Exploring motorcyclist speeding through naturalistic riding data

Abstract

Motorcyclists represent a vulnerable group of road users, often exhibiting elevated crash severity risk due to speeding and limited physical protection. This study explores speeding behavior among motorcyclists using smartphone-based naturalistic data, a relatively novel approach in motorcycle safety research. The aim of the study was to examine which factors influence rider speeding behavior. A total of 1,853 trips (36,169.3 km) from 19 participants were recorded via a phone application capable of detecting speeding and other riding dynamics in real-world traffic conditions. Linear regression was applied to investigate the effect of several potential predictors, including road type, average speed, speed over the limit, indicators of aggressive maneuvers (acceleration, braking), and contextual variables (daytime, weekend). The model explained 72% of the variance in speeding behavior, with the percentage of trip duration spent speeding as a dependent variable. Key predictors included the average speed over the limit, which was strongly positively associated with the observed outcome, and the proportion of motorway driving, displaying a negative association. Harsh accelerations also had a significant positive effect, while factors such as daylight and weekends did not yield significant effects. Findings highlight the potential of smartphone-based data collection for monitoring speeding in naturalistic settings. While several limitations were acknowledged, this study offers a valuable starting point for broader applications of mobile sensing in road safety, with potential implications for tailored feedback, rider coaching, and intelligent transport systems. Abstract Motorcyclists represent a vulnerable group of road users, often exhibiting elevated crash severity risk due to speeding and limited physical protection. This study explores speeding behavior among motorcyclists using smartphone-based naturalistic data, a relatively novel approach in motorcycle safety research. The aim of the study was to examine which factors influence rider speeding behavior. A total of 1,853 trips (36,169.3 km) from 19 participants were recorded via a phone application capable of detecting speeding and other riding dynamics in real-world traffic conditions. Linear regression was applied to investigate the effect of several potential predictors, including road type, average speed, speed over the limit, indicators of aggressive maneuvers (acceleration, braking), and contextual variables (daytime, weekend). The model explained 72% of the variance in speeding behavior, with the percentage of trip duration spent speeding as a dependent variable. Key predictors included the average speed over the limit, which was strongly positively associated with the observed outcome, and the proportion of motorway driving, displaying a negative association. Harsh accelerations also had a significant positive effect, while factors such as daylight and weekends did not yield significant effects. Findings highlight the potential of smartphone-based data collection for monitoring speeding in naturalistic settings. While several limitations were acknowledged, this study offers a valuable starting point for broader applications of mobile sensing in road safety, with potential implications for tailored feedback, rider coaching, and intelligent transport systems.