Abstract

This research presents an innovative computational simulator designed to model and analyze passenger evacuation dynamics in multi-vehicle light rail transit (LRT) systems. The developed web-based simulation tool provides a sophisticated method for assessing emergency egress scenarios, incorporating critical parameters such as vehicle configuration, passenger density, door characteristics, and panic-induced behavioral modifications.

1. Introduction

Passenger safety during emergency evacuations represents a critical challenge in mass transit systems. Light Rail Vehicles (LRVs) present unique evacuation complexities due to their segmented structure, multiple interconnected carriages, and potential passenger capacity variations. Traditional evacuation models often fail to capture the nuanced dynamics of multi-vehicle transit environments, necessitating more advanced computational approaches.

2. Literature Review

2.1 Evacuation Modeling Approaches

Existing literature on evacuation dynamics can be categorized into several key methodological approaches:

1. Fluid Dynamics Models
– Helbing and Molnár (1995) pioneered crowd movement simulation using social force models
– Characterized pedestrian movement as analogous to particle interactions
– Limited in capturing complex behavioral nuances

2.  Agent-Based Simulation
– Pelechano et al. (2005) introduced individual-based evacuation modeling
– Allows for more granular representation of individual passenger behaviors
– Computationally intensive for large-scale scenarios

3. Queuing and Flow Analysis
– Fruin (1971) established fundamental pedestrian movement principles
– Focused on flow rates, door capacity, and movement bottlenecks
– Provided foundational metrics for egress calculations

2.2 Transportation-Specific Evacuation Research

Critical studies in transportation evacuation include:
– Ronchi and Nilsson (2013): Analyzed emergency evacuation in public transport
– Niu et al. (2017): Developed comprehensive models for metro train evacuation scenarios
– Kirytopoulos et al. (2019): Examined risk assessment in mass transit systems

3. Methodology

3.1 Simulation Parameters

The developed simulator incorporates key evacuation dynamics parameters:

1. Vehicle Configuration
– Number of vehicles (1-10)
– Individual vehicle passenger capacity
– Total system passenger load

2. Egress Characteristics
– Door count per vehicle side
– Door width
– Flow rate calculations

3. Behavioral Modeling
– Panic factor coefficient
– Evacuation speed variations
– Behavioral complexity introduction

3.2 Computational Approach

Evacuation Time Calculation

The core evacuation time calculation follows the fundamental formula:

Where:
– Flow Rate = Doors × Door Width × Passenger Movement Coefficient
– Panic Factor: Modifies baseline evacuation dynamics (1.0 – 2.0 range)

3.3 Mathematical Model

The evacuation simulation employs a stochastic flow model:

1. Passenger Distribution:
P_total = N_vehicles × P_vehicle

2. Flow Rate Calculation:
Q = D_count × W_door × V_passenger

3. Evacuation Time:
T_evac = P_total / (Q × F_panic)

4. Simulator Usage Guide

4.1 Input Parameters

1. Vehicle Configuration
– Select number of vehicles (1-10)
– Define passengers per vehicle (50-300 range)

2. Evacuation Dynamics
– Adjust panic factor (1.0-2.0)
– Configure doors per vehicle side (1-8)
– Specify door width (500-2000 mm)

4.2 Simulation Outputs

The simulator provides:
– Total passenger count
– Normal evacuation time
– Panic scenario evacuation time
– Graphical vehicle representation

5. Limitations and Future Research

Current limitations include:
– Simplified behavioral modeling
– Lack of individual agent tracking
– Static environmental assumptions

Recommended future enhancements:
– Integrate machine learning for behavior prediction
– Incorporate real-world sensor data
– Develop more granular panic response models

6. Conclusion

The presented simulator offers a flexible, computational approach to understanding multi-vehicle light rail evacuation dynamics. By providing an accessible web-based tool, researchers and transit planners can rapidly assess potential emergency scenarios.