Using naturalistic data to assess e-cyclist behavior
Journal article, 2016

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

road user interaction

naturalistic data

electric bicycle



Marco Dozza

Vehicle and Traffic Safety Centre at Chalmers

Chalmers, Applied Mechanics, Vehicle Safety

Giulio Bianchi Piccinini

Chalmers, Applied Mechanics, Vehicle Safety

Julia Werneke

Chalmers, Applied Mechanics, Vehicle Safety

Transportation Research Part F: Traffic Psychology and Behaviour

1369-8478 (ISSN)

Vol. 41 217-226

Areas of Advance


Subject Categories

Computer and Information Science




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3/2/2020 1