Driver behavior and characteristics are influential contributing factors to traffic crashes, but current safety analysis tools primarily incorporate infrastructure-related factors affecting crashes. The lack of behavior and characteristic information can create a problem when considering safety applications since some of the most important factors are not included. This could lead to safety solutions that may not work as well as intended. In the AASHTO Highway Safety Manual (HSM), the measures of driver characteristics are divided into several categories such as attention and information processing, vision, perception-reaction time, and speed choice. However, these characteristics are provided at a very high level. Police officers usually report driver characteristics such as gender, age, speeding, blood alcohol content, seat belt use, and distracted driving. While several studies have evaluated the impact of these factors on crash severity, there is a need to incorporate these factors in crash prediction methods to achieve a better picture of the true potential effects on crash severity and frequency for decisions in planning, design, and operations. Research is needed to develop a methodology to incorporate a variety of factors related to driver behavior and characteristics into crash prediction methods to allow for a more comprehensive assessment of existing and expected safety performance, for use in design and operational decision-making, and incorporation into the AASHTO HSM and other safety tools and guidelines.
The objective of this research is to continue and complete the work begun under NCHRP Project 22-47 to develop a methodology to incorporate driver characteristics and behavior into safety prediction methods to estimate the expected crash frequency and severity related to infrastructure features for use in planning, design, and operational decisions.
The research plan should delineate the tasks required to develop a methodology necessary to accomplish the research objective. At a minimum, the research should address the following:
- Update literature review from NCHRP Project 22-47 that evaluated or estimated impacts of driver characteristics and behavior to identify variables that have been found to contribute measurably to the number and severity of motor vehicle crashes, with emphasis on literature published since the NCHRP Project 22-47 work product was prepared;
- Assess the work products from NCHRP Project 22-47 listed above and propose work activities necessary to accomplish the project objectives, within the available budget;
- Using work products from NCHRP Project 22-47, along with any updates or modifications, propose a work plan for developing and validating the predictive methodology to estimate crash frequency and severity;
- Develop a validated predictive methodology that can be used to estimate crash frequency and severity;
- Develop a spreadsheet tool to implement the application of the new methodology;
- Prepare input data sets for the research, the fused and integrated research data sets, data dictionaries, and steps in the data fusion and integration process as they are developed;
- Recommend considerations for developing calibration factors or functions;
- Prepare draft language and commentary for consideration by AASHTO to incorporate the research results in the next update of the AASHTO HSM (herein called AASHTO Deliverable); and
- Develop proposed documentation and guidance to provide practitioners with an enhanced understanding of the effects of driver behavior and characteristics on measureable safety performance to inform planning, design, and operations decisions.
The final deliverables shall include (1) the methodology to incorporate driver characteristics and behavior into safety prediction methods to estimate the expected crash frequency and severity related to infrastructure features for use in planning, design, and operational decisions; (2) a final report documenting the entire project and incorporating all other specified deliverable products of the research, including electronic files of all data used in the project and the results of the analyses conducted with the data as an appendix; (3) a scalable, accessible, changeable electronic presentation of the methodology that can be tailored for specific audiences, and a train-the-trainer toolkit; (4) recommendations for additional research; and (5) a stand-alone technical memorandum titled “Implementation of Research Findings and Products.”