Investigating the impacts of telecommuting on the spatial, temporal, and modal distribution of travel using an agent-based transport simulation model
Bibliographic record
Abstract
Technological advancements over the past few decades have facilitated telecommuting, but its adoption surged significantly when travel restrictions forced workers to work from home during the pandemic. This shift significantly reduced peak-hour traffic flow and congestion, but the impact of this travel demand management strategy on 24-hour travel is not well understood. This study aims to evaluate the impacts of telecommuting on 24-hour traffic flow using an agent-based transport simulator. Methodologically, a nested structure is implemented to generate departure time, mode, and destination choice joint decisions and accommodate inter-dependencies. Given the behavioral differences among different population groups, separate models are implemented for these different groups: commuters, telecommuters, non-workers, students attending school in-person/online. Following the generation of 24-hour activities, activity itineraries are applied within a dynamic agent-based multimodal transport network model using the open-source MATSim platform. This modeling and simulation exercise has been implemented for the entire population of the Okanagan region of British Columbia, Canada. After thorough validation, the simulation results suggest that with the increase in telecommuting population, an increase in all types of non-mandatory travel is predicted to occur near the urban centers during the off-peak hours – resulting in the spreading of the peak over the day.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".