AASK - Aircraft accident statistics and knowledge: A database of human experience in evacuation, derived from aviation accident reports

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in aviation safety. However such models have a heavy dependency on real evacuation data in order to (a) identify the key processes and factors associated with evacuation, (b) quantify variables and parameters associated with the identified factors/processes and finally (c) validate the models. The Fire Safety Engineering Group of the University of Greenwich is undertaking a large data extraction exercise from three major data sources in order to address these issues. This paper describes the extraction and application of data from one of these sources - aviation accident reports. To aid in the storage and analysis of the raw data, a computer database known as AASK (aircraft accident statistics and knowledge) is under development. AASK is being developed to store human observational and anecdotal data contained in accident reports and interview transcripts. AASK comprises four component sub-databases. These consist of the ACCIDENT (crash details), FLIGHT ATTENDANT (observations and actions of the flight attendants), FATALS (details concerning passenger fatalities) and PAX (observations and accounts from individual passengers) databases. AASK currently contains information from 25 survivable aviation accidents covering the period 4 April 1977 to 6 August 1995, involving some 2415 passengers, 2210 survivors, 205 fatalities and accounts from 669 people. In addition to aiding the development of aircraft evacuation models, AASK is also being used to challenge some of the myths which proliferate in the aviation safety industry such as, passenger exit selection during evacuation, nature and frequency of seat jumping, speed of passenger response and group dynamics. AASK can also be used to aid in the development of a more comprehensive approach to conducting post accident interviews, and will eventually be used to store the data directly.

An aircraft accident database of human experience in evacuation derived from aviation accident reports

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in aviation safety. However, such models have a heavy dependency on real evacuation data. The Fire Safety Engineering Group of the University of Greenwich is undertaking a large data extraction exercise in order to address this issue. This paper describes the extraction and application of data from aviation accident reports. To aid in the storage and analysis of the raw data, a computer database known as AASK (Aircraft Accident Statistics and Knowledge) is under development. AASK is being developed to store human observational and anecdotal data contained in accident reports and interview transcripts. AASK currently contains information from 25 survivable aviation accidents covering the period 04/04/77 to 06/08/95, involving some 2415 passengers, 2210 survivors, 205 fatalities and accounts from 669 people. Copyright © 1999 John Wiley & Sons, Ltd.

The collection and analysis of pre-evacuation times derived from evacuation trials and their application to evacuation modelling

This paper presents data relating to occupant pre-evacuation times from university and hospital outpatient facilities. Although the two occupancies are entirely different, they do employ relatively similar procedures: members of staff sweep areas to encourage individuals to evacuate.However the manner in which the dependent population reacts to these procedures is quite different. In the hospital case, the patients only evacuated once a member of the nursing staff had instructed them to do so, while in the university evacuation, the students were less dependent upon the actions of the staff, with over 50% of them evacuating with no prior prompting. In addition, the student pre-evacuation time was found to be dependent on their level of engagement in various activities.

Recommendations on the nature of the passenger response time distribution to be used in the MSC 1033 assembly time analysis based on data derived from sea trials

The passenger response time distributions adopted by the International Maritime Organisation (IMO)in their assessment of the assembly time for passanger ships involves two key assumptions. The first is that the response time distribution assumes the form of a uniform random distribution and the second concerns the actual response times. These two assumptions are core to the validity of the IMO analysis but are not based on real data, being the recommendations of an IMO committee. In this paper, response time data collected from assembly trials conducted at sea on a real passanger vessel using actual passangers are presented and discussed. Unlike the IMO specified response time distributions, the data collected from these trials displays a log-normal distribution, similar to that found in land based environments. Based on this data, response time distributions for use in the IMO assesmbly for the day and night scenarios are suggested

Methodologies employed in the collection, retrieval and storage of human factors information derived from first hand accounts of survivors of the WTC disaster of 11 September 2001

This article provides a broad overview of project HEED (High-rise Evacuation Evaluation Database) and the methodologies employed in the collection and storage of first-hand accounts of evacuation experiences derived from face-to-face interviews of evacuees from the World Trade Center (WTC) Twin Towers complex on September 11, 2001. In particular, the article describes the development of the HEED database. This is a flexible research tool which contains qualitative type data in the form of coded evacuee experiences along with the full interview transcripts. The data and information captured and stored in the HEED database is not only unique, but provides a means to address current and emerging issues relating to human factors associated with the evacuation of high-rise buildings