Key Characteristics of Multimodal Public Transport Across the Entire Commuting Process: Quantitative Evidence from Shanghai
Bibliographic record
Abstract
Determining the critical factors influencing commuters’ choice of public transport is essential for increasing its commuting mode share. This study examines multimodal public transport (metro and bus) across the entire commuting process by utilizing survey data on commuting behavior and Internet‐extracted data. The concept of travel time ratio, defined as the ratio of public transport travel time to car travel time for the same origin–destination (OD), is introduced to perform a quantitative analysis. A classification and regression tree (CART) model is then applied to identify and rank the key characteristics affecting public transport selection for commuting, and a marginal utility analysis quantifies their impact on commuting behavior. The results show that the travel time ratio is the most critical variable influencing commuters’ choice of public transport. Under the same commuting task, the average travel time of public transport is 25% longer than that of cars. This figure can reach nearly 70% for buses and 10% for the metro, which is the main reason for the low efficiency of public transport. Service characteristics optimization has a more substantial impact on increasing the commuting mode share for metro services than for buses. For every 0.1 decrease in the travel time ratio, the average commuting mode share for the metro and buses increased by 3.7% and 2.4%, respectively. To attract more commuters to public transport, it is necessary to maintain the travel time ratio within the range of 1–1.5. For bus services, in addition to improving commuting efficiency, it is essential to optimize convenience characteristics, such as transfer times, walking distance, and service frequency. This includes ensuring no transfers and maintaining a walking distance of less than 880 m. If the walking distance exceeds this threshold, the travel time ratio should be reduced below 1.1. Commuters without a private car demonstrate a higher tolerance for bus services, with a travel time ratio threshold of 1.8, provided that the overall service frequency is within 7 min. When fully served by the metro, if its service distance is within 6 km, the travel time should be comparable to that of cars to remain competitive. This study provides a quantitative basis for increasing the commuting mode share of public transport and improving its service quality.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".