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

Srinivasan, Raghavan; Lan, Bo; Carter, Daniel; Smith, Sarah; Signor, Kari

doi:10.1177/0361198118788192

Cited by 5 publications

(8 citation statements)

References 4 publications

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“…Overall, the total crashes for the entire 24-h day increased 10%–20% (CMFs are 1.087 [all severity] and 1.217 [fatal & injury]). The results are in line with the findings from previous studies that conversion from protected-only to FYA-PPLT could have an adverse effect on safety, especially under heavy traffic conditions ( 4 , 6–8 ).…”

Section: Resultssupporting

confidence: 92%

“…Based on the CMF estimation results, the following can be said about crashes at sites with left-turn signal conversion from protected-only to FYA-PPLT with TOD operation: Overall, the total crashes for the entire 24-h day increased 10%-20% (CMFs are 1.087 [all severity] and 1.217 [fatal & injury]). The results are in line with the findings from previous studies that conversion from protected-only to FYA-PPLT could have an adverse effect on safety, especially under heavy traffic conditions (4,(6)(7)(8). When looking at target crashes over the entire 24h day, Category 1 crashes increased 270%-290% (CMFs are 3.732 [all severity] and 3.919 [fatal & injury]), whereas Category 2 crashes decreased 35%-40% (CMFs are 0.650 [all severity] and 0.594 [fatal & injury]).…”

Section: Cmf Estimation Resultssupporting

confidence: 91%

“…Overall, the resulting CMF estimation showed that crashes over the 24-h period increased slightly (8.7% increase for all severity crashes). This result is in line with the findings from previous studies that conversion from protected-only to FYA-PPLT could have adverse impacts on the safety, especially under heavy traffic conditions (4 , 6–8 ). When looking at fatal and injury crashes only, the increase in fatal and injury crashes (21.7%) was relatively higher than all crashes (8.7%) for the total crash category.…”

Section: Discussionsupporting

confidence: 92%

“…Srinivasan et al developed CMFs for six categories of FYA treatments using the empirical Bayes (EB) method ( 8 ). As one of the six categories of treatments, CMFs were developed using crash data collected from the treated sites with at least one signal conversion from a protected-only left turn to FYA-PPLT with TOD operation.…”

Section: Literature Reviewmentioning

confidence: 99%

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Safety Evaluation for Conversion From Protected-Only Left-Turn Phasing to Time-of-Day Protected-Permissive Left-Turn Phasing Using Flashing Yellow Arrows

Lee

Cunningham

Simpson³

2023

Transportation Research Record: Journal of the Transportation R

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This study investigated the safety effects of the conversion from a protected-only left turn to protected-permissive left turn with flashing yellow arrow (FYA−PPLT) with time-of-day operation. The observational before–after study with the comparison group method was used to develop crash modification factors (CMFs) for the total crashes and two types of target crashes that involve left-turning vehicles on treated approaches (left-turn−same roadway and rear-end crashes). For all potential crashes, crash reports, crash diagrams, and narratives were manually inspected to identify the actual target crashes. The CMFs were separately developed for a full 24-h day and specific time-of-day based on FYA-PPLT operation, and for two severity categories (all severities and fatal & injury). The results showed that the total crashes for 24 h had a slight increase, whereas the target crashes had substantially larger changes (increase in left-turn−same roadway crashes and decrease in rear-end crashes). When looking at the specific time-of-day use of FYA-PPLT, the increase in left-turn−same roadway crashes was significantly higher than the full 24-h period. This implies that engineers could extend protected-only use more on the fringes of the peak periods to mitigate the increase in left-turn-related crashes. The analysis results also showed a trade-off between left-turn–same roadway and rear-end crashes after the signal conversion. The findings reveal how important it is for engineers to properly understand the safety effects on the different categories of target crashes and time periods when considering the signal conversion from a protected-only left turn to FYA-PPLT.

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Section: Resultssupporting

confidence: 92%

Section: Cmf Estimation Resultssupporting

confidence: 91%

Section: Discussionsupporting

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

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Safety Evaluation for Conversion From Protected-Only Left-Turn Phasing to Time-of-Day Protected-Permissive Left-Turn Phasing Using Flashing Yellow Arrows

Lee

Cunningham

Simpson³

2023

Transportation Research Record: Journal of the Transportation R

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“…Nevertheless, the conversion of the left-turn mode was not classified or studied thoroughly, and other crash types such as ANGC and REC were not evaluated. Srinivasan et al (19) collected data from four states-Nevada, North Carolina, Oklahoma, and Oregon-to serve the safety impacts study of FYA. SPFs were developed based on different categories, including permissiveonly, protected-permissive, or protected-only left-turn modes in the ''before'' period and FYA permissive or FYA PPLT modes in the ''after'' period.…”

Section: Literature Reviewmentioning

confidence: 99%

Safety Performance Evaluation of Flashing Yellow Arrow: Time-of-Day Versus 24-Hour Operation

Zhang

2023

Transportation Research Record: Journal of the Transportation R

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The flashing yellow arrow (FYA), as the permissive left-turn indication, has been widely implemented to mitigate driving confusion caused by the permissive steady green indication. The selection of an appropriate FYA operation type, however, remains an open-ended question as few studies have evaluated the safety effectiveness of FYA under different operation types, that is, 24-hour, time-of-day (TOD), and switching from 24-hour to TOD. Moreover, since FYA has been typically used at intersections with a single left-turn lane, its safety effectiveness at intersections with dual left-turn lanes has not been well investigated. To bridge these gaps, this paper focuses on evaluating the safety effectiveness of FYA at intersections with varying numbers of left-turn lanes under different operation types and examining whether the FYA operation type switch affects safety performance. Empirical Bayes before/after studies are conducted for 24-hour, TOD, and switching from 24-hour to TOD FYA operation types using multivariate adaptive regression splines models in comparison with traditional negative binomial models. Safety performance functions are developed for different combinations of crash types and a different number of left-turn lanes. Results show that for intersections with either a single left-turn lane or dual left-turn lanes, 24-hour and TOD FYA operation types reduce crashes by 8.76% to 50%. However, intersections with dual left-turn lanes experience a 31.2% increase in total crashes when switching from 24-hour to TOD FYA operation type, while intersections with a single left-turn lane see a 60% decrease in rear-end crashes.

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Effectiveness of road safety interventions: An evidence and gap map

Goel,

Tiwari,

Varghese

et al. 2024

Campbell Systematic Reviews

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BackgroundRoad Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low‐ and middle‐income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high‐income countries have successfully reduced RTI by using a public health approach and implementing evidence‐based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high‐income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge.ObjectivesThe objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels.Search MethodsThe EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019.Selection CriteriaThe interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post‐crash pre‐hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non‐fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes.Data Collection and AnalysisThe EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case‐control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2.Main ResultsThe EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors (n = 771), road design, infrastructure and traffic control (n = 661), legal and institutional framework (n = 424), post‐crash pre‐hospital care (n = 118) and vehicle factors and protective devices (n = 111). Fatal crashes as outcomes were reported in 1414 records and non‐fatal injury crashes in 1252 records. Among the four intermediate outcomes, speed was most commonly reported (n = 298) followed by alcohol (n = 206), use of seatbelts (n = 167), and use of helmets (n = 66). Ninety‐six percent of the studies were reported from high‐income countries (HIC), 4.5% from upper‐middle‐income countries, and only 1.4% from lower‐middle and low‐income countries. There were 25 systematic reviews of high quality, 4 of moderate quality, and 293 of low quality.Authors' ConclusionsThe EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence‐synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions.

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

Cited by 5 publications

References 4 publications

Safety Evaluation for Conversion From Protected-Only Left-Turn Phasing to Time-of-Day Protected-Permissive Left-Turn Phasing Using Flashing Yellow Arrows

Safety Evaluation for Conversion From Protected-Only Left-Turn Phasing to Time-of-Day Protected-Permissive Left-Turn Phasing Using Flashing Yellow Arrows

Safety Performance Evaluation of Flashing Yellow Arrow: Time-of-Day Versus 24-Hour Operation

Effectiveness of road safety interventions: An evidence and gap map

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