Freight Demand Distribution in a Suburban Area: Calibration of an Acquisition Model with Floating Car Data
Bibliographic record
Abstract
The paper deals with the interaction between freight flows, land use, and economy of a suburban area. In urban freight distribution, the process of freight acquisition of commercial establishments represents a crucial element. Therefore, it is necessary to plan city logistics in order to reduce the negative externalities generated by freight transport without depressing the economic and social vitality. In order to estimate and evaluate the performance and impacts generated by urban freight distribution, Transport System Models (TSMs) play an important role in reproducing and forecasting the process of distribution of final products to retailers and consumers. In recent years, Information and Communication Technologies (ICTs) have made new sources of data in great amounts available at lower costs than data provided from traditional surveys. Among the different data sources, Floating Car Data (FCD), obtained through GPS trackers installed on board the vehicles, offer partial insights into the examined process with lower cost and provide continuous information in space and time. In order to obtain reliable results, it is necessary to combine TSMs and ICTs as observed data could feed Transport System Models and could allow their calibration and therefore their application in real cases. In the above context, the paper deals with TSMs related to estimating urban freight demand flows. The work carried out concerns the specification-calibration-validation of a freight zonal acquisition model by means of FCD related to interzonal trips of commercial vehicles inside the Locride area (Reggio Calabria, Italy). The obtained models, estimated from FCD, could support the a priori evaluation of city logistics measures. The results are more important considering that they are obtained in an area with a lack of transport monitoring technologies. They could be adopted in other similar areas and confirm that adequate planning of city logistics determines advantages for environmental, social, and economic 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".