Managing urban freight electrification: spatial insights into battery electric truck delivery demand
Bibliographic record
Abstract
The operational effectiveness of battery electric trucks (BETs) – a promising solution for mitigating the environmental impacts of urban freight—hinges on their spatiotemporal delivery patterns. Leveraging high-resolution GPS trajectory data from 2,652 BETs operating in Shanghai, this study employs spatial econometric models to examine how urban built environment factors shape BET delivery demand. Results reveal significant temporal variations, with delivery demands peaking during mid-day hours and being closely associated with specific land-use categories such as postal services, retail, and restaurants. Charging station density notably influences BET activities exclusively at mid-day, reflecting operational adjustments to battery limitations. BETs actively avoid congested central urban areas during peak times, highlighting the need for tailored urban freight management strategies, such as establishing dedicated zero-emission logistics zones and optimized charging infrastructure placement. This research advances the methodological understanding of electric freight demand modeling and provides actionable insights for policymakers aiming to enhance urban logistics efficiency and sustainability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".