Development of an integrated urban modelling framework for examining the impacts of work from home on travel behavior
Bibliographic record
Abstract
This paper develops an integrated urban modelling framework (IUMF) to predict how work from home (WFH) decision affects travel behavior. First, it conducts a questionnaire survey among working professionals in Halifax, Canada, to collect data on their socio-demographic characteristics, mode choice, vehicle ownership, and work-arrangement. Bayesian Belief network models are developed using the collected responses to calculate the cumulative probability tables (CPTs) of variables associated with the decision to WFH. Next, the ascertained CPTs are used as input to extend an integrated urban modelling framework (IUMF) that is further utilized to simulate individuals’ work from home choices and travel behavior up to 2025 for Halifax, Canada. Results indicate that around 57% of the workers would like to WFH and 7% wants to relocate closer to workplace. The model forecasts a significant preference for remote work among individuals with offices in the urban core. Results also show that auto mode share is increased to 79% in 2024, whereas transit, walking and biking trips decreased. Average travel distance is higher in the post-pandemic compared to the pre-pandemic, while travel distance of telecommuters is found to be higher than non-telecommuters. Statistically significant differences are observed between telecommuters and non-telecommuters for ‘number of activities’ and ‘distance travelled’ in a day. The outcomes of this study will offer policy makers a better understanding of long-term impacts of WFH on transport and land-use systems and help to develop effective travel demand management strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".