Travelers’ Potential Demand toward Flex-Route Transit: Nanjing, China, Case Study
Bibliographic record
Abstract
As an innovative combination of conventional fixed-route transit and demand-responsive services, flex-route transit is a promising transit option that can address the travel needs of residents in growing low-density suburban and rural areas. This paper assesses potential demand and passengers’ service design preferences prior to the deployment of a flex-route transit service in China. Using the city of Nanjing as an example, a survey was designed and implemented that involves a series of stated-preference experiments in order to (1) examine travelers’ willingness to use flex-route transit services, (2) identify the most promising users, and (3) guide the service design and policymaking before actual operation. Three discrete choice models, namely multinomial logit model, nested logit model, and panel mixed logit model, are applied to describe the mode-choice process using the data collected from the survey. An orthogonal design is used to generate the stated-choice experiments among traditional fixed-route transit, private cars, and hypothetical flex-route transit. Walking time, waiting time, in-vehicle time, and cost are selected as alternative attributes, which vary across each choice scenario. The survey results show that nearly 78% of respondents are willing to try the flex-route transit service. Women, bike-share members, and people who are disabled, retired, or need to transfer to the metro are the target groups of the flex-route transit service. The choice model results also indicate that slack time between checkpoints and vehicle delays should be kept within a reasonable range when designing and operating flex-route transit services.
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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 | 0.001 | 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 | 0.000 |
| 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".