Taxi Driver’s Operation Behavior and Passengers’ Demand Analysis Based on GPS Data
Bibliographic record
Abstract
The existing research outputs paid less attention to the relationship between land use and passenger demand, while the taxi drivers’ searching behavior for different lengths of observation period has not been explored. This paper is based on taxi GPS trajectories data from Shenzhen to explore taxi driver’s operation behavior and passengers’ demand. The taxi GPS trajectories data covers 204 hours in Shenzhen, China, which includes the taxi license number, time, longitude, latitude, speed, and whether passengers are in the taxi vehicle, to track the passenger’s pick-up and drop-off information. This paper focuses on these important topics: exploring the taxi driver operation behavior by the measurements of activity space and the connection between different activity spaces for different time duration; mainly focusing on eight traffic analysis zones (TAZs) of Shenzhen and exploring the customer’s real-time origin and destination demands on a spatial-temporal distribution on weekdays and weekends; taxi station optimization based on the passenger demand and expected customer waiting time distribution. This research can be helpful for taxi drivers to search for a new passenger and passengers to more easily find a taxi’s location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".