Modeling of Commuters’ Mode Choice and Office Location/Relocation Preferences of Business Firms for Informed Decision-Making and Planning: Applications of Stated Preference Methodology
Bibliographic record
Abstract
This study makes use of a stated preference (SP) methodology to examine the mode choice preferences of commuters who commute to the central business district of Ottawa taking into account the addition of a Light Rail Transit (LRT) system that is currently non-existent.As part of a planning perspective, the study also seeks to investigate how telecommuting may influence the location/relocation of offices within a multinucleated region such as the City of Ottawa.Two separate surveys were carried out: A survey on commuters' mode choice within the central business district of Ottawa; and a survey on office location/relocation preferences within the Ottawa municipal area.Each survey elicited SP responses to a number of hypothetical scenarios defined according to the principles of SP experimental design.In addition, information about the characteristics of commuters as well as the profile of companies was also obtained.These surveys yielded the data for the model estimation.A discrete choice modeling approach framework was used to estimate the parameters of the utility function.The innovation in this research is the use of these SP methodologies to understand the role of certain important factors in commuters' mode preferences and the location/relocation preferences of office space within the Ottawa municipal area.Results are presented and discussed in the study.ACKNOWLEDGEMENT This research was carried out under the supervision of Professor A.M. Khan, whose advice, guidance and continuous encouragement during the course of this study is greatly appreciated.His hospitality, ability to delegate responsibility and relentless effort to seek for knowledge gave me the zeal to continuously work towards achieving my career objectives.I would also like to acknowledge the time and effort set aside by Professor Scott Bennett for providing comments on my Stated Preference survey design methodology used to collect data for this study.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".