One of the goals of traffic safety specialists is to improve road geometry and traffic control at high-crash locations to reduce the risk of crash. The most difficult part of site investigation is determining the causes of crashes. This task may be overwhelming, particularly for inexperienced investigators. Experienced investigators often may have difficulties in connecting various pieces of information and knowledge due to the high level of uncertainty, the high complexity of safety impacts, and the gaps in what is known about driver performance during the crash occurrence. An AI method is proposed to help investigators identify the crash causes. A developed knowledge-based system utilizes information from two sources: (1) extracted from the crash database and (2) collected during a site investigation. It connects these pieces of information with relevant safety countermeasures.

We propose to deploy a tree-like structure of a knowledge base allowing for fast search of the solution. The knowledge needed for the tool has been sufficiently documented in published work. The knowledge base structure is transparent and understandable and a user can easily edit it. The safety improvements suggested by RSIT are well justified. The report generated by RSIT can be added to the final investigation report. The developed prototype software called Road Safety Investigation Tool (RSIT) is user-friendly. Its preliminary evaluation has brought promising results. The developed tool can reduce the required size of the investigation team and/or decrease the time spent at the investigated site.

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high-crash locations, safety countermeasures, safety improvements, knowledge-based system, road safety audits, SPR-2951

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Performing Organization

Joint Transportation Research Program

Publisher Place

West Lafayette, IN

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