Exploring Passengers’ Travel Behaviors Based on Elaboration Likelihood Model under the Impact of Intelligent Bus Information
Bibliographic record
Abstract
The ubiquitous intelligent transportation infrastructure in metropolitan cities has enabled bus passengers to access comprehensive (even real-time) bus information. However, the impact of different types of information on passenger behavior is still insufficiently understood. Combining with the theory of information processing path, this study partially fills this gap by adopting an elaboration likelihood model (ELM) suitable for explaining how the various types of intelligent bus information influence passengers’ choice behavior. Six types of intelligent bus information (information of bus lines, estimated travel time, estimated time of arrival, congestion inside bus, road congestion, and bus fare) are used as six independent variables, and passengers’ departure time, travel routes, and travel modes as dependent variables. Valid questionnaire assessments were collected from 285 participants at 4 bus stops equipped with intelligent bus system in Harbin, providing quantitative data to verify each hypothesis. The results show that six types of intelligent bus information to different degrees (significant influence, slight influence, and no significant influence) affect three types of passengers’ choice behaviors; the information of estimated travel time and that of road congestion are both significantly effective in all three types of choice behavior while bus fare has no significant influence. Meanwhile, other types of information have a significant or slight effect on certain behavior. The results of this study can be used to design more reasonable intelligent bus information provision strategies to meet passengers’ requirements.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".