Land use models and sustainable urban mobility plans: An integrative approach for strategic planning
Bibliographic record
Abstract
The notion of integrative and multidisciplinary approach in developing and implementing sustainable urban mobility plans (SUMPs) has been prevalent in the transportation planning agenda for several years now.The benefits of such approach include preparing better and public legitimate plans and promoting cooperative planning culture.In this context, European Commission (EC) currently promotes the concept of the SUMP, which can be defined as a strategic planning framework for the urban multimodal transport system combining multi-disciplinarity, policy analysis and decision making, while its objectives concise with the main pillars of sustainable urban mobility.Furthermore application guidelines for SUMP propose a combination of appropriate techniques and tools, for successful conduction of the activities and fulfilment of the requirements of the planning process.In this context, this paper argues that the use of Land Use Transport Interaction (LUTI) models could enhance the prospect of successful implementation of such plans.Therefore, it explores the possibility of integrating LUTI models in the various phases of a SUMP cycle.To do so, it starts with an investigation and recording of the different types of land use models and their functionality.It then specifies the criteria that someone should use in order to choose the appropriate LUTI model and it proposes a framework for the integration of LUTI models into a SUMP cycle.Finally, it discusses the expected benefits and drawbacks from such integration.The paper concludes that integration of LUTI models into the SUMP cycle, could enhance the strategic and communicative aspects of SUMPs, mainly due to the fact that LUTI models can be used as testing and evaluating tools of alternative 'mobility futures', and as tools to communicate and ensure mutual understanding amongst involved stakeholders and individuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".