Safety Performance Functions of Low-Volume Roadways

2019 ◽  
Vol 2673 (12) ◽  
pp. 798-810 ◽  
Author(s):  
Subasish Das ◽  
Ioannis Tsapakis ◽  
Songjukta Datta
Keyword(s):  
High Volume ◽  
Highway Safety ◽  
Safety Performance ◽  
Improvement Program ◽  
Crash Data ◽  
Safety Issues ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Low Volume ◽  
State And Local

The Fixing America’s Surface Transportation Act (FAST Act) mandates a Highway Safety Improvement Program (HSIP) for all states that “emphasizes a data-driven, strategic approach to improving highway safety on all public roads that focuses on performance.” To determine the predicted crashes on a specific roadway facility, the most convenient and widely used tool is the first edition of Highway Safety Manual (HSM), which provides predictive models [known as safety performance functions (SPFs)] of crash frequencies for different roadways. Low-volume roads (LVRs) are defined as roads located in rural or suburban areas with daily traffic volumes of less than or equal to 400 vehicles per day (vpd). LVRs cover a significant portion of the roadways in the U.S. While much work has been done to develop SPFs for high-volume roads, less effort has been devoted to LVR safety issues. This study used 2013–2017 traffic count, and roadway network and crash data from North Carolina to develop six SPFs for three LVRs, which can be used to predict total crashes, as well as fatal and injury crashes. This study also performed a sensitivity analysis to show the influence of traffic volumes on expected crash frequencies. The SPFs developed in this study can provide guidance to state and local agencies with the means to quantify safety impacts on LVR networks.

Getting to Zero Deaths on Ohio’s Low-Volume Roads

2018 ◽  
Vol 2672 (32) ◽  
pp. 40-48
Author(s):  
Victoria F. Beale ◽  
Derek Troyer ◽  
Alejandro Chock ◽  
Cory Hopwood ◽  
Mike McNeill
Keyword(s):  
Highway Safety ◽  
Improvement Program ◽  
Crash Data ◽  
Societal Benefit ◽  
Low Volume Roads ◽  
Grant Program ◽  
Low Volume ◽  
Safety Countermeasures ◽  
Benefit Cost ◽  
Serious Injuries

Ohio faces the challenge, as do many other states, of how to utilize Highway Safety Improvement Program (HSIP) funding to improve safety on its low-volume roadways while meeting the data-driven safety funding requirements of the Fixing America’s Surface Transportation (FAST) Act. Low-volume roads present unique challenges because data is rarely available and other factors, such as roadway ownership, affect the implementation of safety countermeasures on this system. Beginning in 2014, the Ohio Department of Transportation (ODOT) created a township safety signage grant program designed to address the issues with utilizing HSIP funding on low-volume roadways. The grant program’s goal was to drive down the number of fatalities, serious injuries, and overall crashes occurring on Ohio’s low-volume roads. ODOT took into consideration overriding issues regarding low-volume roads in how it structured the grant program. ODOT also utilized the direction from its Strategic Highway Safety Plan in choosing a safety countermeasure which met the needs of its roadway departure and intersection crash trends. The program has been actively engaged in by Ohio townships and now has a large enough amount of post-safety countermeasure installation data available to quantify its initial success. This paper presents the successful results by highlighting the human capital and comprehensive societal benefit/cost analyses for the first 24 townships with 12 months of post-grant completion crash data.

Safety Performance Functions for Low-Volume Rural Stop-Controlled Intersections

2019 ◽  
Vol 2673 (5) ◽  
pp. 660-669 ◽  
Author(s):  
Steven Y. Stapleton ◽  
Timothy J. Gates ◽  
Raul Avelar ◽  
Srinivas R. Geedipally ◽  
Ramin Saedi
Keyword(s):  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Cross Sectional ◽  
Minor Road ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Low Volume

This study involved the development of safety performance functions for rural, low-volume, minor road stop-controlled intersections in Michigan. Facility types included three-leg stop-controlled (3ST) and four-leg stop-controlled (4ST) intersections under state or county jurisdiction and were sampled from each of Michigan’s 83 counties. To isolate lower-volume rural intersections, major roadway traffic volumes were limited to the range of 400–2,000 vehicles per day (vpd). Data were compiled from several sources for 2,023 intersections statewide. These data included traffic crashes, volumes, roadway classification, geometry, cross-sectional features, and other site characteristics covering the period of 2011–2015. Random effects negative binomial regression models were specified for each stop-controlled intersection type considering factors such as driveway density, lighting presence, turn lane presence, and intersection skew, in addition to volume. To account for the unobserved heterogeneity between counties, mixed effects negative binomial models with a county-specific random effect were utilized. Furthermore, unobserved temporal effects were controlled through the use of a year-specific random effect. Separate models were developed for fatal/injury crashes, property damage crashes, and select target crash types. The analysis found that skew angles of greater than five degrees led to significantly greater crash occurrence for both 3ST and 4ST intersections, while greater than two driveways near the intersection led to significantly greater angle crashes at 4ST intersections. Other factors were found to have little impact on crash occurrence. Comparison with the Highway Safety Manual (HSM) base models showed that the HSM models over-predict crashes on 4ST intersections and 3ST intersections with volumes between 1,200 and 2,000 vpd.

Regional Perspective of Safety Performance Functions and Their Application to Florida Intersections in Suburban Residential and Urban General Context Classification Categories

2021 ◽  
pp. 036119812110084
Author(s):  
Ghalia Gamaleldin ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
John McCombs ◽  
Alan El-Urfali
Keyword(s):  
Negative Binomial ◽  
Regression Tree ◽  
Influential Factors ◽  
Safety Performance ◽  
General Context ◽  
Boosted Regression Tree ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Intersection Types ◽  
The Individual

Safety performance functions (SPFs) are essential tools to help agencies predict crashes and understand influential factors. Florida Department of Transportation (FDOT) has implemented a context classification system which classifies intersections into eight context categories rather than the three classifications used in the Highway Safety Manual (HSM). Using this system, regional SPFs could be developed for 32 intersection types (unsignalized and signalized 3-leg and 4-leg for each category) rather than the 10 HSM intersection types. In this paper, eight individual intersection group SPFs were developed for the C3R-Suburban Residential and C4-Urban General categories and compared with full SPFs for these categories. These comparisons illustrate the unique and regional insights that agencies can gain by developing these individual SPFs. Poisson, negative binomial, zero-inflated, and boosted regression tree models were developed for each studied group as appropriate, with the best model selected for each group based on model interpretability and five performance measures. Additionally, a linear regression model was built to predict minor roadway traffic volumes for intersections which were missing these volumes. The full C3R and C4 SPFs contained four and six significant variables, respectively, while the individual intersection group SPFs in these categories contained six and nine variables. Factors such as major median, intersection angle, and FDOT District 7 regional variable were absent from the full SPFs. By developing individual intersection group SPFs with regional factors, agencies can better understand the factors and regional differences which affect crashes in their jurisdictions and identify effective treatments.

Tools to Identify Safety Issues for a Corridor Safety-Improvement Program

2000 ◽  
Vol 1717 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Jerome P. Breyer
Keyword(s):  
Relational Databases ◽  
Safety Evaluation ◽  
Highway Safety ◽  
New Paradigm ◽  
Safety Hazards ◽  
Improvement Program ◽  
Satellite Systems ◽  
Safety Issues ◽  
Advanced Technologies ◽  
Safety Improvement

The Arizona Department of Transportation (ADOT) recognizes that a new paradigm in highway safety evaluation was brought about by the advent of advanced technologies such as photo log, geographic information systems (GIS), and global-positioning satellite systems. Whereas these technologies are known to serve distinct singular purposes in a highway agency, ADOT has endeavored to explore the possibilities of integrating these technologies for the purpose of providing an all-encompassing perspective of crash history and roadside characteristics in a multimedia display of GIS maps and related photo imagery. The research provides the account of an analytic tool-development process aimed at improving the recognition of highway safety hazards. These hazards might otherwise be apparent if not for the relative complexity of existing relational databases and spatial GIS infrastructure at ADOT. Previous methods of mining data from the ADOT crash databases were limited in functionality as well as in reliability. By promoting the “visualization” of highway safety conditions, the advanced technologies open a wealth of new opportunities in identifying problematic roadside conditions and crash histories. This is expected to lead to an improved economy of implementing safety improvements that are designed properly to mitigate the “real” conditions that can be identified. The research is a companion to the larger, FHWA-sponsored research into establishing a corridor safety-improvement program for Arizona (FHWA Report FHWA-AZ 98-458).

Calibration of the Highway Safety ManualGiven Safety Performance Functions for RuralMultilane Segments and Intersections in Kansas

2017 ◽  
Author(s):  
Syeda Rubaiyat Aziz ◽  
Sunanda Dissanayake
Keyword(s):  
Safety Evaluation ◽  
Highway Safety ◽  
Safety Performance ◽  
Regression Coefficients ◽  
Local Conditions ◽  
Crash Prediction ◽  
Highway Safety Manual ◽  
Satisfactory Level ◽  
Safety Performance Functions ◽  
Predictive Methods

The Highway Safety Manual (HSM) provides models and methodologies for safety evaluation and prediction of safety performance of various types of roadways. However, predictive methods in the HSM are of limited use if they are not calibrated for local conditions. In this study, calibration procedures given in the HSM were followed for rural segments and intersections in Kansas. Results indicated that HSM overpredicts fatal and injury crashes and underpredicts total crashes on rural multilane roadway segments in Kansas. Therefore, existing safety performance functions (SPFs) must be adjusted for Kansas conditions, in order to increase accuracy of crash prediction. This study examined a way to adjust HSM calibration procedures by development of new regression coefficients for existing HSM-given SPF. Final calibration factors obtained through modified SPFs indicated significant improvement in crash prediction for rural multilane segments in Kansas. Additionally, obtained calibration factors indicated that the HSM is capable of predicting crashes at intersections at satisfactory level.

Safety-Based Left-Turn Phasing Decisions for Signalized Intersections

2017 ◽  
Vol 2619 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Kiriakos Amiridis ◽  
Nikiforos Stamatiadis ◽  
Adam Kirk
Keyword(s):  
Urban Areas ◽  
Primary Objective ◽  
Signalized Intersections ◽  
Safety Performance ◽  
Signal Timing ◽  
Left Turn ◽  
Crash Data ◽  
Intersection Safety ◽  
Traffic Volumes

The efficient and safe movement of traffic at signalized intersections is the primary objective of any signal-phasing and signal-timing plan. Accommodation of left turns is more critical because of the higher need for balancing operations and safety. The objective of this study was to develop models to estimate the safety effects of the use of left-turn phasing schemes. The models were based on data from 200 intersections in urban areas in Kentucky. For each intersection, approaches with a left-turn lane were isolated and considered with their opposing through approach to examine the left-turn–related crashes. This combination of movements was considered to be one of the most dangerous in intersection safety. Hourly traffic volumes and crash data were used in the modeling approach, along with the geometry of the intersection. The models allowed for the determination of the most effective type of left-turn signalization that was based on the specific characteristics of an intersection approach. The accompanying nomographs provide an improvement over existing methods and warrants and allow for a systematic and quick evaluation of the left-turn phase to be selected. The models used the most common variables that were already known during the design phase, and they could be used to determine whether a permitted or protected-only phase would suit the intersection when safety performance was considered.

Roadway Context Classification Approach for Developing Regional Safety Performance Functions for Florida Intersections

2020 ◽  
Vol 2674 (2) ◽  
pp. 191-202
Author(s):  
Ghalia Gamaleldin ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
Alan El-Urfali ◽  
Md Imrul Kayes ◽  
...  
Keyword(s):  
Classification System ◽  
Negative Binomial ◽  
Safety Performance ◽  
P Value ◽  
Department Of Transportation ◽  
Florida Department ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Road Width ◽  
Data Elements

Understanding how the type and location of intersections affect crashes is important to reduce these crashes effectively. This paper discusses the development of regional safety performance functions (SPFs) based on a new context classification system developed by the Florida Department of Transportation (FDOT). This classification system (which has not previously been used) categorizes intersections into eight different categories based on land use and other parameters, allowing SPFs to be developed for up to 32 different types of intersections. The Model Inventory of Roadway Elements (MIRE) 2.0 was used as the standard inventory for the data elements collected. Using MIRE 2.0 allows for the procedures conducted in this study to be easily implemented in other states. SPFs were developed for two intersection groups. First, a linear regression model was built to predict missing minor traffic volumes. This statistically significant model ( p-value < 0.05) had an adjusted R-square of 0.7648. Data were collected for over 25 potential predictor variables (including a regional variable for FDOT districts) and used to fit a negative binomial model to each studied intersection group. Some variables (such as major traffic volume) were significant for both groups, but each SPF had unique variables (such as speed limit and road width). Different regions were significant for each group, showing how crashes vary for different intersection types in different regions. By allowing for the development of SPF models for many intersection classifications, FDOT’s context classification system can be used by other agencies to identify crash-influencing factors better for different conditions.

Estimation of Traffic Volume on Rural Local Roads

2000 ◽  
Vol 1719 (1) ◽  
pp. 121-128 ◽  
Author(s):  
William L. Seaver ◽  
Arun Chatterjee ◽  
Mark L. Seaver
Keyword(s):  
Mathematical Model ◽  
Air Quality ◽  
Road Traffic ◽  
Highway Safety ◽  
Alternative Methods ◽  
Safety Issues ◽  
Statistical Procedures ◽  
Local Roads ◽  
Traffic Volumes ◽  
Local Road

Traffic volumes on local roads have not received much attention from highway planners and researchers, although local roads constitute the majority of road mileage in a state. In recent years the need for reliable estimates of vehicle-miles of travel on local roads has been recognized for the analysis of air quality and highway safety issues. To provide a better understanding of traffic volumes on local roads and to explore alternative methods for estimation, data from Georgia were analyzed by using different statistical procedures. The findings of this analysis are presented, along with the results of an attempt to develop a mathematical model for estimation of local road traffic volumes.

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