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.

Systematic Safety Evaluation of Diverging Diamond Interchanges Based on Nationwide Implementation Data

2021 ◽  
pp. 036119812110049
Author(s):  
Ahmed Abdelrahman ◽  
Mohamed Abdel-Aty ◽  
Jinghui Yuan ◽  
Ma’en M. A. Al-Omari
Keyword(s):  
Empirical Bayes ◽  
Safety Evaluation ◽  
Safety Performance ◽  
Cross Sectional ◽  
Crash Frequency ◽  
Left Turn ◽  
Crash Modification Factors ◽  
Diamond Interchanges ◽  
Implementation Data ◽  
Sectional Analysis

Diverging diamond interchanges (DDIs) are designed as an alternative to the conventional diamond interchanges to enhance operational and safety performance. As the popularity of the DDI is increasing and more DDIs are being constructed and proposed, the need has arisen to measure the actual safety benefits of DDIs as compared with the traditional diamond interchanges. This study evaluates the safety of DDIs using three methods: before–after study with comparison group, Empirical Bayes before–after method, and cross-sectional analysis. This study collected a nationwide sample of 80 DDIs in 24 states. The estimated crash modification factors indicated that converting conventional diamond interchange to DDIs could significantly decrease the total, fatal-and-injury, rear-end, and angle/left-turn crashes by 14%, 44%, 11%, and 55%, respectively. Moreover, the developed safety performance functions implied that a longer distance between crossovers/ramp terminals and a lower speed limit on freeway exit ramps are significantly associated with lower crash frequency at diamond interchanges. This study contributes to the existing literature using a relatively large representative sample size, which provides more reliable evaluation results. In addition, this study also explored the effects of different traffic and geometric characteristics on the safety performance of DDIs.

Empirical Bayes Before-After Study to Develop Crash Modification Factors and Functions for High Friction Surface Treatments on Curves and Ramps

2020 ◽  
Vol 2674 (12) ◽  
pp. 505-514 ◽  
Author(s):  
Craig Lyon ◽  
Bhagwant Persaud ◽  
David Merritt ◽  
Joseph Cheung
Keyword(s):  
Friction Surface ◽  
Empirical Bayes ◽  
Crash Frequency ◽  
Run Off ◽  
Innovative Methodology ◽  
Crash Modification Factors ◽  
Using Data ◽  
Benefit Cost ◽  
Crash Types ◽  
Disaggregate Analysis

The intent of the study was to fill a knowledge void by developing high quality crash modification factors (CMFs) and benefit/cost (B/C) ratios for high friction surface treatment (HFST). The state-of-the-art empirical Bayes (EB) before-after methodology was applied to evaluate the effects of this treatment on crashes of various types using data from West Virginia (curve sites), Pennsylvania (curve sites), Kentucky (curve and ramp sites), and Arkansas (ramp sites). The results for curve sites generally indicate substantial and highly significant safety benefits. This is especially so for the primary crash types targeted by HFST programs: run-off-road, wet road, and head-on side-swipe opposite direction crashes (HOSSOD). The results for ramp sites were inconsistent, with substantial benefits for all crashes and injury crashes for Kentucky, negligible effects for these crashes in Arkansas, and substantial and highly significant reductions in wet weather crashes in both states. A disaggregate analysis of the CMF results for curve sites indicated a logical and consistent relationship between CMFs and three variables: friction improvement, traffic volume, and expected crash frequency before treatment. These variables, and an innovative methodology, were used in developing crash modification functions (CMFunctions) that can be applied to determine where, and under what conditions, the treatment can be used most effectively. Such functions are typically not provided for the vast majority of treatments for which CMFs are available, so, in itself, developing them is a significant contribution of this research.

Development of Crash Modification Factors for Uncontrolled Pedestrian Crossing Treatments

2017 ◽  
Vol 2636 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Charlie Zegeer ◽  
Craig Lyon ◽  
Raghavan Srinivasan ◽  
Bhagwant Persaud ◽  
Bo Lan ◽  
...  
Keyword(s):  
The United States ◽  
Crash Risk ◽  
Limited Sample ◽  
Cross Sectional ◽  
Empirical Bayesian ◽  
Treatment Type ◽  
Crash Modification Factors ◽  
Pedestrian Crashes ◽  
Pedestrian Crossing ◽  
Crash Types

The objective of this study was to develop crash modification factors for four treatment types: rectangular rapid-flashing beacon (RRFB), pedestrian hybrid beacon (PHB), pedestrian refuge island (RI), and advance yield or stop markings and signs (AS). From 14 cities throughout the United States, 975 treatment and comparison sites were selected. Most of the treatment sites were selected at intersections on urban, multilane streets, because these locations present a high risk for pedestrian crashes and are where countermeasures typically are needed most. For each treatment site, relevant data were collected on the treatment characteristics, traffic, geometric, and roadway variables, and the pedestrian crashes and other crash types that occurred at each site. Cross-sectional regression models and before–after empirical Bayesian analysis techniques were used to determine the crash effects of each treatment type. All four of the treatment types were found to be associated with reductions in pedestrian crash risk, compared with the reductions at untreated sites. PHBs were associated with the greatest reduction of pedestrian crash risk (55% reduction), followed by RRFBs (47% reduction), RIs (32% reduction), and AS (25% reduction). The results for RRFBs had their basis in a limited sample and must be used with caution.

Insights from Integrated Geo-Location Data for Pedestrian Crashes, Demographics, and Land Uses

2020 ◽  
Vol 2674 (8) ◽  
pp. 720-731
Author(s):  
Rui Guo ◽  
Zhiqiang Wu ◽  
Yu Zhang ◽  
Pei-Sung Lin ◽  
Zhenyu Wang
Keyword(s):  
Low Income ◽  
Fast Food ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Pedestrian Safety ◽  
Marginal Effect ◽  
Land Uses ◽  
Crash Frequency ◽  
Location Data ◽  
Pedestrian Crashes

This study investigates the effects of demographics and land uses on pedestrian crash frequency by integrating the contextual geo-location data. To address the issue of heterogeneity, three negative binomial models (with fixed parameters, with observed heterogeneity, and with both observed and unobserved heterogeneities) were examined. The best fit with the data was obtained by explicitly incorporating the observed and unobserved heterogeneity into the model. This highlights the need to accommodate both observed heterogeneity across neighborhood characteristics and unobserved heterogeneity in pedestrian crash frequency modeling. The marginal effect results imply that some land-use types (e.g., discount department stores and fast-food restaurants) could be candidate locations for the education campaigns to improve pedestrian safety. The observed heterogeneity of the area indicator suggests that priority should be given to more populated low-income areas for pedestrian safety, but attention is also needed for the higher-income areas with larger densities of bus stops and hotels. Moreover, three normally distributed random parameters (proportion of older adults, proportion of lower-speed roads, and density of convenience stores in the area) were identified as having random effects on the probability of pedestrian crash occurrences. Finally, the identification of pedestrian crash hot zone provides practitioners with prioritized neighborhoods (e.g., a list of areas) for developing effective pedestrian safety countermeasures.

scholarly journals Risk Factors for Older Pedestrian Injuries and Fatalities Among Communities in Massachusetts

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 113-113
Author(s):  
Shuangshuang Wang ◽  
Nina Silverstein ◽  
Chae Man Lee ◽  
Frank Porell ◽  
Beth Dugan
Keyword(s):  
Risk Factors ◽  
Healthy Aging ◽  
Health And Safety ◽  
Pedestrian Safety ◽  
The United States ◽  
Time Of Day ◽  
Community Factors ◽  
Crash Data ◽  
The Road ◽  
Pedestrian Crashes

Abstract The number of pedestrian crashes in the United States has increased by 35 percent from 2008 to 2017. Among all pedestrian fatalities in 2017, 48% were pedestrians aged 50 and older, which suggests a disproportionate threat to older residents’ health and safety. Massachusetts has a large older population and is experiencing increased numbers of older pedestrian crashes. This research identified risk factors and community characteristics contributing to older pedestrian crashes and suggests leveraging the state’s age-friendly efforts to speed the implementation of countermeasures. Based on ten-year statewide crash data (2006-2015) and community indicators from the 2018 Massachusetts Healthy Aging Data Report, this study examined 4,472 crashes across Massachusetts that involved pedestrians age 55 and over. The leading reasons for crashes were driver’s inattention, driver’s failure to yield right of way, and driver’s issues with visibility. Older pedestrians were hit while walking in the road, often in crosswalks at intersections. Many factors were found to contribute to older pedestrian crashes: time of day (rush hour), time of year (winter), and community factors (higher rates of disabilities, higher percentage of racial minority residents, higher number of cultural amenities, and lack of dementia-friendly community efforts. Greater awareness of older pedestrian safety risks is needed. Communities highlighted in this research warrant priority attention from planning, health, aging services, and transportation authorities to improve older pedestrian safety.

scholarly journals United States fatal pedestrian crash hot spot locations and characteristics

2021 ◽  
Vol 14 (1) ◽  
pp. 1-23
Author(s):  
Robert James Schneider ◽  
Rebecca Sanders ◽  
Frank Proulx ◽  
Hamideh Moayyed
Keyword(s):  
New York ◽  
New York City ◽  
Low Income ◽  
Hot Spots ◽  
Hot Spot ◽  
Pedestrian Safety ◽  
The Us ◽  
Safety Strategies ◽  
Traffic Volumes ◽  
Pedestrian Crashes

US pedestrian fatalities are at their highest level in nearly three decades and account for an increasing share of total traffic fatalities (16%). To achieve the vision of a future transportation system that produces zero deaths, pedestrian safety must be improved. In this study, we screened the entire US roadway network to identify fatal pedestrian crash “hot spot” corridors: 1,000-meter-long sections of roadway where six or more fatal pedestrian crashes occurred during an eightyear period. We identified 34 hot spot corridors during 2001-2008 and 31 during 2009-2016. While only five corridors were hot spots during both analysis periods, the 60 unique hot spots had remarkably consistent characteristics. Nearly all (97%) were multilane roadways, with 70% requiring pedestrians to cross five or more lanes. More than three-quarters had speed limits of 30 mph or higher, and 62% had traffic volumes exceeding 25,000 vehicles per day. All had adjacent commercial retail and service land uses, 72% had billboards, and three-quarters were bordered by low-income neighborhoods. Corridors with these characteristics clearly have the potential to produce high numbers of pedestrian fatalities. We also used hierarchical clustering to classify the hot spots based on their roadway and surrounding landuse characteristics into three types: regional highways, urban primary arterial roadways, and New York City thoroughfares. Each context may require different safety strategies. Our results support a systemic approach to improve pedestrian safety: Agencies should identify other roadway corridors with similar characteristics throughout the US and take actions to reduce the risk of future pedestrian fatalities.

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.

Crash Modification Factors for the Flashing Yellow Arrow Treatment at Signalized Intersections

2018 ◽  
Vol 2672 (30) ◽  
pp. 142-152 ◽  
Author(s):  
Raghavan Srinivasan ◽  
Bo Lan ◽  
Daniel Carter ◽  
Sarah Smith ◽  
Kari Signor
Keyword(s):  
North Carolina ◽  
Empirical Bayes ◽  
Evaluation Method ◽  
Signalized Intersections ◽  
Left Turn ◽  
Flashing Yellow Arrow ◽  
Crash Modification Factors ◽  
Treatment Category ◽  
Using Data ◽  
Yellow Arrow

This paper presents the results of an evaluation of the flashing yellow arrow (FYA) treatment using data from signalized intersections in Nevada, North Carolina, Oklahoma, and Oregon. The evaluation method was an empirical Bayes before–after analysis. The treatments were divided into seven categories depending on the phasing system in the before period (permissive, protected–permissive, or protected), phasing system in the after period (FYA permissive or FYA protected–permissive), the number of roads where the FYA was implemented (one road or both roads), and the number of legs at the intersections (three or four). The first five treatment categories involved permissive or protected–permissive phasing in the before period. Intersections in these five treatment categories experienced a reduction in the primary target crashes under consideration: left turn crashes and left turn with opposing through crashes. The reduction ranged from 15% to 50%, depending on the treatment category. Intersections that had at least one protected left turn phase in the before period and had FYA protected–permissive left turn phase in the after period experienced an increase in left turn crashes and left turn with opposing through crashes, indicating that replacing a fully protected left turn with FYA will likely cause an increase in left turn crashes.

Safety Evaluation of Roundabouts in Georgia

2019 ◽  
Vol 2673 (7) ◽  
pp. 641-651
Author(s):  
Franklin E. Gbologah ◽  
Angshuman Guin ◽  
Michael O. Rodgers
Keyword(s):  
Empirical Bayes ◽  
Safety Evaluation ◽  
The United States ◽  
Small Sample ◽  
Data Availability ◽  
Evaluation Procedure ◽  
Crash Frequency ◽  
Fatal Crashes ◽  
Bayes Analysis ◽  
Departments Of Transportation

U.S. roundabout growth has been significant in recent years and many published studies have documented significant safety benefits of roundabouts. However, the safety benefits for a roundabout may vary from region to region depending on many local factors. Therefore, transportation agencies can make more informed implementation decisions with local safety evaluations rather than published national findings. However, roundabouts are relatively new in the United States and most departments of transportation, including Georgia, are often hindered by the data availability requirements of the state-of-the-art empirical Bayes analysis evaluation procedure. This current study provides a safety evaluation of 23 Georgia roundabouts. It adopts a time-dependent form of the Highway Safety Manual predictive (empirical Bayes) method to estimate potential crash reductions across all crashes and all injury/fatal crashes. The method extends the empirical Bayes procedure towards a full Bayesian analysis. The findings indicate a 37–48% reduction in average crash frequency for all crashes and a 51–60% reduction in average crash frequency for injury/fatal crashes at four-leg roundabouts that were converted from stop-controlled and conventional intersections. In addition, when analyzed as a group, three-leg and four-leg roundabouts converted from stop-controlled and conventional intersections collectively experienced 56% reduction in average crash frequency for all crashes and 69% reduction in injury/fatal crashes. The study did not consider five-leg roundabouts because of small sample size and concerns about the form of the safety performance function. The adopted methodology offers departments of transportation with data availability challenges an alternative evaluation framework that retains the positive attributes of empirical Bayes analysis.

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.

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