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Record W4406195629 · doi:10.1016/j.trpro.2024.12.247

A conceptual framework for integrating urban freight deliveries with public transportation and crowdsourcing

2025· article· en· W4406195629 on OpenAlexaff
Fernando Zingler, Navneet Vidyarthi

Bibliographic record

VenueTransportation research procedia · 2025
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsConcordia UniversityHEC Montréal
Fundersnot available
KeywordsCrowdsourcingTransport engineeringConceptual frameworkPublic transportConceptual modelBusinessComputer scienceEngineeringWorld Wide WebSociologyDatabase

Abstract

fetched live from OpenAlex

The transformations in urban freight transportation resulting from the increase in e-commerce require novel approaches for integrating these flows with traditional passenger flows in the urban environment. The idea of integrating freight and passenger movements has recently been investigated in academia, aiming to utilize the spare capacity of public transportation to move cargo, thereby reducing congestion in densely populated areas. This paper proposes a conceptual framework for integrating freight deliveries and passenger transportation using the same vehicle and schedules, using scheduled lines (passenger transportation services), ridesharing alternatives (share-a-ride), and crowd shipping. Examples from real-world operations and quantitative analysis of systems are reviewed to construct a framework to assess the fundamental issues with integration. Different planning levels are presented, and integration issues are described, considering aspects of real-world examples in cities around the globe. This paper discusses how freight transportation can be integrated into city planning to reduce the externalities attributed to these operations while promoting a more economical and sustainable option for same-day deliveries.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.297
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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