scholarly journals Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

2021 ◽  
Vol 13 (18) ◽  
pp. 10086
Author(s):  
Thanapong Champahom ◽  
Sajjakaj Jomnonkwao ◽  
Chinnakrit Banyong ◽  
Watanya Nambulee ◽  
Ampol Karoonsoontawong ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Random Effect ◽  
Crash Severity ◽  
Spatial Correlations ◽  
Driver Training ◽  
Crash Frequency ◽  
Spatial Approach ◽  
Crash Reduction ◽  
Best Fit ◽  
Intra Class Correlation Coefficient

Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.

scholarly journals Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

2021 ◽  
Vol 13 (11) ◽  
pp. 6214
Author(s):  
Bumjoon Bae ◽  
Changju Lee ◽  
Tae-Young Pak ◽  
Sunghoon Lee
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Random Effect ◽  
Temporal Correlation ◽  
Negative Binomial Regression ◽  
Spatiotemporal Analysis ◽  
Parameter Estimates ◽  
Crash Frequency ◽  
Crash Frequency Modeling ◽  
Using Data

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.

Safety Impacts at Intersections on Curved Segments

2005 ◽  
Vol 1908 (1) ◽  
pp. 130-140 ◽  
Author(s):  
Peter T. Savolainen ◽  
Andrew P. Tarko
Keyword(s):  
High Speed ◽  
Negative Binomial ◽  
National Standards ◽  
Crash Severity ◽  
Department Of Transportation ◽  
Crash Frequency ◽  
Lighting Conditions ◽  
Fatal Crashes ◽  
Road Crash ◽  
Research Findings

Indiana geometric design policy, consistent with national standards, allows for the design of intersections on superelevated curves if other solutions are prohibitively expensive. Consequently, the Indiana Department of Transportation (DOT) has built a number of such intersections. Following a series of fatal crashes at one of these intersections, Indiana DOT made a decision to avoid designing intersections on segments with steep superelevation. This design restriction calls for expensive alternatives, such as realigning roads or adding grade separations. This research was done to determine whether superelevated intersections were more hazardous than similar intersections located on tangents and, if so, to determine what combination of factors made this true. The research focused on two-way stop-controlled intersections where the mainline was a high-speed four-lane divided highway located on a superelevated curve. An attempt was made to analyze as many factors as possible by using appropriate comparison techniques. Negative binomial models were developed to determine the statistical relationship between crash occurrence and intersection geometric characteristics, including curvature of the main road. Crash severity and the joint impact of curvature with weather and lighting conditions were examined by using binomial comparisons of proportions. Research findings show significant increases in crash frequency and severity at intersections located on superelevated curves.

scholarly journals Performance of Right-Turn Lane Designs at Intersections

2021 ◽  
Author(s):  
Satish Ukkusuri ◽  
Lu Ling ◽  
Tho V. Le ◽  
Wenbo Zhang
Keyword(s):  
Influencing Factors ◽  
Traffic Safety ◽  
Traffic Control ◽  
Negative Binomial ◽  
Random Effect ◽  
Research Literature ◽  
Crash Risk ◽  
Related Factors ◽  
Crash Frequency ◽  
The Impact

Right-turn lane (RTL) crashes are among the most key contributors to intersection crashes in the US. Different right turn lanes based on their design, traffic volume, and location have varying levels of crash risk. Therefore, engineers and researchers have been looking for alternative ways to improve the safety and operations for right-turn traffic. This study investigates the traffic safety performance of the RTL in Indiana state based on multi-sources, including official crash reports, official database, and field study. To understand the RTL crashes' influencing factors, we introduce a random effect negative binomial model and log-linear model to estimate the impact of influencing factors on the crash frequency and severity and adopt the robustness test to verify the reliability of estimations. In addition to the environmental factors, spatial and temporal factors, intersection, and RTL geometric factors, we propose build environment factors such as the RTL geometrics and intersection characteristics to address the endogeneity issues, which is rarely addressed in the accident-related research literature. Last, we develop a case study with the help of the Indiana Department of Transportation (INDOT). The empirical analyses indicate that RTL crash frequency and severity is mainly influenced by turn radius, traffic control, and other intersection related factors such as right-turn type and speed limit, channelized type, and AADT, acceleration lane and AADT. In particular, the effects of these factors are different among counties and right turn lane roadway types.

Combined Crash Frequency–Crash Severity Evaluation of Geometric Design Decisions

2015 ◽  
Vol 2521 (1) ◽  
pp. 54-63 ◽  
Author(s):  
M. Scott Shea ◽  
Thanh Q. Le ◽  
Richard J. Porter
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
High Sensitivity ◽  
Negative Binomial Regression ◽  
Crash Severity ◽  
Crash Frequency ◽  
Design Decisions ◽  
Crash Data ◽  
Safety Performance Function ◽  
Traffic Characteristics

This paper quantified the effects of freeway ramp spacing and auxiliary lane presence on crash frequency and crash severity. Crash frequencies were predicted with a safety performance function, and crash severities were estimated with what was termed a “severity distribution function.” The paper then demonstrated how to combine quantitative knowledge related to the effects of ramp spacing and auxiliary lane presence on both crash frequency and severity into a framework for assessing the overall crash cost for different ramp configurations. Geometric features, traffic characteristics, and crash data were collected for 404 freeway segments in California and Washington State. Negative binomial regression models and multinomial logit regression models were used to estimate the effects of ramp spacing and auxiliary lane presence on expected crash frequencies and crash severities, respectively. Results showed that expected multiple-vehicle crash frequency increased as ramp spacing decreased. Meanwhile, there was a decrease in the proportion of severe crashes (fatal, incapacitating injury) with a decrease in ramp spacing, even though the overall frequency of these severe crashes remained relatively unchanged. Providing an auxiliary lane was expected to decrease crash frequency, although this reduction appeared to be primarily in crashes that were less severe (possible injury and property damage only). The findings appeared to effectively capture the complex relationships between geometric designs and operations and the high sensitivity between speed and crash severity. The paper provided quantitative tools for making informed freeway and interchange design decisions where ramp spacing and auxiliary lanes were considerations.

Predicting expressway crash frequency using a random effect negative binomial model: A case study in China

2017 ◽  
Vol 98 ◽  
pp. 214-222 ◽  
Author(s):  
Zhuanglin Ma ◽  
Honglu Zhang ◽  
Steven I-Jy Chien ◽  
Jin Wang ◽  
Chunjiao Dong
Keyword(s):  
Negative Binomial ◽  
Random Effect ◽  
Negative Binomial Model ◽  
Binomial Model ◽  
Crash Frequency

scholarly journals Cardiovascular hospitalisation before and during the financial crisis among occupations in Taiwan

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Y Chang
Keyword(s):  
Financial Crisis ◽  
Sex Education ◽  
Negative Binomial ◽  
Random Effect ◽  
Unskilled Worker ◽  
Research Database ◽  
Skilled Workers ◽  
Occupational Groups ◽  
2008 Financial Crisis ◽  
The 2008 Financial Crisis

Abstract Background Cardiovascular diseases (CVD) were related to financial stress. Little was known about the effects of financial crisis on cardiovascular health by occupations. This study examined CVD hospitalisations before and during the 2008 financial crisis among five occupational groups in Taiwan. Methods Data were collected from the Taiwan Survey on Hypertension, Hyperglycemia and Hyperlipidaemia 2007, including 4,673 participants aged 20 and above, categorized into five types of occupations, i.e., professional & manager (PM), office clerk & administrative staff (OA), skilled work (SW), unskilled worker (UW) and non-worker (NW). We abstracted their CVD hospitalisation records in the three years before (September 2005 to August 2008) and during the 2008 financial crisis (September 2008 to August 2011) from the National Health Insurance Research Database. Using incidence rate ratios (IRRs), we compared CVD hospitalisation of the first, second, third year from September 2008 to the three-year average before September 2008 for five occupational groups. Random effect negative binomial models were performed to estimate IRRs. Results After adjusting for covariates including age, sex, education, smoking, alcohol drinking, exercise and body mass index, there was an increase of CVD hospitalisation incidence for NW in the first year of the financial crisis (IRR=1.46, 95% Confidence Interval [95% CI]=1.19-1.77); in the second year, SW had a raised risk of CVD hospitalisation (IRR= 2.71, 95% CI = 1.59-4.60). For all occupational groups, the incidence rates of CVD hospitalisation reached the peak in the third year (PM: IRR=2.68, 95% CI = 1.05-6.83; OA: IRR=2.70, 95% CI = 1.18-6.19; SW: IRR=5.13, 95% CI = 2.89-9.09; UW: IRR=2.12, 95% CI = 1.02-4.41; NW: IRR=1.85, 95% CI = 1.18-2.67). Conclusions CVD hospitalisation of all occupations were affected by the financial crisis; when non-workers were the early victims, skilled workers may be the most vulnerable in the 2008 financial crisis. Key messages This study investigated the effects of the 2008 financial crisis on cardiovascular disease hospitalization by five occupational types in Taiwan. All occupations, particularly skilled workers, were affected by the financial crisis.

610 Self-Reported Sleep and Circadian Characteristics Predict Future Substance Use: A Longitudinal Analysis from the NCANDA Study

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A240-A240
Author(s):  
Brant Hasler ◽  
Jessica Graves ◽  
Meredith Wallace ◽  
Stephanie Claudatos ◽  
Fiona Baker ◽  
...  
Keyword(s):  
Risk Factors ◽  
Substance Use ◽  
Sleep Duration ◽  
Daytime Sleepiness ◽  
Parental Education ◽  
Negative Binomial ◽  
Longitudinal Design ◽  
Random Effect ◽  
Cannabis Use ◽  
Increased Risk

Abstract Introduction Growing evidence indicates that sleep characteristics predict later substance use and related problems during adolescence and young adulthood. However, most prior studies have assessed a limited range of sleep characteristics, studied only a narrow age span, and included relatively few follow-up assessments. Here, we used multiple years of data from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study, which spans the adolescent period with an accelerated longitudinal design, to examine whether multiple sleep characteristics in any year predict substance use the following year. Methods The sample included 831 participants (423 females; age 12–21 years at baseline) from NCANDA. Sleep variables included the previous year’s circadian preference, sleep quality, daytime sleepiness, timing of midsleep (weekday and weekend), and sleep duration (weekday and weekend). Each sleep variable’s association with the subsequent year’s substance use (cannabis use or alcohol binge severity) across years 1–5 was tested separately using generalized linear mixed models (zero-inflated Negative Binomial for cannabis; ordinal for binge severity) with age, sex, race, visit, parental education, previous year’s substance use (yes/no) as covariates and subject as a random effect. Results With regard to cannabis use, greater eveningness and shorter weekday sleep duration predicted an increased risk for additional days of cannabis use the following year, while greater eveningness and later weekend midsleep predicted a greater likelihood of any cannabis use the following year. With regard to alcohol binge severity, greater eveningness, greater daytime sleepiness, and shorter sleep duration (weekday and weekend) all predicted an increased risk for more severe alcohol bingeing the following year. Post-hoc stratified analyses indicated that some of these associations may differ between high school-age and college-age participants. Conclusion Our findings extend prior work, indicating that eveningness and later sleep timing, as well as shorter sleep duration, especially on weekdays, are risk factors for future cannabis use and alcohol misuse. These results underscore a need for greater attention to sleep characteristics as potential risk factors for substance use in adolescents and young adults and may inform future areas of intervention. Support (if any) Grants from NIH: R01AA025626 (Hasler) and U01AA021690 (Clark) and UO1 AA021696 (Baker & Colrain)

scholarly journals GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 362
Author(s):  
Arshad Jamal ◽  
Tahir Mahmood ◽  
Muhamad Riaz ◽  
Hassan M. Al-Ahmadi
Keyword(s):  
Real Time ◽  
Control Charts ◽  
Negative Binomial ◽  
Highway Safety ◽  
Monitoring Methods ◽  
Count Data Models ◽  
Crash Data ◽  
Safety Surveillance ◽  
Study Results ◽  
Best Fit

Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

scholarly journals Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019 ◽  
Vol 11 (23) ◽  
pp. 6643 ◽  
Author(s):  
Lee ◽  
Guldmann ◽  
Choi
Keyword(s):  
Negative Binomial ◽  
Age Groups ◽  
Urban Environments ◽  
Vehicle Miles Traveled ◽  
Crash Frequency ◽  
Rural Environments ◽  
Lower Likelihood ◽  
Conditional Autoregressive ◽  
The Relationship ◽  
Binomial Models

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

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