Improving Safety in Hauling Operations: Predicting and Analyzing Collision Probabilities with Discrete Event Simulation

Ensuring safety in off-highway raw material handling systems is critical, as the high risk of truck collisions poses a significant threat to both human lives and mining equipment, leading to costly damages.While various safety and risk assessment methods exist-such as probabilistic models (e.g., Fault Tree Analysis), reliability-based models (e.g., Failure Mode and Effect Analysis), simulation-based models (e.g., Agent-Based Models), and incident analysis frameworks (e.g., Tripod)-most struggle to capture the complexity of dynamic traffic interactions.These methods often lack the flexibility to accurately model real-world conditions and require substantial computational resources, making them impractical for real-time applications.This study proposes a discrete-event simulation (DES) approach, which provides a time-based simulation of discrete events and effectively manages randomness, process interactions, and resource constraints.DES can outperform static or probabilistic models in simulating truck traffic flow and conducting real-time accident analysis, offering a more practical solution for operational safety studies compared to high-level systemic or agent-based models.The proposed model simulates truck movements across a road network that reflects a realistic mine layout.The model then develops and evaluates various collision scenarios while capturing real-time truck interactions throughout the entire road network.The simulation modeling was performed using the OpenCLSim, an open-source library for rule-driven scheduling and comparison of cyclic logistics strategies.The results highlight areas and locations on the road network with high collision probability, particularly mid-road and junction locations.Based on these high-risk areas, different operational scenarios, along with dynamic shove-truck allocation and scheduling, can help enhance safety and decrease the probability of collisions.Furthermore, additional enhancements, including signage, speed limits, adaptive traffic control and automated vehicle-to-vehicle communication systems, are recommended to improve driver responses to changing road conditions and congestion, offering a flexible and computationally efficient approach to safety management.

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Published
2025-01-01
DOI
https://doi.org/10.3850/978-981-94-3281-3_esrel-sra-e2025-p8086-cd
Primary Topic
Occupational Health and Safety Research
Type
article
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article

Improving Safety in Hauling Operations: Predicting and Analyzing Collision Probabilities with Discrete Event Simulation

Javad Sattarvand, Amin Moniri-Morad, Fedor Baart, Masoud Soleymani Shishvan et al.
Occupational Health and Safety Research
article

Improving Safety in Hauling Operations: Predicting and Analyzing Collision Probabilities with Discrete Event Simulation

Javad Sattarvand, Amin Moniri-Morad, Fedor Baart, Masoud Soleymani Shishvan, Malihe Goli
article en

Abstract

Ensuring safety in off-highway raw material handling systems is critical, as the high risk of truck collisions poses a significant threat to both human lives and mining equipment, leading to costly damages.While various safety and risk assessment methods exist-such as probabilistic models (e.g., Fault Tree Analysis), reliability-based models (e.g., Failure Mode and Effect Analysis), simulation-based models (e.g., Agent-Based Models), and incident analysis frameworks (e.g., Tripod)-most struggle to capture the complexity of dynamic traffic interactions.These methods often lack the flexibility to accurately model real-world conditions and require substantial computational resources, making them impractical for real-time applications.This study proposes a discrete-event simulation (DES) approach, which provides a time-based simulation of discrete events and effectively manages randomness, process interactions, and resource constraints.DES can outperform static or probabilistic models in simulating truck traffic flow and conducting real-time accident analysis, offering a more practical solution for operational safety studies compared to high-level systemic or agent-based models.The proposed model simulates truck movements across a road network that reflects a realistic mine layout.The model then develops and evaluates various collision scenarios while capturing real-time truck interactions throughout the entire road network.The simulation modeling was performed using the OpenCLSim, an open-source library for rule-driven scheduling and comparison of cyclic logistics strategies.The results highlight areas and locations on the road network with high collision probability, particularly mid-road and junction locations.Based on these high-risk areas, different operational scenarios, along with dynamic shove-truck allocation and scheduling, can help enhance safety and decrease the probability of collisions.Furthermore, additional enhancements, including signage, speed limits, adaptive traffic control and automated vehicle-to-vehicle communication systems, are recommended to improve driver responses to changing road conditions and congestion, offering a flexible and computationally efficient approach to safety management.

University of Nevada, Reno (US), Ministry of Infrastructure and Water Management (NL), Delft University of Technology (NL)
Openalex Percentile: Top 59%
Occupational Health and Safety Research
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