A paper titled “Investigation of the speeding behavior of motorcyclists through an innovative smartphone application“, authored by Armira Kontaxi, Apostolos Ziakopoulos and George Yannis is published in Traffic Injury Prevention.  Using risk exposure and riding behavior indicators calculated from smartphone sensor data, Generalized Linear Mixed-Effects Models are calibrated to correlate the percentage of riding time over the speed limit with other riding behavior indicators. Results indicate that the parameters of trip duration, distance driven during risky hours, morning peak hours and the number of harsh accelerations are all determined as statistically significant and positively correlated with the percentage of speeding time. Additionally, the provision of rider feedback and riding during afternoon peak hours are statistically significant and correlated with decreased percentages of speeding time.