Shared Mobility for Last-Mile Delivery: Design, Operational Prescriptions, and Environmental Impact
Bibliographic record
Abstract
Two socioeconomic transformations, namely, the booms in the sharing economy and retail e-commerce, lead to the prospect where shared mobility of passenger cars prevails throughout urban areas for home delivery services. Logistics service providers as well as local governments are in need of evaluating the potentially substantial impacts of this mode shift, given their economic objectives and environmental concerns. This paper addresses this need by providing new logistics planning models and managerial insights. These models characterize open-loop car routes, car drivers’ wage-response behavior, interplay with the ride-share market, and optimal sizes of service zones within which passenger vehicles pick up goods and fulfill the last-mile delivery. Based on theoretical analysis and empirical estimates in a realistic setting, the findings suggest that crowdsourcing shared mobility is not as scalable as the conventional truck-only system in terms of the operating cost. However, a transition to this paradigm has the potential for creating economic benefits by reducing the truck fleet size and exploiting additional operational flexibilities (e.g., avoiding high-demand areas and peak hours, adjusting vehicle loading capacities, etc.). These insights are insignificantly affected by the dynamic adjustment of wages and prices of the ride-share market. If entering into this paradigm, greenhouse gas emissions may increase because of prolonged car trip distance; on the other hand, even exclusively minimizing operating costs incurs only slightly more emissions than exclusively minimizing emissions. The online appendix is available at https://doi.org/10.1287/msom.2017.0683 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".