An Efficient Approach for Identifying Potential Bus Passenger Demand Based on Multisource Data
Bibliographic record
Abstract
Big data provide massive samples and resources for exploring the operating rules of public transportation. This article proposes a method that combines multiple data sources to identify potential bus passenger flows, aiming to address the issue of insufficient identification accuracy with a single data source. First, the spatially weighted K‐means algorithm and improved DBSCAN algorithm are designed to partition traffic zones and residents’ travel flow OD is extracted based on mobile phone signaling data. Second, using bus IC card data and vehicle trajectory data, a method for identifying bus passenger boarding and alighting stops based on spatiotemporal clustering is proposed and the bus passenger flow OD for each traffic zone is calculated. By comparing the resident travel flow OD with the bus passenger flow OD, we set a threshold for the potential bus passenger demand proportion. Finally, the analysis is conducted using actual data from a city in central China. The city is divided into 43 traffic zones, with the maximum bus passenger flow proportion between zones being 14.9%, the minimum being 5.0%, and the average being 7.2%. The initial threshold for the potential bus passenger demand proportion is thus set to 7.2%, and a sensitivity analysis is conducted by gradually decreasing the threshold in increments of 0.5% to 6.7%, 6.2%, 5.7%, and 5.2%. The corresponding potential bus passenger demand OD pairs between traffic zones are identified as 419, 358, 245, 151, and 51. Urban managers should focus on the 51 pairs with relatively large potential flows to gradually optimize and balance the development of the bus network based on actual conditions. The method proposed provides important theoretical and practical support for effectively optimizing urban bus networks. However, there are limited indicators for identifying potential passenger flows; in the future, more multidimensional indicators will be taken into consideration.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".