Passenger behavior in trains during emergency situations

dell’Olio, Luigi; Ibeas, Ángel; Barreda, Rosa; Sañudo, Roberto

doi:10.1016/j.jsr.2013.05.005

Cited by 29 publications

(8 citation statements)

References 21 publications

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“…However, we now know that if the driver had continued to travel outside the tunnel rather than stopping as per the routine, many passengers in other wagons would have survived rather than suffocating in the tunnel. This and many other examples show the importance of applying "common sense" or "operational discretion" in conjunction with standard operating procedures, as well as the need to understand both the ostensive and performative aspects [49,50]. Such common sense has been theorized as "mindfulness" in the literature that relates to high-reliability organisations [51] and has also been modeled as a maturity framework [52].…”

Section: Literature Related To Dichotomy In Routines and Proceduresmentioning

confidence: 99%

Learning How to Learn from Disasters through a Comparative Dichotomy Analysis: Grenfell Tower and Hurricane Katrina Case Studies

et al. 2021

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We describe two well-established, practice-based Master’s programmes as examples of existing competence development opportunities for practitioners and how such links between theory and practice can be developed and taught within the Higher Education (HE) context. We hypothesise that learning from major failures is essential in linking theory with practice in both engineering and management education. We investigate how to train emergency response teams on coping with, and learning from, rare events; a major challenge to other practitioners in the fields of safety and risk management. Comparison is undertaken between two disasters—Hurricane Katrina in the USA and the relatively recent Grenfell Tower in the UK—using a balanced dual approach of paradoxes, a dichotomy. In this paper, we demonstrate the enhancement of both engineering and management education. This was achieved through using the two case studies to emphasize the relevance of incorporating advanced mental modelling approaches for root cause analysis in training and by comparing the two cases with respect to the black swan and black elephant concepts. It is recommended that future training has a balanced approach that encompasses the outlined features of dichotomies.

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Section: Literature Related To Dichotomy In Routines and Proceduresmentioning

confidence: 99%

Learning How to Learn from Disasters through a Comparative Dichotomy Analysis: Grenfell Tower and Hurricane Katrina Case Studies

et al. 2021

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“…They concluded that the length of outdoor track segments in the metro system and weather conditions have a significant association with the frequency of service interruptions. When it comes to predicting disruption impact (d), the literature can usually be divided into micro-level research (a) based on passenger behavior research [7,8], and mesolevel research (b), including travel demand [9][10][11][12][13], impact duration [14], and response strategies [15] of the transportation system. In this paper, we focus on predicting travel demand under disruptions at the mesoscopic level.…”

Section: Introductionmentioning

confidence: 99%

A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System

2021

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Predicting evacuation demand, including its generation and dissipation process, for urban rail transit systems under disruptions, such as line and station closure, often requires comprehensive historical data recorded under homogeneous situations. However, data under disruptions are hard to collect due to various reasons, which makes traditional methods impractical in evacuation demand prediction. To address this problem from the modeling perspective, we develop a data-efficient approach to predict evacuation demand for urban rail transit systems under disruptions. Our model-based approach mainly uses historical data obtained from the natural state, when no shocks take place. We first formulate the mathematical representation of the evacuation demand for every type of urban rail transit station. Input variables in this step are location features related to the station under the disruption, as well as an origin–destination matrix under the natural state. Then, based on these mathematical expressions, we develop a simulation system to imitate the spatio-temporal evolution of evacuation demand within the whole network under disruptions. The transport capacity drop under disruptions is used to describe the disruption situation. Several typical scenarios from the Shanghai metro network are used as examples to implement the proposed method. The results show that our method is able to predict the generation and dissipation processes of evacuation demand, as well model how severely stations will be affected by given disruptions. One general observation we draw from the results is that the most vulnerable stations under disruption, where the locations peak evacuation demand occurs, are mainly turn-back stations, closed stations, and the transfer stations near closed stations. This paper provides new insight into evacuation demand prediction under disruptions. It could be used by transport authorities to better respond to the urban rail transit system disruption.

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“…In specific disasters, such as fires and earthquakes, the evacuation could be a complex process due to lots of evacuees and differentiating decision behaviors. Some literature has exhibited an interest in investigating this issue in conjunction with the safety of some buildings [1,2]. Because of limited space facilities, there are many uncertain factors in the process of flow evacuation, including physical conflicts that lead to evacuation chokepoints, where secondary disasters like stampede frequently occur.…”

Section: Introductionmentioning

confidence: 99%

Modeling Human Evacuating Behavior in Limited Space Based on Cellular Automata Model

Wen

Zhao

et al. 2020

Complexity

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The study of evacuation for buildings with limited space is an important part of improving evacuation efficiency and preventing stampedes. A building evacuation model was proposed based on cellular automata simulation considering different crowd states. Different flow sizes under layout environments with the same facilities as well as evacuation efficiency, bottleneck area density, and escape routes choice under the orderly and disorderly distribution conditions have also been analyzed. The results show that the initial disorderly distribution state is superior to the orderly distribution state in terms of the evacuation efficiency index. The former provides evacuees with maximum room for the corridor and the exit, with the overall evacuation density being lower than that of the latter. Evacuation along the central corridor provides more room compared to that of the two flanks, which explains why evacuees prefer to occupy the central area when space is limited, and this is detrimental to the moving capacity.

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Passenger behavior in trains during emergency situations

Cited by 29 publications

References 21 publications

Learning How to Learn from Disasters through a Comparative Dichotomy Analysis: Grenfell Tower and Hurricane Katrina Case Studies

Learning How to Learn from Disasters through a Comparative Dichotomy Analysis: Grenfell Tower and Hurricane Katrina Case Studies

A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System

Modeling Human Evacuating Behavior in Limited Space Based on Cellular Automata Model

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