Introducing naturalistic cycling data: What factors influence bicyclists' safety in the real world?
Artikel i vetenskaplig tidskrift, 2014

Presently, the collection and analysis of naturalistic data is the most credited method for understanding road user behavior and improving traffic safety. Such methodology was developed for motorized vehicles, such as cars and trucks, and is still largely applied to those vehicles. However, a reasonable question is whether bicycle safety also can benefit from the naturalistic methodology, once collection and analyses are properly ported from motorized vehicles to bicycles. This paper answers this question by showing that instrumented bicycles can also collect analogous naturalistic data. In addition, this paper shows how naturalistic cycling data from 16 bicyclists can be used to estimate risk while cycling. The results show that cycling near an intersection increased the risk of experiencing a critical event by four times, and by twelve times when the intersection presented some form of visual occlusion (e.g., buildings and hedges). Poor maintenance of the road increased the risk tenfold. Furthermore, the risk of experiencing a critical event was twice as large when at least one pedestrian or another bicyclist crossed the bicyclist’s trajectory. Finally, this study suggests the two most common scenarios for bicycle accidents, which result from different situations and thus require different countermeasures. The findings presented in this paper show that bicycle safety can benefit from the naturalistic methodology, which provides data able to guide development and evaluation of (intelligent) countermeasures to increase cycling safety.

Författare

Marco Dozza

Chalmers, SAFER - Fordons- och Trafiksäkerhetscentrum

Chalmers, Tillämpad mekanik, Fordonssäkerhet

Julia Werneke

Chalmers, SAFER - Fordons- och Trafiksäkerhetscentrum

Chalmers, Tillämpad mekanik, Fordonssäkerhet

Transportation Research Part F: Traffic Psychology and Behaviour

1369-8478 (ISSN)

Styrkeområden

Transport

Ämneskategorier

Data- och informationsvetenskap

Psykologi