Analyzing injury severity of rear-end crashes involving large trucks using a mixed logit model: A case study in North Carolina

2020 ◽  
pp. 1-14
Author(s):  
Pengfei Liu ◽  
Wei (David) Fan
Keyword(s):  
North Carolina ◽  
Logit Model ◽  
Injury Severity ◽  
Mixed Logit ◽  
Mixed Logit Model

Modeling head-on crash severity on NCDOT freeways: a mixed logit model approach

2019 ◽  
Vol 46 (4) ◽  
pp. 322-328 ◽  
Author(s):  
Pengfei Liu ◽  
Wei (David) Fan
Keyword(s):  
Logit Model ◽  
Injury Severity ◽  
Safety Information ◽  
Mixed Logit ◽  
Weather Condition ◽  
Mixed Logit Model ◽  
Young Drivers ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
Model Approach

This study employs a mixed logit model approach to evaluate contributing factors that significantly affect the severity of head-on crashes. The head-on crash data are collected from Highway Safety Information System (HSIS) from 2005 to 2013 in North Carolina. The effects that vehicle, driver, roadway, and environmental characteristics have on the injury severity of head-on crashes are examined. The results of this research demonstrate that adverse weather, young drivers, rural roadways, and pickups are found to be better modeled as random-parameters at specific injury severity levels, while others should remain fixed. Also, the model results indicate that driving under the influence of alcohol or drugs, grade or curve roadway configuration, old drivers, high speed limit, motorcycles will increase the injury severity of head-on crashes. Adverse weather condition, two-way divided road, traffic control, young drivers, and pickups will decrease the injury severity of head-on crashes.

A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model

2010 ◽  
Vol 42 (6) ◽  
pp. 1751-1758 ◽  
Author(s):  
Joon-Ki Kim ◽  
Gudmundur F. Ulfarsson ◽  
Venkataraman N. Shankar ◽  
Fred L. Mannering
Keyword(s):  
Logit Model ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Mixed Logit ◽  
Motor Vehicle Crashes ◽  
Mixed Logit Model ◽  
Vehicle Crashes ◽  
Pedestrian Injury

Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways

2014 ◽  
Vol 72 ◽  
pp. 105-115 ◽  
Author(s):  
Qiong Wu ◽  
Feng Chen ◽  
Guohui Zhang ◽  
Xiaoyue Cathy Liu ◽  
Hua Wang ◽  
...  
Keyword(s):  
Logit Model ◽  
Injury Severity ◽  
Mixed Logit ◽  
Mixed Logit Model ◽  
Vehicle Crashes ◽  
Model Based

scholarly journals Exploring the Effect of Bicycle Infrastructure on Car Usage: A Case Study in Huhhot, China

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Meiying Jian ◽  
Xiaojuan Li ◽  
Jinxin Cao ◽  
Zhenyu Liu
Keyword(s):  
Logit Model ◽  
Stated Preference ◽  
Mixed Logit ◽  
Random Coefficients ◽  
Decision Makers ◽  
Walking Distance ◽  
Mixed Logit Model ◽  
Rational Allocation ◽  
Bicycle Infrastructure

This study aimed to quantitatively investigate the effect of bicycle infrastructure on car usage. The mixed logit model with random coefficients was used to capture the differences in individuals’ preferences. Based on data from a stated preference survey conducted in Huhhot, China, the estimated results showed that the mixed logit model provides better fitting than the standard logit model. Considerable variations were found in individuals’ attitudes toward the use of cars and bicycles. Riding a bicycle is preferred by most individuals. Furthermore, based on the constraints for maintaining the effect on car usage equal, the equivalent change in parking fees for improvement in bicycle infrastructures was estimated. The results showed that the effect of a 100 m reduction in walking distance to bicycle stations on the probability of driving is the same as that of an approximately 2.00 yuan/h (US 0.30$/h) increase in the parking fees, and the effect of providing bike lanes is in line with additional parking fees of approximately 3.00 yuan/h (US 0.45$/h). The findings of this study can be an important reference for decision makers to consider improvements in bicycle systems and rational allocation of infrastructure investment and road resources.

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