Risk-type density diagrams by crash type on two-lane rural roads
Bibliographic Data
| ID | 12597291 |
|---|---|
| Authors | Gianluca Dell’Acqua (0000-0001-8697-7477, Ingegneria dei Trasporti (Italy)), Francesca Russo (0000-0002-1120-8953, Ingegneria dei Trasporti (Italy), corresponding author), Salvatore Antonio Biancardo (0000-0003-2567-7977, Ingegneria dei Trasporti (Italy)) |
| Year | 2013 |
| Volume | 16 |
| Issue | 10 |
| Pages | 1297-1314 |
| Publication date | 2013-05-24 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Risk Research (JOURNAL) |
| Journal identifiers | ISSN: 1366-9877 • E-ISSN: 1466-4461 |
| Publisher | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/13669877.2013.788547 |
| OpenAlex | W1997088137 |
| Language | EN |
| Citations received | 2 |
| References cited | 11 |
The research presented here aims to plot density diagrams per road crash risk type to identify all possible scenarios where driving is less than safe. The starting point was the prediction of injury crash rate on horizontal homogeneous segments of two-lane rural roads for three main injurious crash types (head-on/side collisions, rear-end crashes, and single-vehicle run-off-road crashes) as observed on the network. A careful analysis of the database shows that a wide variety of factors appear to be influenced or associated with the crash dynamic, as follows: the road scenario (combination of infrastructure and environmental conditions found at the site at the time of the crash), mean lane width, the horizontal curvature indicator (measurement of the curvature change rate), and mean speed. Crashes recorded from 2003 to 2010, of which 1597 were injurious, and 645 resulted only in damage to property, were analyzed on more than 3700 km of road network in Southern Italy. Generalized estimating equations with a negative binomial distribution were implemented. Risk-type density charts were plotted to thoroughly identify all possible combinations of existing explicative variables producing hazardous conditions on the road. The different shades in the diagrams represent different ranges of injurious crash rates: the white band shows low levels, while a black band shows high values. It is not possible to consider working on an explanatory variable to reduce hazardous conditions on the road network without also considering how this variation might affect the influence of the remaining explanatory variables on crash phenomena and, consequently, on the predictive model. The risk maps make it possible to keep under control in a simple and immediate approach the way each variable as a result of variations of a part or of all
Crash · Curvature · Econometrics · Geometry · Negative binomial distribution · Statistics · Transport engineering · Variable (mathematics · Automotive and Human Injury Biomechanics · Computer Science · Engineering · Environmental Science · Mathematics · Traffic and Road Safety · Urban Transport and Accessibility
| Citations per year | 0.17 |
|---|---|
| Citation span | 2014 - 2016 (3) |
| Highly cited | No |