Past Presidents' Award for Merit in Transportation Engineering: Assessing Transportation Policy Using an Activity-Based Microsimulation Model of Travel Demand
Why this work is in the frame
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Bibliographic record
Abstract
For over 50 years, travel demand models have provided decision support for transportation infrastructure planning. However, the current emphasis on travel demand management policies, which allows for more efficient use of existing road and transit capacity, requires improved methods of analysis. This paper presents a new operational prototype microsimulation model of travel and activity scheduling for household agents (TASHA). This model provides more precise outputs than current state-of-practice models with little increase in supporting data requirements. The functionality of the model is summarized. The model is well-suited to assess alternative hours (e.g., flexible working hours and telecommuting), high-occupancy vehicle lanes and intelligent transportation system initiatives. The model is applied to several transportation policy problems in Toronto to demonstrate the potential benefits of the TASHA modeling approach. The model shows significant promise, although more research is needed before the TASHA system is ready for large-scale implementation.
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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 | 0.001 |
| 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 it