The Highway Safety Manual (HSM) procedures provide robust methods for conducting site-specific safety analyses known as micro-level analyses. These micro-level analyses can either be in reaction to evaluating alternatives to fix an identified black spot (i.e., reactive), or as part of an entirely new facility planned for an area (i.e., proactive). Micro-level analysis procedures such as the predictive method and Crash Modification Factors presented in the HSM are significant tools that highway agencies are beginning to integrate into their safety management and design procedures and practices. Micro-level analysis tools are suitable for analysis of specific intersections or roadway segments. They make it possible to consider the safety impacts of alternative design features such as the number or type of lanes, shoulder width, or intersection control. In contrast to micro-level analysis procedures for site-specific or corridor situations, macro-level analysis procedures perform analyses on an area-wide basis – entire neighborhoods, cities, and/or regions. Macro-level analysis procedures can be used to incorporate safety prediction into area-wide, long-range transportation system planning, programming, and policy development. The idea of predicting crashes within a given area, such as census tracts or traffic analysis zones (TAZs), allows agencies to identify safety concerns that may not be apparent by examining crash patterns at an intersection or segment scale. This could potentially allow planners to address safety concerns prior to their full materialization. For example, a macro-level model may indicate that changing demographics could result in a sharp uptick in fatalities among the older driver population in a particular section of a city. As a result, agencies could implement wider lane striping and larger sign lettering in that area, or work to improve transit service to older populations. Moreover, macro-level models would help quantify that uptick such that a robust cost benefit analysis could be used to justify one or several of these investments in response to this changing demographic. The ability to analyze and respond to these types of concerns is not currently accounted for in the HSM Predictive Methods. To avoid reactive expensive infrastructure retrofits, macro-level predictive models developed to a standard consistent with the Predictive Method in Part C of the HSM would make it possible to integrate area-wide quantitative safety into planning procedures and programs.
The objectives of this research are to develop validated and demonstrated quantitative macro-level safety prediction models and a quantitative safety planning chapter for the AASHTO HSM intended for use by transportation practitioners at all levels. This includes a guidance document on the development and application of these models, methods to integrate the model results into planning procedures, and electronic analysis tools for applying the models in practice. The research results are intended as a new chapter for inclusion in a future edition of the HSM. The results should address a broad range of safety planning level issues related to macro-level models such as, but not limited to, geography, demographics, transportation modes and modal interaction, existing or planned land-use and/or transportation projects, model transferability, calibration needs, and associated data limitations.
A kick-off teleconference of the research team and NCHRP shall be scheduled within 1 month of the contract’s execution. The work plan should be divided into two phases with tasks, with each task described in detail. Phase 1 culminating in the submission of an interim report will consist of, at a minimum, an outline or framework of the proposed chapter; preliminary models; and validation process including completed, planned, and anticipated pilot studies to validate the models. The interim report will describe the work completed in the Phase 1 tasks and provide an updated work plan for the Phase 2 tasks to complete the project objectives. The updated Phase 2 work plan should address the manner in which the proposer intends to use the information developed in Phase 1 to satisfy the project objectives. The interim report and panel meeting should occur after the expenditure of no more than 35 percent of the project budget. Development of electronic analysis tools for applying the models in practice shall be limited to no more than 10 percent of the overall project budget. There must be a face-to-face meeting with NCHRP to discuss the interim report. No work shall be performed on Phase 2 without NCHRP approval. At the completion of the research, the final deliverables at a minimum shall include: (1.) A final report documenting the entire project, incorporating all other specified deliverables that also includes an executive summary and the research team’s recommendation of research needs and priorities for related research. (2.) A stand-alone comprehensive compilation of materials, macro-level safety prediction models, practitioner guidance for use of the models, and chapter text intended for incorporation in a format suitable as a new chapter for possible adoption in the next edition of the HSM. (3.) The electronic analysis tools and accompanying stand-alone guidance document for applying the macro-level models. (4.) An electronic or PowerPoint presentation describing the project background, objective, research method, findings, and guidelines that summarizes the project and that can be tailored for specific audiences. (5.) A webinar on the results of the research and the deliverables. (6.) Recommendations for the development of a full suite of data-driven tools (e.g., spreadsheets, software, checklists, and/or enhancements to existing electronic tools) for application of the models based on the research results, including content, budget, and program requirements. (7.) A stand-alone technical memorandum titled “Implementation of Research Findings and Products”. Proposers may recommend additional deliverables to support the project objectives.
STATUS: Research in progress.