The objective of the present study is twofold: (i) to explore driving behaviour of motorcyclists while speeding, based on detailed driving analytics collected by smartphone sensors, and (ii) to investigate whether personalized feedback can improve motorcyclist behaviour. The objectives are achieved through a naturalistic driving experiment with a sample of 20 motorcyclists based on a smartphone application developed within the framework of the BeSmart project. Using risk exposure and driving behaviour indicators calculated from smartphone sensor data, Generalized Linear Mixed-Effects Models are calibrated to correlate the percentage of driving time over the speed limit with other driving behaviour indicators. Results indicate that the parameters of trip duration, distance driven during risky hours, morning peak hours and the number of harsh accelerations have all been determined as statistically significant and positively correlated with the percentage of speeding time. Additionally, driver feedback and afternoon peak hours are statistically significant and negatively correlated with the percentage of speeding.
| ID | pc430 |
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| Tags | big data, driver behaviour, motorcyclists, naturalistic driving, speed, statistical modelling, telematics |
