Using Traffic Simulation and Geographic Information Systems in Truck Route Planning
Bibliographic record
Abstract
The rapid increase in truck traffic put many cities in the forefront to deal with the economic and environmental challenges associated with it. Many studies have been conducted in the realm of truck freight movement yet there remains a need for research tools to support the important role of cities in truck route planning. This paper argues that traffic simulation linked to emission models coupled with geographic information systems (GIS) can be used effectively to support truck route planning process in cities. To demonstrate the usefulness of these tools, this paper presents the application of traffic simulation and GIS in evaluating the truck route alternatives in the City of Hamilton, Canada. The truck route alternatives are compared using network system usage and performance indicators generated through TRAFFIC, the application used for traffic simulation. Some useful evaluation indicators are derived using GIS that reflect the main considerations of the truck route master plan. The evaluation results show that there is negligible difference between the proposed truck route alternatives from the existing truck routes in terms of measures and derived indicators. The traffic simulation linked to an emission model effectively provides useful measures and indicators that support the evaluation of truck route alternatives. The maps generated through GIS serve as a discussion platform in the evaluation of truck route alternatives. These tools can be further tested in truck route planning for other cities.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".