Impact of IoT System Imperfections and Passenger Errors on Cruise Ship Evacuation Delay
Abstract
:1. Introduction
- Errors that the passengers may make in following instructions. Such errors can be caused by panic, human error or the lack of visibility of direction panels, and the difficulty of understanding loudspeaker announcements due to noise.
Related Work
2. The Simulation Framework
- The first part is the simulation software AnyLogic 8.8-8.8.2: Please state the version number of the software. I have added the version number. in which the layout of the physical where the evacuation occurs is incorporated. The “pedestrian” software library of AnyLogic adopts the Social Potential Field model to determine the direction of the movement of each evacuee.
- Secondly, we add a path-planning module written in Python that computes the evacuation direction for evacuees based on ANS, which is described below. This module transfers the computed instructions to the AnyLogic simulation software at each simulation step when the movement instructions need to be updated.
- ANS is implemented in our simulator to move evacuees along the path with minimum delay, avoiding the harmful effects caused by dynamic hazards. ANS assumes knowledge about the propagation of hazards (velocity and direction), and the average and maximum delay across each edge in the paths.
- As hazards progress in the simulation, ANS calculates the direction each evacuee should take to avoid hazards. In a real evacuation, this direction should be computed and then communicated to each evacuee via a wired or wireless network. In previous work, the possible delays of this communication were not taken into account.
- However, wireless or wired networks and computational servers that are used for decision making are likely to experience congestion, especially in emergency situations when decisions and communications are frequently updated and many messages are sent to evacuees and to the staff in the ship. This congestion can cause delays in updates regarding the navigation direction, and transmitting network packets and hence messages can be lost, and decisions may lead to errors due to the arrival of delayed instructions or facts, used by decision algorithms, that have been modified by events [43,44]. While most prior work neglects these effects, the present paper specifically evaluates their effect on the time required for the evacuation.
- Also, the passengers being evacuated may themselves be unable to follow the instructions they receive due to noise, panic, or misunderstanding.
- Thus, these delays and possible errors due to Information and Communication Technology (ICT), including network packet losses, as well as the possible effects of panic or misunderstandings by the evacuees, will be simulated and evaluated in this paper.
2.1. The Supporting IoT System
- Delays in the reception of the EMRs at the locations of the EDPs throughout the vessel. These delays can be caused by LAN delays and congestion, and DC delay and congestion during an emergency.
- Errors made by evacuees in following instructions from the EDPs during an emergency evacuation due to confusion and panic.
2.2. System Parameters for the Simulation
2.3. Layout of the Simulation Framework
3. Impact of Delays in Computing and Communications on Passenger Evacuation
Evaluation of the Average Evacuation Time
4. The Effect of Uncertain Passenger Behaviour
The Average Evacuation Time
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Evacuation Review | Focus | Related work |
Crowd monitoring | [4,6,25] | |
Disaster detection & prediction | [3,6,25] | |
Evacuation modelling | [6,11,15,19] | |
Evacuation path planning | [6,11,15,28,30] | |
Ship Accident | Accident type | Related work |
Ship sinking | [12,13] | |
Ship collision | [14] | |
Evacuation Simulation | Simulation model | Related work |
Agent-based model | [1,8,9,17,20,32] | |
Social force model | [10,21,23,24] | |
Social potential field model | [33] | |
Flow model | [17,20] | |
Path Planning | Planning method | Related work |
A* | [5] | |
Swarm optimization algorithm | [26] | |
Proactiv & reactive method | [29] | |
Cognitive packet network-based method | [7,38,39] | |
OPEN | [31] | |
Social potential field | [7] | |
Temporally ordered routing algorithm | [34] | |
Directional pathfinding method | [35] | |
Table-driven method | [36] | |
ANS | [37] | |
Dinic algorithm | [40] | |
Minimum spanning tree | [41] | |
Hazard potential field | [42] | |
Risk Analysis | Analysis method | Related work |
Bayesian network | [16] | |
Failure modes and effects analysis & analytic hierarchy process & fuzzy rule-based Bayesian reasoning & ER | [22] | |
Person Localization | Method | Related work |
Hybrid optimized fuzzy threshold extreme learning machine | [27] | |
Decision Rule Learning | Method | Related work |
Coevolutionary fuzzy rule miner | [2] | |
Evacuation Analysis & Layout Optimization | Method | Related work |
FDS+EVAC | [18] | |
Wireless Energy Transmission | Method | Related work |
Four-stage transmission | [43] | |
Activity & Data Detection | Method | Related work |
Multi-armed bandit | [44] |
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Ma, Y.; Gelenbe, E.; Liu, K. Impact of IoT System Imperfections and Passenger Errors on Cruise Ship Evacuation Delay. Sensors 2024, 24, 1850. https://doi.org/10.3390/s24061850
Ma Y, Gelenbe E, Liu K. Impact of IoT System Imperfections and Passenger Errors on Cruise Ship Evacuation Delay. Sensors. 2024; 24(6):1850. https://doi.org/10.3390/s24061850
Chicago/Turabian StyleMa, Yuting, Erol Gelenbe, and Kezhong Liu. 2024. "Impact of IoT System Imperfections and Passenger Errors on Cruise Ship Evacuation Delay" Sensors 24, no. 6: 1850. https://doi.org/10.3390/s24061850
APA StyleMa, Y., Gelenbe, E., & Liu, K. (2024). Impact of IoT System Imperfections and Passenger Errors on Cruise Ship Evacuation Delay. Sensors, 24(6), 1850. https://doi.org/10.3390/s24061850