The Impact of Advanced Traveler Information Services on Travel Time and Schedule Delay Costs
Bibliographic record
Abstract
This study develops an analytical dynamic mixed-equilibrium model to describe the transportation system performance with Route Planning and Guidance (RPG) services. The model considers travelers' departure time and route choices, and their propensity to subscribe to the service at different charge levels. We investigate the effect of the service charge on three parties: (i) equipped drivers; (ii) society in general; and (iii) the transportation management agency. The results show that the objectives of these parties are not always accommodative and may conflict with each other. Specifically, a numerical example illustrates that, under certain conditions, the society overall benefits from the service because all travelers are better off in terms of total generalized travel cost (i.e., travel time cost plus schedule delay cost). The surprising result, however, is that the network may become more severely congested. This seemingly paradoxical phenomenon is due to the much lower schedule delay cost incurred by travelers with RPG services, which more than compensates for the higher travel time associated with a more congested network. These results indicate the importance of considering RPG services from a more comprehensive perspective. In particular, it is important to include schedule delay cost as part of the benefit assessment of RPG services, in addition to the common measure of travel time cost.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".