The Use of Road Microsimulation Software within BIM Environments: A Preliminary Assessment
Bibliographic record
Abstract
The use of Building Information Modelling (BIM) is becoming increasingly adopted worldwide to support the creation and management of digital environments in which physical and functional features of a facility are modelled. Due to its characteristic flexibility and interoperability, it rapidly extends beyond the boundaries of applications for which it was originally conceived, thus being implemented in other contexts such as the infrastructure sector. Indeed, during each phase of the construction process for building a linear infrastructure, a suitable design of a traffic control plan to optimising traffic flows proves crucial, both for drivers and builders. In this context, there arises the need to perform a comprehensive analysis, combining infrastructure design and building issues with transportation theory principles. For this purpose, the paper proposes a comparative analysis of two different approaches relying on BIM-based tools, namely integrated (i.e., BIM environments with an in-built module for transport simulation tasks) and an interfaceable solution (i.e., BIM environments offering a customised interface for external mobility simulation software). Specifically, after an overview of the issues involved, the two solutions analysed are customised according to the software packages adopted, and a numerical application is carried out. The goal of the paper is to perform a preliminary analysis on the potential of such frameworks by means of comparative evaluation aimed at identifying pros and cons of the two approaches, as well as the best field of application, according to the effort required from the designer, the degree of flexibility offered, and the target pursued.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".