Making Evidence-based Crash Risk Estimation Routine by using the SESA Process
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 be...
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| Format: | Article |
| Language: | English |
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Australasian College of Road Safety
2020-02-01
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| Series: | Journal of Road Safety |
| Online Access: | https://acrs.org.au/files/papers/arsc/2020/Making%20evidence-based%20crash%20risk%20estimation%20routine%20by%20using%20the%20SESA%20process.pdf |
| _version_ | 1818888606049107968 |
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| author | Shane Turner Paul Durdin Shendi Mani |
| author_facet | Shane Turner Paul Durdin Shendi Mani |
| author_sort | Shane Turner |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T16:55:47Z |
| format | Article |
| id | doaj.art-5120108da24344f28bd56baa5e2de300 |
| institution | Directory Open Access Journal |
| issn | 2652-4260 2652-4252 |
| language | English |
| last_indexed | 2024-12-19T16:55:47Z |
| publishDate | 2020-02-01 |
| publisher | Australasian College of Road Safety |
| record_format | Article |
| series | Journal of Road Safety |
| spelling | doaj.art-5120108da24344f28bd56baa5e2de3002022-12-21T20:13:25ZengAustralasian College of Road SafetyJournal of Road Safety2652-42602652-42522020-02-01311405010.33492/JRS-D-19-00125Making Evidence-based Crash Risk Estimation Routine by using the SESA ProcessShane TurnerPaul DurdinShendi ManiAchieving 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.https://acrs.org.au/files/papers/arsc/2020/Making%20evidence-based%20crash%20risk%20estimation%20routine%20by%20using%20the%20SESA%20process.pdf |
| spellingShingle | Shane Turner Paul Durdin Shendi Mani Making Evidence-based Crash Risk Estimation Routine by using the SESA Process Journal of Road Safety |
| title | Making Evidence-based Crash Risk Estimation Routine by using the SESA Process |
| title_full | Making Evidence-based Crash Risk Estimation Routine by using the SESA Process |
| title_fullStr | Making Evidence-based Crash Risk Estimation Routine by using the SESA Process |
| title_full_unstemmed | Making Evidence-based Crash Risk Estimation Routine by using the SESA Process |
| title_short | Making Evidence-based Crash Risk Estimation Routine by using the SESA Process |
| title_sort | making evidence based crash risk estimation routine by using the sesa process |
| url | https://acrs.org.au/files/papers/arsc/2020/Making%20evidence-based%20crash%20risk%20estimation%20routine%20by%20using%20the%20SESA%20process.pdf |
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