title: Prediction of accidents at full green and green arrow traffic lights in Switzerland with the aid of configuration-specific features.
abstract: In this study it was endeavored to predict full green and green arrow accidents at traffic lights, using configuration-specific features. This was done using the statistical method known as Poisson regression. A total of 45 sets of traffic lights (criteria: in an urban area, with four approach roads) with 178 approach roads were investigated (the data from two approach roads was unable to be used). Configuration-specific features were surveyed on all approach roads (characteristics of traffic lanes, road signs, traffic lights, etc.), traffic monitored and accidents (full green and green arrow) recorded over a period of 5 consecutive years. It was demonstrated that only between 23 and 34% of variance could be explained with the models predicting both types of accidents. In green arrow accidents, the approach road topography was found to be the major contributory factor to an accident: if the approach road slopes downwards, the risk of a green arrow accident is approximately five and a half times greater (relative risk, RR = 5.56) than on a level or upward sloping approach road. With full green accidents, obstructed vision plays the major role: where vision can be obstructed by vehicles turning off, the accident risk is eight times greater (RR = 8.08) than where no comparable obstructed vision is possible. From the study it emerges that technical features of traffic lights are not able to control a driver's actions in such a way as to eradicate error. Other factors, in particular the personal characteristics of the driver (age, sex, etc.) and accident circumstances (lighting, road conditions, etc.), are likely to make an important contribution to explaining how an accident occurs.
authors.affiliation: Swiss Council for Accident Prevention bfu, CP, CH-3001 Bern, Switzerland. m.hubacher@bfu.ch
authors.forename: Markus
authors.initials: M
authors.lastname: Hubacher
authors.forename: Roland
authors.initials: R
authors.lastname: Allenbach
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D006801:Humans; D016012:Poisson Distribution; D011897:Random Allocation; D012189:Retrospective Studies; D012449:Safety; D013557:Switzerland