Aimed at the riding safety issue of food delivery riders in China who mainly travel by electric bikes, a naturalistic cycling study was conducted by collecting the naturalistic cycling data of dozens of food delivery riders in Changsha, China, to identify their riding characteristics. It was found that the participating food delivery riders are mainly undereducated young male adults, and the primary reason for them to take the job is the flexible working hours. Furthermore, they frequently work overtime and admit to often committing risky riding behaviors to deliver food on time. The analysis of their riding trajectories indicates that they delivered orders all day long, rather than just at mealtimes. They mainly work within 3 km of the delivery station, and the average riding radius was 2.39 km. Male riders, riders working less than one year, and riders with high school education had a relatively fast riding speed. These findings provide valuable new insights for agencies to understand the riding characteristics of food delivery riders and to formulate the appropriate countermeasures to improve their occupational safety.

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.

With the acceleration of population aging, the elderly driving safety issue is increasingly prominent. Method: With the crash data of Pennsylvania from 2010 to 2019, this study exclusively discusses features of single-vehicle hit-fixed-object crashes (SVHFOCs), one of the most common and deadliest crash types for elderly drivers. Results: Firstly, we demonstrate that elderly drivers are much more likely to be injured and killed than young drivers in SVHFOCs by checking crash consequences. The descriptive analysis indicates that elderly drivers have very different crash features from young drivers. They are found to drive with more caution in many aspects, such as more low-speed local travels, fewer illegal behaviors, fewer nighttime travels, etc. Then, a logistic regression model is built to find the factors significantly influencing the severity of SVHFOCs from driver, vehicle, roadway, and environment. The estimation results indicate that female sex, not wearing a seatbelt, DUI, rural area, and SUV involvement tend to be associated with more severe SVHFOCs. Additionally, illumination, weather, and road type could also significantly affect crash severity. Especially, SVHFOCs in adverse weather, in dark conditions, and at intersections are found to be less severe, which implies that elderly drivers might drive more carefully in complex environments. Practical Applications: These findings are expected to provide new insights for agencies in formulating customized measures to prevent elderly drivers from being involved in SVHFOCs.