Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes

Hossain, Md Mahmud; Zhou, Huaguo; Das, Subasish

doi:10.1016/j.aap.2023.107217

Accident Analysis & Prevention

2023

DOI: 10.1016/j.aap.2023.107217

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Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes

Md Mahmud Hossain

Huaguo Zhou

Subasish Das

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Cited by 3 publications

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Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

Seo,

Park,

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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The unstructured-textual crash descriptions recorded by police officers is rarely utilized, despite containing detailed information on traffic situations. This lack of utilization is mainly due to the difficulty in analyzing text data, as there is currently no innovative methodology for extracting meaningful information from it. Given limitations and challenges in analyzing traffic crash descriptions, this study developed a methodology to classify significant words in unstructured data that describe traffic crash scenarios into standardized data. Ultimately, a natural language processing technique, specifically a bidirectional encoder representation from transformer (BERT), was used to extract meaningful information from crash descriptions. This BERT-based model effectively extracts information on the exact collision point and the pre-crash vehicle maneuver from crash descriptions. Its practical approach allows for the interpretation of traffic crash descriptions and outperforms other natural language processing models. Importantly, this method of extracting crash scene information from traffic crash descriptions can aid in better comprehending the unique characteristics of traffic crashes. This comprehension can ultimately aid in the development of appropriate countermeasures, leading to the prevention of future traffic crashes.

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Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

Seo,

Park,

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Severity Analysis of Secondary Crashes on High-Speed Roadways: Pattern Recognition Using Association Rule Mining

Hossain,

Abbaszadeh Lima,

Zhou

2024

Transportation Research Record: Journal of the Transportation R

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Secondary crashes (SCs) are a major concern, posing additional safety threats to both non-involved vehicles and incident responders. The objective of this study was to identify the affiliated factors contributing to SCs on roadways with a speed limit of 55 mph or above. Traditional police-investigated crash dataset was analyzed, spanning more than four years (January 2016–February 2020) for the entire state of Alabama. As the crash database did not directly include information on SCs and did not allow for linking a primary crash with a subsequent SC, a data extraction process was developed to identify SCs and understand their characteristics. Association rule mining (ARM) was applied to identify crash patterns based on maximum injury severity levels. The generated rules were filtered based on support, confidence, and lift, and then validated by the lift increase criterion. The results revealed complex relationships between risk factors and severity of SCs. In relation to SCs with injuries, single-vehicle crashes were frequently observed during peak hours and when drivers swerved to avoid objects/persons/vehicles. In contrast, concerning SCs with possible/no injuries, single-vehicle collisions were more likely to occur when drivers failed to notice objects/persons/vehicles and were involved in speeding. On urban interstates, single-vehicle SCs were frequently associated with injuries, while rear-end SCs were often linked to possible/no injuries. The findings of this study can be helpful in enhancing existing traffic incident management programs to mitigate the occurrence of SCs.

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Using Perceptual Cycle Model and Text Mining to Investigate Ambulance Traffic Crashes

Das,

Chakraborty,

Sheykhfard

et al. 2024

Transportation Research Record: Journal of the Transportation R

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Ambulance crashes constitute a matter of utmost concern within public health, posing potential risks to both patients and emergency responders. Despite this critical importance, investigating the underlying causes of these collisions is difficult because of the scarcity of comprehensive and relevant datasets. To bridge this research gap and gain valuable insights, the present study embarked on a mission to shed light on the causative factors behind ambulance-related crashes. To achieve this objective, this study adopted a meticulous approach, collecting narrative descriptions from ten special investigation reports published by the National Highway Traffic Safety Administration. These reports were selected as they offered in-depth accounts of real-life ambulance crashes, rendering them an invaluable resource for analyzing the multifaceted aspects leading to such incidents. Central to this investigation was the utilization of the Perceptual Cycle Model (PCM), a well-established and comprehensive framework that facilitates a systematic examination of the various stages leading to a crash. The study examined the key influential factors associated with ambulance crashes by employing PCM and text mining. The results reveal diverse factors contributing to ambulance crashes, including varied causes, driver actions, and post-crash scenarios, providing a holistic understanding of road safety. The outcomes of this study will bolster the safety of ambulance operations, safeguard patients and personnel, and ensure the efficient delivery of life-saving emergency services to those in need.

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Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes

Cited by 3 publications

References 60 publications

Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

Text Classification Modeling Approach on Imbalanced-Unstructured Traffic Accident Descriptions Data

Severity Analysis of Secondary Crashes on High-Speed Roadways: Pattern Recognition Using Association Rule Mining

Using Perceptual Cycle Model and Text Mining to Investigate Ambulance Traffic Crashes

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