Bicyclist injury severity classification using a random parameter logit model

Das, Subasish; Tamakloe, Reuben; Zubaidi, Hamsa; Obaid, Ihsan; Rahman, M. Ashifur

doi:10.1016/j.ijtst.2023.02.001

Cited by 6 publications

(2 citation statements)

References 50 publications

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“…Moreover, 91.13% of the accidents in the C1 accident cluster occurred at intersections. It has been noted that the probability of serious injury increases when traffic signal lights are present at intersections, which may be due to high traffic flow, leading to an increased risk for cyclists [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance

Dong,

Zhang,

Wang

et al. 2024

PLoS ONE

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Bicycle safety has emerged as a pressing concern within the vulnerable transportation community. Numerous studies have been conducted to identify the significant factors that contribute to the severity of cyclist injuries, yet the findings have been subject to uncertainty due to unobserved heterogeneity and class imbalance. This research aims to address these issues by developing a model to examine the impact of key factors on cyclist injury severity, accounting for data heterogeneity and imbalance. To incorporate unobserved heterogeneity, a total of 3,895 bicycle accidents were categorized into three homogeneous sub-accident clusters using Latent Class Cluster Analysis (LCA). Additionally, five over-sampling techniques were employed to mitigate the effects of data imbalance in each accident cluster category. Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. The optimal BN models for each accident cluster type provided insights into the key factors associated with cyclist injury severity. The results indicate that the key factors influencing serious cyclist injuries vary heterogeneously across different accident clusters. Female cyclists, adverse weather conditions such as rain and snow, and off-peak periods were identified as key factors in several subclasses of accident clusters. Conversely, factors such as the week of the accident, characteristics of the trafficway, the season, drivers failing to yield to the right-of-way, distracted cyclists, and years of driving experience were found to be key factors in only one subcluster of accident clusters. Additionally, factors such as the time of the crash, gender of the cyclist, and weather conditions exhibit varying levels of heterogeneity across different accident clusters, and in some cases, exhibit opposing effects.

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Section: Discussionmentioning

confidence: 99%

Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance

Dong,

Zhang,

Wang

et al. 2024

PLoS ONE

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“…Comparing the ME values reveals that roads in urban areas have higher fatality risks, as these roads often have other types of vehicles that tend to travel at higher speeds at night. Additionally, reduced visibility can intensify crash severity [40].…”

Section: Discussionmentioning

confidence: 99%

Crash Severity Analysis of Young Adult Motorcyclists: A Comparison of Urban and Rural Local Roadways

Champahom,

Se,

Aryuyo

et al. 2023

Applied Sciences

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In developing nations, motorcycles are a ubiquitous mode of transportation on local road networks, contributing to a substantial frequency of traffic accidents and fatalities. These occurrences exhibit significant discrepancies between urban and rural road environments. Specifically, urban roads exhibit greater traffic density and more robust law enforcement presence compared to their rural counterparts, factors that invariably influence vehicular speeds. Consequently, these variations in speed are associated with the adoption of riskier behaviors by motorists, with a pronounced impact on Young Adult Motorcyclists (YAMs) who demonstrate a heightened propensity for engaging in perilous activities, such as operating a motorcycle while under the influence or executing excessively close maneuvers. This study seeks to examine the factors that influence injury severity among YAM on local roads by contrasting urban and rural roadways, taking into account the different roadway contexts. Data from motorcycle crashes on local roads in Thailand between 2018 and 2020 were analyzed using the Random Parameter Logit Models with Unobserved Heterogeneity in Means and Variances (RPLMV) approach to investigate the relationship between contributing factors and YAM injury severity. The findings revealed several critical insights into the factors influencing the severity of motorcycle accidents, particularly among YAM on local urban roads. It is evident that a confluence of factors, including the absence of a valid driver’s license, exceeding designated speed limits, and the involvement of male drivers, significantly elevates the probability of fatal outcomes in these accidents. Our analysis has also unveiled intriguing patterns in nighttime accidents involving motorcycles, where those with functioning vehicle lights and those occurring under the cover of darkness without proper illumination share notable similarities in terms of severity. Furthermore, our research has emphasized that accidents transpiring outside city confines during nighttime hours with adequate lighting exhibit a negligible impact on higher crash severity. The key findings advocate for the development and implementation of targeted policy recommendations and countermeasures to alleviate the severity of accidents involving YAM. This includes a proposition to bolster law enforcement efforts, particularly in regions beyond city borders, and to enforce strict adherence to regulations concerning driver’s license verifications. Our study offers a crucial foundation for future research and policy development aimed at improving road safety and reducing the severity of motorcycle accidents, with the ultimate goal of safeguarding the lives of YAM.

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A temporal statistical assessment of the effectiveness of bicyclist safety helmets in mitigating injury severities in vehicle/bicyclist crashes

Alnawmasi,

Alogaili,

Rangaswamy

et al. 2024

Analytic Methods in Accident Research

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Bicyclist injury severity classification using a random parameter logit model

Cited by 6 publications

References 50 publications

Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance

Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance

Crash Severity Analysis of Young Adult Motorcyclists: A Comparison of Urban and Rural Local Roadways

A temporal statistical assessment of the effectiveness of bicyclist safety helmets in mitigating injury severities in vehicle/bicyclist crashes

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