A random parameters regional quantile analysis for the varying effect of road-level risk factors on crash rates

2021 ◽  
Vol 29 ◽  
pp. 100153
Author(s):  
Jinjun Tang ◽  
Weiqi Yin ◽  
Chunyang Han ◽  
Xinyuan Liu ◽  
Helai Huang
Keyword(s):  
Risk Factors ◽  
Random Parameters ◽  
Crash Rates

Analysis of Crash Rates at Freeway Diverge Areas using Bayesian Tobit Modeling Framework

2019 ◽  
Vol 2673 (4) ◽  
pp. 652-662 ◽  
Author(s):  
Yanyong Guo ◽  
Zhibin Li ◽  
Tarek Sayed
Keyword(s):  
Risk Factors ◽  
Tobit Model ◽  
Information Criteria ◽  
Random Parameters ◽  
Modeling Framework ◽  
Crash Rates ◽  
Tobit Models ◽  
Heterogeneous Effects ◽  
The Impact ◽  
Diverge Areas

The goal of this study is to evaluate the impact of various risk factors on crash rates at freeway diverge areas. Crash rates data for a three-year period from 367 freeway diverge areas were used for analysis. Four candidate Tobit models were developed and compared under the Bayesian framework: a traditional Tobit model; a random parameters Tobit (RP-Tobit) model; a grouped random parameters Tobit (GRP-Tobit) model; and a random intercept Tobit (RI-Tobit). The results showed that the RP-Tobit model performs best with highest value of Rd2 as well as lowest Mean Absolute Deviance (MAD) and Deviance Information Criteria (DIC), indicating the importance of accounting for unobserved heterogeneity to improve the model fit. Both the GRP-Tobit and the RI-Tobit models provide better performance than the traditional Tobit model. The model results showed that crash rates at freeway diverge areas were positively associated with mainline annual average daily traffic (AADT) and negatively associated with ramp AADT, indicating the different mechanisms of the impact of traffic volume on crash rates at freeway diverge areas. Lane-balanced design and high speed limits at freeway diverge areas have a negative effect on crash rates. The number of lanes on mainline and ramp length have significant heterogeneous effects on crash rates across observations. The RP-Tobit model provides a more comprehensive understanding of the heterogeneous effects of risk factors on crash rates across observations.

scholarly journals A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity

2017 ◽  
Vol 99 ◽  
pp. 184-191 ◽  
Author(s):  
Qiang Zeng ◽  
Huiying Wen ◽  
Helai Huang ◽  
Xin Pei ◽  
S.C. Wong
Keyword(s):  
Injury Severity ◽  
Tobit Model ◽  
Random Parameters ◽  
Crash Rates

Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression

2019 ◽  
Vol 15 (2) ◽  
pp. 1867-1884 ◽  
Author(s):  
Qiang Zeng ◽  
Qiang Guo ◽  
S. C. Wong ◽  
Huiying Wen ◽  
Heilai Huang ◽  
...  
Keyword(s):  
Random Parameters ◽  
Tobit Regression ◽  
Crash Rates ◽  
Spatio Temporal

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

2021 ◽  
Vol 11 (17) ◽  
pp. 7819
Author(s):  
Fulu Wei ◽  
Zhenggan Cai ◽  
Zhenyu Wang ◽  
Yongqing Guo ◽  
Xin Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Spatial Correlation ◽  
Traffic Safety ◽  
Logit Model ◽  
Unobserved Heterogeneity ◽  
Crash Severity ◽  
Random Parameters ◽  
Crash Data ◽  
Random Parameters Logit ◽  
Single Vehicle

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.

scholarly journals Socioeconomic Status, Health and Lifestyle Settings as Psychosocial Risk Factors for Road Crashes in Young People: Assessing the Colombian Case

2021 ◽  
Vol 18 (3) ◽  
pp. 886
Author(s):  
Andrea Serge ◽  
Johana Quiroz Montoya ◽  
Francisco Alonso ◽  
Luis Montoro
Keyword(s):  
Risk Factors ◽  
Socioeconomic Status ◽  
Protective Factors ◽  
Road Traffic ◽  
Principal Component ◽  
The Self ◽  
Related Factors ◽  
Cross Sectional ◽  
Traffic Crashes ◽  
Crash Rates

The social determinants of health influence both psychosocial risks and protective factors, especially in high-demanding contexts, such as the mobility of drivers and non-drivers. Recent evidence suggests that exploring socioeconomic status (SES), health and lifestyle-related factors might contribute to a better understanding of road traffic crashes (RTCs). Thus, the aim of this study was to construct indices for the assessment of crash rates and mobility patterns among young Colombians who live in the central region of the country. The specific objectives were developing SES, health and lifestyle indices, and assessing the self-reported RTCs and mobility features depending on these indices. A sample of 561 subjects participated in this cross-sectional study. Through a reduction approach of Principal Component Analysis (PCA), three indices were constructed. Mean and frequency differences were contrasted for the self-reported mobility, crash rates, age, and gender. As a result, SES, health and lifestyle indices explained between 56.3–67.9% of the total variance. Drivers and pedestrians who suffered crashes had higher SES. A healthier lifestyle is associated with cycling, but also with suffering more bike crashes; drivers and those reporting traffic crashes have shown greater psychosocial and lifestyle-related risk factors. Regarding gender differences, men are more likely to engage in road activities, as well as to suffer more RTCs. On the other hand, women present lower healthy lifestyle-related indices and a less active implication in mobility. Protective factors such as a high SES and a healthier lifestyle are associated with RTCs suffered by young Colombian road users. Given the differences found in this regard, a gender perspective for understanding RTCs and mobility is highly suggestible, considering that socio-economic gaps seem to differentially affect mobility and crash-related patterns.

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

2019 ◽  
Vol 11 (7) ◽  
pp. 2071 ◽  
Author(s):  
Yanyong Guo ◽  
Yao Wu ◽  
Jian Lu ◽  
Jibiao Zhou
Keyword(s):  
Risk Factors ◽  
Unobserved Heterogeneity ◽  
Multinomial Logit ◽  
Model Parameters ◽  
License Plate ◽  
Speed Limit ◽  
Random Parameters ◽  
Logit Regression ◽  
Multinomial Logit Regression ◽  
The Impact

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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