Urban Air Mobility for Last-Mile Transportation: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urban air mobility (UAM) is a revolutionary approach to transportation in densely populated cities. UAM involves using small, highly automated aircraft to transport passengers and goods at lower altitudes within urban and suburban areas, aiming to transform how people and parcels move within these environments. On average, UAM can reduce travel times by 30% to 40% for point-to-point journeys, with even greater reductions of 40% to 50% in major cities in the United States and China, compared to land transport. UAM includes advanced airborne transportation options like electric vertical takeoff and landing (eVTOL) aircraft and unmanned aerial vehicles (UAVs or drones). These technologies offer the potential to ease traffic congestion, decrease greenhouse gas emissions, and substantially cut travel times in urban areas. Studying the applications of eVTOLs and UAVs in parcel delivery and passenger transportation poses intricate challenges when examined through the lens of operations research (OR). By OR approaches, we mean mathematical programming, models, and solution methods addressing eVTOL- and UAV-aided parcel/people transportation problems. Despite the academic and practical importance, there is no review paper on eVTOL- and UAV-based optimization problems in the UAM sector. The present paper, applying a systematic literature review, develops a classification scheme for these problems, dividing them into routing and scheduling of eVTOLs and UAVs, infrastructure planning, safety and security, and the trade-off between efficiency and sustainability. The OR methodologies and the characteristics of the solution methods proposed for each problem are discussed. Finally, the study gaps and future research directions are presented alongside the concluding remarks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it