Bibliographic record
Abstract
UrbanSim is an integrated transportation land-use model that has been under development since the late 1990s. It has received a significant amount of attention in the integrated modeling community. It is well known for its disaggregated approach. A number of papers describing the application of UrbanSim have appeared in the formal and gray literatures. Some of these papers report on successful applications of UrbanSim with little description of the amount of effort required to develop an operational model. Those that do report on the effort and challenges of using UrbanSim suggest that substantial data and human resources are required. One recent report quantifies the human resource requirements as an interdisciplinary team of four for three years. This reputation makes many potential users think twice before developing an UrbanSim model. We believe the best way to evaluate UrbanSim for a new region is by having a sense of how it can be used, and how much effort is required to do so. Understanding UrbanSim, however, does not require having a fully operational model. This paper is aimed at researchers and institutions that would like to evaluate UrbanSim, but are concerned about the effort required to do so. Based on two applications (Brussels in Belgium and Lausanne in Switzerland), it describes a procedure to develop a prototype UrbanSim model and how to use it to evaluate UrbanSim for application to a new region. Its objective is to motivate, describe, comment and illustrate a procedure for an efficient evaluation of the use of UrbanSim. Its main contributions are threefold. First, it develops a procedure by which a prototype UrbanSim model can be developed for evaluation purposes in a new region. Second, it provides an analysis of the effort required to do so. Finally, in so doing it advances knowledge in the field of transportation and land-use modeling by helping modelers to evaluate the use of UrbanSim for a particular study region, in a rigorous and systematic way.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".