Assessing the Spatial Sustainability of Urban Bus Transit: A Socioeconomic Sustainability Perspective
Bibliographic record
Abstract
To promote the sustainable development of urban bus transit, this paper introduces the concept of spatial sustainability for bus networks. This concept aims to achieve a balance in the spatial distribution of bus routes, optimizing both social and economic benefits. The paper presented a novel approach that utilizes multisource big data to assess the spatial sustainability of bus transit, with a specific focus on the existing transit‐supportive area (TSA) method. In this study, we employed various data sources, including mobile phone signaling data, residential travel survey data, map API data, and bus credit card data. These datasets were used to identify regions where peak‐hour ridership exceeded a defined threshold. Using Nanjing as a case study, we defined the bus sustainable development zone (BSDZ) by evaluating areas where peak‐hour bus ridership exceeded 10 trips per grid. The study then compared the BSDZ with both the bus support zone and the bus stop service zone. In addition, we conducted a comparative analysis between the BSDZ and TSA, as well as the existing bus service areas. The findings revealed a spatial imbalance in the current bus network and highlighted areas where optimization was needed. The identified BSDZs offered valuable insights that can serve as a reference for future bus route optimization in Nanjing, contributing to a more balanced and efficient transit system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".