A multinomial logit model of motorcycle crash severity at Australian intersections

Vajari, Mohammad Abrari; Aghabayk, Kayvan; Sadeghian, Mohammad; Shiwakoti, Nirajan

doi:10.1016/j.jsr.2020.02.008

Cited by 102 publications

(58 citation statements)

References 45 publications

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“…For motorcycle crashes, non-clear weather is significantly and positively associated with FS injuries in rural SV crashes (marginal effect 4.52%), which is consistent with previous research [37], because rural China has poor traffic conditions, such as waterlogged and narrower roads. In addition, motorcycles do not have satisfactory stability because of two tires [70].…”

Section: Discussionsupporting

confidence: 90%

“…However, there is a significant negative correlation between male drivers and FS injuries in rural motorcycle SV crashes, with average marginal effects of −2.73%. Vajari et al [37] reached a similar conclusion by analyzing the severity of motorcycle crashes at Australian intersections. These findings are of interest to comprehensively elucidate the variable effects of male driver on rural SV crash severity and validate the necessity of regression analysis based on different vehicle types.…”

Section: Discussionmentioning

confidence: 68%

“…This is generally consistent with the previous studies. For example, Vajari et al [37] and Cerwick et al [72] analyzed motorcycle crashes and truck crashes, respectively, and both suggested that the probability of a serious crash was significantly higher on weekends than on weekdays. These findings underscore the importance of educating drivers to remain vigilant after long off-duty hours, such as on weekends, and provide theoretical references for formulating scientific management measures.…”

Section: Discussionmentioning

confidence: 99%

“…Among various logit approaches, the multinomial logit (MN-logit) model has been widely used due to its simple structure and easy-to-read parameters. For example, the MN-logit model was calibrated to analyze the risk factors of motorcycle crashes and pedestrian-vehicle crashes, respectively [37,38]. Subsequently, a binary logit model was employed to fit roadside accidents and suggested that rural crashes should be investigated separately [22].…”

Section: Unobserved Heterogeneity In Crash Analysismentioning

confidence: 99%

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Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

et al. 2021

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The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Unobserved Heterogeneity In Crash Analysismentioning

confidence: 99%

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Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

et al. 2021

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“…Considering the substantial differences between different vehicle types, crossvehicle-types modeling was recommended. For example, a crash severity regression function related to motorcycle and truck crashes was established, respectively, to explore the risk factors [27,28]. Establishing a regression model across vehicle types can obtain targeted findings and improve the model fit; this may be due to the fact that a crash dataset containing a specific vehicle type has more homogeneity compared with a crash dataset containing all vehicle types [29].…”

Section: Safety Covariates Of Rural Single-vehicle Crashesmentioning

confidence: 99%

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

et al. 2021

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Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.

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Vehicle and Pedestrian Crash Risk Modeling in Arabian Gulf Region

AlKheder

2023

Lecture Notes in Networks and Systems

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A multinomial logit model of motorcycle crash severity at Australian intersections

Cited by 102 publications

References 45 publications

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

Vehicle and Pedestrian Crash Risk Modeling in Arabian Gulf Region

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