Telecommuting and sustainable travel: Reduction of overall travel time, increases in non-motorised travel and congestion relief?
Bibliographic record
Abstract
Existing research has concluded that shares of telecommuting are low but stable, increase with distance from the workplace and that telecommuting may reduce commuting-related travel. Its effect on work and non-work travel are subject to rebound effects and, thus, still debated. Additionally, telecommuting does not necessarily occur entirely at home. The paper studies telecommuting’s potential as a sustainable mobility tool in Canada to reduce overall travel time and peak hour travel, and to increase non-motorised travel. Do types of telecommuting arrangements have varying relationships with these studied travel patterns? Using time use data from the 2005 Canadian General Social Survey, studied outcomes are regressed on telecommuting arrangements (all day home working, part-day home working and a combination of other locations and home and/or workplace) and other personal characteristics. Depending on telecommuting arrangements and travel outcomes, results vary. Working from home is associated with decreases in overall travel time by 14 minutes and increases in odds of non-motorised travel by 77%. Other forms of telecommuting yield different results. Telecommuters may be more likely to avoid peak hours when they do take trips. Types of telecommuting arrangements have different impacts on sustainable travel outcomes that should be considered depending on policy priorities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".