Using Behavioral Economics to Optimize Safer Undergraduate Late-Night Transportation
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| Title: | Using Behavioral Economics to Optimize Safer Undergraduate Late-Night Transportation |
|---|---|
| Language: | English |
| Authors: | Brett W. Gelino (ORCID |
| Source: | Journal of Applied Behavior Analysis. 2024 57(1):117-130. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Undergraduate Students, Student Transportation, Evening Programs, Student Behavior, School Safety, Student Needs, Simulation, Preferences |
| DOI: | 10.1002/jaba.1029 |
| ISSN: | 0021-8855 1938-3703 |
| 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 "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. |
| Abstractor: | As Provided |
| Notes: | https://osf.io/qrd2w |
| Entry Date: | 2024 |
| Accession Number: | EJ1418362 |
| Database: | ERIC |
| 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 "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. |
|---|---|
| ISSN: | 0021-8855 1938-3703 |
| DOI: | 10.1002/jaba.1029 |
