Using naturalistic data to assess e-cyclist behavior
Artikel i vetenskaplig tidskrift, 2015

In Europe, the use of electric bicycles is rapidly increasing. This trend raises important safety concerns: Is their use compatible with existing infrastructure and regulations? Do they present novel safety issues? How do they impact other traffic? Monitoring e-cyclist behavior in real traffic would allow us to begin addressing these concerns. This study instrumented electric bicycles to monitor e-cyclists’ behavior in a naturalistic fashion. Data was collected from 12 bicyclists, each of whom rode an instrumented bicycle for two weeks. In total, 1500 km worth of data were collected, including 88 critical events (crashes and near-crashes). Analysis of these critical events identified pedestrians, light vehicles and other bicycles as main threats to a safe ride. Other factors also contributed to crash causation, such as being in proximity to a crossing or encountering a vehicle parked in the bicycle lane. A comparison between electric and traditional bicycles was enabled by the availability of data from a previous study a year earlier, which collected naturalistic cycling data from traditional bicycles using the same instrumentation as in this study. Electric bicycles were found to be ridden faster, on average, than traditional bicycles, in addition to interacting differently with other road users. The results presented in this study also suggest that countermeasures to bicycle crashes should be different for electric and traditional bicycles. Finally, increasing electric bicycle conspicuity appears to be the easiest, most obvious way to increase their safety.

Cycling safety

countermeasures.

electric bicycle

naturalistic data

road user interaction

Författare

Marco Dozza

Chalmers, SAFER - Fordons- och Trafiksäkerhetscentrum

Chalmers, Tillämpad mekanik, Fordonssäkerhet

Giulio Bianchi Piccinini

Chalmers, Tillämpad mekanik, Fordonssäkerhet

Julia Werneke

Chalmers, Tillämpad mekanik, Fordonssäkerhet

Transportation Research Part F: Traffic Psychology and Behaviour

1369-8478 (ISSN)

Vol. 41 217-226

Styrkeområden

Transport

Ämneskategorier

Data- och informationsvetenskap

Psykologi

DOI

10.1016/j.trf.2015.04.003

Mer information

Skapat

2017-10-08