Potential Effects of Automatic Vehicle Location and Computer-Aided Dispatch Technology on Paratransit Performance: A Simulation Study
Bibliographic record
Abstract
Automatic vehicle location (AVL) and computer-aided dispatch (CAD) systems have afforded a unique opportunity for public transit agencies to integrate these technologies in their paratransit systems for improved productivity and reliability. This opportunity has also prompted widespread interest in quantifying the benefits that can be attained from such technological enhancement. This research assesses the potential effects of AVL and CAD on the productivity and service reliability of a paratransit system. A simulation model that can realistically model AVL and CAD functionality is used in the investigation. Many cases representing variations in operating environment, such as service area, demand intensity, and proportion of real-time demand trips, are simulated for a sensitivity analysis under three assumed operational improvements—en route diversion, dwell time reduction, and periodic reoptimization. The results indicate that although AVL and CAD effectiveness varies from case to case, on average, these systems can help to substantially improve paratransit performance.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".