What about Telecommuting in Activity-Based Models? Towards Post-Covid Travel Demand Analysis
Bibliographic record
Abstract
The global prevalence of the COVID-19 pandemic has necessitated a reevaluation of the popularity of tele-activities. This pandemic and the resulting implementation of lockdown measures have led to many new everyday activity patterns based on information and communication technology (ICT). This thesis examines the impact of pandemic lockdown measures on the development and sustainability of individuals' ICT-influenced behaviour, specifically telecommuting.The dissertation commences with an introductory section comprising two preparatory chapters. These chapters elucidate the underlying motives that have propelled the research endeavor and provide a comprehensive assessment of the existing literature in the field. Upon examining the empirical scheduling models in the activity-based modelling literature, a common simplifying assumption, known as the "fixed work-hour during the day" or "skeleton assumption," was discovered. This assumption, which many models often accept, detrimentally affects these models' prediction power and reliability. Based on discussions and reviews in the two preparatory chapters, the dissertation proceeds with four empirical studies examining various facets of in-person and remote activity participation from distinct perspectives. The analyses provide evidence that a skeleton framework's underlying assumption is unlikely to align with the actual condition in the foreseeable future. The initial analysis chapter examines individuals' immediate response to the pandemic lockdown. The objective is to identify a range of activities that might be incorporated into daily schedules while staying at home. This chapter demonstrates the widespread use of ICT for a multitude of tasks. In the subsequent analysis chapter, attention is directed toward examining the impact of observable and latent attitudinal variables on selecting ICT for work-related activities. This chapter demonstrates the significance of using attitudinal characteristics as useful parameters for predicting persons' behaviour. The third analysis chapter employes datasets to elucidate emerging patterns in allocating time across diverse activities, focusing on work-related endeavors. This chapter illustrates the trajectory of changes in the employment of ICT, beginning with the onset of the COVID-19 pandemic and extending into the post-pandemic period. The fourth analysis chapter examines the permanence of newly formed habits by projecting the sequence of stated choices using panel data. The recent chapters suggest that telecommuting will continue and should be considered a crucial component of future scheduling models. The last chapter of the thesis reviews the research findings and highlights the significant contributions made by the study. Additionally, the chapter offers recommendations for future research directions.
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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 | 0.004 | 0.021 |
| 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 | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".