Autonomous Ride-Hailing Services: A Scalable Heuristic Approach for Efficient Transportation
Bibliographic record
Abstract
One significant aspect of smart cities is the emergence of Autonomous Electric Vehicles (AEVs), which are poised to revolutionize transportation systems, offering an intelligent transportation system that surpasses traditional modes in terms of reducing carbon emissions, environmental pollution, congestion, transportation costs, and wait times. In this context, ride-hailing systems will play a pivotal role by leveraging shared AEVs to optimize transportation efficiency. This paper presents an in-depth exploration of an efficient Autonomous Driving System (ARH) that leverages a fleet of autonomous electric vehicles, enabling riders to seamlessly schedule trips while the intelligent system allocates suitable vehicles and integrates multiple ride requests. Our proposed solution aims to minimize arrival times for both AEVs and passengers by accepting the maximum number of feasible requests. To strike a balance between passenger satisfaction and system profitability, this study introduces a novel heuristic approach called Minimization of Passenger and Vehicle Time (MPVT). By adopting this approach, we aim to overcome the challenges associated with complexity and deliver a satisfactory experience for passengers while maximizing profitability for the system operator.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".