carried out by the Health and Safety Executive in investigating the disaster at the Hillsborough Stadium on 15 April 1989. The individual aspects investigated include examination and testing of crush barriers, collapse load calculations, development of a model to predict crowd pressures and estimation of the number of spectators entering the West Terrace of the ground.
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paper_title: Dynamics of crowd disasters: An empirical study

paper_content:

Many observations of the dynamics of pedestrian crowds, including various self-organization phenomena, have been successfully described by simple many-particle models. For ethical reasons, however, there is a serious lack of experimental data regarding crowd panic. Therefore, we have analyzed video recordings of the crowd disaster in Mina/Makkah during the Hajj in 1426H on 12 January 2006. They reveal two subsequent, sudden transitions from laminar to stop-and-go and "turbulent" flows, which question many previous simulation models. While the transition from laminar to stop-and-go flows supports a recent model of bottleneck flows [D. Helbing, Phys. Rev. Lett. 97, 168001 (2006)], the subsequent transition to turbulent flow is not yet well understood. It is responsible for sudden eruptions of pressure release comparable to earthquakes, which cause sudden displacements and the falling and trampling of people. The insights of this study into the reasons for critical crowd conditions are important for the organization of safer mass events. In particular, they allow one to understand where and when crowd accidents tend to occur. They have also led to organizational changes, which have ensured a safe Hajj in 1427H.
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paper_title: Crowd Security Detection based on Entropy Model

paper_content:

Identifying the terror attack, illegal public gathering or other mass events risks by utilizing cameras is an important concern both in crowd security area and in pattern recognition research area. This paper provides a physical entropy model to measure the crowd security level. The entropy model was created by identifying individuals’ moving velocity and the related probability. The individuals are represented by Harris Corners in videos, thus to avoid the time-consuming human recognition task. Simulation experiment and video detection experiments were conducted, verified that in the disordered state, the entropy is higher; while in ordered state, the entropy is much lower; when the crowd security has a sudden change, the entropy will change. It was verified that the entropy is the applicable indicator of crowd security. By recognizing the entropy mutation, it is possible to automatically detect the abnormal crowd behavior and to set the warning alarm.
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paper_title: Data-Driven Modeling of Pedestrian Crowds

paper_content:

Numerous crowd disasters occur each year at large gatherings around the world. Unfortunately, the information about the (spatio-temporal) development of these events tend to be qualitative rather than quantitative. Video recordings from the crowd disaster in Mina, Kingdom of Saudi Arabia, on the 12th of January 2006, where hundreds of pilgrims lost their lives during the annual Muslim pilgrimage to Makkah, gave the possibility to scientifically evaluate the dynamics of the crowd. Based on the insights from the analysis of the crowd disaster, new tools and measures to detect and avoid critical crowd conditions have been proposed, and some of them have been implemented in order to reduce the likelihood of similar disasters in the future. In order to enable the revision of previous works and the analysis of the crowd disaster mentioned above, algorithms used for video-tracking have been introduced. The novelty of this work concerns not only the algorithms themselves, but also the uniqueness and quantity of data on which the algorithms have been validated and calibrated.
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paper_title: Social identification moderates the effect of crowd density on safety at the Hajj

paper_content:

Crowd safety is a major concern for those attending and managing mass gatherings, such as the annual Hajj or pilgrimage to Mecca (also called Makkah). One threat to crowd safety at such events is crowd density. However, recent research also suggests that psychological membership of crowds can have positive benefits. We tested the hypothesis that the effect of density on safety might vary depending on whether there is shared social identification in the crowd. We surveyed 1,194 pilgrims at the Holy Mosque, Mecca, during the 2012 Hajj. Analysis of the data showed that the negative effect of crowd density on reported safety was moderated by social identification with the crowd. Whereas low identifiers reported reduced safety with greater crowd density, high identifiers reported increased safety with greater crowd density. Mediation analysis suggested that a reason for these moderation effects was the perception that other crowd members were supportive. Differences in reported safety across national groups (Arab countries and Iran compared with the rest) were also explicable in terms of crowd identification and perceived support. These findings support a social identity account of crowd behavior and offer a novel perspective on crowd safety management.
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paper_title: Abnormal crowd behavior detection using social force model

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In this paper we introduce a novel method to detect and localize abnormal behaviors in crowd videos using Social Force model. For this purpose, a grid of particles is placed over the image and it is advected with the space-time average of optical flow. By treating the moving particles as individuals, their interaction forces are estimated using social force model. The interaction force is then mapped into the image plane to obtain Force Flow for every pixel in every frame. Randomly selected spatio-temporal volumes of Force Flow are used to model the normal behavior of the crowd. We classify frames as normal and abnormal by using a bag of words approach. The regions of anomalies in the abnormal frames are localized using interaction forces. The experiments are conducted on a publicly available dataset from University of Minnesota for escape panic scenarios and a challenging dataset of crowd videos taken from the web. The experiments show that the proposed method captures the dynamics of the crowd behavior successfully. In addition, we have shown that the social force approach outperforms similar approaches based on pure optical flow.
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paper_title: Loveparade 2010: Automatic video analysis of a crowd disaster

paper_content:

On July 24, 2010, 21 people died and more than 500 were injured in a stampede at the Loveparade, a music festival, in Duisburg, Germany. Although this tragic incident is but one among many terrible crowd disasters that occur during pilgrimage, sports events, or other mass gatherings, it stands out for it has been well documented: there were a total of seven security cameras monitoring the Loveparade and the chain of events that led to disaster was meticulously reconstructed. In this paper, we present an automatic, video-based analysis of the events in Duisburg. While physical models and simulations of human crowd behavior have been reported before, to the best of our knowledge, automatic vision systems that detect congestions and dangerous crowd turbulences in real world settings were not reported yet. Derived from lessons learned from the video footage of the Loveparade, our system is able to detect motion patterns that characterize crowd behavior in stampedes. Based on our analysis, we propose methods for the detection and early warning of dangerous situations during mass events. Since our approach mainly relies on optical flow computations, it runs in real-time and preserves privacy of the people being monitored.
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paper_title: Crowd Density Estimation Using Texture Analysis and Learning

paper_content:

This paper presents an automatic method to detect abnormal crowd density by using texture analysis and learning, which is very important for the intelligent surveillance system in public places. By using the perspective projection model, a series of multi-resolution image cells are generated to make better density estimation in the crowded scene. The cell size is normalized to obtain a uniform representation of texture features. In order to diminish the instability of texture feature measurements, a technique of searching the extrema in the Harris-Laplacian space is also applied. The texture feature vectors are extracted from each input image cell and the support vector machine (SVM) method is utilized to solve the regression problem of calculating the crowd density. Finally, based on the estimated density vectors, the SVM method is used again to solve the classification problem of detecting abnormal density distribution. Experiments on real crowd videos show the effectiveness of the proposed system.
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paper_title: Inferring Crowd Conditions from Pedestrians' Location Traces for Real-Time Crowd Monitoring during City-Scale Mass Gatherings

paper_content:

There is a need for event organizers and emergency response personnel to detect emerging, potentially critical crowd situations at an early stage during city-wide mass gatherings. In this work, we introduce and describe mathematical methods based on pedestrian-behavior models to infer and visualize crowd conditions from pedestrians' GPS location traces. We tested our approach during the 2011 Lord Mayor's Show in London by deploying a system able to infer and visualize in real-time crowd density, crowd turbulence, crowd velocity and crowd pressure. To collection location updates from festival visitors, a mobile phone app that supplies the user with event-related information and periodically logs the device's location was distributed. We collected around four million location updates from over 800 visitors. The City of London Police consulted the crowd condition visualization to monitor the event. As an evaluation of the usefulness of our approach, we learned through interviews with police officers that our approach helps to assess occurring crowd conditions and to spot critical situations faster compared to the traditional video-based methods. With that, appropriate measure can be deployed quickly helping to resolve a critical situation at an early stage.
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paper_title: Continuum modeling of crowd turbulence

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With the growth in world population, the density of crowds in public places has been increasing steadily, leading to a higher incidence of crowd disasters at high densities. Recent research suggests that emergent chaotic behavior at high densities-known collectively as crowd turbulence-is to blame. Thus, a deeper understanding of crowd turbulence is needed to facilitate efforts to prevent and plan for chaotic conditions in high-density crowds. However, it has been noted that existing algorithms modeling collision avoidance cannot faithfully simulate crowd turbulence. We hypothesize that simulation of crowd turbulence requires modeling of both collision avoidance and frictional forces arising from pedestrian interactions. Accordingly, we propose a model for turbulent crowd simulation, which incorporates a model for interpersonal stress and acceleration constraints similar to real-world pedestrians. Our simulated results demonstrate a close correspondence with observed metrics for crowd turbulence as measured in known crowd disasters. Language: en
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paper_title: Data-Driven Modeling of Pedestrian Crowds

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Numerous crowd disasters occur each year at large gatherings around the world. Unfortunately, the information about the (spatio-temporal) development of these events tend to be qualitative rather than quantitative. Video recordings from the crowd disaster in Mina, Kingdom of Saudi Arabia, on the 12th of January 2006, where hundreds of pilgrims lost their lives during the annual Muslim pilgrimage to Makkah, gave the possibility to scientifically evaluate the dynamics of the crowd. Based on the insights from the analysis of the crowd disaster, new tools and measures to detect and avoid critical crowd conditions have been proposed, and some of them have been implemented in order to reduce the likelihood of similar disasters in the future. In order to enable the revision of previous works and the analysis of the crowd disaster mentioned above, algorithms used for video-tracking have been introduced. The novelty of this work concerns not only the algorithms themselves, but also the uniqueness and quantity of data on which the algorithms have been validated and calibrated.
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paper_title: A Novel Wireless Sensor Network Architecture for Crowd Disaster Mitigation

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Disasters aroused due to dynamic movement of large, uncontrollable crowds are ever increasing. The inherent real-time dynamics of crowd need to be tightly monitored and alerted to avoid such disasters. Most of the existing crowd monitoring systems is difficult to deploy, maintain, and dependent on single component failure. This research work proposes novel network architecture based on the key technologies of wireless sensor network and mobile computing for the effective prediction of causes of crowd disaster particularly stampedes in the crowd and thereby alerting the crowd controlling station to take appropriate actions in time. In the current implemented version of the proposed architecture, the smart phones act as wireless sensor nodes to estimate the probability of occurrence of stampede using data fusion and analysis of embedded sensors such as tri-axial accelerometers, gyroscopes, GPS, light sensors etc. The implementation of the proposed architecture in smart phones provides light weight, easy to deploy, context aware wireless services for effective crowd disaster mitigation.
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paper_title: An Overview on Soft Computing Techniques

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Soft computing is a term applied to a field within computer science which is characterized by the use of inexact solutions to computationally-hard tasks such as the solution of NP-complete problems, for which an exact solution cannot be derived in polynomial time. This paper explains about the soft computing and its components briefly, also explains the need use and efficiency of its components. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve tractability, robustness and low solution cost.
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paper_title: An aggregate approach to model evacuee behavior for no-notice evacuation operations

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This study proposes an aggregate approach to model evacuee behavior in the context of no-notice evacuation operations. It develops aggregate behavior models for evacuation decision and evacuation route choice to support information-based control for the real-time stage-based routing of individuals in the affected areas. The models employ the mixed logit structure to account for the heterogeneity across the evacuees. In addition, due to the subjectivity involved in the perception and interpretation of the ambient situation and the information received, relevant fuzzy logic variables are incorporated within the mixed logit structure to capture these characteristics. Evacuation can entail emergent behavioral processes as the problem is characterized by a potential threat from the extreme event, time pressure, and herding mentality. Simulation experiments are conducted for a hypothetical terror attack to analyze the models’ ability to capture the evacuation-related behavior at an aggregate level. The results illustrate the value of using a mixed logit structure when heterogeneity is pronounced. They further highlight the benefits of incorporating fuzzy logic to enhance the prediction accuracy in the presence of subjective and linguistic elements in the problem.
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paper_title: Simulation of agent behavior in a goal finding application

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There has been increasing interest in simulation of agent behavior in the context of agent based modeling. This paper concentrates on the use of fuzzy logic in simulating agent based behavior. Our approach is decomposed into two levels. The higher level addresses the agent's goal finding behavior and lower level addresses collision detection and avoidance behavior. Our approach focuses on modeling individual behavior as well as group behavior. Individuals constantly adjust their behavior according to the dynamic factors in the environment. We hypothesize that people with similar characteristics such as race, age, and gender are more likely to collaborate with each other in order to reach a goal. This paper describes an agent based system implementation for crowd behavior. The simulation evaluates different evacuation and damage control decision making strategies beforehand, which allows the execution of the most effective evacuation scheme during real-time emergency scenario.
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paper_title: Research on pedestrian evacuation simulation based on fuzzy logic

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Pedestrian evacuation simulation has many important applications. For example, based on the pedestrian evacuation simulation results one can predict the evacuation time and evaluate the performances of pedestrian facilities. This paper derives a mathematical model for the pedestrian evacuation based on fuzzy logic. In this fuzzy model for the pedestrian evacuation, each pedestrian?s behavior is linguistically depicted by some simple qualitative physical and psychological laws. These qualitative laws are then transformed into a quantitative mathematical model using a fuzzy modeling approach. The evacuation simulation results based on the proposed fuzzy model are consistent with the observed phenomena for the pedestrian evacuation. Moreover, the simulations show that the parameters involved in the proposed fuzzy model have prominent influences on the evacuation time and dynamical features of pedestrian evacuation.
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paper_title: Social force model for pedestrian dynamics

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It is suggested that the motion of pedestrians can be described as if they would be subject to ``social forces.'' These ``forces'' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can also be applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: first, a term describing the acceleration towards the desired velocity of motion; second, terms reflecting that a pedestrian keeps a certain distance from other pedestrians and borders; and third, a term modeling attractive effects. The resulting equations of motion of nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.
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paper_title: An intelligence-based route choice model for pedestrian flow in a transportation station

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We developed an artificial neural network (ANN) model to mimic route choice behaviour in crowds which achieved a prediction accuracy of 86%.We demonstrated the feasibility of applying the ANN approach to decision-making in pedestrian flows. Both safety and comfort level inside the stations are potentially improved.This model is useful for both station design and daily operation, as escalators are a critical transportation facility in transportation stations.This ANN approach provides a rapid method for engineers to estimate the loadings of escalators, even for new stations, so that they can optimise their utilisation to achieve maximum efficiency. This study proposes a method that uses an artificial neural network (ANN) to mimic human decision-making about route choice in a crowded transportation station. Although ANN models have been developed rapidly and widely adopted in various fields in the last three decades, their application to predict human decision-making in pedestrian flows is limited, because the video clip technology used to collect pedestrian movement data in crowded conditions is still primitive. Data collection must be carried out manually or semi-manually, which requires extensive resources and is time consuming. This study adopts a semi-manual approach to extract data from video clips to capture the route choice behaviour of travellers, and then applies an ANN to mimic such decision-making. A prediction accuracy of 86% (ANN model with ensemble approach) is achieved, which demonstrates the feasibility of applying the ANN approach to decision-making in pedestrian flows.
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paper_title: Intelligent agents in a goal finding application for homeland security

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Orderly and efficient evacuations are the key in saving lives and are of considerable interest to homeland security. There has been a considerable interest in simulation of intelligent agent behavior in the context of agent based modeling. This paper combines Genetic Algorithm (GA) with Neural Networks (NN) to explore how intelligent agents can look for exits during an evacuation. The agents have the capability to adapt their behavior in the environment and formulate their response by learning from the environment. Our approach focuses on modeling individual behavior as well as group behavior. Individuals constantly adjust their behavior according to the dynamic factors in the environment. We are developing crowdmodeling and emergency behavior modeling capability in a goal finding application. This paper examines an intelligent agentbased evacuation that can help plan emergency evacuations, run numerous event-driven evacuation scenarios, support research in the areas of human behavior, and model the movement of responders and security personnel. The result of this simulation was very promising as we are able to observe the agents use GA and NN to learn how to find the various exits.
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paper_title: An Artificial Neural-network Based Predictive Model for Pre-evacuation Human Response in Domestic Building Fire

paper_content:

The post-1993 WTC attack study (Proulx and Fahy, In: Proceedings of ASIAFLAM’95—An International Conference on Fire Science and Engineering, Hong Kong, 1995, pp 199–210) revealed that occupants took 1–3 h to leave the 110-storey buildings, and the pre-movement reactions could account for over two-thirds of the overall evacuation time. This indicates that a thorough understanding of the pre-evacuation behavioral response of people under fire situations is of prime importance to fire safety design in buildings, especially for complex and ultra high-rise buildings. In view of the stochastic (the positions of the occupants) and fuzzy (uncertainty) nature of human behavior (Fraser-Mitchell, Fire Mater 23:349–355, 1999), conventional linear and polynomial predictive methods may not satisfactorily predict the people’s response. An alternative approach, Adaptive Network based Fuzzy Inference System (ANFIS), is proposed to predict the pre-evacuation behavior of peoples, which is an artificial neural network (ANN) based predictive model and integrates fuzzy logic (if-then rules) and neural network (based on back propagation learning procedures The ANFIS learning architecture can be trained by structured human behavioral data, and different fuzzy human decision rules. The applicability in simulating human behavior in fire is worth exploring.
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paper_title: Intelligent Exit-Selection Behaviors during a Room Evacuation

paper_content:

A modified version of the existing cellular automata (CA) model is proposed to simulate an evacuation procedure in a classroom with and without obstacles. Based on the numerous literature on the implementation of CA in modeling evacuation motions, it is notable that most of the published studies do not take into account the pedestrian's ability to select the exit route in their models. To resolve these issues, we develop a CA model incorporating a probabilistic neural network for determining the decision-making ability of the pedestrians, and simulate an exit-selection phenomenon in the simulation. Intelligent exit-selection behavior is observed in our model. From the simulation results, it is observed that occupants tend to select the exit closest to them when the density is low, but if the density is high they will go to an alternative exit so as to avoid a long wait. This reflects the fact that occupants may not fully utilize multiple exits during evacuation. The improvement in our proposed model is valuable for further study and for upgrading the safety aspects of building designs.
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paper_title: A Bayesian network model for evacuation time analysis during a ship fire

paper_content:

We present an evacuation model for ships while a fire happens onboard. The model is designed by utilizing Bayesian networks (BN) and then simulated in GeNIe software. In our proposed model, the most important factors that have significant influence on a rescue process and evacuation time are identified and analyzed. By applying the probability distribution of the considered factors collected from the literature including IMO, real empirical data and practical experiences, the trend of the rescue process and evacuation time can be evaluated and predicted using the proposed model. The results of this paper help understanding about possible consequences of influential factors on the security of the ship and help to avoid exceeding evacuation time during a ship fire.
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paper_title: Dynamic decision making for dam-break emergency management – Part 1: Theoretical framework

paper_content:

Abstract. An evacuation decision for dam breaks is a very serious issue. A late decision may lead to loss of lives and properties, but a very early evacuation will incur unnecessary expenses. This paper presents a risk-based framework of dynamic decision making for dam-break emergency management (DYDEM). The dam-break emergency management in both time scale and space scale is introduced first to define the dynamic decision problem. The probability of dam failure is taken as a stochastic process and estimated using a time-series analysis method. The flood consequences are taken as functions of warning time and evaluated with a human risk analysis model (HURAM) based on Bayesian networks. A decision criterion is suggested to decide whether to evacuate the population at risk (PAR) or to delay the decision. The optimum time for evacuating the PAR is obtained by minimizing the expected total loss, which integrates the time-related probabilities and flood consequences. When a delayed decision is chosen, the decision making can be updated with available new information. A specific dam-break case study is presented in a companion paper to illustrate the application of this framework to complex dam-breaching problems.
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paper_title: Modelling dwelling fire development and occupancy escape using Bayesian network

paper_content:

Abstract The concept of probabilistic modelling under uncertainty within the context of fire and rescue through the application of the Bayesian network (BN) technique is presented in this paper. BNs are capable of dealing with uncertainty in data, a common issue within fire incidents, and can be adapted to represent various fire scenarios. A BN model has been built to study fire development within generic dwellings up to an advanced fire situation. The model is presented in two parts: part I deals with “initial fire development” and part II “occupant response and further fire development”. Likelihoods are assessed for states of human reaction, fire growth, and occupant survival. Case studies demonstrate how the model functions and provide evidence that it could be used for safety assessment, planning and accident investigation. Discussion is undertaken on how the model could be further developed to investigate specific areas of interest affecting dwelling fire outcomes.
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paper_title: Forecasting model for pedestrian distribution under emergency evacuation

paper_content:

Pedestrian distribution forecasting on the road network is developed to support the evacuation decision-making. The numbers of evacuees distributed on each road link are stochastic, uncertain and multi-dependent. Therefore, a Gaussian Bayesian networks (GBN) based forecasting model is presented, considering the pedestrian flow characteristics, optimization of evacuation route and evacuation decision-making. In the forecasting model, the route choice probabilities obtained by minimizing evacuation time are applied to correct the regression coefficients of GBN. Finally, an example is provided to illustrate the usefulness of this model. Research shows that this model not only reflects the complexity and dynamics of evacuation process but also performs an accurate forecasting on the time development of the pedestrian distributed in the evacuation space.
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paper_title: Modeling panic in ship fire evacuation using dynamic Bayesian network

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In this paper, we model passengers' panic during a ship fire by considering its most influential factors. The qualitative factors are quantified, allowing us to study passengers' panic in a probabilistic manner. Considering the time-varying nature of these factors, we update the state of the factors over time. We utilize a dynamic Bayesian network (DBN) to model passengers' panic, this allows us to represent probabilistic and dynamic elements. By defining several worst-case scenarios and running the simulations, we demonstrate how panic can dynamically vary from passenger to passenger with different physical (mental) conditions. Furthermore, we show how this panic can threaten passengers' health during the evacuation process. The impact of panic on the evacuation time is also investigated. The results in this paper are valuable inputs for rescue teams and marine organizations that aim to mitigate property damages and human fatalities.
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paper_title: RETRACTED: <Emergency Response Plans Optimization for Unexpected Environmental Pollution Incidents using an Open Space Emergency Evacuation Model

paper_content:

The objective of this research is to model the crowd evacuation process providing dynamic spatial–temporal distribution information, which can minimize the human exposure risk (death or casualties) on the population implied by a specific evacuation policy when he or she was exposed to adverse effects under accidents. An open space evacuation model based on the stochastic Markov process was introduced here to estimate the spatial–temporal distribution of the evacuees during evacuation covering the estimation of affected areas, space discretization, nodes and links creation, etc. Then, according to the solution of the Markov process providing the expected distribution of the evacuees in the nodes of the area as a function of time and the dose–response relationship, the people’s health effects (e.g., death, several kinds of injuries, etc.) suffered in the evacuation process can be calculated, so that the accident’s health consequence can be determined. Finally, different emergency response policies can be evaluated CT ED with corresponding health consequence, so that the emergency policy can be optimized. © 2012 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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paper_title: Architectural space planning using Genetic Algorithms

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This paper presents a system which can find out space planning for a single flat, arrangement of several flats on a single floor and extend the design for each floor and find out collective plan for a multi-storey apartment building. At each level it generates a plan which supports quick evacuation in case of adversity. Starting with design specifications in terms of constraints over spaces, use of Genetic Algorithm leads to a complete set of consistent conceptual design solutions named topological solutions. These topological solutions which do not presume any precise definitive dimension correspond to the sketching step that an architect carries out from the design specifications on a preliminary design phase in architecture. Further, door placement algorithm has been proposed with modifications in existing Dijkstra's algorithm and dimensions analysis is carried out for the designs selected by the user. If the user wishes to generate a plan for many floors, inputs are taken accordingly and plan is generated which is efficient in terms of evacuation.
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paper_title: Method to determine the locations of tsunami vertical evacuation shelters

paper_content:

The 2004 Indian Ocean tsunami and the 2011 Great Tohoku Japan earthquake and tsunami focused a great deal of the world’s attention on the effect of tsunamis on buildings and infrastructure. When a tsunami impacts structures in a coastal community, the structures are often not strong enough to withstand the forces and may collapse. Therefore, to maximize the survival probability, people evacuate to higher ground or move outside the inundation zone. However, this is not always possible because of short warning times for near-field tsunamis. Thus, sheltering-in-place or “sheltering-near-place” using vertical evacuation should be considered as an alternative approach to lateral evacuation from a tsunami inundation zone. This paper presents the method and results of a study to develop and demonstrate a methodology that applied genetic optimization to determine optimal tsunami shelter locations with the goal of reducing evacuation time, thereby maximizing the probability of survival for the population in a coastal community. The City of Cannon Beach, Oregon, USA, was used as an illustrative example. Several cases were investigated ranging from a single shelter to multiple shelters with locations of high elevation already in place near the city. The method can provide decision-support for the determination of locations for tsunami vertical evacuation shelters. The optimum location of the shelter(s), which was found to vary depending on the number of shelters considered, can reduce the evacuation time significantly, thereby reducing the number of fatalities and increasing the safety of a community.
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paper_title: Dynamic Differential Evolution for Emergency Evacuation Optimization

paper_content:

Emergency evacuation in public places has become the hot area of research in recent years. Emergency evacuation route assignment is one of the complex dynamic optimization problems in emergency evaluation. This paper proposed the modified dynamic differential evolution algorithm and studied the emergency evacuation, then applied the multi-strategy dynamic differential evolution for emergency evacuation route assignment in public places. We use the Wuhan Sport Center in Wuhan China as the experiment scenario to test the performance of the proposed algorithm. The results show that the proposed algorithm can effectively solve the complex emergency evacuation route assignment problem.
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paper_title: GP Generation of Pedestrian Behavioral Rules in an Evacuation Model Based on SCA

paper_content:

This paper presents a research in the context of pedestrian dynamics according to Situated Cellular Agent (SCA), a Multi-Agent Systems approach whose roots are on Cellular Automata (CA). The aim of this work is to apply Genetic Programming (GP) approach, a well known Machine Learning method belonging to the family of Evolutionary Algorithms, to generate suitable behavioral rules for pedestrians in an evacuation scenario. The main contribution of this work is in the design of a testset of GP generated behaviors to represent basic behavioral models of evacuees populating a only locally known environment, a typical scenario for CA-based models.
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paper_title: Office layout plan evaluation system using evacuation simulation considering other agents' action

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In this paper, we propose an office layout plan evaluation system using evacuation simulation considering other agents' action. The proposed system evaluates office layout plans for polygonal space generated by the office layout support system using genetic algorithm. In the proposed system, the office layout plan is given, and then the agents move under the conditions. Each agent decides the escape route based on the information about office layout, impassable spaces, crowded areas, other agents' action and so on, and goes to the entrance. Based on the behavior of agents, the evaluation on the maximum time for escape, average speed, the number of agents who could not reach entrance and so on are carried out. We carried out a series of computer experiments in order to demonstrate the effectiveness of the proposed system and confirmed that the proposed system can evaluate layout plans.
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paper_title: Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach

paper_content:

Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space–time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.
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paper_title: Multi-ant colony system for evacuation routing problem with mixed traffic flow

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Evacuation routing problem with mixed traffic flow is complex due to the interaction among different types of evacuees. The positive feedback mechanism of single ant colony system may lead to congestion on some optimum routes. Like different ant colony systems in nature, different components of traffic flow compete and interact with each other during evacuation process. In this paper, an approach based on multi-ant colony system was proposed to tackle evacuation routing problem with mixed traffic flow. Total evacuation time is minimized and traffic load of the whole road network is balanced by this approach. The experimental results show that this approach based on multi-ant colony system can obtain better solutions than single ant colony system and solve mixed traffic flow evacuation problem with reasonable routing plans.
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paper_title: A research on human emergency evacuation based on revised ACO-CA

paper_content:

In a sudden natural disaster, a large number of people may get detained within a hazardous space. In recent years, the number of such incidents has increased in China, which motivates the research of artificial intelligence-based simulations of emergency rescue and evacuation. This paper proposesam ethodological approach that combined with ant colony optimization and Cellular automata integrating simulation and optimizationto study the complexity and randomness characteristics of human behaviors under the emergency evacuation, for solving an optimal emergency evacuation planning problem in an emergency shelter. The path residual pheromone and heuristic factors of the Ant colony algorithm in this integrated model are treated as personal behavior difference and aggregation, which can be treated as the herb behavior factors and the shortest path first factors,reflecting the randomness and interaction in the process of population evacuation. Through using ant colony algorithm to calculate the transition probability of the interaction among the neighboring cells, and updating the pheromone through taboo list based on local optimal path of the ant colony, the cells can finally finish the simulation of safe evacuation under the principle of optimal transition rules. The method could effectively simulate the delayed population and achieve the simulation of the population distribution in the evacuation area when the natural disaster happens. It would also offer a scientific reference for the research of population emergency evacuation.
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paper_title: An Adaptive Multi-Objective Artificial Bee Colony with crowding distance mechanism

paper_content:

In this work, we propose an Adaptive Multi-Objective Artificial Bee Colony (A-MOABC) Optimizer that uses Pareto dominance procedure with taking the advantage of crowding distance and windowing mechanism. The employed bees use an adaptive windowing mechanism to select their own leaders and alter their positions. Besides, onlookers update their positions by using food sources presented by employed bees. Pareto dominance notion is used to show the quality of the food sources. Employed or onlooker bees which find poor quality food sources turn into scout bee to search other areas. The suggested method uses crowding distance technique in order to keep diversity in the archive. The method adaptively adjusts the limits of objective function values in the archive iteration by iteration. The experimental results indicate that the proposed approach not only thoroughly competitive compared to other algorithms considered in this work but also finds the result with greater precision.
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paper_title: Behavior-Based Simulation of Real-Time Crowd Evacuation

paper_content:

Emergency evacuation has many applications in computer animation, virtual reality, architecture planning, safety science, etc. However, current methods most focus on the agent-based modeling and simulation. These simulation results can not consider the human behavior fully and their reliabilities are doubtable. This paper presents a new method to simulate the large-scale crowds in real-time and verify the evacuation data in complex environment. Through analyzing the characteristics of human behavior in emergent condition, a mixed geometry-based ant colony evacuation model is firstly proposed. Then, many behaviors of human are considered to calculate the best evacuation path, including autonomous avoidance, human's warning time, and preferential path selecting. The experimental results show that it is an effective method to simulate large-scale crowds in real time, because the verification makes the simulation more reliable as well as making human behavior logical and the virtual scene realistic.
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paper_title: Population Classification in Fire Evacuation: A Multiobjective Particle Swarm Optimization Approach

paper_content:

In an emergency evacuation operation, accurate classification of the evacuee population can provide important information to support the responders in decision making; and therefore, makes a great contribution in protecting the population from potential harm. However, real-world data of fire evacuation is often noisy, incomplete, and inconsistent, and the response time of population classification is very limited. In this paper, we propose an effective multiobjective particle swarm optimization method for population classification in fire evacuation operations, which simultaneously optimizes the precision and recall measures of the classification rules. We design an effective approach for encoding classification rules, and use a comprehensive learning strategy for evolving particles and maintaining diversity of the swarm. Comparative experiments show that the proposed method performs better than some state-of-the-art methods for classification rule mining, especially on the real-world fire evacuation dataset. This paper also reports a successful application of our method in a real-world fire evacuation operation that recently occurred in China. The method can be easily extended to many other multiobjective rule mining problems.
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paper_title: Particle swarm and NSGA-II based evacuation simulation and multi-objective optimization

paper_content:

Because of the high-dense population and complex structure, the large public building faces a unique challenge in developing effective emergency evacuation plans. And due to the large scale and numbers of evacuees in real evacuation, real tests are impractical. Therefore, the simulation of evacuation becomes a wonderful choice in program planning. Particle Swarm is as one of the multi-agent based simulation method that can simulate complex behaviors of individuals. NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a kind of optimization method for multi-objective optimization problem. In this paper, we propose a novel multi-objective evolutionary algorithm (named as PNMO, Particle swarm & NSGA-II based Multi-objective Optimization) which simulates evacuation process as well as optimizing the generated evacuation plans. The experiment shows that this method possesses superior performance in evacuation planning.
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paper_title: Formative studies for dynamic wayfinding support with in-building situated displays and mobile devices

paper_content:

There is a significant disparity between wayfinding support services available in outdoor and in-building locations. Services such as Google Maps and in-car GPS allow users to examine unknown outdoor locations in advance as well as receive guidance en-route. In contrast, there is relatively little digital technology to support users in complex building architectures, e.g. institution buildings where users are generally limited to using traditional signage or asking for directions at the reception. However, recent advances in pervasive digital display technology are enabling a new range of possibilities and are making this topic increasingly subject to study. In this paper, we describe five formative studies involving 39 participants using situated digital displays, a Person Locator Kiosk, and personal mobile devices. We report our findings by gaining insights and feedback from users in order to develop wayfinding assistance for visitors in an in-building environment.
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paper_title: Presenting evacuation instructions on mobile devices by means of location-aware 3D virtual environments

paper_content:

Natural and man-made disasters present the need to efficiently and effectively evacuate the people occupying the affected buildings. Providing appropriate evacuation instructions to the occupants of the building is a crucial aspect for the success of the evacuation. This paper presents an approach for giving evacuation instructions on mobile devices based on interactive location-aware 3D models of the building. User's position into the building is determined by using active short-range RFID technology. A preliminary user evaluation of the system has been carried out in the building of our Department.
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paper_title: A RFID-Based Hybrid Building Fire Evacuation System on Mobile Phone

paper_content:

Building fire is a common disaster happening in our daily life that causes unfortunate casualties and deaths. Successfully escaping from fire depends on the design of evacuation route and time as most of the damage of fire is caused due to lack of evacuation equipments or poor design of the emergency route. In this research work, we designed a hybrid building fire evacuation system (HBFES) on a mobile phone using Radio Frequency Identification (RFID) techniques. The system will be implemented at Tamkang University on Lanyang campus where a central fire alarm system has been installed. Location Based Service (LBS) and several existing computer or mobile phone applications, namely Viewpoint Calculator, Path planner, and MobiX3D viewer will be used on the system to rapidly calculate the reliable evacuation routes when building fire takes place.
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paper_title: Evacuation Time Analysis and Optimization for Distributed Emergency Guiding Based on Wireless Sensor Networks

paper_content:

This paper proposes a load-balancing framework for emergency guiding based on wireless sensor networks. We design a load-balancing guiding scheme and derive an analytical model in order to reduce the total evacuation time of indoor people. The guiding scheme can provide the fastest path to an exit for people based on the evacuation time estimated by the analytical model. This is the first distributed solution which takes the corridor capacity and length, exit capacity, concurrent move, and people distribution into consideration for estimating evacuation time and planning escape paths. Through the proposed framework, the congestion of certain corridors and exits can be released to significantly reduce the total evacuation time. Analytical and simulation results show that our approach outperforms existing works, which can prevent people from following the local optimal guiding direction with the longer evacuation time in total. We also implement a prototype, called Load-balancing Emergency Guiding System (LEGS), which can compare evacuation time and guiding directions of existing schemes and ours under different people distribution.
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paper_title: Handheld Augmented Reality Indoor Navigation with Activity-Based Instructions

paper_content:

We present a novel design of an augmented reality interface to support indoor navigation. We combine activity-based instructions with sparse 3D localisation at selected info points in the building. Based on localisation accuracy and the users' activities, such as walking or standing still, the interface adapts the visualisation by changing the density and quality of information shown. We refine and validate our design through user involvement in pilot studies. We finally present the results of a comparative study conducted to validate the effectiveness of our design and to explore how the presence of info points affects users' performance on indoor navigation tasks. The results of this study validate our design and show an improvement in task performance when info points are present, which act as confirmation points and provide an overview of the task.
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paper_title: Indoor Navigation with Minimal Infrastructure

paper_content:

We describe an indoor navigation system based on dead reckoning localization, "augmented photos", and interactive methods that simplify the process of orientation. Our navigator does not require a fixed infrastructure for the recalibration of inertial data but it can improve its performance if a minimal localization infrastructure is available.
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paper_title: New Framework of Intelligent Evacuation System of Buildings

paper_content:

On the basis of the analysis about the traditional evacuation and lifesaving facilities, this paper adopts high-tech technological means (e.g. advanced intelligent information-monitoring technique, artificial intelligent technique, computer technology, etc.), integrates the function of building evacuation, and establishes an intelligent evacuation system. This system overcomes the disadvantages and defects of the current intelligent evacuation system, and realizes the intelligent dynamic guidance of the personnel evacuation under the real fire scene through the main control module of the intelligent evacuation system. It aims to actually realize the intellectualization according to the dynamic change of the fire scene, and make the personnel evacuation more scientific, rapid and safer.
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paper_title: Soft Computing: Overview and Recent Developments in Fuzzy Optimization

paper_content:

Soft Computing (SC) represents a significant paradigm shift in the aims of computing, which reflects the fact that the human mind, unlike present day computers, possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain and lacking in categoricity. At this juncture, the principal constituents of Soft Computing (SC) are: Fuzzy Systems (FS), including Fuzzy Logic (FL); Evolutionary Computation (EC), including Genetic Algorithms (GA); Neural Networks (NN), including Neural Computing (NC); Machine Learning (ML); and Probabilistic Reasoning (PR). In this work, we focus on fuzzy methodologies and fuzzy systems, as they bring basic ideas to other SC methodologies. The other constituents of SC are also briefly surveyed here but for details we refer to the existing vast literature. In Part 1 we present an overview of developments in the individual parts of SC. For each constituent of SC we overview its background, main problems, methodologies and recent developments. We focus mainly on Fuzzy Systems, for which the main literature, main professional journals and other relevant information is also supplied. The other constituencies of SC are reviewed shortly. In Part 2 we investigate some fuzzy optimization systems. First, we investigate Fuzzy Sets we define fuzzy sets within the classical set theory by nested families of sets, and discuss how this concept is related to the usual definition by membership functions. Further, we will bring some important applications of the theory based on generalizations of concave functions. We study a decision problem, i.e. the problem to find a ”best” decision in the set of feasible alternatives with respect to several (i.e. more than one) criteria functions. Within the framework of such a decision situation, we deal with the existence and mutual relationships of three kinds of ”optimal decisions”: Weak Pareto-Maximizers, Pareto-Maximizers and Strong Pareto-Maximizers particular alternatives satisfying some natural and rational conditions. We also study the compromise decisions maximizing some aggregation of the criteria. The criteria considered here will be functions defined on the set of feasible alternatives with the values in the unit interval. In Fuzzy mathematical programming problems (FMP) the values of the objective function describe effects from choices of the alternatives. Among others we show that the class of all MP problems with (crisp) parameters can be naturally embedded into the class of FMP problems with fuzzy parameters. Finally, we deal with a class of fuzzy linear programming problems. We show that the class of crisp (classical) LP problems can be embedded into the class of FLP ones. Moreover, for FLP problems we define the concept of duality and prove the weak and strong duality theorems. Further, we investigate special classes of FLP interval LP problems, flexible LP problems, LP problems with interactive coefficients and LP problems with centered coefficients. We present here an original mathematically oriented and unified approach.
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Title: Intelligent Evacuation Management Systems: A Review

Section 1: INTRODUCTION
Description 1: Write an introduction that provides an overview of the significance and scope of Intelligent Evacuation Management Systems (IEMS) in crowd management during large-scale events and everyday crowded spaces.

Section 2: CROWD MONITORING
Description 2: Describe technologies and methods used for crowd monitoring, including GPS-based tracking, Bluetooth-based tracking, computer vision-based tracking, RFID, and IR transmitters and receivers.

Section 3: GPS Based Tracking
Description 3: Explain how GPS technology is used for tracking crowds, including specific case studies and applications.

Section 4: Bluetooth Based Tracking
Description 4: Outline the use of Bluetooth technology for crowd tracking and its application in various events.

Section 5: Computer Vision Based Tracking
Description 5: Discuss computer vision techniques for tracking crowds and the challenges associated with these methods in congested scenarios.

Section 6: RFID
Description 6: Detail the application of RFID for crowd monitoring, including specific implementations and limitations.

Section 7: IR Transmitter and Receiver
Description 7: Describe recent developments in using IR transmitters and receivers for crowd monitoring.

Section 8: PREDICTION OF CROWD DISASTER
Description 8: Review methods for predicting crowd disasters, focusing on the critical conditions of crowd density, speed, and flow, and the distinction between video-based and nonvideo-based prediction techniques.

Section 9: Video Based Crowd Disaster Prediction
Description 9: Discuss video-based methods for predicting crowd disasters, including specific metrics and warning signs used in these systems.

Section 10: Nonvideo Based Prediction of Crowd Disaster
Description 10: Explain nonvideo-based techniques for predicting crowd disasters, especially those leveraging smartphone technologies and wireless sensors.

Section 11: EVACUATION MODELLING VIA SOFT COMPUTING METHODS
Description 11: Review the use of soft computing methods such as fuzzy logic, artificial neural networks, probabilistic graphical models, and evolutionary computing for evacuation modelling.

Section 12: Fuzzy Logic Based Evacuation Models
Description 12: Outline different fuzzy logic-based approaches to evacuation modelling, including specific examples and case studies.

Section 13: Overview of Evacuation Models Using Artificial Neural Networks
Description 13: Discuss the application of artificial neural networks in evacuation scenarios and their effectiveness in modelling pedestrian behavior.

Section 14: Evacuation Modelling via Probabilistic Graphical Models
Description 14: Describe the use of probabilistic graphical models like Bayesian networks and Markov networks for evacuation predictions and decision-making.

Section 15: Typical Evolutionary Computing Based Evacuation Models
Description 15: Review evolutionary computing approaches, including genetic algorithms and swarm intelligence models, for solving evacuation problems.

Section 16: EVACUATION PATH GUIDELINES
Description 16: Describe methods and technologies for providing evacuation path guidelines, including the use of mobile phones, visual aids, and wireless sensor networks.

Section 17: DISCUSSION AND CONCLUSION
Description 17: Summarize the key findings and insights from the review, emphasizing the importance of integrating various methods for effective evacuation management.

Section 18: ACKNOWLEDGMENTS
Description 18: Acknowledge the contributions and support received for the research work, including any funding or institutional support.