Temporal instability of truck volume composition on non-truck-involved crash severity using uncorrelated and correlated grouped random parameters binary logit models with space-time variations

Meng, Fang; Sze, N.N.; Song, Chunqiao; Chen, Tiantian; Zeng, Yiping

doi:10.1016/j.amar.2021.100168

Cited by 33 publications

(11 citation statements)

References 64 publications

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“…Studies in the literature show that crash injury severity models tend to suffer from temporal instability, that is, the factors contributing to crash injury severity vary quite extensively over different time periods ( 34 , 36 , 47 , 48 ). Researchers employ the log-likelihood ratio (LR) test to determine whether temporal instability exists or a combined model, with all the years’ aggregated data, is sufficient.…”

Section: Methodsmentioning

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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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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Section: Methodsmentioning

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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“…These indicate that the means of the fixed variables and standard deviations of the random variables are significantly different from zero (with at least 90% confidence) ( 29 ). In addition, retaining variables that are significant at the 90% level not only allows more variables to be included in the model to ensure consistency in the definition of categorical variables, but also explains part of the random error in the dependent variable ( 34 ). Table 5 presents the estimation results of the random parameter logit model considering the heterogeneity of means.…”

Section: Resultsmentioning

confidence: 99%

“…Table 5 presents the estimation results of the random parameter logit model considering the heterogeneity of means. A McFadden pseudo R-squared is a likelihood-based calculation that responds to the fit of the models ( 34 ). Both models in this paper have a reasonable fit with McFadden pseudo R-squared values of 0.48 and 0.60, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Factors Associated With the Severity of Motor Vehicle Crashes Involving Electric Motorcycles and Electric Bicycles: A Random Parameters Logit Approach With Heterogeneity in Means

Wang

Jin

Chen

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Exploring the heterogeneity of factors influencing the severity of electric bicycle crashes and electric motorcycle crashes can help target accident prevention policies to improve traffic safety. Therefore, this paper establishes a mean heterogeneity random parameter logit model using crash data from 2016 to 2020 in Guangxi to explore the different factors influencing the severity of crashes involving electric motorcycles and electric bicycles. The results show that the key influences on crash severity differ in electric motorcycle crashes and electric bicycle crashes. At the same time, some common factors affect the two types of crashes to different degrees. In addition, the complex interaction effects of unobserved heterogeneity were considered to explore the random parameters of the two types of crashes. The effect of unobserved heterogeneity on the distribution characteristics of the random parameters was then determined. For example, in electric motorcycle crashes, street lighting at night has a random parameter characteristic. The likelihood of serious crashes decreased when both street lighting at night and vehicle left turn were involved, and decreased when both street lighting at night and no signal control were involved. In electric bicycle crashes, large trucks have a random parameter characteristic. The likelihood of serious crashes increased when both large trucks and motor vehicle lights not turned on were involved, and increased when both large trucks and visibility less than or equal to 200 meters were involved. The results provide a basis for improving the road safety of electric motorcycles and electric bicycles.

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“…Using crash data from California and random parameters logit models, they found there were significant differences between crashes involving male and female drivers, and both male and female related crashes exhibited statistically significant temporal instability over the five-year period considered. Meng et al ( 28 ) examined the crash severity for non-truck-involved crashes given the presence of trucks. The authors developed uncorrelated and correlated grouped random parameters binary logit models for each year using crash data from 2016 to 2019 on eight freeway segments of Shandong in China.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Temporal Stability of Contributing Factors to Truck-Involved Crashes at Work Zones in South Carolina

Ahmed

Siddiqui²,

Huynh

2022

Transportation Research Record: Journal of the Transportation R

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This study examines factors that contributed to truck-involved work zone crash injury in South Carolina, a state which experienced a rise in the number of injury crashes from 2014 to 2018. The outcome of interest is injury or property damage only (PDO) crashes. A binary logit model is developed using South Carolina statewide work zone crash data from 2014 to 2018. It considers various factors, including vehicle, crash, roadway, environment, day and time, and driver-related characteristics. Of particular interest to this study is the temporal stability of the contributing factors from year to year so that a long-term mitigating strategy can be developed. To this end, the test for parameter transferability is used, and it confirms that separate models should be used for each year, except for 2014. The only factor found to be temporally stable across all years (i.e., statistically significant in 2015–2018 models) is airbag deployment. Given that nearly all the factors are temporally unstable, an importance measure is introduced to enable transportation agencies to rank factors and develop countermeasures that target persistent contributing factors. Based on the importance measure, the top three ranked factors that contributed to work zone truck-involved crash injury in South Carolina from 2015 to 2018 are: (i) airbags deployed, (ii) tie between rear-end crashes and crashes on primary roadways, and (iii) crashes in dark conditions.

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Temporal instability of truck volume composition on non-truck-involved crash severity using uncorrelated and correlated grouped random parameters binary logit models with space-time variations

Cited by 33 publications

References 64 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

Factors Associated With the Severity of Motor Vehicle Crashes Involving Electric Motorcycles and Electric Bicycles: A Random Parameters Logit Approach With Heterogeneity in Means

Analysis of Temporal Stability of Contributing Factors to Truck-Involved Crashes at Work Zones in South Carolina

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