scholarly journals Investigating hazardous factors affecting freeway crash injury severity with real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors

2021 ◽  
Author(s):  
Xuan Zhang ◽  
Huiying Wen ◽  
Toshiyuki Yamamoto ◽  
Qiang Zeng
Keyword(s):  
Real Time ◽  
Logit Model ◽  
Injury Severity ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Weather Data ◽  
Factors Affecting ◽  
Conditional Autoregressive ◽  
Crash Injury Severity

scholarly journals Factors affecting packed and unpacked fluid milk consumption

2009 ◽  
Vol 55 (No. 11) ◽  
pp. 557-563
Author(s):  
O. Kilic ◽  
C. Akbay ◽  
G. Yildiz Tiryaki
Keyword(s):  
Logit Model ◽  
Household Head ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Milk Consumption ◽  
Consumer Survey ◽  
Factors Affecting ◽  
Female Household Head ◽  
Consumer Survey Data ◽  
Fluid Milk

This article identifies consumer characteristics associated with preferences toward fluid milk alternatives. Using consumer survey data from Samsun province of Turkey and Multinomial Logit model, unpacked and packed fluid milk preferences were analyzed. Based on the results, 14.1% of respondents consumed only unpacked fluid milk, 58.2% consumed only packed fluid milk and 27.7% of respondents consumed both unpacked and packed fluid milk at least once a weak. Multinomial Logit model results indicated that better educated household head, higher income households, younger and female household head and people who agree with “unpacked milk is not healthy” consume more packed fluid milk than do others. Moreover, consumers who agree with statement “price of packed milk is expensive compare to unpacked milk” were less likely to consume packed fluid milk than do others.

scholarly journals Investigation of Contributing Factors to Traffic Crashes and Violations: A Random Parameter Multinomial Logit Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jaeyoung Lee ◽  
Xing Li ◽  
Suyi Mao ◽  
Wen Fu
Keyword(s):  
Logit Model ◽  
Random Parameter ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Contributing Factors ◽  
Traffic Crashes ◽  
Factors Affecting ◽  
Effective Strategies ◽  
Traffic Violations ◽  
Afternoon Peak

This study investigates contributing factors to traffic violations by seriousness. The traffic violations are divided into four categories by seriousness (unintentional violation, minor violation, serious violation, and crash with violation). The results of the random parameter multinomial logit model indicate that various factors potentially affect the severity of traffic violations. The key findings include the following: (1) female drivers are more likely to commit minor violations; (2) drivers from an area with a longer travel time to work and a higher proportion of driving to work are more likely to have minor violations and serious violations, while those from the high-income area are less likely; (3) drivers are more likely to be associated with a more minor infraction during the afternoon peak (4 p.m.–6 p.m.). The results from this study are expected to be beneficial for policymakers and traffic police to comprehend the factors affecting violations and implement effective strategies to minimize the number and seriousness of traffic violations.

scholarly journals Analyzing Severity of Vehicle Crashes at Highway-Rail Grade Crossings: Multinomial Logit Modeling

2015 ◽  
Author(s):  
Wei (David) Fan ◽  
Martin R. Kane ◽  
Elias Haile
Keyword(s):  
Logit Model ◽  
High Speed ◽  
Injury Severity ◽  
Weather Conditions ◽  
Multinomial Logit Model ◽  
The United States ◽  
Multinomial Logit ◽  
Vehicle Crashes ◽  
Grade Crossings ◽  
The Impact

The purpose of this paper is to develop a nominal response multinomial logit model (MNLM) to identify factors that are important in making an injury severity difference and to explore the impact of such explanatory variables on three different severity levels of vehicle-related crashes at highway-rail grade crossings (HRGCs) in the United States. Vehicle-rail and pedestrian-rail crash data on USDOT highway-rail crossing inventory and public crossing sites from 2005 to 2012 are used in this study. A multinomial logit model is developed using SAS PROC LOGISTICS procedure and marginal effects are also calculated. The MNLM results indicate that when rail equipment with high speed struck a vehicle, the chance of a fatality resulting increased. The study also reveals that vehicle pick-up trucks, concrete, and rubber surfaces were more likely to be involved in more severe crashes. On the other hand, truck-trailer vehicles in snow and foggy weather conditions, development area types (residential, commercial, industrial, and institutional), and higher daily traffic volumes were more likely to be involved in less severe crashes. Educating and equipping drivers with good driving habits and short-term law enforcement actions, can potentially minimize the chance of severe vehicle crashes at HRGCs.

scholarly journals Exploratory multinomial logit model–based driver injury severity analyses for teenage and adult drivers in intersection-related crashes

2015 ◽  
Vol 17 (4) ◽  
pp. 413-422 ◽  
Author(s):  
Qiong Wu ◽  
Guohui Zhang ◽  
Yusheng Ci ◽  
Lina Wu ◽  
Rafiqul A. Tarefder ◽  
...  
Keyword(s):  
Logit Model ◽  
Injury Severity ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Model Based

A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes

2015 ◽  
Vol 80 ◽  
pp. 76-88 ◽  
Author(s):  
Cong Chen ◽  
Guohui Zhang ◽  
Rafiqul Tarefder ◽  
Jianming Ma ◽  
Heng Wei ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Logit Model ◽  
Injury Severity ◽  
Hybrid Approach ◽  
Multinomial Logit Model ◽  
Multinomial Logit

Analyzing influencing factors of crash injury severity incorporating FARS data

2021 ◽  
pp. 1-11
Author(s):  
Zhijian Zhang ◽  
Yubin Jiang ◽  
Zhijun Chen ◽  
Yubing Xiong
Keyword(s):  
Bayesian Network ◽  
Influencing Factors ◽  
Logit Model ◽  
Prediction Accuracy ◽  
Traffic Accident ◽  
Injury Severity ◽  
Drunk Driving ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Fatal Crash

The purpose of this study is to deeply analyze the influencing factors of drivers’ traffic accident casualties and reduce the occurrence of casualties. From the FARS database of the National Highway Safety Administration (NHTSA), 93248 traffic accident data were extracted as analysis samples. On this basis, the Bayesian network and multinomial logit model are established. The constructed model was tested from the perspective of model prediction accuracy and variables importance. Bayesian networks are used to analyze the interrelationships among influencing factors, and multinomial logit models are used to compare and evaluate the impact of different variables on the injury severity under different circumstances. The results show that: the prediction accuracy of the Bayesian network model and multinomial logit model is 64.57% and 65.97%, respectively. The Bayesian network reference analyses indicate that injury severity is affected by the crash factors, and there are various interactions between the various factors. The multinomial logit model analyses indicate that the factors including drivers’ age, female driver, rural roads, drunk driving, drug driving, crash time, side collision accident, etc. could significantly increase injury severity. Airbags are more effective in reducing fatal crash than injury crash. The probability of accidents caused by drug driving drivers is greater than drunk driving, drunk driving drivers are 1.79 times and 2.34 times more likely to suffer an injury severity and fatal injury severity in crashes as compared to a no injury severity, respectively, and drug driving is 1.93 times and 2.6 times, respectively. Seat belts may avoid 92.2% of fatalities. Roadside guardrail reduces the incidence of fatal crash better than injury crash. Fatal injuries severity and injury severity are 1.124 times and 1.141 times more likely to occur during the 0 : 00 to 6 : 00 as compared to no injuries, respectively, etc.

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