Unobserved heterogeneity and temporal instability in the analysis of work-zone crash-injury severities

Islam, Mouyid Bin; Alnawmasi, Nawaf; Mannering, Fred

doi:10.1016/j.amar.2020.100130

Cited by 95 publications

(33 citation statements)

References 36 publications

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“…For instance, researchers have found that factors affecting injury severity outcomes from crashes involving aggressive and non-aggressive drivers varied significantly in each of the years from 2015 to 2017 ( 36 ). Islam et al ( 37 ) also demonstrated temporal instability in work-zone crashes from 2012 to 2017 in Florida. Behnood and Mannering ( 38 ) investigated the effect of time of day on injury severity outcomes in large truck crashes in Los Angeles from 2010 to 2017 and found there was temporal instability in the factors.…”

mentioning

confidence: 93%

“…That is, the factors affecting crash injury severity may not be constant over time because of fundamental changes in human behavior, perceptions toward various risks, vehicle technology, and socio-economic conditions (34). There are several studies in the literature which suggest that the confounding factors of injury severity outcomes vary over time (34)(35)(36)(37)(38). For instance, researchers have found that factors affecting injury severity outcomes from crashes involving aggressive and non-aggressive drivers varied significantly in each of the years from 2015 to 2017 (36).…”

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confidence: 99%

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Investigating the Temporal Instability in Injury Severity Outcomes of Clear and Adverse Weather Crashes on Rural Mountainous Highways

Ahmed

2021

Transportation Research Record

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Analysis of driver injury severity based on weather conditions on rural highways is limited in the literature. Such analyses provide insights useful to policymakers in optimizing the allocation of limited resources based on weather conditions. Furthermore, if there is a possibility of factors exhibiting temporal instability, then an aggregate analysis can lead to erroneous allocation of funds. In this study, separate models for clear and adverse weather conditions were developed for each of the years from 2015 to 2019 using crash data from a rural mountainous highway corridor. A random-intercept Bayesian logistic approach was used to analyze the dichotomous injury severity response and capture the between-crash variance. An efficient Markov chain Monte Carlo sampling technique known as the No-U-Turn Hamiltonian Monte Carlo was employed to sample the posterior distributions of parameter estimates. Likelihood ratio tests provided statistical significance of the temporal instability and also the differences in driver injury severities resulting from clear and adverse weather crashes. While most of the variables demonstrated temporal instability, some factors exhibited temporal stability over a short period of time and only during clear weather conditions. Findings from the separate models suggest that there are major differences in both the combination and magnitude of the significant contributing factors. Implementation of confirmatory warning signs, variable message signs, connected vehicle technology, strict enforcements during different times and locations, and driver awareness programs have been recommended as suitable countermeasures. The findings and recommendations could potentially help in guiding the respective agencies in formulating injury severity mitigation policies and strategies.

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confidence: 93%

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confidence: 99%

Investigating the Temporal Instability in Injury Severity Outcomes of Clear and Adverse Weather Crashes on Rural Mountainous Highways

Ahmed

2021

Transportation Research Record

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“…Extensive studies demonstrated that the factors of accident injury severity showed temporal instability [20]. To find out whether the analyzed data share the same property, two groups of likelihood ratio tests were conducted [16,[26][27][28]. The test for the first group was to verify whether the model estimated by any two-year data has temporal instability under certain lighting conditions.…”

Section: Likelihood Ratio Testmentioning

confidence: 99%

Temporal Stability Analysis of Lighting Conditions in Traffic Accidents

Hou¹,

Xing

Li³

et al. 2022

Safety

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Different lighting conditions can result in accidents of different levels of severity. However, current studies lack the consideration of the heterogeneity and temporal stability of accident data under various lighting conditions. Therefore, three years’ worth of data were used to investigate the critical factors of accident severity. The random parameters logit model was employed to investigate the influence of different lighting conditions on temporal stability and heterogeneity. The critical factors affecting injury severity were also identified. The temporal stability and transferability of the models were investigated by a series of likelihood ratio tests. Based on different lighting conditions (daylight conditions, and night-time conditions with street lighting on), six models were established. Three kinds of accident injury severity levels were classified: property damage only (PDO), severe injury (SI), and fatal injury (FI). The estimation results showed contributing factors of accident severity were significantly different between the two kinds of lighting conditions. Additionally, accidents showed temporal instability. The proposed method can provide a guide for infrastructure construction, operation, and maintenance in traffic-safety management.

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“…[ 13 , 14 ] presented the statistical analysis by using motorcyclist injuries data and road accident data. References [ 15 , 16 ] presented excellent work in monitoring road accidents.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Road Accidents and Injuries Using Variance Chart under Resampling and Having Indeterminacy

Aslam

Albassam

2021

IJERPH

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Unobserved heterogeneity and temporal instability in the analysis of work-zone crash-injury severities

Cited by 95 publications

References 36 publications

Investigating the Temporal Instability in Injury Severity Outcomes of Clear and Adverse Weather Crashes on Rural Mountainous Highways

Investigating the Temporal Instability in Injury Severity Outcomes of Clear and Adverse Weather Crashes on Rural Mountainous Highways

Temporal Stability Analysis of Lighting Conditions in Traffic Accidents

Monitoring Road Accidents and Injuries Using Variance Chart under Resampling and Having Indeterminacy

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