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 Safety Impacts of Push-Button and Countdown Timer on Nonmotorized Traffic at Intersections

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Bei Zhou ◽  
Arash M. Roshandeh ◽  
Shengrui Zhang
Keyword(s):  
Maximum Likelihood ◽  
Traffic Safety ◽  
Negative Impact ◽  
Unobserved Heterogeneity ◽  
Likelihood Method ◽  
Impact Factors ◽  
Random Parameters ◽  
Push Button ◽  
Left Turn ◽  
Crash Data

This paper applies the random parameters negative binominal model to investigate safety impacts of push-button and countdown timer on pedestrians and cyclists at urban intersections. To account for possible unobserved heterogeneity which could vary from one intersection to another, random parameters model is introduced. A simulation-based maximum likelihood method using Halton draws is applied to estimate the maximum likelihood of random parameters in the model. Dataset containing pedestrians’ and cyclists’ crash data of 1,001 intersections from Chicago is utilized to establish the statistical relationship between crash frequencies and potential impact factors. LIMDEP (Version 9.0) statistical package is utilized for modeling. The parameter estimation results indicate that existence of push-button and countdown timer could significantly reduce crash frequencies of pedestrians and cyclists at intersections. Increasing number of through traffic lanes, left turn lanes, and ratio of major direction AADT to minor direction AADT, tend to increase crash frequencies. Annual average daily left turn traffic has a negative impact on pedestrians’ safety, but its impact on cyclists’ crash frequency is statistically insignificant at 90% confidence level. The results of current study could provide important insights for nonmotorized traffic safety improvement projects in both planning and operational levels.

scholarly journals Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis

2020 ◽  
Vol 17 (8) ◽  
pp. 2768 ◽  
Author(s):  
Qiang Zeng ◽  
Wei Hao ◽  
Jaeyoung Lee ◽  
Feng Chen
Keyword(s):  
Real Time ◽  
Spatial Correlation ◽  
Logit Model ◽  
Weather Conditions ◽  
Crash Severity ◽  
Ordered Logit ◽  
Ordered Logit Model ◽  
Generalized Ordered Logit ◽  
Severity Levels ◽  
Generalized Ordered Logit Model

This study presents an empirical investigation of the impacts of real-time weather conditions on the freeway crash severity. A Bayesian spatial generalized ordered logit model was developed for modeling the crash severity using the hourly wind speed, air temperature, precipitation, visibility, and humidity, as well as other observed factors. A total of 1424 crash records from Kaiyang Freeway, China in 2014 and 2015 were collected for the investigation. The proposed model can simultaneously accommodate the ordered nature in severity levels and spatial correlation across adjacent crashes. Its strength is demonstrated by the existence of significant spatial correlation and its better model fit and more reasonable estimation results than the counterparts of a generalized ordered logit model. The estimation results show that an increase in the precipitation is associated with decreases in the probabilities of light and severe crashes, and an increase in the probability of medium crashes. Additionally, driver type, vehicle type, vehicle registered province, crash time, crash type, response time of emergency medical service, and horizontal curvature and vertical grade of the crash location, were also found to have significant effects on the crash severity. To alleviate the severity levels of crashes on rainy days, some engineering countermeasures are suggested, in addition to the implemented strategies.

Estimating Incident Duration Considering the Unobserved Heterogeneity of Risk Factors for Trucks Transporting HAZMAT on Expressways

2019 ◽  
Vol 2673 (2) ◽  
pp. 232-242 ◽  
Author(s):  
Jungyeol Hong ◽  
Reuben Tamakloe ◽  
Dongjoo Park ◽  
Yoonhyuk Choi
Keyword(s):  
Large Scale ◽  
Traffic Accidents ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Economic Losses ◽  
Random Parameters ◽  
Crash Data ◽  
Incident Duration ◽  
Fixed Model ◽  
Accident Response

Traffic accidents involving vehicles transporting hazardous materials (HAZMAT) on expressways not only delay traffic flow but can also cause large-scale casualties and socio-economic losses. Therefore, rapid response to and prevention of these accidents is important to minimize such loss. To ensure more efficient accident response, this study applied a random parameter hazard-based Weibull modeling approach to measure the relationship between crash characteristics and accident duration for trucks transporting HAZMAT. The study focuses on finding the key factors that have an impact on the accident duration of these vehicles as well as a statistical method to estimate the accident duration. The analysis is based on raw crash data from 2007 to 2017, obtained from the Korea Expressway Corporation, of crashes that involved HAZMAT trucks. The study found that crashes occurring during peak times of the day; crashes occurring on segments at the mainline, ramp, and roadways with a guardrail; and the number of vehicles involved in a crash, result in random parameters. In addition, the weather, season, crash severity, truck size, crash location, type of accident report, roadside features (e.g., guardrails), and status after a crash, can be used to explain the accident duration. The random parameters hazard-based model is found to have a better fit than a fixed model since it is able to capture the unobserved heterogeneity in the hazard function.

scholarly journals Modeling of single-vehicle crash severity at intersections based on full Bayesian model

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Zhenggan Cai ◽  
Xin Li ◽  
Long Chen ◽  
Xiaoyan Wu ◽  
Xu Yao
Keyword(s):  
Logit Model ◽  
Bayesian Model ◽  
Drunk Driving ◽  
Crash Severity ◽  
Seat Belts ◽  
Middle Aged ◽  
Elderly Drivers ◽  
Fatal Crash ◽  
Sample Data ◽  
Single Vehicle

In order to explore the factors that have a significant impact on the severity of single-vehicle crashes at intersections, we take 2940 crashes at intersections in Zibo City from 2017 to 2019 as samples. And then, we use the sample data to establish a Bayesian logit model. The modeling results show that the probability of fatal crash of male drivers is 0.53 times of that of female drivers. The probability of fatal crash of unused seat belts is 2.41 times of that of using seat belts. The probability of fatal crash of drunk driving is 3.75 times of that of non-drunk driving. The probability of fatal crash of elderly drivers is 1.47 times of that of middle-aged drivers. The probability of fatal crash in the night is 1.46 times of that in the morning peak.

scholarly journals Heterogeneidad de preferencias y experimentos de elección: Aplicación de un logit con parámetros aleatorios a la demanda de nueces

2011 ◽  
Vol 4 (8) ◽  
pp. 105
Author(s):  
Dena M. Camarena ◽  
Ana I. Sanjuán
Keyword(s):  
Logit Model ◽  
Choice Experiment ◽  
Mixed Logit ◽  
Stated Preferences ◽  
Attribute Level ◽  
Random Parameters ◽  
Random Parameters Logit ◽  
Independence Of Irrelevant Alternatives

Consumers’ stated preferences towards walnuts are studied by means of a choice experiment, with a double objective: first, to identify the main attributes searched by consumer at purchase and second, to analyse the chances for the introduction into the Spanish market of the Pecan variety. From this study, commercial guidelines may be derived, that helps distribution and import companies to commercialise this nut. A mixed or random parameters logit is estimated which relaxes the IIA property (independence of irrelevant alternatives) present in the logit model with fixed parameters. In a mixed logit, coefficients of each attribute/level vary randomly across consumers, reflecting the heterogeneity of individuals’ preferences. This model also allows estimate efficiently the parameters when each individual chooses several times, as in the present study.

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