scholarly journals Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Daiquan Xiao ◽  
Quan Yuan ◽  
Shengyang Kang ◽  
Xuecai Xu
Keyword(s):  
Random Effects ◽  
Traffic Safety ◽  
Injury Severity ◽  
Probit Model ◽  
Probit Analysis ◽  
Novice Drivers ◽  
Error Terms ◽  
Time Specific ◽  
Crash Types ◽  
Crash Injury Severity

This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers.

Recursive Bivariate Probit Analysis of Injury Severity and Non-Truck Improper Actions in Large Truck-Related Crashes on Florida Suburban Roads

2021 ◽  
pp. 036119812199714
Author(s):  
Zhenyu Wang ◽  
Abhijit Vasili ◽  
Runan Yang ◽  
Pei-Sung Lin
Keyword(s):  
Injury Severity ◽  
Probit Model ◽  
Probit Analysis ◽  
Truck Driver ◽  
Bivariate Probit ◽  
Contributing Factors ◽  
Recursive Bivariate Probit ◽  
Annual Average Daily Traffic ◽  
Study Results ◽  
Two Factors

This study investigated the hierarchical connection among injury severity, non-truck improper actions, and contributing factors in large-truck-involved crashes. Data for 4 years (2011–2014) of crashes that involved a large truck (≥ 10,000 lb) and a non-truck vehicle were collected from suburban roads in Florida, U.S. A recursive bivariate probit model was fitted with collected data to identify the cause-effect chain, including contributing factors influenced by improper actions, the effects of improper actions on injury severity, and contributing factors indirectly affecting injury severity in large-truck-related crashes. Study results indicate that non-truck vehicle improper actions, such as excessive speed, careless driving, failure to yield right-of-way, and others, significantly increase the likelihood of fatal and severe injury in large-truck crashes, and factors such as crash month, darkness, intersection-related, surface and shoulder width, truck parking, truck driver age, non-truck driver age, and non-truck alcohol/drug impaired indirectly influence injury severity through their impacts on non-truck improper actions. Two factors—truck right-turn and non-truck driver physical defects—affect injury severity and non-truck improper actions simultaneously. Other factors, including crash year, annual average daily traffic, speed limit, crash type, truck type, truck speed, truck alcohol/drug-impaired, and motorcycle involvement, directly contribute to injury severity in large-truck crashes and have no influence on non-truck improper actions.

scholarly journals Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Kairan Zhang ◽  
Mohamed Hassan
Keyword(s):  
Traffic Safety ◽  
Injury Severity ◽  
Random Parameter ◽  
Probit Model ◽  
Weather Conditions ◽  
Work Zone ◽  
Work Zones ◽  
Road Project ◽  
Key Indicators ◽  
The Impact

Egypt’s National Road Project is a large infrastructure project aiming to upgrade the existing network of 2500 kilometers as well as constructing new roads of 4000 kilometers to meet today’s need. Increasing highway work zones eventually direct the challenges for traffic safety and mobility. Realizing the need for mitigating the impact of such a challenging scenario, this paper aims to investigate and identify the factors of work zone rear-end crash severity. In this regard, a random parameter ordered probit model was applied to analyze data on the Egyptian long-term highway work zone projects during the period of 2010 to 2017. The factors of speeding and foggy weather conditions are found to be the key indicators for modeling the random parameters. Besides, during the weekend and at nighttime, there is a higher risk of rear-end crash in work zones, while heavy and passenger vehicles are at greater risk in this regard. It is anticipated that the findings of this study would facilitate transport agencies in developing effective measures to ensure safe mobility across work zones.

Driver Education Curriculum Outline

1998 ◽  
Vol 42 (20) ◽  
pp. 1396-1400 ◽  
Author(s):  
Lawrence P. Lonero ◽  
Kathryn M. Clinton ◽  
Douglas M. Black
Keyword(s):  
Program Development ◽  
Traffic Safety ◽  
Traditional Education ◽  
Education Curriculum ◽  
Driver Education ◽  
Novice Drivers ◽  
New Developments ◽  
Effective Role ◽  
Education Model ◽  
Crash Reduction

The purpose of the AAA Foundation for Traffic Safety outline project was to initiate program development which could lead to “reinventing” a more intensive, comprehensive, and effective driver education system, which could lead to crash reduction in novice drivers. The project reviewed knowledge in a number of areas — driver education effectiveness, novice drivers' needs, and methods of instruction and behavioral influence. The traditional education model used for driver education is inadequate, and fundamental changes in content, methods, and organization are needed. New developments and synergies among education methods, training technologies, organizational change, and demand for quality promise a new and more effective role for driver education in the 21st Century.

Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models

2017 ◽  
Vol 108 ◽  
pp. 172-180 ◽  
Author(s):  
Wen Cheng ◽  
Gurdiljot Singh Gill ◽  
Taha Sakrani ◽  
Mohan Dasu ◽  
Jiao Zhou
Keyword(s):  
Injury Severity ◽  
Weather Data ◽  
Crash Frequency ◽  
Crash Injury Severity

Program Participation Behavior of Nonindustrial Forest Landowners: A Probit Analysis

1996 ◽  
Vol 28 (2) ◽  
pp. 323-336 ◽  
Author(s):  
Venkatarao Nagubadi ◽  
Kevin T. McNamara ◽  
William L. Hoover ◽  
Walter L. Mills
Keyword(s):  
Probit Model ◽  
Probit Analysis ◽  
Program Participation ◽  
Willingness To Participate ◽  
Private Forest ◽  
Sources Of Information ◽  
Cost Share ◽  
Assistance Programs ◽  
Nonindustrial Private Forest ◽  
Nipf Landowners

AbstractThis study provides an analysis of nonindustrial private forest (NIPF) landowners' participation in forestry assistance programs. A probit model was used for data collected from a random sample of 329 Indiana landowners. The analysis revealed that total land owned, commercial reasons for ownership, government sources of information, and membership in forestry organizations influenced NIPF landowners' program participation. Age, fear of loss of property rights, and duration since the first wooded tract was acquired also influenced program participation. Location of landowners' residence on their wooded land and landowners' knowledge of and willingness to participate in a conservation easement influenced the participation in cost-share programs.

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