Generalized Ordered Logit Model Showing Effects of Constituency and Descriptive Representation on Reputations for Women’s Advocacy

2021 ◽  
pp. 251-252
Author(s):  
Qiang Zeng ◽  
Wei Hao ◽  
Jaeyoung Lee ◽  
Feng Chen

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.


2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878162 ◽  
Author(s):  
Tao Wang ◽  
Jun Chen ◽  
Chen Wang ◽  
Xiaofei Ye

A thorough crash modeling effort was made to examine e-bicyclists’ injury severity, using a generalized ordered logit model, which can capture the ordinal nature of injury severity and allow for heterogeneity across observations. A number of factors associated with injury severity of e-bicyclists are identified, including older e-bicyclists, heavy truck involved, e-bicyclist at fault, e-bicyclist turn left, e-bicyclist cross the road, driver turn right, industrial area, weekday, and tree separation. Safety countermeasures and interventions are thus proposed based on the modeling results, including developing educational programs for specific age groups (e.g. older e-bicyclists, female e-bicyclists, and inexperienced drivers), launching safety campaigns, improving geometric design and traffic control in low-developed area, curve roads, and signalized intersections. Moreover, some interesting research topics are also suggested, such as examining head-on e-bike crash mechanism, crash mechanism between e-bicyclists and heavy trucks and motorcycles, and safety effects of different separation treatments on e-bicyclists’ injury severity from kinetic and kinematic perspectives.


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