Inferring the Purposes of using Ride-Hailing Services through Data Fusion of Trip Trajectories, Secondary Travel Surveys, and Land Use Data
Bibliographic record
Abstract
This paper presents a data fusion methodology for inferring trip purposes from GPS trajectories of ride-hailing services in Toronto. The methodology has a discrete choice model at its core that predicts the most probable purpose distributions using only basic trip-related information such as approximate pick-up and drop-off locations, trip start times, and land use characteristics around the origins and destinations. The choice model is estimated using revealed trip purpose data from a small-sample travel survey augmented by land use information from an enhanced point of interest database and the census. The methodology is applied to the trajectories of commercial ride-hailing trips made in Toronto between September 2016 and September 2018. For the core choice model, multinomial, nested, and mixed multinomial logit models are compared. Validation of the inferred trip purposes using the trip purpose proportions from another independent survey (not used in choice model estimation) reveal that the multinomial logit model can infer ride-hailing trip purpose distribution with reasonable accuracy. The inferred purpose distribution explains the nature of ride-hailing trips and provides important context of travel demand generated by the services. The results indicate that although ride-hailing services are mostly used for discretionary activities, they also play important roles in daily commuter travel. A quarter of the total weekday ride-hailing trips were made for work- and school-related activities. With increasing ridership, these services may start influencing conventional travel modes and thereby adversely affect the level of traffic congestion and transit ridership in the city.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".