Highway Safety Improvement Program Research Articles

Safety and Economic Evaluation of the Highway Safety Improvement Program: Is there a Return on Investment?

The Highway Safety Improvement Program (HSIP) is a Federal-aid program aimed at achieving a significant reduction in traffic fatalities and serious injuries on all public roads. Projects are selected based on the potential reduction of severe crashes and the greatest return on investment. In this paper, a step-by-step process and methodology were developed to evaluate HSIP projects. The process and method were implemented to evaluate HSIP projects executed in Wisconsin between 2013 and 2019. Safety effectiveness evaluation and economic assessment were conducted using the Empirical Bayes (EB) method. Crash cost benefit of implemented projects was quantified to find the benefit–cost (B/C) for a horizon of 10 years and observed period of analysis. With data available from project evaluations, Crash Modification Factors (CMFs) for common treatments were developed. A total of 64 HSIP projects were evaluated. B/C ratios greater than one were observed in 43 projects. For a 10-year horizon, the aggregated B/C ratio was 2.71. Alternatively, using the observed data during the study period of each project, the observed overall crash cost benefit was equal to $72 million which corresponds to a B/C ratio of 1.10 (benefit already surpassed project costs at 3–5 years). Approximately 536 crashes were prevented which translates to seven lives saved, 380 injuries prevented, and avoided 1,067 property damage losses.

Traffic volume prediction on low-volume roadways: a Cubist approach

ABSTRACT A significant aspect of the U.S. Department of Transportation’s Highway Safety Improvement Program (HSIP) rulemaking is the prerequisite that states must gather and utilize Model Inventory of Roadway Elements (MIRE) for all public paved roads, including low-volume roadways (LVR). States are particularly not equipped with the ability to collect traffic volumes of LVRs due to issues such as budgetary constraints. One alternative is to estimate traffic volumes of LVRs using regression or machine learning (ML) models. The present study accomplishes this by developing a ML framework to estimate traffic volumes on LVRs. By using available traffic counts on low-volume roads in Minnesota, this study applies and validates three different ML models (random forest, support vector regression, and Cubist) to estimate traffic volumes. The models include various traffic and non-traffic (e.g. demographic and socio-economic) variables. Overall, the Cubist model shows better performance compared to support vector regression and random forests. Additionally, the Cubist approach provides rule-based equations for different subsets of data. The findings of this study can be beneficial for transportation communities associated with LVRs.

Analysis of Single-Vehicle Roadway Departure Crashes on Rural Curved Segments Accounting for Unobserved Heterogeneity

Roadway departure crashes are one of the core emphasis areas in Strategic Highway Safety Plans (SHSP). These crashes, especially on rural roads, lead to a disproportionately higher number of fatalities and serious injuries. The focus of this study is to identify and quantify the factors affecting injury-severity outcomes for single-vehicle roadway departure (SV-RwD) crashes on rural curved segments in Minnesota. The crash data are extracted from the Highway Safety Information System (HSIS) from 2010 to 2014. This study applied a mixed logit with heterogeneity in means and variances approach to model driver-injury severity. The approach accounts for possible unobserved heterogeneity in the data resulting from driver, roadway, traffic, environmental conditions, or any combination of these attributes. This analysis adds value to the growing body of literature because it uncovers some unobserved heterogeneity in the form the attributes specific to driver-injury severities in contrast to the standard mixed logit approach. The model results indicate that there is a complex interaction of driver characteristics and actions (male drivers, aged below 30 years of age, and unsafe speed), roadway and traffic characteristics (two-lane undivided road, county roadways, and low traffic volume), environmental conditions (adverse weather, cloudy weather, dark conditions, and dry surface conditions), and vehicle characteristics (vehicle type—sport utility vehicle involved in rollover crashes). The results also provide some evidence of the effectiveness of a highway curve safety improvement program implemented in one of the Minnesota Department of Transportation (DOT) districts.

Enhancing Statewide Annual Average Daily Traffic Estimation with Ubiquitous Probe Vehicle Data

Annual average daily traffic (AADT) is a critical input into many transportation applications, particularly safety reporting. For example, the Highway Safety Improvement Program in the U.S. requires states to make AADT data for all public paved roadways accessible by 2026. Because collecting traffic counts on every network segment is prohibitively expensive, a method capable of accurately estimating AADT on unmonitored segments is of great value to state DOTs. The ubiquitous probe vehicle data present a great opportunity to this end. This paper presents an enhanced method for statewide AADT estimation by leveraging such data in Kentucky. The use of the probe data is explored in two ways. First, an annual average daily probes (AADP) variable is derived from hourly probe counts; second, a betweenness centrality (BC) variable is calculated using probe speeds. Including both variables and using the random forest model results in model performance that exceeds those previously reported for statewide applications. Incorporating AADP and BC improves the accuracy of AADT estimates by 30%–37% for all roads and 23%–43% for highways in functional classes 5–7, compared with only using sociodemographic and roadway characteristics. These results demonstrate the value of the probe data for enhancing AADT estimation. The analysis further shows that on roadways having more than 53 AADP or an average of 2.2 probe counts per hour, the median and the mean absolute percent errors are below 20% and 25%, respectively. These findings have practical implications for state DOTs wanting to maximize the utility of probe vehicle data.

Estimating Baseline Numbers for Safety Measure Target Setting in Virginia

The Federal Highway Administration (FHWA) established the Safety Performance Management program (Safety PM) to support the Highway Safety Improvement Program. The Safety PM Final Rule requires state departments of transportation (DOTs) to establish and report safety targets annually. FHWA does not identify a specific methodology to use when establishing safety targets. Many state DOTs apply annual growth/decline factors to previous-year safety measures. However, state DOTs also have flexibility to use a data-driven process. The Virginia Department of Transportation (VDOT) recently pursued the development of a more robust data-driven safety target setting methodology. This paper presents a methodology for establishing safety target baselines for several measures, including (1) fatalities, (2) serious injuries, and (3) nonmotorized fatalities and serious injuries. Predictive models were developed for establishing a baseline for 2019 targets and were further refined for 2020 targets. The predictive models include macro-level inputs and were developed for monthly, VDOT district-level outcomes. Performance measure data from 2018 were withheld from models for validation purposes and 2018–2020 model inputs were forecasted based on recent data. As 2019 data become available, the models should incorporate newer data and new models should be developed for revised 2020 and beyond predictions, as necessary. Refined models should include additional data elements as predictors, include more years of data to increase sample size, and capture moments when unobserved annual factors (i.e., unobserved underlying macro-level trends) begin to change.

Safety Performance Functions of Low-Volume Roadways

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

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.

Pavement friction management – artificial neural network approach

ABSTRACTThe Highway Safety Improvement Program (HSIP) requires state highway agencies to improve their roadway network safety through a ‘strategic’ and ‘data-driven’ approach. As part of HSIP, the Federal Highway Administration mandates that states develop a Pavement Friction Management System to reduce the rate of fatal and injury-causing crashes and prioritise their safety improvement projects based on the crash risk. This paper aims to predict the rate of wet and dry vehicle crashes based on surface friction, traffic level, and speed limit using an artificial neural network (ANN). Three learning algorithms, Levenberg–Marquardt, conjugate gradient, and resilient back-propagation, were examined to train the network. Levenberg–Marquardt produced the best precision and was used to develop the model. The results of the study suggest that the ANN model can reliably predict the rate of crashes. The prediction model can be used as a scale to prioritise safety improvement projects based on the rate of fatal and injury-causing crashes.

Oregon’s All Roads Transportation Safety Program: Data-Driven Program to Improve Safety on All Public Roads

Historically, the Oregon Department of Transportation (DOT) has spent the Highway Safety Improvement Program (HSIP) funding only on state highways. However, half of the fatalities and serious injuries occur on nonstate roadways. To address this concern and to comply with the federal requirement that the HSIP funding be spent on all public roads, the Oregon DOT developed a jurisdictionally blind safety program, known as the All Roads Transportation Safety (ARTS) Program, to address safety problems on all public roads in Oregon. The objective of the ARTS Program is the same as that of the HSIP: to reduce fatalities and serious injuries on all public roads using a data-driven approach. The ARTS Program has two components: hot spot and systemic. For the first round of the ARTS Program, safety projects were selected by using a combination of a selection approach and an application-based approach. For the hot spot component, potential safety projects were identified by using crash history, and the projects were prioritized on the basis of benefit–cost ratios. The systemic component—with three emphasis areas: roadway departure, intersection, and pedestrian and bicycle—was an application-based process in which the Oregon DOT and local agencies competed for available safety funding. Systemic projects were prioritized on the basis either of benefit–cost ratios (roadway departure and intersection projects) or cost-effectiveness index (pedestrian and bicycle projects). Through collaboration with various stakeholders, the Oregon DOT was able to select safety projects on all public roads with a data-driven approach. It is expected that these projects will significantly improve safety on all Oregon roads by reducing fatal and serious injury crashes.

High-Risk Rural Road Safety Study and Determining the Crash-Reduction Factors for High-Risk Rural-Road Usage

More than 32,000 motorists are killed on U.S. roads and streets annually, and approximately 54% of the accidents occur on rural roads. In an attempt to address and reduce these fatalities, the current transportation act, the Safe, Accountable, Flexible, Efficient Transportation Equality Act: A Legacy for Users (SAFETY-LU), elevated the Highway Safety Improvement Program (HSIP) to a core program and included a $90,000,000 High-Risk Rural Road Program (HRRRP) to address and significantly reduce traffic fatalities and incapacitating injuries on rural major or minor collectors, and/or rural local roads. While there were many challenges to properly implement the HRRRP in counties, this study provided important information that was needed to identify the predominant crash types on HRRRP-eligible roads and compiled a list of countermeasures for the predominant crash types that were identified on Kansas’ high-risk rural roads. For the gathered countermeasures, crash reduction factors (CRFs) were also provided from the literature review, and their values were validated by conducting interviews with Kansas county engineers/officials. This study provided valuable information for the county engineers and local government officials while they worked on improving the safety of high-risk rural roads using HRRRP funds.

Optimal funding allocation strategies for safety improvements on urban intersections

Urban intersections crashes cause significant economic loss. The safety management process undertaken by most states in the United States is referred to as Highway Safety Improvement Program and consists of three standardized steps: (i) identification of critical crash locations, (ii) development of countermeasures, and (iii) resource allocation among identified crash locations. Often these three steps are undertaken independently, with limited detail of each step at the state planning agencies. The literature review underlines the importance of the third step, and the lack of sophisticated tools available to state planning agencies for leveraging information obtained from the first two steps. Further, non-strategic approaches and unavailability of methods for evaluating policies may lead to sub-optimal funding allocation. This paper overcomes these limitations and proposes multiple optimal resource allocation strategies for improvements at urban intersections that maximize safety benefits, under budget and policy constraints. Proposed policy measures based on benefits maximization (economic competitiveness), equitable allocation (equity), and relaxation of mutually exclusiveness (multiple alternatives at one location) produce significantly different alternative and fund allocation. The proposed models are applied to selected intersections in four counties of southeast Michigan. Results reinforce the applicability of the strategies/policies and tools developed in this paper for safety project funding allocation on critical urban intersections.

Examining Fatal Crash Reductions by First Harmful Events since the Introduction of the Federal Highway Safety Improvement Program

The federal Highway Safety Improvement Program (HSIP) has been associated with the reduction in fatal crashes since 2006, but the reasons for the reduction remain largely unknown. This paper examines the reduction in fatal crashes in terms of different types of first harmful events that can provide insight into crash causes and prevention strategies. In this study, fatal crashes were categorized into four types: overturn, collision with motor vehicle in transport, collision with fixed object, and collision with nonmotorist. Fixed-effects and mixed-effects Poisson models were used to estimate the magnitudes of fatal crash reduction by first harmful events for each state. Fatal crashes due to collisions with nonmotorists and motor vehicles in transport have been reduced by 10% and 5.3%, respectively, compared with the 2001 to 2005 period. Fatal crashes due to overturn and collision with a fixed object decreased in some states but remained unchanged or increased in other states. Nevertheless, the numbers of national fixed-object and overturn fatal crashes have been reduced by 3% and 0.7%, respectively, as a whole. This study also investigated possibilities that could be associated with the magnitudes of the reductions, for example, the different traffic laws among states. It was found that although different safety improvement projects were implemented to target the various types of crashes, the improvements were also likely to be beneficial to other crash types. These are referred to as spillover effects. Nationally, fatal crashes have decreased since the introduction of the HSIP partly because of the reduction in fatal crashes due to collisions with nonmotorists and motor vehicles in transport and partly because of spillover effects.

Holistic Approach to Reduce Rural Roadway Departure Crashes

Two-lane rural roadways generate a disproportionate number of fatal and serious injury crashes. Many safety measures to date have reduced crash severity. As roadway design guides have evolved, they have incorporated changes that address safety needs. Advanced vehicle designs have incorporated features that protect occupants. Regulations have addressed risky driver behavior. Technological improvements have reduced emergency response times and transport times to trauma centers. To mitigate rural roadway departure issues, the engineering, education, enforcement, and emergency response (4E) approach has recently been implemented by some highway transportation agencies. Including nonengineering mitigation measures along with engineering mitigations, such as improved roadway and roadside design, and maintenance practices has had positive effects on rural roadway departure crashes. Improved vehicle designs have had positive effects on severity. Improved emergency response has had positive effects on survival rate. Education and enforcement efforts have had positive effects on driver behavior. The Moving Ahead for Progress in the 21st Century Act emphasizes an application of the 4E approach with full flexibility in the use of Highway Safety Improvement Program and Highway Safety Program funds. This paper advances a holistic analytical approach by coordinating the use of the 4Es and identifies the need for research to support and validate the proposed optimization approach. A data-driven model is presented for optimizing the allocation of 4E resources at the program level to address rural roadway departure crash issues. The same model can be applied at the project level.

Road Safety Analysis in the United States

The Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users required each of the states to develop a strategic highway safety plan and establish a Highway Safety Improvement Program (HSIP) as a core program. The HSIP required states to submit annual 5% reports describing at least 5% of their most hazardous locations, approaches being used to improve safety at these locations, and their effectiveness in improving safety. A review of FHWA's 2009 5% reports indicated that most state departments of transportation were still using basic safety analysis procedures such as frequency, rate, or safety index. Understanding that traditional methods are fraught with issues, biases, and false assumptions, recent research has opened doors to more advanced methodologies in the form of two safety analysis tools: SafetyAnalyst and the Highway Safety Manual (HSM). Unlike basic traditional methods, the empirical Bayes approach used in these tools involves rigorous calculations and requires safety performance functions in addition to roadway characteristics, traffic volume, and crash information. The data requirements and statistical complexity of these new safety tools are considered by many a hindrance to their adoption, but the levels of increasing complexity with additional requirements allow states to adopt at their own speed. The intent of this paper is to document the current engineering-related safety analysis practices and states’ perspectives toward adopting and implementing SafetyAnalyst and the HSM. The perspectives of several states on implementation of SafetyAnalyst were obtained through a survey and are discussed in this paper. This survey mainly helps the practitioners, administrators, and researchers to understand the various engineering-related safety analysis methods used across states and the states’ perceptions on shifting to newer tools.

Evaluation of Effectiveness of the Federal Highway Safety Improvement Program

The Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users established the Highway Safety Improvement Program (HSIP), which authorized about $1.3 billion/year from 2006 to 2009 for highway safety projects. The HSIP aims “to achieve a significant reduction in fatalities and serious injuries on all public roads,” and the number of national traffic fatalities seems to have decreased at about the same time. This study sought to evaluate the effectiveness of the HSIP in reducing fatal crashes in the United States. The study adopted fixed-effect panel models and multilevel mixed-effect models to deal with random fluctuations both before and after introduction of the HSIP and state-specific effects. The results show a drop of about 7.5% in national traffic fatalities since introduction of the HSIP compared with the average for 2001 to 2005, but the magnitude of reduction varied by state. States’ safety-related spending did not increase after introduction of the HSIP. Increased federal safety funding was offset by reduced state funding (crowd-out effect). The magnitude of states’ fatal-crash reduction was highly associated with years of available crash data, prioritizing method, and use of roadway inventory data. Moreover, states that prioritized hazardous sites by using more detailed roadway inventory data and the empirical Bayes method had the greatest reductions; all of those states relied heavily on the quality of their crash data systems. This study found that effectiveness of the HSIP in reducing national fatal crashes is very likely attributable to mandated reporting requirements, which helped states allocate safety spending more effectively and efficiently. It also suggests that more consistent and reliable crash data will allow states to employ more sophisticated prioritization methods and make better highway safety investment decisions.

Evaluation of Texas Department of Transportation Safety Improvement Index as a Prioritization Tool

In accordance with the federally mandated Highway Safety Improvement Program, every state is required to “develop and implement, on a continuous basis, a highway safety improvement program which has the overall objective of reducing the number and severity of crashes.” As part of this program, the Texas Department of Transportation (DOT) uses a formula known as the Safety Improvement Index (SII) for identifying, ranking, and selecting eligible projects. The SII is used to rank projects for potential funding by giving priority to projects that have a high benefit–cost ratio. Because the SII has not been updated within the past two decades, there is a need to determine whether the current formulation should be revised or updated. The objective of this study was to evaluate the SII in its current functional form and in its usefulness to rank and prioritize projects for safety improvement. The evaluation procedure proposed in this study used sensitivity analyses to study the effects of input variables on the SII. The results of the analysis indicated that although changes in the value of input variables affected the SII output, the ranking of projects usually was not affected, with the exception of the crash reduction factor variable. Thus, the same projects would be selected for safety improvement, even if different values were used in the SII. It is therefore recommended that the current formulation of the SII and the value of input variables used in the formula be retained by the Texas DOT to prioritize safety improvement projects.

Road Safety Audit

Fatalities and injuries from traffic crashes are one of the most critical concerns among transportation professionals in the United States and in the rest of the world. Certain preventive measures can help to significantly reduce crashes. One such tool, the road safety audit (RSA), is proactive and effective when used to address traffic safety issues. FHWA of the U.S. Department of Transportation has been aggressive in educating roadway agencies and promoting RSAs as a proven safety tool. Arizona formally adopted the use of RSAs in 2006 under its Highway Safety Improvement Program funded by FHWA. The authors were involved in several RSAs conducted throughout Arizona. This paper discusses the Arizona RSA program, including issues and recommendations identified through two dozen audits conducted to date, success rates of RSAs conducted in various jurisdictions, and lessons learned from them.

Identifying Pedestrian High-Crash Locations as Part of Florida’s Highway Safety Improvement Program: A Systematic Approach

From 1997 to 2001, pedestrian fatalities represented 25.9% (2,065 fatalities) of all traffic fatalities in Florida. The latest available statewide crash data from the Florida Department of Highway Safety and Motor Vehicles reveals 8,487 pedestrian crashes, resulting in 510 deaths and 7,894 injuries, in 2001. However, a methodology is not currently available to identify pedestrian high-crash locations in Florida as part of the Highway Safety Improvement Program (HSIP). A study was conducted to provide the framework for the systematic identification of pedestrian high-crash locations on the state highway system as part of the HSIP. The study methodology uses the Poisson distribution to determine abnormally high pedestrian crash frequencies in a year for 1-mi roadway segments. Four-lane and six-lane divided roadways with continuous sidewalks on both sides of the road in Miami-Dade County were included. The crash data, the latest available from the crash database of the Florida Department of Transportation, correspond to the years 1997, 1998, and 1999. A χ2 goodness-of-fit test was performed to determine how well the data could be modeled by a Poisson process. The goodness-of-fit test was significant at the 2.5% level for the 1999 data, at the 10% level for the 1998 data, and less than 1% for the 1997 data. With a confidence level of at least 90%, a pedestrian crash frequency of three crashes in a 1-mi segment was found to be abnormally high for the fourlane divided facilities. For the six-lane divided facilities, four pedestrian crashes per 1-mi segment were established as the threshold value. From these threshold values, 22 1-mi segments were identified as pedestrian high-crash locations in Miami-Dade County for 1999.

An Expert System to Analyze Hazardous Intersections

The detailed analysis of highway locations that have been identified as hazardous within the framework of a state Highway Safety Improvement Program (HSIP) requires a great deal of time and money to perform. Given the limited resources available to state highway agencies, whose task it is to perform such analyses, it seems a worthwhile effort to automate this “location analysis.” And, given the nature of location analysis problems, which are based on judgement rather than mathematical models, it was known a prior; that conventional automation techniques were not appropriate for this application. A prototype microcomputer‐based expert system, which performs location analysis, was developed to assess the feasibility of applying this new technology to location analysis problems. This paper describes the prototype system, and presents a brief discussion of some of the issues that were addressed when building the system.