The impact of time aggregation and travel time models on time-dependent routing solutions
Bibliographic record
Abstract
Traffic and congestion have a big impact on the performance of transportation systems. Travel time models are required to calculate trip durations and arrival times when traffic information is available. These models rely on the availability of detailed information about the traffic state. With the growing availability of onboard devices, we can now capture very precise data with a very high frequency. The challenge is now to efficiently use these data to solve routing problems and evaluate routing solutions. A key question that emerges is how to determine the best compromise between a huge amount of very precise data and a smaller volume of aggregated data. In this article, we analyze the impact of time aggregation on the performance of the main travel time models, namely the link travel model (LTM), the flow speed model (FSM) and the smoothed travel time model (STTM). We also analyze the impact of different time aggregation levels on these models. Our results show that all models share similar performance, particularly with large intervals. Finally, we show that the LTM largely respects the FIFO property, which is an important hypothesis for routing algorithms.
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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 | 0.004 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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 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".