Users' views on current and future real‐time bus information systems
Bibliographic record
Abstract
SUMMARY Actual bus arrival times often deviate from the posted schedules due to a variety of factors; hence, providing real‐time bus information can improve service quality. This study examined users' views and perceptions towards the possible future availability of real‐time bus information systems in Calgary, Alberta, Canada. A face‐to‐face paper‐based survey was conducted to collect the data. Various statistics and methods, such as ANOVA tests, ordinal regression and binary logistic regression, were used to analyse the data. The results showed that 35.5% of the respondents either agreed or strongly agreed that the current information system deterred or discouraged them from using public transport. In addition, a significant portion of respondents (82%) stated that they board the first arriving bus, even though it may take a longer in‐vehicle time to complete the trip, because of uncertainty regarding the arrival time of the next alternative bus with a shorter in‐vehicle travel time. A majority of the respondents (88%) indicated that real‐time transit information would not be necessary if bus headways are less than 10 minutes. As for preferred information content, information on the next bus arrival time received the highest priority. In general, Light Rail Transit (LRT) users showed the least interest in real‐time information. Women, younger riders, current car users and infrequent transit users showed a higher interest in real‐time information. Display boards at bus stops were perceived to be the most preferred medium to get en‐route information, whereas a website/call centre was stated to be the preferred media for pre‐trip information. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".