Time is money: Effects of time-sensitive incentive on risky driving among on-demand food-delivery motorcyclists in Taiwan

The gig economy has witnessed prevalent risky driving among on-demand food-delivery (ODFD) drivers worldwide, including speeding, red-light running, and improper lane-changing. While studies have linked this trend to digital platforms' business models, there remains a research gap in understanding how time-sensitive incentive strategies impact ODFD workers' risky driving during real-world operations due to limited evidence in naturalistic driving conditions. Using unmanned aerial vehicles (UAV) videography collected at signalized intersections in Taiwan, we examined risk-taking behavior among ODFD drivers under two different incentive structures. We analyzed two indicators of risky driving: discharge acceleration and weaving maneuvers, derived from 0.1-second resolution vehicular trajectories extracted using AI-based video recognition software. We compared these indicators across three driver groups: non-ODFD motorcyclists and two ODFD motorcyclist groups from Taiwan's major ODFD platforms—UberEats and Foodpanda. We found that UberEats motorcyclists, operating under the "cumulative basis model", where incentives depend on the number of completed tasks accumulated within a strict period, consistently exhibited harsher discharge acceleration and more frequent leftward weaving than non-ODFD motorcyclists. In contrast, Foodpanda motorcyclists, under the "temporary basis model" with higher pay rates during high-demand bonus hours, engaged in even riskier driving by increasing rightward weaving frequency, particularly during bonus hours. Our intra-ODFD analysis extends beyond prior work comparing ODFD and non-ODFD drivers, highlighting how incentive structures subtly influence ODFD motorcyclists' risk-taking behavior and contributing to road safety literature within the ODFD sector. Regulations should mitigate road risks externalized by the incentive strategies prevalently used by digital platforms in the gig economy.