Bibliographic record
Abstract
A larger amount of funding and attention are going toward highway infrastructure of Ontario for rehabilitation, maintenance and construction projects. These rehabilitation and maintenance activities on highways involve lane closures, which reduce the traffic throughput and cause delays for the road users. The impact of these activities is very important and has led to research into improvements of work zones in Ontario. To prevent the significant cost that these construction delays have on the general public, contractors are required to keep highway lanes open during the peak traffic hours and work at night. However, working at night may reduce the quality of the work by increasing cold joints and construction joints in the pavement, and may increase the amount of time needed to complete the work. Therefore, finding a balance between the times that the lanes can be closed and the times they should be kept open requires an accurate prediction of the construction work zone throughputs, which can increase the efficiency of the contractor work, save money and reduce the user delay costs. \n \nConsequently, this study which has been funded by the Ministry of Transportation of Ontario (MTO) Highway Infrastructure Innovation Funding Program (HIIFP) involves measurement of highway construction work zones throughput of Southern Ontario, to determine the factors affecting the throughput. It has been carried out in partnership with researchers at the University of Toronto. For this study, a manual counting method for collecting throughput data has been employed. This involved data collection of variables such as heavy vehicles which had not been included in previous studies. This provides the visual confirmation of queuing and assists in evaluating the intensity of work activity at the work zones. New generic models for throughput have been developed in this research to better describe current state-of-the practice on Southern Ontario highways. Furthermore, a better functioning highway specific model was developed to calculate the throughput of the MTO Southern Ontario Highway network. In addition to development of these new models, this project involved further development and refinement to a spreadsheet based model SZUDA (Simplified work Zone User Delay Analysis) that uses normal hourly traffic flows to calculate the resulting queue for that entire hour and approximate user delay cost associated with road user delay. \n \nOverall, the thesis describes a methodology for collection of data in work zones. This involved collection of data during 2009 and 2010 Ontario construction season. Furthermore, the data were then used to develop more reliable generic and highway specific models for the MTO. These models can be used to determine when and how work zones should be established. Finally the refined SZUDA model and case studies demonstrate the impact of various work zone configurations on the traveling public.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".