Estimation Markov Decision Process of Multimodal Trip Chain between Integrated Transportation Hubs in Urban Agglomeration Based on Generalized Cost
Bibliographic record
Abstract
An efficient multimodal transportation network is crucial to the development of urban agglomeration. Rapid transfer of travelers between integrated transportation hubs is essential for long-distance multimodal trip chains. The Markov Decision Process framework was estimated to explore the optimal transfer trip chain of different income groups, given that the states are considered nodes between hubs, the reward functions are calculated by using the generalized travel cost between states after travelers make action decision, and the actions between states contain bus, subway, taxi, and walk. The optimal trip chain can be obtained through a value iteration algorithm. In the case study, multimodal transfer trip chains of different types between Beijing Capital International Airport and Beijingxi Station in Beijing-Tianjin-Hebei urban agglomeration were constructed by MDP to compare the optimal trip chains of various groups. The findings of this study are as follows: (1) long-distance travelers always prefer to choose the unimodal fewer transfers trip chain between hubs; (2) long-distance travelers are more likely to choose the trip chain with more transfers more than long waiting time; (3) individual income difference affects the generalized cost of trip chains and also influences the optimal choice of trip chain through the MDP framework. One potential application of this study is to complement the research on the transfer behavior of multimodal trip chains in long-distance travel, which can be used to help management alleviate the excessive pressure of passenger flow between integrated hubs due to the sudden colossal travel flow during severe weather days or holidays.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".