Future Scenarios for Traffic Information and Management
Bibliographic record
Abstract
The limited coordination between public and private actors in the fields of traffic information and management has led to reduced efficiency and sometimes undesirable situations. The main objective of the Strategic Council for Traffic Information and Traffic Management installed by the Dutch Ministry of Transportation is to develop a joint strategy for the development and the organization of traffic information and traffic management by public authorities and private parties. This strategy will outline future developments and related actions, as well as the organization and roles of the relevant actors for traffic management and information activities. To satisfy these requirements, a proposed scenario-based approach entails sketching different scenarios to describe the situations in 2015, 2020, and 2028 for public and private stakeholders involved in traffic management and traffic information. The approach to determine these scenarios, the scenarios themselves, and their implications are described. The developed scenarios were built around the dimension of freedom of choice of the traveler. After extreme scenarios were identified, possible scenarios were sketched and were linked to instruments and to multiple objectives. On the basis of the scenarios, no-regret activities (those beneficial regardless of scenario) were identified as part of the robust strategy forming essential elements for all possible scenarios. These no-regret activities reflect an important outcome of the project; they entail setting up the value chain of traffic information, setting up a data warehouse to share all relevant data (including the functional and technical standards), and preparing for integrated network management and cooperative systems.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".