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Abstract
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Key Findings
• The case studies presented in this paper demonstrate how more robust road safety assessments are possible using the SESA process and various crash prediction tools
• This process can be used to estimate the all-injury and fatal-and-serious injury crash risk at an existing intersection and for various improvement options, even when the intersections have complex and unusual layouts.
• That new crash risk assessment methods like X-KEMM-X can be incorporated into the process to develop even better predictions of expected severe crash risks.
Achieving safe system or vision zero outcomes at high-risk urban intersections, especially priority cross-roads and high- volume traffic signals, is a major challenge for most cities. Even after decades of crash analysis and improvement works many of these intersections still perform poorly. While best practice for optimising the efficiency of intersections requires the use of modelling tools, like Sidra, this is rarely the case with optimising road safety outcomes. This is despite the large number of evidence-based safety analysis models and tools that are now available to understand intersection crash risk. This paper outlines the SESA (Site-specific Evidence-based Safety Analysis) Process that has been developed to enable transport professionals to estimate and predict crash risk at intersections and other sites. This process utilises existing crash risk estimation tools (based on crash prediction models and crash reduction factors), relevant road safety research, crash severity factors, professional judgement and crash data to predict the underlying crash risk at intersections (and other sites) and the effectiveness of improvement options. The output includes both the number and return period of 'all injury' and 'fatal and serious injury (FSI)' crashes for each option. The paper includes three applications of the process to high risk intersections in three New Zealand cities, consisting of two priority cross-roads and one high speed roundabout. The case studies demonstrate how the process can be used to assess intersection features and improvement options that are not covered within the available crash estimation tools.
Keywords Evidence-base crash analysis, crash risk estimation, crash prediction models, safety performance functions (SPFs), X-KEMM-X
Language: en |