Comparative analysis of Bayesian quantile regression models for pedestrian injury severity at signalized intersections

2019 ◽  
pp. 1-22 ◽  
Author(s):  
Xuecai Xu ◽  
Xiangjian Luo ◽  
Daiquan Xiao ◽  
S. C. Wong
Keyword(s):  
Comparative Analysis ◽  
Quantile Regression ◽  
Regression Models ◽  
Injury Severity ◽  
Signalized Intersections ◽  
Bayesian Quantile Regression ◽  
Pedestrian Injury

scholarly journals Comparison of Bayesian quantile and frequency-oriented regressions in studying the trend of discharge changes in several hydrometric stations of Gorganroud basin in Iran.

2021 ◽  
Author(s):  
Khalil Ghorbani ◽  
Meysam Salarijazi ◽  
Sedigheh Bararkhanpour ◽  
Laleh Rezaei Ghaleh
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Regression Models ◽  
Trend Detection ◽  
Data Series ◽  
Northern Iran ◽  
Bayesian Quantile Regression ◽  
Temperature And Precipitation ◽  
Quantile Regression Model ◽  
Discharge Rates

Climate change causes fluctuations in temperature and precipitation. As a result, it affects the discharge of rivers, the most important consequence of which is the tendency toward extreme events such as torrential rains and widespread droughts. River discharge is one of the most important climatic and hydrological parameters. Investigating the changes in this parameter is one of the main prerequisites in the management and proper use of water resources and rivers. Most trend detection studies are based on analyzing changes in the mean or middle of the data. They do not provide information on how changes occur in different data ranges. Therefore, to investigate parameter changes in a different range of the data series, various regression models were proposed. Frequentist quantile regression and Bayesian quantile regression models were used to estimate their trend and trend slope in different quantiles of discharge in different seasons of the year for Arazkouseh, Tamar, and Galikesh stations of Gorganroud basin in northern Iran with the statistical period of 1346–1396 (1966–2016). The results show that in most seasons of the year, high discharge rates for all 3 stations have decreased with a steep slope, and only in summer, Tamar and Galikesh stations have had an increasing trend, but low discharge rates have not changed significantly. Spatially, the discharge values at Arazkouseh station have a decreasing trend with a higher slope rate, and in terms of time, the most decreasing trend has been in spring. Comparing the models also shows that the Bayesian quantile regression model provides more accurate and reliable results than the frequency-oriented quantile regression model. In general, quantile regression models are useful for predicting and estimating extreme high and low discharge changes for better management to reduce flood and drought damage.

scholarly journals Development of a Partial Proportional Odds Model for Pedestrian Injury Severity at Intersections

2020 ◽  
Vol 32 (4) ◽  
pp. 559-571
Author(s):  
Xi Lu ◽  
Zhuanglin Ma ◽  
Steven I-Jy Chien ◽  
Ying Xiong
Keyword(s):  
Injury Severity ◽  
Signalized Intersections ◽  
Proportional Odds ◽  
Contributing Factors ◽  
Explanatory Variables ◽  
Large Size ◽  
Pedestrian Injury ◽  
Proportional Odds Assumption ◽  
Hit And Run ◽  
Ordered Response

Pedestrian injury in crashes at intersections often results from complex interaction among various factors. The factor identification is a critical task for understanding the causes and improving the pedestrian safety. A total of 2,614 crash records at signalized and non-signalized intersections were applied. A Partial Proportional Odds (PPO) model was developed to examine the factors influencing Pedestrian Injury Severity (PIS) because it can accommodate the ordered response nature of injury severity. An elasticity analysis was conducted to quantify the marginal effects of contributing factors on the likelihood of PIS. For signalized intersections, seven explanatory variables significantly affect the likelihood of PIS, in which five explanatory variables violate the Proportional Odds Assumption (POA). Local driver, truck, holiday, clear weather, and hit-and-run lead to higher likelihood of severer PIS. For non-signalized intersections, six explanatory variables were found significant to the PIS, in which three explanatory variables violate the POA. Young and adult drivers, senior pedestrian, bus/van, divided road, holiday, and darkness tend to increase the likelihood of severer PIS. The vehicles of large size and heavy weight (e.g. truck, bus/van) are significant factors to the PIS at both signalized and non-signalized intersections. The proposed PPO model has demonstrated its effectiveness in identifying the effects of contributing factors on the PIS.

Age and pedestrian injury severity in motor-vehicle crashes: A heteroskedastic logit analysis

2008 ◽  
Vol 40 (5) ◽  
pp. 1695-1702 ◽  
Author(s):  
Joon-Ki Kim ◽  
Gudmundur F. Ulfarsson ◽  
Venkataraman N. Shankar ◽  
Sungyop Kim
Keyword(s):  
Injury Severity ◽  
Motor Vehicle ◽  
Motor Vehicle Crashes ◽  
Logit Analysis ◽  
Vehicle Crashes ◽  
Pedestrian Injury

scholarly journals Forecasting Large Price Declines of the Nikkei Using the S&P 500 Implied Volatility

2016 ◽  
Vol 8 (1) ◽  
pp. 58
Author(s):  
Chikashi Tsuji
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Implied Volatility ◽  
Predictive Power ◽  
Downside Risk ◽  
Price Changes ◽  
Empirical Results ◽  
Left Tail

This paper empirically examines the forecast power of the previous day’s US implied volatility for large declines of the Nikkei by using several versions of quantile regression models. All our empirical results suggest that the previous day’s US S&P 500 implied volatility has forecast power for large price drops of the Nikkei 225 in Japan. Since we repeatedly and carefully tested the several left tail risks in price changes of the Nikkei and we also tested by using some different versions of quantile regression models, our evidence of the predictive power of the S&P 500 implied volatility for downside risk of the Nikkei is very robust.

scholarly journals Investigation into Interactions between Accident Consequences and Traffic Signs: A Bayesian Bivariate Tobit Quantile Regression Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xuecai Xu ◽  
Željko Šarić
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Close Relation ◽  
Warning Signs ◽  
Material Damage ◽  
Accident Rate ◽  
Traffic Signs ◽  
Accident Severity ◽  
Severity Levels ◽  
The Republic

This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia and explore the correlation between accident severity levels and heterogeneity attributed to unobserved factors. The data from 460 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, Bayesian bivariate Tobit quantile regression models were proposed, in which the bivariate framework addressed the correlation of residuals with Bayesian approach, while the Tobit quantile regression model accommodated the heterogeneity due to unobserved factors. By comparing the Bayesian bivariate Tobit quantile and mean regression models, the proposed quantile models showed priority to mean model. Results revealed that (1) low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death, or injury; (2) average speed limit exhibited a close relation with accident rate; and (3) the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injury.

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