@article{ref1,
title="Using behavioral economics to optimize safer undergraduate late-night transportation",
journal="Journal of applied behavior analysis",
year="2023",
author="Gelino, Brett W. and Graham, Madison E. and Strickland, Justin C. and Glatter, Hannah W. and Hursh, Steven R. and Reed, Derek D.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Many 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 &quot;safe&quot; option, (b) pay an escalating fee for an on-demand rideshare service, or (c) pick a free, immediately available &quot;unsafe&quot; 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.<p /> <p>Language: en</p>",
language="en",
issn="0021-8855",
doi="10.1002/jaba.1029",
url="http://dx.doi.org/10.1002/jaba.1029"
}