| dc.contributor.author | Bridgelall, Raj | |
| dc.description.abstract | Advanced air mobility (AAM) is a sustainable aviation initiative to deliver cargo and passengers in urban and regional locations by electrified drones. The widespread expectation is that AAM adoption worldwide will help to reduce pollution, reduce transport costs, increase accessibility, and enable a more reliable and resilient supply chain. However, most countries lack regulations that legalize AAM. A fragmented regulatory approach hampers the progress of business prospectors and international organizations concerned with human welfare. Therefore, amidst high uncertainty, knowledge of indicators that can predict the propensity for AAM adoption will help nations and organizations plan for drone use. This research finds predictive indicators by assembling a unique dataset of 36 economic, social, environmental, governance, land use, technology, and transportation indicators for 204 nations. Subsequently, the best of 12 different machine learning models ranks the predictive importance of the indicators. The gross domestic product (GDP) and the regulatory quality index (RQI) developed by the Worldwide Governance Indicators (WGI) project were the two top predictors. Just as importantly, the poor predictors were as follows: the social progress index developed by the Social Progress Imperative, the WGI rule-of-law index, land use characteristics such as rural and urban proportions, borders on open waterways, population density, technology accessibility such as electricity and cell phones, carbon dioxide emission level, aviation traffic, port traffic, tourist arrivals, and roadway fatalities. | en_US |
| dc.rights | In copyright. Permission to make this version available has been granted by the author and publisher. | |
| dc.title | Predicting Advanced Air Mobility Adoption by Machine Learning | en_US |
| dc.type | Article | en_US |
| dc.description | Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM). | en_US |
| dc.date.accessioned | 2023-05-23T21:50:30Z | |
| dc.date.available | 2023-05-23T21:50:30Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://hdl.handle.net/10365/33171 | |
| dc.subject | social progress index | en_US |
| dc.subject | sustainable aviation | en_US |
| dc.subject | rule-of-law | en_US |
| dc.subject | regulatory quality index | en_US |
| dc.subject | government effectiveness index | en_US |
| dc.subject | political stability index | en_US |
| dc.subject | logistics performance index | en_US |
| dc.identifier.orcid | 0000-0003-3743-6652 | |
| dc.identifier.citation | Bridgelall, Raj. "Predicting Advanced Air Mobility Adoption by Machine Learning." Standards, DOI:10.3390/standards3010007, 3(1):70-83, March 2023. | en_US |
| dc.description.sponsorship | The U.S. Department of Transportation supported this work, grant number [Mountain Plains Consortium]. | en_US |
| dc.description.uri | https://www.ugpti.org/about/staff/viewbio.php?id=79 | |
| dc.language.iso | en_US | en_US |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.contributor.organization | Upper Great Plains Transportation Institute | |
| ndsu.college | College of Business | |
| ndsu.department | Transportation and Logistics | |
| dc.identifier.doi | DOI:10.3390/standards3010007 | |
