COVID-19 and Teleworking: Lessons, Current Issues and Future Directions for Transport and Land-Use Planning
Bibliographic record
Abstract
Teleworking has been considered to be one of the emanating behaviors from the pandemic that may become long-lasting. Wider adoption of teleworking may fundamentally change urban mobility and spaces across cities. However, knowledge about the potential implications of teleworking on urban transport and land-use systems post-pandemic is limited. Through a comprehensive review of existing teleworking studies, this research identifies gaps in the literature, discusses major issues for exploration and suggests future research directions. It also explores ways to utilize teleworking as an effective travel demand management strategy. Analysis shows that teleworking has the potential to substantially change city landscapes and can assist in reducing traffic congestion, greenhouse gas emissions, and energy use. Priority areas for further research are identified, such as in-home activities, residential location choice, non-work trip patterns, and energy consumption decisions of teleworkers for a clearer understanding of the relationship between teleworking and urban systems. Analysis also reveals several planning and policy challenges surrounding teleworking, including digital divide, urban sprawling, and transformation of city centers, among others. To fully realize the benefits of teleworking, planners need to reconfigure community design principles to promote mixed-use, lively, and vibrant neighborhoods where people can both live and work. At the same time, governments should consider providing incentives to both organizations and employees with an aim to retain teleworking. Results of this paper will be highly beneficial to transport and land-use researchers, planners, and policy makers.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".