OPTIMIZING THE POTENTIAL OF HIGHWAY SAFETY INVESTMENT

Highway safety management aims to prevent crashes and reduce the resulting frequency and severity within the limit of available resources. The identification of potentially hazardous sites and investment on safety treatments have been fundamental to fulfill this goal. However, having many highway safety improvement projects in hand, safety professionals need to evaluate several different alternatives and allocate the limited funds to the ones that would provide the highest return on investment. Hence, prioritizing the safety projects based on their potential to achieve the greatest safety benefit is crucial. Ineffective prioritization can distribute funds to locations with less potential for improvement while higher-risk sites remain untreated. Prior to the widespread use of the Highway Safety Manual (HSM), highway safety analysts prioritized candidate safety improvement projects using simplistic safety metrics based only on past performance (e.g., crash history, rates, and costs). However, these metrics lack precision and are limited by several methodological weaknesses. Published in 2010, the HSM provides a comprehensive guideline for evaluating safety improvements that facilitates the use of advanced safety performance measures including “Excess Expected Crashes (EEC)”. This metric is dependent on two estimates: expected crashes by Empirical Bayes (EB) method and predicted crashes by Safety Performance Functions (SPFs). It is obtained by taking the difference between these estimates and better reflect a site’s safety improvement potential. Nowadays, several agencies and state departments of transportation use EEC for prioritizing projects. While EEC is a preferred measure, it comes with a few limitations. The use of EEC alone may not identify all sites with promise, as it purposefully ignores the real maximum potential reduction, that is, to zero. Further, if SPFs (and EEC) are based on combined crashes of all severities, the focus may be placed on the less serious crashes that comprises the substantial proportion. However, focusing only on the most severe crashes often leads to small sample size problems that hinders the development of meaningful prediction models. Moreover, EEC compares the safety performance of a site to the average location for that roadway type and traffic volume but fails to represent the magnitude of the overall number of crashes occurring at that site, more preciously EB estimates. Additionally, the ever-present need for choosing the most appropriate SPFs that can balance between the model quality and available data remains. The research conducted in this dissertation aims to propose a more comprehensive approach to prioritize the potential for safety improvement of proposed projects. To illustrate the concepts developed in this work, a case study (a project prioritization scheme named Strategic Highway Investment Formula for Tomorrow (SHIFT) by Kentucky Transportation Cabinet (KYTC)) is presented using crash and roadway data from state-maintained roads in Kentucky. This scheme used EEC of total crashes for project ranking. Their desire to further improve the safety ranking methodology manifested the impetus for this research. This dissertation offers recommendations regarding the choice of SPFs and integration of crash severity and EB estimate for project prioritization. It also offers a novel technique of considering severity by incorporating future goals of fatalities into the project prioritization metric-EEC. Finally, it presents a framework for developing a multifactor safety scoring technique using Kentucky data and recommends replacing the dependency on EEC only with this new safety score for prioritizing safety projects. In the process of conducting the research, a tool was developed that automates the safety score estimation and ranking with efficiency. This technique and tool can be customized according to a jurisdiction’s safety needs and therefore, can be used for any state or country’s safety investment

THE RELATIONSHIP BETWEEN ROADWAY HOMOGENEITY AND NETWORK COVERAGE FOR NETWORK SCREENING

In the context of transportation safety engineering, network screening is a method of identifying and prioritizing high-risk locations for potential safety investment. Since its release, the Highway Safety Manual (HSM) has facilitated the adoption of Safety Performance Functions (SPF) to predict the number of crashes for the network screening of any facility type. The predictive model becomes more reliable when developed from crash data with homogeneous roadway segments and this homogeneity can be attained by applying specific geometric attributes to the dataset. The caveat to this method is the requirement of adjustment factors (AFs) to adjust the predicted estimate for the segments which have different geometric characteristics compared to the base attributes. Though AFs are available from several sources, particularly the HSM and CMF Clearinghouse, there are still many attributes for various roadways for which the AFs have not been estimated yet. The absence of appropriate AFs limits the use of such crash prediction models for network screening. In that case, a generic SPF can be developed from the entire network without applying any base conditions and, the reliability of the model is compromised. The goal of this study is to evaluate the trade-offs between a more reliable SPF (that requires more AFs) and a relatively less reliable SPF (that requires fewer AFs). This leads to the following question this research attempts to answer: “Are the benefits of AFs for network screening worth the cost of developing them?” Recommended by the HSM, this study uses “Excess Expected Crashes (EEC)”, a metric derived from the SPF and historical crash data for ranking potential sites for improvement. The study analyses found that segment rank is nearly insensitive to the choice of the SPF and developing AFs may not justify the cost of network screening. On the other hand, an SPF developed from the entire roadway data might not work as well for project-level analysis (a combination of several segments) or estimating the benefit-cost ratio for a site. This is because the magnitudes of the EEC are crucial for such cases and the generic SPF overestimates the EEC compared to SPFs developed from specific sets of attributes. for most of the segments. Therefore, the major finding of the thesis is that a generic SPF is sufficient when sites are needed to be ranked, but specific SPFs perform better when a benefit-cost analysis is required

Incorporating Crash Severity and Continuous Improvement of SHIFT

The Strategic Highway Investment Formula for Tomorrow (SHIFT) is the Kentucky Transportation Cabinet\u27s data-informed approach for comparing capital improvement projects and prioritizing limited transportation funds. SHIFT 2022 incorporates advancements in methods and flexibility. This project revises the SHIFT crash data safety metric. The crash data safety metric from the previous version of SHIFT was excess expected crashes (EECs). It is computed using the total number of crashes of all severities. Locations with a higher proportion of severe (fatal and injury) crashes received the same weight as locations with an equal number of property damage only crashes. This project redefines the SHIFT crash data safety metric, increasing the weight of serious (KAB) crashes while still accounting for the potential to reduce less serious crashes. It also attends to the five-year and ultimate goals of Kentucky’s Strategic Highway Safety Plan by developing a metric sensitive to these policy goals. The five-year goal is represented by a new definition of EEC (the difference between expected crashes, the Empirical Bayes estimate, and the number of systemwide crashes when the goal is achieved). The ultimate goal is represented by the potential to reduce crashes on all road sections to zero, which is the EB estimate itself

Incorporating Variability in KYTC Planning and Project Development

Transportation planning and design decisions are typically made using point estimates of traffic and safety measures. This report addresses the uncertainty of data used to make these decisions. It focuses on two data elements — project safety ratings and volume estimates from traffic counts. For safety ratings, the Kentucky Transportation Cabinet’s Strategic Highway Investment Formula for Tomorrow (SHIFT) crash history score is used as a case study. Confidence intervals are developed for the Cabinet’s new safety metric, which incorporates estimates of future crashes by severity as well as excess expected crashes above safety goals. For traffic counts, the report develops confidence intervals based on daily and monthly expansion factors across multiple permanent count recording stations. Results should help users of these data better understand the range of data the point estimates represent and the likelihood that projects based on these plans and designs will function as expected

Safety Analysis for SHIFT Implementation

The Strategic Highway Investment Formula for Tomorrow (SHIFT) program research team evaluated the program’s 2018 safety component and developed a new methodology to rank the safety needs of 2020 projects. For the current year, the research team suggests replacing the SHIFT 2018 formulas (based on three naive crash measures) with a new metric, Excess Expected Crashes, formulated from the most current safety analysis guidelines available in the Highway Safety Manual. In addition, the research team developed a user-friendly network screening tool serving as a method to prioritize projects. Another automation tool was developed that will help safety professionals evaluate and prioritize projects in an efficient manner, replacing the laborious manual calculation needed for each project. This report details the new methodology and tools

Analysis of Traffic Crash Data in Kentucky 2017-2021

Each year the Kentucky Transportation Center publishes Analysis of Traffic Crash Data in Kentucky, a report that reviews state-level crash data from the previous five years. This report summarizes crash data for 2017 through 2021. In addition to presenting a review of crash data, the report also contains safety performance functions (SPFs) for nine roadway types. Because SPFs were derived using 2017-2021 data, the models predict crashes for a five-year period. Users must adjust the model by adding a coefficient of 1/5 to models to avoid overestimating crashes by a factor of five

Crash Modification Factor Recommendation List

Practitioners use Crash Modification Factors/Functions (CMFs) to calculate the number of crashes expected once a countermeasure has been implemented. When evaluating design alternatives, CMFs can be used in conjunction with safety performance functions (SPFs) to derive crash predictions. The Federal Highway Administration (FHWA) maintains the CMF Clearinghouse as a repository for CMFs. Contributions are submitted by researchers across the U.S., Canada, and throughout the world. Typically, multiple CMFs are associated with a single countermeasure. Some CMFs only apply to specific facility or crash types. Furthermore, CMF quality varies, and some only apply to specific facility and/or crash types, regions, or times. Using the Clearinghouse to identify an appropriate CMF for a given situation demands considerable time and significant expertise. This report describes the development and implementation of a spreadsheet-based tool that Kentucky Transportation Cabinet staff and the agency’s design consultants can use to select CMFs most appropriate for the state’s highways and conditions. Guidance instructs users on use of the tool. A web-based form was also developed, which must be filled out and submitted to KYTC’s CMF committee when a CMF is planned for use on a project

Incorporating Crash Severity and Continuous Improvement of SHIFT

SPR 21-603The Strategic Highway Investment Formula for Tomorrow (SHIFT) is the Kentucky Transportation Cabinet's data-informed approach for comparing capital improvement projects and prioritizing limited transportation funds. SHIFT 2022 incorporates advancements in methods and flexibility. This project revises the SHIFT crash data safety metric. The crash data safety metric from the previous version of SHIFT was excess expected crashes (EECs). It is computed using the total number of crashes of all severities. Locations with a higher proportion of severe (fatal and injury) crashes received the same weight as locations with an equal number of property damage only crashes. This project redefines the SHIFT crash data safety metric, increasing the weight of serious (KAB) crashes while still accounting for the potential to reduce less serious crashes. It also attends to the five-year and ultimate goals of Kentucky\u2019s Strategic Highway Safety Plan by developing a metric sensitive to these policy goals. The five-year goal is represented by a new definition of EEC (the difference between expected crashes, the Empirical Bayes estimate, and the number of systemwide crashes when the goal is achieved). The ultimate goal is represented by the potential to reduce crashes on all road sections to zero, which is the EB estimate itself

Safety Analysis for SHIFT Implementation

The Strategic Highway Investment Formula for Tomorrow (SHIFT) program research team evaluated the program’s 2018 safety component and developed a new methodology to rank the safety needs of 2020 projects. For the current year, the research team suggests replacing the SHIFT 2018 formulas (based on three naive crash measures) with a new metric, Excess Expected Crashes, formulated from the most current safety analysis guidelines available in the Highway Safety Manual. In addition, the research team developed a user-friendly network screening tool serving as a method to prioritize projects. Another automation tool was developed that will help safety professionals evaluate and prioritize projects in an efficient manner, replacing the laborious manual calculation needed for each project. This report details the new methodology and tools