Dynamic Bayesian Network-Based Escape Probability Estimation for Coach Fire Accidents
Coach emergency escape research is an effective measure to reduce casualties under serious vehicle fire accidents. A novel experiment method employing a wireless transducer was implemented and the head rotation speed, rotation moment and rotation duration were collected as the input variables for the classification and regression tree (CART) model. Based on this model, the classification result explicitly pointed out that the exit searching efficiency was evolving. By ignoring the last three unimportant factors from the Analytic Hierarchy Process (AHP), the ultimate Dynamic Bayesian Network (DBN) was built with the temporal part of the CART output and the time-independent part of the vehicle characteristics. Simulation showed that the most efficient exit searching period is the middle escape stage, which is 10 seconds after the emergency signal is triggered, and the escape probability clearly increases with the efficient exit searching. Furthermore, receiving emergency escape training contributes to a significant escape probability improvement of more than 10%. Compared with different failure modes, the emergency hammer layout and door reliability have a more significant influence on the escape probability improvement than aisle condition. Based on the simulation results, the escape probability will significantly drop below 0.55 if the emergency hammers, door, and aisle are all in a failure state.
- Record URL:
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03535320
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Supplemental Notes:
- © 2021 Chenyu Zhou et al.
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Authors:
- Chenyu Zhou
- Xuan Zhao
- Qiang Yu
- Rong Huang
- Publication Date: 2021-3-30
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 193-204
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Serial:
- PROMET-Traffic & Transportation
- Volume: 33
- Issue Number: 2
- Publisher: University of Zagreb
- ISSN: 0353-5320
- EISSN: 1848-4069
- Serial URL: https://traffic2.fpz.hr/index.php/PROMTT
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Bayes' theorem; Buses; Crash risk forecasting; Escape systems; Probability; Vehicle fires
- Subject Areas: Highways; Passenger Transportation; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01831437
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 22 2021 9:13AM