# The Development and Demonstration of an Enhanced Risk Model for the Evacuation Process of Large Passenger Vessels

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Literature Review

#### 2.2. Background

- Evaluation of the situation/ship condition;
- The mustering of passengers (and preparation of LSAs);
- Abandonment to the survival craft/water;
- Survival in the survival craft/at sea;
- Rescue/safe haven.

#### 2.3. Methods and Tools

## 3. Risk Model

#### 3.1. Model Structure—Event Tree Analysis

- The mustering process;
- The lifeboat or other LSA availability and preparation;
- The transfer toward and embarkation to the lifeboats or other LSAs during the abandonment phase;
- The lifeboat lowering, as part of the abandonment phase;
- The lifeboat or other LSAs clearing off the shipside during abandonment and whether not clearing off its life-threatening;
- Survival while waiting for rescue, either in a lifeboat, in another LSA, or at sea.

#### 3.2. Model Structure—Bayesian Networks

#### 3.3. Risk Calculation Considerations

_{a}, which corresponds to the unsafe lowering scenario, and the BN 09

_{b}, which refers to the fatalities in case of clearing fails. Additionally, the EMSA III study provides percentages of 80% and 5% for the cases of fast and slow flooding, respectively [10]. Thus, for the actual exposed population, 80% of the total exposed population is assumed for the case of untenable conditions, and 5% for the cases of failing to muster, failing to transfer, and embarking to the lifeboats during the abandonment. For fire/explosion accidents, the study provides percentages of 7.5% and 2.5% for the cases of an uncontrollable fire leading to a total loss of the ship and a contained fire in high-density spaces, respectively [10]. So, for the actual exposed population, 7.5% of the total exposed population is assumed for the case of an uncontrollable fire, and 2.5% for the cases of failing to transfer and embark to lifeboats during the abandonment process. In the case that a fire is rapidly extinguished, only 0.1% of the total exposed population is considered by expert judgment. For the rest of the undesired events, which correspond to either the survival in a lifeboat/LSA or at sea, the total population is assumed (100%) as the actual exposed population.

- The probability of LSA unavailability is approximately 10% of the probability of conventional lifeboat unavailability, based on expert judgment.
- The probability of transfer and embarkation during abandonment is considered the same for both lifeboats and LSAs.
- The probability of not surviving in an LSA is double the probability of not surviving in a lifeboat, according to expert judgment.

#### 3.4. Risk Model Review

_{FX}, i.e., “Reach Muster Station” and BN 04

_{FX}, i.e., “Transfer and Embark to lifeboats”. The focus of the revision was the fire effluents, i.e., heat, smoke, and toxicity, and the possibility of debris from the fire, and consequently, their effect on the probability of blocked routes during either mustering or abandonment. A single “route blocked” parameter was considered for both networks, affected by debris and fire effluents parameters. However, toxicity could not be quantified due to its complexity and lack of data, and thus it remained as a qualitative parameter. Regarding the Bayesian network that refers to the availability of lifeboats, as it is far less complex, only simple considerations were made. The only major consideration, as the lifeboats may be damaged also from fire, were the influence of the “Accident Category” parameter and the “Damaged in Accident” parameter, which were thoroughly discussed in terms of quantification. All three BNs were graphically presented in Section 3.2 and Appendix A. Finally, developing a Bayesian network for defining untenable conditions in fire accidents, due to the various contradictions regarding its parameters, and thus the complexity of modeling a fire situation, was not suggested by experts. Instead, direct quantification of the frequencies for the spreading scenarios that are presented in the fire/explosion generic event tree, i.e., rapidly extinguished, contained, and uncontrolled, was preferred and achieved through previous studies [10,22].

#### 3.5. Risk Control Option

## 4. Results

#### 4.1. Bayesian Inference

^{−1}for collision accidents and for all passenger ships worldwide, while the reference study calculates it as 1.51 × 10

^{−1}. Additionally, the BNs 02

_{FL}and 02

_{FX}calculate the probability of the observed parameter “Reach Muster Station” as 5.2 × 10

^{−2}for the general case (no evidence given), while the aforementioned study estimates the same probability as 3.3 × 10

^{−2}.

#### 4.2. Sensitivity Analysis

#### 4.3. Baseline Scenario Results

^{−3}taken from the EMSA III study [10]; scaling factors, as presented in Section 3.3 (ship en route: 3.44 × 10

^{−1}, the ship being struck: 5.16 × 10

^{−1}, and water ingress: 3.33 × 10

^{−1}); the probability of a person being killed during an evacuation, i.e., the probability of occurrence for that undesired event; and the utility function, namely the passengers and crew threatened in each case. This product, for the baseline scenario of collision accidents for cruise ships, on a worldwide scale, is estimated as a PLL of 1.14 × 10

^{−1}per ship/year. If multiplied by 30 years, i.e., the assumed ship lifetime, it results in an overall PLL of 3.42 per ship/lifetime.

#### 4.4. Risk Reduction Demonstration

_{FL}, presented in Figure 7. The updated probabilities are then provided to the respective event tree gates, and through a recalculation, the event tree delivers an updated PLL value. Indicatively, the updated collision event tree is shown in Figure 12, highlighting the updated result, namely the residual risk, and the updated probabilities together with the respective event gates. Then, the residual risk is simply subtracted from the initial baseline risk, resulting in risk reduction in the case of flooding accidents.

## 5. Discussion and Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

_{FL}and 02

_{FX}, which are presented in Figure 5 and Figure 6 previously. Grey nodes correspond to parameters that are given evidence, green nodes represent qualitative parameters, yellow nodes correspond to the output parameters for each Bayesian network, and cyan nodes are all the rest probabilistic model parameters that are somehow influencing the output parameter.

**Figure A4.**A Bayesian network for transfer and embarkation to lifeboats in flooding accidents (04

_{FL}).

**Figure A5.**A Bayesian network for transfer and embarkation to lifeboats in fire/explosion accidents (04

_{FX}).

**Figure A6.**A Bayesian network for lifeboat lowering in both flooding and fire/explosion accidents (05).

**Figure A8.**A Bayesian network for survival in the lifeboat in flooding and fire/explosion accidents (07).

**Figure A9.**A Bayesian network for survival at sea in both flooding and fire/explosion accidents (08).

**Figure A10.**A Bayesian network for fatalities in unsafe lowering in both flooding and fire/explosion accidents (09a).

## Appendix B

## References

- EMSA. Annual Overview of Marine Casualties and Incidents 2021; European Maritime Safety Agency: Lisbon, Portugal, 2021. [Google Scholar]
- Chae, C.J.; Kim, K.H.; Kang, S.Y. Limiting Ship Accidents by Identifying Their Causes and Determining Barriers to Application of Preventive Measures. J. Mar. Sci. Eng.
**2021**, 9, 302. [Google Scholar] [CrossRef] - IMO. SOLAS Consolidated Edition; International Maritime Organization: London, UK, 2020. [Google Scholar]
- Lee, D.; Kim, H.; Park, J.H.; Park, B.J. The Current Status and Future Issues in Human Evacuation from Ships. Saf. Sci.
**2003**, 41, 861–876. [Google Scholar] [CrossRef] - MSC.1/Circ.1533; Revised Guidelines On Evacuation Analysis For New And Existing Passenger Ships. International Maritime Organization Maritime Safety Committee: London, UK, 2016.
- SSE 4/3—Safety Objectives And Functional Requirements of The Guidelines On Alternative Design And Arrangements For SOLAS Chapters II-1 AND III-Report of the Correspondence Group. Submission by Sweden; International Maritime Organization Ship System and Equipment Sub-Committee: London, UK, 2017.
- Boulougouris, E.; Vassalos, D.; Stefanidis, F.; Karaseitanidis, G.; Karagiannidis, L.; Amditis, A.; Ventikos, N.; Kanakidis, D.; Petrantonakis, D.; Liston, P. SafePASS-Transforming Marine Accident Response. In Proceedings of the 8th Transport Research Arena TRA 2020, Helsinki, Finland, 27–30 April 2020. [Google Scholar]
- Hamann, R. BMVI Study on Safety Model for Life-Saving Appliances-Risk Model; DNV GL: Hamburg, Germany, 2019. [Google Scholar]
- Liu, Y.; Zhang, H.; Zhan, Y.; Deng, K.; Dong, L. Evacuation Strategy Considering Path Capacity and Risk Level for Cruise Ship. J. Mar. Sci. Eng.
**2022**, 10, 398. [Google Scholar] [CrossRef] - Konovessis, D.; Hamann, R.; Eliopoulou, E.; Luhmann, H.; Cardinale, M.; Routi, A.-L.; Kujanpaa, J.; Bertin, R.; Harper, G.; Pang, E. Risk Acceptance Criteria and Risk Based Damage Stability, Final Report, Part 2: Formal Safety Assessment; DNV GL: Høvik, Norway, 2015. [Google Scholar]
- Vassalos, D.; Paterson, D.; Mauro, F.; Murphy, A.; Mujeeb-Ahmed, M.P.; Michalec, R.; Boulougouris, E. A Multi-Level Approach for Flooding Risk Estimation and Assessment of Passenger Ships. In Proceedings of the SNAME Maritime Convention, Houston, TX, USA, 27–29 September 2022. [Google Scholar]
- Hu, M.; Cai, W. Research on the Evacuation Characteristics of Cruise Ship Passengers in Multi-Scenarios. Appl. Sci.
**2022**, 12, 4213. [Google Scholar] [CrossRef] - Vassalos, D.; Bole, M.; Vassalos, G.C.; Bole, M.; Kim, H.S.; Majumber, J. Advanced Evacuation Analysis–Testing the Ground on Ships. In Proceedings of the 2nd International Conference on Pedestrian and Evacuation Dynamics, Greenwich, UK, 20–22 August 2003. [Google Scholar]
- Liu, L.; Zhang, H.; Xie, J.; Zhao, Q. Dynamic Evacuation Planning on Cruise Ships Based on an Improved Ant Colony System (IACS). J. Mar. Sci. Eng.
**2021**, 9, 220. [Google Scholar] [CrossRef] - Sarshar, P.; Granmo, O.C.; Radianti, J.; Gonzalez, J.J. A Bayesian Network Model for Evacuation Time Analysis during a Ship Fire. In Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2013–2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013, Singapore, 16–19 April 2013; pp. 100–107. [Google Scholar]
- Ventikos, N.P.; Zagkliveri, T.; Kopsacheilis, I.; Annetis, M.; Pollalis, C.D.; Sotiralis, P. Reducing Ship Evacuation Time: The Case of a Rail Platform for Integrating Novel LSA Lifeboats on Ship Architectural Structures. In Proceedings of the 1st International Conference on the Stability and Safety of Ships and Ocean Vehicles, Glasgow, UK, 6–11 June 2021. [Google Scholar]
- MSC-MEPC.2/Circ.12/Rev.2; Revised Guidelines for Formal Safety Assessment (FSA) for Use in the IMO Rule-Making Process. Iinternational Maritime Organization Maritime Safety and Marine Environmental Protection Committees: London, UK, 2018.
- Papanikolaou, A.; Hamann, R.; Lee, B.S.; Mains, C.; Olufsen, O.; Vassalos, D.; Zaraphonitis, G. GOALDS—Goal Based Damage Ship Stability and Safety Standards. Accid. Anal. Prev.
**2013**, 60, 353–365. [Google Scholar] [CrossRef] [PubMed] - Zaraphonitis, G.; Bulian, G.; Lindroth, D.; Hamann, R.; Luhmann, H.; Cardinale, M.; Routi, A.-L.; Bertin, R.; Harper, G. Evaluation of Risk from Raking Damages Due to Grounding, Final Report; DNV GL: Høvik, Norway, 2015. [Google Scholar]
- Spouge, J.; Skjong, R. Risk Acceptance Criteria and Risk Based Damage Stability. Final Report, Part 1: Risk Acceptance Criteria; DNV GL: Høvik, Norway, 2015. [Google Scholar]
- MSC 75/5/2; Formal Safety Assessment—Bulk Carriers. Submitted by Japan; International Maritime Organization Maritime Safety Committee: Tokyo, Japan, 2002.
- MSC 85/17/1; Formal Safety Assessment—Cruise Ships. Submitted by Denmark; International Maritime Organization Maritime Safety Committee: London, UK, 2008.
- MSC 85/INF.3; Formal Safety Assessment—RoPax Ships. Submitted by Denmark; International Maritime Organization Maritime Safety Committee: London, UK, 2008.
- Vassalos, D.; Mujeeb-Ahmed, M.P.; Papanikolaou, A.; Antonini, A. Conception and Evolution of the Probabilistic Methods for Ship Damage Stability and Flooding Risk Assessment. J. Mar. Sci. Eng.
**2021**, 9, 667. [Google Scholar] [CrossRef] - Spyrou, K.J.; Koromila, I.A. A Risk Model of Passenger Ship Fire Safety and Its Application. Reliab. Eng. Syst. Saf.
**2020**, 200, 106937. [Google Scholar] [CrossRef] - Matsuoka, T.; Mitomo, N.; Kaneko, F. Evaluation of Occurrence Frequencies of Marine Accidents by Event Tree Analysis. In Probabilistic Safety Assessment and Management; Springer: London, UK, 2004; pp. 3269–3274. [Google Scholar] [CrossRef]
- Raiyan, A.; Das, S.; Islam, M.R. Event Tree Analysis of Marine Accidents in Bangladesh. Procedia Eng.
**2017**, 194, 276–283. [Google Scholar] [CrossRef] - Sotiralis, P.; Louzis, K.; Ventikos, N.P. The Role of Ship Inspections in Maritime Accidents: An Analysis of Risk Using the Bow-Tie Approach. Proc. Inst. Mech. Eng. O J. Risk Reliab.
**2019**, 233, 58–70. [Google Scholar] [CrossRef] - Jensen, F.V.; Nielsen, T.D. Bayesian Networks and Decision Graphs. Statistics for Engineering and Information Science; Springer: New York, NY, USA, 2007; ISBN 0-387-95259-4. [Google Scholar]
- Ventikos, N.P.; Sotiralis, P.; Annetis, E. A Combined Risk-Based and Condition Monitoring Approach: Developing a Dynamic Model for the Case of Marine Engine Lubrication. Transp. Saf. Environ.
**2022**, 4, tdac020. [Google Scholar] [CrossRef] - Ventikos, N.P.; Sotiralis, P.; Drakakis, M. A Dynamic Model for the Hull Inspection of Ships: The Analysis and Results. Ocean. Eng.
**2018**, 151, 355–365. [Google Scholar] [CrossRef] - Sotiralis, P.; Ventikos, N.P.; Hamann, R.; Golyshev, P.; Teixeira, A.P. Incorporation of Human Factors into Ship Collision Risk Models Focusing on Human Centred Design Aspects. Reliab. Eng. Syst. Saf.
**2016**, 156, 210–227. [Google Scholar] [CrossRef] - Zhang, D.; Yan, X.P.; Yang, Z.L.; Wall, A.; Wang, J. Incorporation of Formal Safety Assessment and Bayesian Network in Navigational Risk Estimation of the Yangtze River. Reliab. Eng. Syst. Saf.
**2013**, 118, 93–105. [Google Scholar] [CrossRef] - BayesFusion GeNIe Modeler. Available online: https://www.bayesfusion.com/genie/ (accessed on 23 November 2022).
- Loucks, D.P.; van Beek, E.; Stedinger, J.R.; Dijkman, J.P.M.; Villars, M.T. Model Sensitivity and Uncertainty Analysis. In Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications; UNESCO: Paris, France, 2005; pp. 255–290. ISBN 9231039989. [Google Scholar]
- Castillo, E.; Gutiérrez, J.M.; Hadi, A.S. Sensitivity Analysis in Discrete Bayesian Networks. IEEE Trans. Syst. Man Cybernetics Part A Syst. Hum.
**1997**, 27, 412–423. [Google Scholar] [CrossRef] [Green Version] - Kjærulff, U.; van den Gaag, L.C. Making Sensitivity Analysis Computationally Efficient. Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000). arXiv
**2000**, arXiv:1301.3868. [Google Scholar] [CrossRef] - ISO ISO Guide 73:2009; Risk Management—Vocabulary. Available online: https://www.iso.org/standard/44651.html (accessed on 22 November 2022).
- MSC.1/Circ.1212; Guidelines on Alternative Design and Arrangements for SOLAS Chapters II-1 and III. International Maritime Organization Maritime Safety Committee: London, UK, 2006.

**Figure 1.**A high-level event sequence for collision accidents that was adapted from the GOALDS project [18].

**Figure 2.**Event sequence in emergency cases which highlights the risk model focus, adapted from SSE 4/3 (2017) [6].

**Figure 7.**The Bayesian network for “Reaching Muster Station” in flooding accidents. Grey nodes: generic evidence (accident category = collision, ship type = cruise, and sea area = worldwide), blue nodes: RCO parameter (Dynamic Exit Signs), green nodes: qualitative parameter, yellow nodes: output parameter.

**Figure 8.**A Bayesian network for the mustering process, including Dynamic Exit Signs, with “Reach Muster Station” as the target parameter for the sensitivity analysis (accident category = collision, ship type = cruise, and sea area = worldwide).

**Figure 9.**A tornado chart (generated from the GeNIe modeler) of the Bayesian network referring to the mustering process, by assuming Dynamic Exit Signs, for 100% of the spread for the calculated value.

**Figure 11.**Event tree calculations and results for the baseline scenario of fire/explosion accidents.

**Figure 12.**Event tree calculations and results for collision accidents, by considering the Dynamic Exit Signs’ RCO.

ID | Descriptive Bayesian Network Title |
---|---|

01 | Capsize/Untenable Conditions, for flooding accidents |

02_{FL} ^{1} | Reaching Muster Station, for flooding accidents |

02_{FX} ^{1} | Reaching Muster Station, for fire/explosion accidents |

03_{FL} ^{1} | Lifeboat Availability, for flooding accidents |

03_{FX} ^{1} | Lifeboat Availability, for fire/explosion accidents |

04_{FL} ^{1} | Transfer and Embark to the Lifeboat, for flooding accidents |

04_{FX} ^{1} | Transfer and Embark to the Lifeboat, for fire/explosion accidents |

05 | Lifeboat Lowering, for both flooding and fire/explosion accidents |

06 | Lifeboat Clearing, for flooding accidents |

07 | Survive in the Lifeboat, for both flooding and fire/explosion accidents |

08 | Survive at Sea, for both accident types |

09a | Fatality in Unsafe Lowering, for both accident types |

09b | Capsize and Evacuate, to estimate the probability of fatality when clearing fails, for flooding accidents |

^{1}FL (flooding) and FX (fire/explosion) indicators distinguish BNs for flooding and fire accidents.

Collision | Grounding | Contact | |||
---|---|---|---|---|---|

Water ingress | 0.333 | Water ingress | 0.305 | Hull breach | 0.857 |

Being struck | 0.516 | Hull breach | 0.857 | Icebergs, bridges, and offshores | 0.400 |

En route | 0.334 | En route | 0.424 | En route | 0.424 |

Minor damage | 0.270 | Sinking | 0.567 | Sinking | 0.200 |

ID | Description of Results | Acc. Type ^{1} | Probability |
---|---|---|---|

01 | Probability of capsize/untenable conditions, applicable only for flooding accidents. | CN | 0.1384 |

GR | 0.1381 | ||

CT | 0.1384 | ||

02_{FL} | Probability of successful mustering, referring only to flooding accidents. | CN | 0.9734 |

GR | 0.9737 | ||

CT | 0.9735 | ||

02_{FR} | Probability of successful mustering, referring only to fire/explosion accidents. | FX | 0.9933 |

03_{FL} | Probability of lifeboat availability, referring only to flooding accidents. | CN | 0.8523 |

GR | 0.9012 | ||

CT | 0.8523 | ||

03_{FR} | Probability of lifeboat availability, referring only to fire/explosion accidents. | FX | 0.9209 |

04_{FL} | Probability of successful transfer and embarkation to lifeboats during abandonment, referring only to flooding accidents. | CN | 0.9909 |

GR | 0.9909 | ||

CT | 0.9909 | ||

04_{FR} | Probability of successful transfer and embarkation to lifeboats during abandonment, referring only to fire/explosion accidents. | FX | 0.9992 |

05 | Probability of unsafe lowering, applicable for all types of accidents. | ALL | 0.0088 |

Probability of no lowering/stuck, applicable for all types of accidents. | ALL | 0.0030 | |

06 | Probability of successful lifeboat clearing, applicable only for flooding accidents. | FL | 0.9486 |

07 | Probability of survival in the lifeboat, applicable for all types of accidents. | ALL | 0.9988 |

08 | Probability of surviving at sea, applicable for all types of accidents. | ALL | 0.8892 |

09_{a} | Probability of fatalities in the case of unsafe lowering, applicable only for flooding accidents. | FL | 0.0006 |

09_{b} | Probability of capsize and successful evacuation, applicable only for flooding accidents (to estimate fatalities when clearing fails). | CN | 0.0459 |

GR | 0.0462 | ||

CT | 0.0459 |

^{1}Accident type may refer to all accidents, flooding (FL) accidents, collision (CN) accidents, grounding (GN) accidents, contact (CT) accidents, or fire/explosion (FX) accidents.

**Table 4.**Results for ΔR (lives saved per ship/lifetime) for the RCO considered, per accident type and in total.

# | Parameter and Evidence (100% Parameter Spread) | dp |
---|---|---|

1 | Being Retrieved by the Crew = Yes|Accident Category = Collision | 0.0668 |

2 | Flooding = Slow|Ship type = Cruise, Accident Category = Collision | 0.0254 |

3 | Discover Other Signals = Yes | 0.0123 |

4 | Trapped/Lost = Yes|Dynamic Exit Signs = No, Excessive Heel/Trim = No, Extreme Acceleration = No, Escape Route Blocked = Yes | −0.0125 |

5 | Dynamic Exit Signs = Yes | 0.0131 |

6 | Trapped/Lost = Yes|Dynamic Exit Signs = Yes, Excessive Heel/Trim = No, Extreme Acceleration = No, Escape Route Blocked = Yes | −0.0125 |

7 | Escape Route Blocked = Yes|Flooding = Slow, TTC = 10 to 15 min | −0.0126 |

8 | Escape Route Blocked = Yes|Flooding = Slow, TTC = 15 to 20 min | −0.0157 |

9 | Escape Route Blocked = Yes|Flooding = Fast, TTC = 10 to 15 min | −0.0028 |

10 | Escape Route Blocked = Yes|Flooding = Fast, TTC = 15 to 20 min | −0.0035 |

11 | Escape Route Blocked = Yes|Flooding = Slow, TTC = 20 to 25 min | −0.0146 |

12 | Trapped/Lost = Yes|Dynamic Exit Signs = No, Excessive Heel/Trim = Yes, Extreme Acceleration = No, Escape Route Blocked = No | −0.0054 |

13 | Weather/Wave = Hs 2|Sea Area = Worldwide | 0.0040 |

14 | Acoustic Alarm not Followed = Yes|Age = less than 50 | −0.0320 |

15 | Escape Route Blocked = Yes|Flooding = Fast, TTC = 20 to 25 min | −0.0032 |

16 | TTC = 10 to 15 min|Weather/Wave = Hs 3 | −0.0091 |

17 | TTC = 10 to 15 min|Weather/Wave = Hs 4 | −0.0066 |

18 | Escape Route Blocked = Yes|Flooding = Slow, TTC = 25 to 30 min | −0.0126 |

19 | TTC = 10 to 15 min|Weather/Wave = Hs 2 | −0.0092 |

20 | Acoustic Alarm not Followed = Yes|Age = between 50 and 70 | −0.0158 |

**Table 5.**Risk model results for the baseline scenario in terms of PLL per ship/year for all accidents of interest, including the frequency of casualties and results from previous studies.

Accident Type ^{1} | Casualties Ship/Year | EMSA III [10] (Cruise Ships) | FSA for Cruise Ships [22] | Risk Model (Cruise Ships) |
---|---|---|---|---|

CN | 6.36 × 10^{−3} | 6.57 × 10^{−2} | 2.27 × 10^{−1} | 1.14 × 10^{−1} |

GR | 7.48 × 10^{−3} | 2.20 × 10^{−1} | 1.49 × 10^{−1} | |

CT | 8.23 × 10^{−3} | 1.30 × 10^{−2} | 9.93 × 10^{−2} | |

GR + CT | 3.34 × 10^{−1} | 2.33 × 10^{−1} | 2.48 × 10^{−1} | |

FX | 7.86 × 10^{−3} | 2.10 × 10^{−2} | 1.70 × 10^{−2} | 2.54 × 10^{−2} |

T | 4.21 × 10^{−1} | 5.20 × 10^{−1} | 3.89 × 10^{−1} |

^{1}Accident type may refer to collision (CN) accidents, grounding (GN) accidents, contact (CT) accidents, or fire/explosion (FX) accidents. Total (T) refers to the total risk.

**Table 6.**Results for ΔR (lives saved per ship/lifetime) for the RCO considered, per accident type and in total.

RCO | Collision | Grounding | Contact | Fire | Total |
---|---|---|---|---|---|

Dynamic Exit Signs | 3.38 × 10^{−1} | 5.05 × 10^{−1} | 3.33 × 10^{−1} | 7.83 × 10^{−2} | 1.30 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ventikos, N.P.; Sotiralis, P.; Annetis, M.; Podimatas, V.C.; Boulougouris, E.; Stefanidis, F.; Chatzinikolaou, S.; Maccari, A.
The Development and Demonstration of an Enhanced Risk Model for the Evacuation Process of Large Passenger Vessels. *J. Mar. Sci. Eng.* **2023**, *11*, 84.
https://doi.org/10.3390/jmse11010084

**AMA Style**

Ventikos NP, Sotiralis P, Annetis M, Podimatas VC, Boulougouris E, Stefanidis F, Chatzinikolaou S, Maccari A.
The Development and Demonstration of an Enhanced Risk Model for the Evacuation Process of Large Passenger Vessels. *Journal of Marine Science and Engineering*. 2023; 11(1):84.
https://doi.org/10.3390/jmse11010084

**Chicago/Turabian Style**

Ventikos, Nikolaos P., Panagiotis Sotiralis, Manolis Annetis, Vasileios C. Podimatas, Evangelos Boulougouris, Fotios Stefanidis, Stefanos Chatzinikolaou, and Alessandro Maccari.
2023. "The Development and Demonstration of an Enhanced Risk Model for the Evacuation Process of Large Passenger Vessels" *Journal of Marine Science and Engineering* 11, no. 1: 84.
https://doi.org/10.3390/jmse11010084