Elderly Pedestrian Fatal Crash-Related Contributing Factors: Applying Empirical Bayes Geometric Mean Method

2019 ◽  
Vol 2673 (8) ◽  
pp. 254-263
Author(s):  
Subasish Das ◽  
Apoorba Bibeka ◽  
Xiaoduan Sun ◽  
Hongmin “Tracy” Zhou ◽  
Mohammad Jalayer
Keyword(s):  
Empirical Bayes ◽  
Reporting System ◽  
Geometric Mean ◽  
The United States ◽  
Contributing Factors ◽  
Fatal Crash ◽  
Road Users ◽  
Fatal Crashes ◽  
Vulnerable Road Users ◽  
Pedestrian Crashes

Recent statistics show that around 20% of all pedestrian fatalities (1,002 out of 5,376) in 2015 were pedestrians over the age of 65. There is a need to identify issues associated with elderly pedestrian crashes to develop effective countermeasures. This study aimed to determine the key associations between contributing factors of elderly pedestrian crashes. The authors analyzed three years (2014 to 2016) of elderly pedestrian fatal crashes from the Fatality Analysis Reporting System in the United States by using empirical Bayes (EB) data mining. The findings of this study revealed several association patterns with high crash potential for elderly pedestrians that include backing vehicle-related crashes for female pedestrians (especially those aged 79 and above), segment-related crashes at night for 65 to 69 year-old male pedestrians, crossing an expressway at night for male pedestrians, especially the 65 to 69 year group, failure to yield while crossing at intersections, and crashes occurring in the dark with poor street lighting. The findings of this study could help authorities determine effective countermeasures for this group of vulnerable road users.

Strategies to Promote the Safety of Vulnerable Road Users in Developing and Emerging Countries: South African Experience

2003 ◽  
Vol 1846 (1) ◽  
pp. 26-30 ◽  
Author(s):  
Hubrecht Ribbens
Keyword(s):  
South Africa ◽  
Middle East ◽  
The United States ◽  
Emerging Countries ◽  
Sub Saharan Africa ◽  
Successful Implementation ◽  
Road Traffic Safety ◽  
Road Users ◽  
Vulnerable Road Users ◽  
The Caribbean

Road casualties are discussed from a worldwide perspective. More than 80% of annual traffic casualties occur in developing and emerging countries in Asia, Latin America, the Caribbean, sub-Saharan Africa, and the Middle East. Vulnerable road users such as pedestrians and bicyclists are a major road safety problem in these countries. In Asia, Africa, the Caribbean, and the Middle East, more than 40% of annual road fatalities involve pedestrians compared with less than 20% in Europe and the United States. The focus of this study is South Africa’s strategy to promote the safety of vulnerable road users. The extent of casualties among vulnerable road users and contributing factors are highlighted. Over the last decade, pedestrian fatalities have gradually and steadily declined in South Africa. This study describes the various policies, strategies, and action plans developed and implemented by different government levels in South Africa to promote road traffic safety, particularly the safety of vulnerable road users such as pedestrians and bicyclists. Barriers to successful implementation are also pointed out. Apart from applying a holistic approach by involving all relevant disciplines, a coordinated and sustained effort of all government levels was encouraged. Joint-venture funding projects among different government levels was emphasized to improve hazardous pedestrian locations. The role of the private sector in South Africa to promote pedestrian safety is also discussed. Practical guidelines are presented for developing and emerging countries to promote the safety of vulnerable road users.

scholarly journals Precipitation and Fatal Motor Vehicle Crashes: Continental Analysis with High-Resolution Radar Data

2019 ◽  
Vol 100 (8) ◽  
pp. 1453-1461 ◽  
Author(s):  
Scott E. Stevens ◽  
Carl J. Schreck ◽  
Shubhayu Saha ◽  
Jesse E. Bell ◽  
Kenneth E. Kunkel
Keyword(s):  
United States ◽  
High Resolution ◽  
Motor Vehicle ◽  
Radar Data ◽  
The United States ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Fatal Crash ◽  
Fatal Crashes ◽  
High Resolution Radar

AbstractMotor vehicle crashes remain a leading cause of accidental death in the United States, and weather is frequently cited as a contributing factor in fatal crashes. Previous studies have investigated the link between these crashes and precipitation typically using station-based observations that, while providing a good estimate of the prevailing conditions on a given day or hour, often fail to capture the conditions present at the actual time and location of a crash. Using a multiyear, high-resolution radar reanalysis and information on 125,012 fatal crashes spanning the entire continental United States over a 6-yr period, we find that the overall risk of a fatal crash increases by approximately 34% during active precipitation. The risk is significant in all regions of the continental United States, and it is highest during the morning rush hour and during the winter months.

“Eyes on the Street”: The Impact of Tennessee's Emergency Cellular Telephone Program on Alcohol-Related Fatal Crashes

1999 ◽  
Vol 45 (4) ◽  
pp. 453-466 ◽  
Author(s):  
Stewart J. D'Alessio ◽  
Lisa Stolzenberg ◽  
W. Clinton Terry
Keyword(s):  
Time Series ◽  
Reporting System ◽  
Highway Safety ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Cellular Telephone ◽  
Fatal Crash ◽  
Fatal Crashes ◽  
The Impact ◽  
Percent Decline

Using longitudinal data drawn from Tennessee's Fatality Analysis Reporting System (FARS) and a multiple time-series research design, the authors assessed whether an emergency cellular telephone program, established on April 1, 1995, reduced alcohol-related fatal crashes. Maximum-likelihood results revealed a 2.5 percent decline in the alcohol-related fatal crash rate on roads serviced by the program. No significant change in the monthly percentage of fatal crashes attributed to drunk drivers was observed on roads where the program was not implemented. Emergency cellular telephone programs show promise as an effective and relatively inexpensive means for improving highway safety.

Long-Term Effect of Universal Helmet Law Changes on Motorcyclist Fatal Crashes: Comparison Group and Empirical Bayes Approaches

2017 ◽  
Vol 2637 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Jaeyoung Lee ◽  
Mohamed Abdel-Aty ◽  
Jung-Han Wang ◽  
Chanyoung Lee
Keyword(s):  
Empirical Bayes ◽  
Brain Injuries ◽  
Comparison Group ◽  
The United States ◽  
Regression To The Mean ◽  
Fatal Crashes ◽  
Before And After ◽  
The Law ◽  
1960S And 1970S

A motorcyclist helmet is considered important safety equipment because it prevents or minimizes head and brain injuries, which are often fatal. Hence, in the 1960s and 1970s, most of the states in the United States enacted the universal helmet law (UHL) requiring all motorcyclists to wear helmets. Many researchers have examined the effect of the helmet law changes by using before-and-after studies and found that repealing the law had a negative effect on motorcyclists. In this study, the authors have attempted to explore the long-term impacts of repeal and reinstatement of the UHL by using 13 to 16 years of data. A before-and-after study with a comparison group and empirical Bayes methods was adopted to account for the passage of time and its effect on other factors such as exposure, maturation, trend, and regression-to-the-mean bias. A range of safety performance functions was developed on the basis of counties and parishes, and the expected fatal motorcycle crashes were calculated. The results showed that the UHL repeal still had significant effects on motorcycle fatal crash counts even 7 to 12 years after the repeal of the law. The crash modification factors showed that the UHL repeal increased the number of motorcycle fatal crashes by 15% to 41%, whereas reinstatement of the UHL decreased it by 21% to 27%. It is expected that the results from this study could be helpful for state policy makers to clearly understand the effects of the UHL on reducing motorcycle fatal crashes.

Generalized Linear Model for Automobile Fatality Rate Prediction in R

2020 ◽  
pp. 137-161
Author(s):  
Gao Niu ◽  
Alan Olinsky
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Descriptive Analysis ◽  
Real Data ◽  
The United States ◽  
Contributing Factors ◽  
Statistical Software ◽  
Validation Metrics ◽  
Fatal Crash ◽  
Accident Data

This chapter demonstrates the descriptive and statistical modeling function in R. The automobile fatal accident data of the United States is extracted from the Fatality Analysis Reporting System (FARS). The model will be used to understand significant contributing factors of automobile accident death when a fatal crash happens. First, descriptive analysis is performed by basic R functions and packages. Then, generalized linear model (GLM) with logit link function is explored and constructed. Finally, multiple validation metrics are introduced and calculated to ensure the reasonability and accuracy of the predictions. The focus of this chapter is to demonstrate the power and flexibility of the most popular Open Source Statistical Software (OSSS) through a real data analysis.

Identifying Fatality Risk Factors for the Commercial Vehicle Driver Population

2019 ◽  
Vol 2673 (9) ◽  
pp. 297-310 ◽  
Author(s):  
Mouyid Islam ◽  
Seckin Ozkul
Keyword(s):  
Risk Factors ◽  
Age Groups ◽  
Critical Issue ◽  
Medical Attention ◽  
Heavy Vehicles ◽  
Contributing Factors ◽  
Fatal Crash ◽  
Fatal Crashes ◽  
System Data ◽  
The U.S

Commercial/large-truck fatal crash involvement by drivers of different age groups is a critical issue for the trucking industry. Escalating safety concerns related to these heavy vehicles serving the freight economy in the U.S. have an impact national freight reliability and economic growth. This study identifies major contributing factors leading to large-truck fatal crashes for four age groups of driver: <30, 30–49, 50–65, and 65+. The analysis in this study is based on five years (2012–2016) of Fatality Analysis Reporting System data and provides an overall picture of risk factors in large-truck fatal crashes. In total, 30 variables were found to be significant in the logit models, indicating varying risks associated with large-truck drivers of these four age groups. Model results indicate different risk factors associated with driver characteristics, spatial and temporal characteristics, vehicle and vehicle maneuvering characteristics, and environmental conditions at the time of the crashes. Identifying the risk factors for different age groups of drivers is important so proper countermeasures can be implemented from the perspective of human factors (e.g., safe speed choice, fatigue), roadway engineering (e.g., design of roadside barriers, radius of ramps), enforcement (e.g., presence of law enforcement personnel at critical locations), and emergency medical attention in remote areas. Considering the aging of the truck driver population in the U.S. and around the world, the findings of this study are vital to understand better the importance of safety in relation to large-truck fatal crashes.

Midblock Pedestrian Crash Predictions in a Systemic, Risk-Based Pedestrian Safety Process

2019 ◽  
Vol 2673 (11) ◽  
pp. 420-432 ◽  
Author(s):  
Wesley Kumfer ◽  
Libby Thomas ◽  
Laura Sandt ◽  
Bo Lan
Keyword(s):  
Systemic Risk ◽  
Management Practices ◽  
Safety Management ◽  
Pedestrian Safety ◽  
The United States ◽  
Motor Vehicles ◽  
Contributing Factors ◽  
Data Types ◽  
Level Data ◽  
Pedestrian Crashes

Although pedestrian fatalities and injuries in the United States decreased for decades at a rate similar to vehicule occupant fatalities, recent years have seen substantial increases in the pedestrian fatality counts and rate. Most concerning is that the growth in pedestrian fatalities seems to be outstripping any gains in safety. There may be many contributing factors to these increases, including changes in population dynamics, vehicular design, and travel trends, but under more traditional, crash-focused roadway safety management practices, systemic risk patterns are difficult to discern and address. Moreover, locations of risk for pedestrians may be overlooked because important, network-level data types are not collected or analyzed, and pedestrian crashes are often relatively infrequent at specific locations. This paper presents the results of efforts to develop the data profile and analysis methods for a risk-based, systemic pedestrian safety approach. Using 8 years of segment data from the entire street network of the city of Seattle, the research team developed safety performance functions for two types of collision between motor vehicles and pedestrians. These predictive models were used, in conjunction with identified risk factors and countermeasures effectiveness data, to develop a systemic screening tool to identify sites that may benefit from treatment. The end goal of this research is a framework that allows practitioners to identify and prioritize locations within a jurisdiction that are risky for pedestrians and to identify and implement effective, appropriate treatments at many such locations.

Evaluation of Protected Left-Turn Phasing and Leading Pedestrian Intervals Effects on Pedestrian Safety

2021 ◽  
pp. 036119812110255
Author(s):  
Elissa Goughnour ◽  
Daniel Carter ◽  
Craig Lyon ◽  
Bhagwant Persaud ◽  
Bo Lan ◽  
...  
Keyword(s):  
New York ◽  
Empirical Bayes ◽  
Pedestrian Safety ◽  
The United States ◽  
Public Health Issue ◽  
Crash Frequency ◽  
Left Turn ◽  
Crash Modification Factors ◽  
Pedestrian Crashes ◽  
Benefit Cost

Pedestrian safety is an important public health issue for the United States, with pedestrian fatalities representing approximately 16% of all traffic-related fatalities in 2016. Nationwide, transportation agencies are increasing their efforts to implement engineering-based improvements that increase pedestrian safety. These agencies need statistically rigorous crash modification factors (CMFs) to demonstrate the safety effectiveness of such countermeasures, and to apply in benefit–cost analyses to justify their implementation. This study focused on developing CMFs for two countermeasures that show promise for improving pedestrian safety: protected or protected/permissive left-turn phasing, and leading pedestrian intervals (LPIs). Data were acquired from four North American cities that had installed one or both of the countermeasures of interest: Chicago, IL; New York City, NY; Charlotte, NC; and Toronto, ON. The empirical Bayes before–after study design was applied to estimate the change in expected crash frequency for crashes following treatment. The protected left-turn phasing evaluation showed a benefit in reducing vehicle–vehicle injury crashes, but did not produce statistically significant results for vehicle–pedestrian crashes. For those crashes a disaggregate analysis did reveal that this treatment could be especially beneficial where pedestrian volumes exceed 5,500 per day. The LPI evaluation showed a statistically significant reduction in vehicle–pedestrian crashes with an estimated CMF of 0.87.

Neighbourhood Influences on Vehicle-Pedestrian Crash Severity

2019 ◽  
pp. 102-121
Keyword(s):  
High Risk ◽  
Socioeconomic Factors ◽  
Crash Severity ◽  
Relative Importance ◽  
Contributing Factors ◽  
Counter Measures ◽  
Road Users ◽  
Pedestrian Crashes ◽  
Crash Modelling ◽  
Partial Dependence

Socioeconomic factors are known to be contributing factors to vehicle-pedestrian crashes. Although several studies have examined the socioeconomic factors related to the locations of crashes, few studies have considered the socioeconomic factors of the neighbourhoods where road users live in vehicle-pedestrian crash modelling. In vehicle-pedestrian crashes in the Melbourne metropolitan area, 20% of pedestrians, 11% of drivers, and only 6% of both drivers and pedestrians had the same postcode for the crash and residency locations. Therefore, an examination of the influence of socioeconomic factors of their neighbourhoods, and their relative importance will contribute to advancing knowledge in the field, as very limited research has been conducted on the influence of socioeconomic factors of both the neighbourhoods where crashes occur and where pedestrians live. In this chapter, neighbourhood factors associated with road users' residents and location of crash are investigated using BDT model. Furthermore, partial dependence plots are applied to illustrate the interactions between these factors. The authors found that socioeconomic factors account for 60% of the 20 top contributing factors to vehicle-pedestrian crashes. This research reveals that socioeconomic factors of the neighbourhoods where road users live and where crashes occur are important in determining the severity of crashes, with the former having a greater influence. Hence, road safety counter-measures, especially those focussing on road users, should be targeted at these high-risk neighbourhoods.

Wreck on the Highway: Intersectionality of Driver Culpability, Tetrahydrocannabinols, Other Intoxicants and Fatalities in Washington State

2019 ◽  
Vol 2673 (11) ◽  
pp. 127-140 ◽  
Author(s):  
Youngki Woo ◽  
Dale W. Willits ◽  
Mary K. Stohr ◽  
Craig Hemmens ◽  
Staci Hoff
Keyword(s):  
Risky Behavior ◽  
Reporting System ◽  
Driver Behavior ◽  
Driving Behavior ◽  
Washington State ◽  
Additional Research ◽  
Driver Safety ◽  
Fatal Crash ◽  
Fatal Crashes ◽  
Logistic Regressions

Given the legalization of recreational cannabis in 2012 in Washington State and recent mixed results regarding the effects of cannabis on driver safety, the paper examines the link between delta-9-tetrahydrocannabinols (THC) and driver behavior, including speeding and driver errors which may have contributed to a particular fatal crash. The current study utilized data from the Washington State Fatality Analysis Reporting System Analytical File (WA FARS) from 2008 to 2016. A series of logistic regressions were employed to compare THC-positive and -negative drivers, as well as drivers who tested positive for other intoxicants. The results of the study were mixed; it was found that delta-9-THC positively predicted speeding, but not other driver errors. Interestingly, carboxy-THC, a non-psychoactive chemical which can be detected for a longer period of time, was a significant predictor of both speeding and driver errors. This research further demonstrates that cannabis is predictive of risky behavior by drivers in fatal crashes, though it is not nearly as strong a predictor as alcohol. Additional research is needed to understand better why carboxy-THC is a stronger and more robust predictor of poor driving behavior than delta-9-THC.

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