Using behavioral economics to optimize safer undergraduate late‐night transportation

Abstract

AbstractMany universities sponsor student‐oriented transit services that could reduce alcohol‐induced risks but only if services adequately anticipate and adapt to student needs. Human choice data offer an optimal foundation for planning and executing late‐night transit services. In this simulated choice experiment, respondents opted to either (a) wait an escalating delay for a free university‐sponsored “safe” option, (b) pay an escalating fee for an on‐demand rideshare service, or (c) pick a free, immediately available “unsafe” option (e.g., ride with an alcohol‐impaired driver). Behavioral‐economic nonlinear models of averaged‐choice data describe preference across arrangements. Best‐fit metrics indicate adequate sensitivity to contextual factors (i.e., wait time, preceding late‐night activity). At short delays, students preferred the free transit option. As delays extend beyond 30 min, most students preferred competing alternatives. These data depict a policy‐relevant delay threshold to better safeguard undergraduate student safety.