Identification of the factors influencing speeding behaviour of food delivery e-bikers in China with the naturalistic cycling data

Abstract

With the rapid growth of the gig economy in China, millions of food delivery e-bikers are making their living by rushing on the street. Speeding is one of their most common risky riding behaviours, leading to severe traffic crashes. Based on 2-month naturalistic cycling data of 46 full-time food delivery e-bikers in Changsha, their speeding behaviour is deeply studied with the individual daily speeding proportion being taken as the speeding indicator. A beta regression model is built to identify the factors significantly influencing the indicator. The estimation results reveal that female riders, middle-aged riders and riders with a bachelor’s degree are less likely to engage in speeding. The same result is indicated for those working longer or experiencing more crashes. Additionally, holidays and riding distance are found to have significantly positive influences. Finally, some countermeasures are proposed to prevent speeding among food delivery e-bikers.