The ITS Application of Mobile Phones to Solve the SignalizedIntersections’ Problems in Suburb of Bangkok, Thailand
Bibliographic record
Abstract
The Application of Intelligent Transportation Systems (ITS) of Rural Roads Department in Thailand have been developed more than 15 years. In this project, the mitigation of traffic signalization system is proposed for two intersections as applied for 3 legs and 4 legs intersections respectively. The collected traffic data that, were used by manual traffic counts, were compared with the image sensing software's data. These data were corrected and predicted for the fine tuning of traffic signalization under the statistic methods which were the Gamma Distribution with Density, Bootstrapping for directly generating inputting data and the Method of Maximum Likelihood (ML) to estimate the parameters. At the moment, traffic signalization system of two intersections cannot be synchronized by the cable network. However, it can be solved problems and remedied traffic situations by using of Mobile Phones' Network with the Private Cloud Service (PCS). There are three I-Cloud Servers which are VDO Collector, VDO Processor and Application Server. In addition, the calculation of traffic density at each leg of intersections is taken into consideration. This means that using of image sensing software under Lucas-Kanade Method is proposed for the reducing of processing times. In case of traffic density, the tracking of each vehicle-group within 40 meters of the first range will be shown the groups of queue lengths and the average speed of vehicles. Moreover, these factors from 40 meters to the out of range of tickers under the image sensing software from CCTVs will be also taken into account. The effective traffic cycle times (ETCT) will decrease of the queue length and increase of the speed-vehicles by the skips of phasing within the optimization of traffic cycle times (OTCT). Finally, traffic policemen at the traffic controllers will change the phases promptly so as to remedy the traffic situations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".