Charging Station Planning for Electric Ride-Hailing Taxi Services
Bibliographic record
Abstract
The development of the transportation sector towards green infrastructures plays a critical role towards mitigating climate change. Specifically, the trend of transitioning from Gas to Electric Vehicles (EVs) requires city planning for the optimal placements of EV Charging Stations (CS). With a future vision of converting all vehicles to electric, this paper examines the impact of CS locations placement on normal taxi operations. Optimized placement of stations have a significant impact on charger utilization and minimize charging queues leading to lower user wait times. A simulator was developed for modeling the taxi service in the Manhattan Borough of New York City. Factors such as taxi regions, DC stations, as well as real user data were used to closely mimic the system. The Particle Swarm Optimizer (PSO) was developed to optimize the CS locations showing the consequences of optimized CS placement on the taxi infrastructure in Manhattan. The results showed that the city of NYC would need approximately 80 DC fast CS, or 480 level 2 chargers placed mostly within midtown Manhattan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".