Mapping the interplay of work-arrangement, residential location, and activity engagement within an integrated model
Bibliographic record
Abstract
Virtual activities, such as teleworking have been identified as major travel demand management strategies to tackle traffic congestion and emission. However, integrated models, which are capable of testing such strategies, have not yet been properly extended to capture emerging activity patterns. This study fills-up the literature gap by implementing individuals’ work-arrangement and introducing in-home (IH) virtual activities within an integrated Transport, Land-use, and Emission (iTLE) modeling framework. First, it conducts a household travel survey, which collected socio-economic, activity-travel, and work-related information through a questionnaire survey in the Halifax Regional Municipality (HRM), Nova Scotia , Canada. Next, it develops a work-arrangement choice model based on the mixed-logit modelling (MXL) approach and implements it within iTLE. After that, individuals’ daily activity programs are generated in a sequential manner utilizing a Markov Chain Monte Carlo (MCMC) modelling approach considering their work-arrangement, employment, and vehicle ownership. To demonstrate the application of the developed tool, individuals’ socio-demographics, residential-location, work-location, and activity-participation are longitudinally simulated up to 2031. The analysis of teleworking parameters revealed distinct clusters and individual preferences, emphasizing the significance of personalized approaches in formulating teleworking strategies. Most non-teleworkers stay closer to downtown than full-teleworkers while clustered behavior is observed among hybrid workers for residential location choice. Increase is observed for shopping, dining-out, and IH maintenance/discretionary activities while decrease is observed for out-of-home work. The outcomes of this paper will be helpful for policymakers and transport researchers to understand the evolution of work-arrangement and examine subsequent impacts on transportation and land-use systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".