Crowd Accidents Research Articles

An improved Cellular Automata model to simulate the behavior of high density crowd and validation by experimental data

In this article we present an improved version of the Cellular Automata floor field model making use of a sub-mesh system to increase the maximum density allowed during simulation and reproduce phenomena observed in dense crowds. In order to calibrate the model’s parameters and to validate it we used data obtained from an empirical observation of bidirectional pedestrian flow. A good agreement was found between numerical simulation and experimental data and, in particular, the double outflow peak observed during the formation of deadlocks could be reproduced in numerical simulations, thus allowing the analysis of deadlock formation and dissolution. Finally, we used the developed high density model to compute the flow-ratio dependent fundamental diagram of bidirectional flow, demonstrating the instability of balanced flow and predicting the bidirectional flow behavior at very high densities. The model we presented here can be used to prevent dense crowd accidents in the future and to investigate the dynamics of the accidents which already occurred in the past. Additionally, fields such as granular and active matter physics may benefit from the developed framework to study different collective phenomena.

PERFORMANCE EVALUATION OF CLASSIFIERS ON ACTIVITY RECOGNITION FOR DISASTERS MITIGATION USING SMARTPHONE SENSING

Activity recognition (ARs) is a classification problem that cuts across many domains. The introduction of ARs accuracy which may be significantly low with decision tree algorithm and the use of smartphone sensing in previous studies has proven its relevance for effective disaster mitigation in our society. Smartphone sensing is an approach found to be useful for activity recognition to monitor people in large gatherings due to the power of embedded sensors on the handheld devices. In this paper, a multitask activity recognition architecture is proposed for proper monitoring of people in large gatherings to control disaster occurrences in crowd, flood, road and fire accidents using related activity scenario in time of danger. We implement the proposed architecture to determine the outcome of activity recognized with K-nearest neighbour (KNN) for k= 3 and 4 to compare performance to that of weka using accelerometer and digital compass (dc) sensors on the same dataset. The results of ARs accuracy of 100% and 99% in weka, 85% and 89% with KNN shows an improved performance in both tools. The performance of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Naives baye (NB), Decision tree (DT), against KNN were investigated using precision, recall and f-measure in weka as well. The results show significant improvement with performance parameters on accelerometer and dc against the use of accelerometer sensor only with KNN and DT having low number of classified activity recognized on training and testing data.

Empirical investigation on safety constraints of merging pedestrian crowd through macroscopic and microscopic analysis

A recent crowd stampede during a New Year's Eve celebration in Shanghai, China resulted in 36 fatalities and over 49 serious injuries. Many of such tragic crowd accidents around the world resulted from complex multi-direction crowd movement such as merging behavior. Although there are a few studies on merging crowd behavior, none of them have conducted a systematic analysis considering the impact of both merging angle and flow direction towards the safety of pedestrian crowd movement. In this study, a series of controlled laboratory experiments were conducted to examine the safety constraints of merging pedestrian crowd movements considering merging angle (60°, 90° and 180°) and flow direction under slow running and blocked vision condition. Then, macroscopic and microscopic properties of crowd dynamics are obtained and visualized through the analysis of pedestrian crowd trajectory data derived from video footage. It was found that merging angle had a significant influence on the fluctuations of pedestrian flows, which is important in a critical situation such as emergency evacuation. As the merging angle increased, mean velocity and mean flow at the measuring region in the exit corridors decreased, while mean density increased. A similar trend was observed for the number of weaving and overtaking conflicts, which resulted in the increase of mean headway. Further, flow direction had a significant impact on the outflow of the individuals while blocked vision had an influence on pedestrian crowd interactions and merging process. Finally, this paper discusses safety assessments on crowd merging behaviors along with some recommendations for future research. Findings from this study can assist in the development and validation of pedestrian crowd simulation models as well as organization and control of crowd events.

Crowd Disaster Risk Identification in Large Sport Venues

A significant number of crowd accidents have occurred in sport venues in the past as well as recently. Statistics of crowd disasters were discussed and the results of time and location are 1509 deaths and 10642 injuries, incidence of crowd accidents in grandstand areas is up to 54%. Serious security risks still exist, and we should pay more attention to sports building safety. Different stages of accidents include crowd gathering, trigger factors, accident happened and rescue failure. Finally, crowd disaster risk in sport venues was identified. This paper aims to lay a foundation to improve comprehensive disaster prevention system in stadium.

Analysis of Compressed Force in Crowds

The risk of dying or injuring caused by force accumulated among crowds is great in the crowd accidents. This kind of force could even bend steel barriers or tear down brick walls. Compressed force between pedestrians may result in the deformation and compression of pedestrians' body, or serious psychological problems like shortness of breath. The movement and force of crowds are analyzed from the view of microcosm at high density. The compression and deformation are emphasized. The social model has been modified to meet the requirements of very high density which is the non-ideal condition. The model allows moving bodies to be touched more closely. Crowds can even be overlapped. The force will accumulate and propagate especially at dense crowds because of the overlap. It is elastical deformation that results in the force spreading and accumulating through the crowds, which is the important cause of the disaster. The elastical deformation and the movement are correlated closely. The failure of crowd simulation software at high density lies in neglecting the compressed phenomena and the force propagating. The objective of this article is to develop a micro-simulation model to reveal crowd movement at the non-ideal condition by modeling and simulating the force and movement of pedestrians.