A novel approach to collision hotspot identification accounting for regression to the mean and trend
<p dir="ltr">This research considers a Bayesian analysis of crash data in an attempt to predict, from a group of potential collision hotspot sites, which of these sites could benefit from treatment with a road safety scheme. Intrinsic to the analysis is the identification of trend an...
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2015
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| _version_ | 1864513523128729600 |
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| author | Timo Hoffmann (10888739) |
| author2 | Lee Fawcett (19774536) Neil Thorpe (19774539) Fabio Galatioto (19774542) Karsten Kremer (19774545) Ane Münch (19774548) Peter Slater (9260498) |
| author2_role | author author author author author author |
| author_facet | Timo Hoffmann (10888739) Lee Fawcett (19774536) Neil Thorpe (19774539) Fabio Galatioto (19774542) Karsten Kremer (19774545) Ane Münch (19774548) Peter Slater (9260498) |
| author_role | author |
| dc.creator.none.fl_str_mv | Timo Hoffmann (10888739) Lee Fawcett (19774536) Neil Thorpe (19774539) Fabio Galatioto (19774542) Karsten Kremer (19774545) Ane Münch (19774548) Peter Slater (9260498) |
| dc.date.none.fl_str_mv | 2015-11-12T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.5339/jlghs.2015.itma.36 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_novel_approach_to_collision_hotspot_identification_accounting_for_regression_to_the_mean_and_trend/27144600 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Commerce, management, tourism and services Transportation, logistics and supply chains Mathematical sciences Statistics Bayesian Analysis Crash Data Collision Hotspots Road Safety Scheme Time Series Analysis Predictive Distribution |
| dc.title.none.fl_str_mv | A novel approach to collision hotspot identification accounting for regression to the mean and trend |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This research considers a Bayesian analysis of crash data in an attempt to predict, from a group of potential collision hotspot sites, which of these sites could benefit from treatment with a road safety scheme. Intrinsic to the analysis is the identification of trend and site-specific regression to the mean (RTM) effects. As in a standard retrospective before-after study to evaluate the effectiveness of a change in e.g. the geometric design of an intersection, observed collision rates are adjusted using values from a suitable crash prediction model (CPM). In any year, collision rates, which are unusually high/low will be suitably depressed/inflated according to the posterior distributions for collision rates at each site, hence giving a more realistic summary of safety in that year. Where site characteristic information (e.g. annual figures for average speed or traffic flow) for use in the CPM is limited, standard techniques from time series analysis are employed to exploit any time dependent (autoregressive) structure observed in historical collision rates at each site. The Bayesian posterior predictive distribution is then used to predict collision rates at each site in future years, having adjusted for trend, RTM and any autoregression in collision rates at each site. This equips road safety practitioners with the necessary methodology to identify, and possibly treat, such locations before these collisions occur and this has the potential to help, inform, and direct, investment in future road safety schemes. In this research, crash data from the United Kingdom and Germany were analysed, and results have shown that this methodology is transferable between regions. The methodology is currently being implemented into a prototype software application to be tested by local road safety practitioners later in the year in the UK.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Journal of Local and Global Health Science, title discontinued as of (2017)<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.5339/jlghs.2015.itma.36" target="_blank">https://dx.doi.org/10.5339/jlghs.2015.itma.36</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_88b99488a33b6c0e691c1f3ca02c3366 |
| identifier_str_mv | 10.5339/jlghs.2015.itma.36 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27144600 |
| publishDate | 2015 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A novel approach to collision hotspot identification accounting for regression to the mean and trendTimo Hoffmann (10888739)Lee Fawcett (19774536)Neil Thorpe (19774539)Fabio Galatioto (19774542)Karsten Kremer (19774545)Ane Münch (19774548)Peter Slater (9260498)Commerce, management, tourism and servicesTransportation, logistics and supply chainsMathematical sciencesStatisticsBayesian AnalysisCrash DataCollision Hotspots RoadSafety SchemeTime Series AnalysisPredictive Distribution<p dir="ltr">This research considers a Bayesian analysis of crash data in an attempt to predict, from a group of potential collision hotspot sites, which of these sites could benefit from treatment with a road safety scheme. Intrinsic to the analysis is the identification of trend and site-specific regression to the mean (RTM) effects. As in a standard retrospective before-after study to evaluate the effectiveness of a change in e.g. the geometric design of an intersection, observed collision rates are adjusted using values from a suitable crash prediction model (CPM). In any year, collision rates, which are unusually high/low will be suitably depressed/inflated according to the posterior distributions for collision rates at each site, hence giving a more realistic summary of safety in that year. Where site characteristic information (e.g. annual figures for average speed or traffic flow) for use in the CPM is limited, standard techniques from time series analysis are employed to exploit any time dependent (autoregressive) structure observed in historical collision rates at each site. The Bayesian posterior predictive distribution is then used to predict collision rates at each site in future years, having adjusted for trend, RTM and any autoregression in collision rates at each site. This equips road safety practitioners with the necessary methodology to identify, and possibly treat, such locations before these collisions occur and this has the potential to help, inform, and direct, investment in future road safety schemes. In this research, crash data from the United Kingdom and Germany were analysed, and results have shown that this methodology is transferable between regions. The methodology is currently being implemented into a prototype software application to be tested by local road safety practitioners later in the year in the UK.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Journal of Local and Global Health Science, title discontinued as of (2017)<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.5339/jlghs.2015.itma.36" target="_blank">https://dx.doi.org/10.5339/jlghs.2015.itma.36</a></p>2015-11-12T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.5339/jlghs.2015.itma.36https://figshare.com/articles/journal_contribution/A_novel_approach_to_collision_hotspot_identification_accounting_for_regression_to_the_mean_and_trend/27144600CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/271446002015-11-12T09:00:00Z |
| spellingShingle | A novel approach to collision hotspot identification accounting for regression to the mean and trend Timo Hoffmann (10888739) Commerce, management, tourism and services Transportation, logistics and supply chains Mathematical sciences Statistics Bayesian Analysis Crash Data Collision Hotspots Road Safety Scheme Time Series Analysis Predictive Distribution |
| status_str | publishedVersion |
| title | A novel approach to collision hotspot identification accounting for regression to the mean and trend |
| title_full | A novel approach to collision hotspot identification accounting for regression to the mean and trend |
| title_fullStr | A novel approach to collision hotspot identification accounting for regression to the mean and trend |
| title_full_unstemmed | A novel approach to collision hotspot identification accounting for regression to the mean and trend |
| title_short | A novel approach to collision hotspot identification accounting for regression to the mean and trend |
| title_sort | A novel approach to collision hotspot identification accounting for regression to the mean and trend |
| topic | Commerce, management, tourism and services Transportation, logistics and supply chains Mathematical sciences Statistics Bayesian Analysis Crash Data Collision Hotspots Road Safety Scheme Time Series Analysis Predictive Distribution |