A cross-comparison of different techniques for modeling macro-level cyclist crashes

Guo, Yanyong; Osama, Ahmed; Sayed, Tarek

doi:10.1016/j.aap.2018.01.015

Cited by 109 publications

(53 citation statements)

References 40 publications

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“…The more roads that contain more vehicles, the greater the likelihood of collision. Previous studies [21][22][23][24][25][26][27][28][29][30][31][32][33]39] reported similar results. According to Kang [47], bus stops can substantially increase pedestrian volume, as well.…”

Section: Discussionsupporting

confidence: 74%

“…Hadayeghi et al [26] developed crash prediction models with geographically weighted Poisson regressions and investigated local spatial variations in the relationship between the number of crashes and potential transportation planning predictors, such as land use, socio-economic and demographic features, traffic volume, road network characteristics, dwelling units, and employment type. Guo et al [27] investigated the determinants of crashes involving cyclists, using a comprehensive list of covariates at the TAZ level in Vancouver, Canada. Using the macro-level crash approach with Bayesian statistical models, they found that bicycle and vehicle exposure measures, households, and commercial area density were positively associated with cyclist crashes.…”

Section: Introductionmentioning

confidence: 99%

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Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019

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As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

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Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019

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“…The parameters of explanatory variables in standard MNL are assumed to be fixed across observations, indicating that the impact of each explanatory variable is the same across observations [18]. However, this assumption is somehow contrary to the fact that the effect of explanatory variable varies across observations.…”

Section: Random Parameters Multinomial Logit Regressionmentioning

confidence: 99%

Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression

Guo

Lü

et al. 2019

Sustainability

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Understanding the risk factors of e-bike collisions can improve e-bike riders’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates risk factors that significantly contribute to the severity of e-bike collisions. Two months of e-bike collision data were collected in the city of Ningbo, China. A random parameters multinomial logit regression (RP-MNL) is proposed to account for the unobserved heterogeneity across observations. A fixed parameters multinomial logit regression (FP-MNL) is estimated and compared with the RP-MNL under the Bayesian framework. The full Bayesian approach based on Markov chain Monte Carlo simulation is employed to estimate the model parameters. Both parameter estimates and odds ratio (OR) are used to interpret the impact of risk factors on the severity of e-bike collisions. The model comparison results show that RP-MNL outperforms FP-MNL, indicating that accommodating the unobserved heterogeneity across observations could improve the model fit. The model estimation results show that age, gender, e-bike behavior, license plate, bicycle type, location, and speed limit are statistically significant and associated with the severity of e-bike collisions. Furthermore, four risk factors, i.e., gender, e-bike behavior, bicycle type, and speed limit, are found to have heterogeneous effects on severity of e-bike collisions, appearing in the form of random parameters in the statistical model.

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“…The three factors that have been widely considered in the literature are cyclists' age, gender, and exposure to cycling. Generally speaking, older cyclists (particularly those aged over 65) as well as males tend to have a higher crash risk [4,14,15], and cyclists with a greater exposure to cycling (commonly measured by cycling distance, cycling frequency, or cycling time) also have a higher possibility of being involved in crashes [16,17]. Besides, a number of environmental factors regarding the context where the crashes took place, for example, the time of day [18,19] and weather conditions [20], have also been found to be related to cyclists' road safety outcomes.…”

Section: Factors Related To Crashes Involving Cyclistsmentioning

confidence: 99%

Personality and Behavioral Predictors of Cyclist Involvement in Crash-Related Conditions

Zheng

et al. 2019

IJERPH

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In recent years, the increasing rate of road crashes involving cyclists with a disproportionate overrepresentation in injury statistics has become a major concern in road safety and public health. However, much remains unknown about factors contributing to cyclists’ high crash rates, especially those related to personal characteristics. This study aims to explore the influence of cyclist personality traits and cycling behaviors on their road safety outcomes using a mediated model combining these constructs. A total of 628 cyclists completed an online questionnaire consisting of questions related to cycling anger, impulsiveness, normlessness, sensation seeking, risky cycling behaviors, and involvement in crash-related conditions in the past year. After the psychometric properties of the employed scales were examined, the relationships among the tested constructs were investigated using structural equation modeling. The results showed that cyclists’ crash risks were directly predicted by risky cycling behaviors and cycling anger, and the effects of cycling anger, impulsiveness, as well as normlessness on crash risks, were mediated by cycling behaviors. The current findings provide insight into the importance of personality traits in impacting cycling safety and could facilitate the development of evidence-based prevention and promotion strategies targeting cyclists in China.

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A cross-comparison of different techniques for modeling macro-level cyclist crashes

Cited by 109 publications

References 40 publications

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression

Personality and Behavioral Predictors of Cyclist Involvement in Crash-Related Conditions

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