Efektifitas Load Balancing Dalam Mengatasi Kemacetan Lalu Lintas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstrak. Kemacetan jalan raya merupakan permasalahan umum di setiap kota yang memerlukan penanganan serius. Pemecahan permasalahan kemacetan jalan raya tidak hanya dapat diselesaikan dengan hanya meningkatkan kualitas dan kuantitas infrastruktur, namun juga manajemen lalu lintas. Pada artikel diusulkan suatu metode untuk mengurangi kemacetan lalu lintas, yaitu dengan menyeimbangkan beban ke berbagai ruas jalan yang disebut dengan load balancing. Melalui metode ini diharapkan beban lalu lintas terbagi secara merata ke berbagai jalur alternatif sedemikian sehingga antrian panjang kendaraan dapat dihindari. Evaluasi efektifitas dari metode load balancing ini dilakukan melalui simulasi dengan mengimplementasikan salah satu bidang ilmu Matematika, yaitu teori Antrian. Simulasi dibuat dengan menggunakan toolbox SimEvents yang dijalankan pada software MATLAB.Kata Kunci: load balancing, kemacetan, lalu lintas, sim-events, matlabAbstract. (the effectiveness of load balancing in reducing the road traffic congestion) Road congestion is a common problem in any city that needs serious handling. The solution of the road congestion problems can not only be solved by simply improving the quality and quantity of infrastructure, but also the traffic management. In this article, we proposed a method to reduce the traffic congestion by balancing the vehicle loads to a various road segments, called as load balancing. Through this method, it is expected that the traffic load is evenly distributed to various alternative routes, such that, long queues and traffic jam can be avoided. Evaluation of the load balancing’s effectiveness is performed through a simulation by implementing the Queueing Theory. Simulations are created using the SimEvents toolbox that runs on MATLAB software.Keywords: load balancing, road congestion, traffic, simevents, matlab.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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 it