Identifying crash risk factors and high risk locations on an interstate network
| Autor: | Matthew J. Heaton, E. Shannon Tass, Kaitlin E. Gibson |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
050210 logistics & transportation Operations research Computer science 05 social sciences Bayesian probability Crash risk Computer security computer.software_genre 01 natural sciences Point process 010104 statistics & probability symbols.namesake 0502 economics and business symbols Systemic approach 0101 mathematics Statistics Probability and Uncertainty human activities Spatial analysis Gaussian process computer |
| Zdroj: | Statistical Modelling. 18:95-112 |
| ISSN: | 1477-0342 1471-082X |
| Popis: | Highway safety improvement projects are identified by using either (i) a site-specific or (ii) a systemic approach. In the site-specific approach, locations for improvements are ranked according to different performance measures such as critical crash rate, expected crash rate or equivalent property damage only. Alternatively, in the systemic approach, roadway characteristics such as number of lanes, shoulder width, etc. are flagged as a ‘risk’ (or ‘preventative’) feature that increases (decreases) the risk of negative outcomes. Using the Highway Safety Information System database, we seek to merge the two approaches by, first, identifying roadway factors associated with an increased occurrence of car crashes (features we call ‘risk factors’) and, subsequently, identifying roadway segments with a higher crash risk. Specifically, we model the locations of crashes as a realization from a spatial point process. We then parameterize the associated intensity surface of this spatial point process as the sum of a regression on roadway characteristics and spatially correlated error terms. Thus, through the regression piece, we identify hazardous roadway features and through the spatially correlated error terms, we identify locations of high risk. |
| Databáze: | OpenAIRE |
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