Transport Planning, Organisation and Management
Bibliographic record
Abstract
High traffic volumes in transport networks are currently among the key issues addressed by municipal authorities of contemporary cities.The complex nature of traffic and its consequences, such as e.g.emission of exhausts and noise, negative environmental impact, costs, time, deterioration of public space, requires appropriate transport planning, organisation and management conducted in a comprehensive manner, aimed at maintaining balance in the transport system.On the other hand, efficient transport management and organisation requires information concerning transport behaviour patterns observed in the travelling population.What proves to be crucial is the knowledge of the travel source, destination as well as the travelling mode and individual traffic routes.Measurements conducted for purposes of traffic modelling do not usually allow for precise analysis and visualisation of characteristics of transport multimodality.For the sake of sustainable development of transport, modal split must be oriented towards eco-friendly solutions, and thus also towards an increase in the share of multimodal travel.Such a need also stems from the growing dynamics of travelling and relocation of traffic generators and absorbers.The authors conducted studies of transfers made in the travelling population on a real time basis, i.e. with increased accuracy compared to traditional research methods typically applied so far.Under the Green Travelling project, mobile surveys of transfers made in the territory of the city of Gliwice (Upper Silesian conurbation, Poland) were conducted by monitoring transfers of persons through tracking of mobile phones featuring GPS receivers.The studies were aimed to enable identification of the inhabitants' transfers, paying special attention to the chains generated in the process.Such an approach makes it possible to account for the specificity of multimodal travels and their dynamic evolution in traffic modelling.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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".