Indicator of Highway Conditions on Traffic Density Levels

Yesy Diah Rosita; Ronny Makhfuddin Akbar; Fajar Indra Kurniawan

doi:10.29138/ijeeit.v6i1.2133

Yesy Diah Rosita Engineering Faculty, Universitas Islam Majapahit, Indonesia
Ronny Makhfuddin Akbar Engineering Faculty, Universitas Islam Majapahit, Indonesia
Fajar Indra Kurniawan Engineering Faculty, Universitas Islam Majapahit, Indonesia

DOI: https://doi.org/10.29138/ijeeit.v6i1.2133

Keywords: computer vision, decisions, rankings, traffic jams

Abstract

Traffic problems, especially congestion which is the impact of high levels of traffic density, are problems that have not been properly resolved, especially in big cities in Indonesia. In this article, 7 features of road conditions are proposed on the level of traffic density. These features include the number of vehicles, the average speed of the vehicle, the area of the main road, the length of traffic that has been determined, the duration of traffic congestion, and the time of traffic congestion. Determining the level of traffic density can be determined by utilizing the MCDM hybrid technique to produce information that can be used to support decision-making in traffic management or other policies such as formulating policies on the buying and selling of private vehicles. The number of indicators related to computer vision for presenting information on the level of traffic density requires a large amount of time and memory. Besides that, the perception of a decision maker in determining priority weights also influences the results of the information on the level of traffic density.

Downloads

Download data is not yet available.

References

Agustina, Z., Dewi, I., Rahayu, T., & Efendi, S. (2022). Hubungan Jarak, Waktu, Kecepatan dan Volume Lalu Lintas pada Ruas Jalan Karya di Kota Medan. SEMNASTEK UISU, 111–116.

Alfani, A. F., Mujib, M. A., & Ikhsan, F. A. (2020). Tingkat Kemacetan dan Realita Transportasi di Jalan Letjen. SOSEARCH: Social Science Educational Research, 1(1), 1–12.

Chen, Y., & Hu, W. (2020). Robust vehicle detection and counting algorithm adapted to complex traffic environments with sudden illumination changes and shadows. Sensors (Switzerland), 20(9). https://doi.org/10.3390/s20092686

Chen, Y. W., & Shiu, J. M. (2022). An implementation of YOLO-family algorithms in classifying the product quality for the acrylonitrile butadiene styrene metallization. International Journal of Advanced Manufacturing Technology, 119(11–12), 8257–8269. https://doi.org/10.1007/s00170-022-08676-5

Eltarabishi, F., Omar, O. H., Alsyouf, I., & Bettayeb, M. (2020). Multi-criteria decision making methods and their applications-A literature review. Proceedings of the International Conference on Industrial Engineering and Operations Management, March, 2654–2663.

Gomaa, A., Minematsu, T., Abdelwahab, M. M., Abo-Zahhad, M., & Taniguchi, R. ichiro. (2022). Faster CNN-based vehicle detection and counting strategy for fixed camera scenes. Multimedia Tools and Applications, 81(18), 25443–25471. https://doi.org/10.1007/s11042-022-12370-9

Gomides, T. S., de Grande, R. E., Souza, F. S. H., & Guidoni, D. L. (2020). A Traffic Management System to Minimize Vehicle Congestion in Smart Cities. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2020-Octob, 1439–1444. https://doi.org/10.1109/SMC42975.2020.9283122

Jahan, A., & Edwards, K. L. (2015). A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design. Materials and Design, 65, 335–342.

Jayady, A., Hidayat, T., Qomariyah, E., Suriyani, B. B., Husain, M. N., Caniago, A., Rusmardiana, A., Pulungan, D. R., Eteruddin, H., Timotius, E., & Nurhayati, S. (2021). Decision Support System with Multi Criteria Decision Making Technique. Journal of Physics: Conference Series, 1933(1), 0–8. https://doi.org/10.1088/1742-6596/1933/1/012017

Kholik, A., Harjoko, A., & Wahyono, W. (2020). Classification of Traffic Vehicle Density Using Deep Learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(1), 69. https://doi.org/10.22146/ijccs.50376

Lahinta, F., Zainuddin, Z., & Syarif, S. (2019). Vehicle Detection and Counting to Identify Traffic Density in The Intersection of Road Using Image Processing. ICOST. https://doi.org/10.4108/eai.2-5-2019.2284706

Moazzam, Md. G., Haque, M. R., & Uddin, M. S. (2019). Image-Based Vehicle Speed Estimation. Journal of Computer and Communications, 07(06), 1–5. https://doi.org/10.4236/jcc.2019.76001

Neupane, B., Horanont, T., & Aryal, J. (2022). Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network. Sensors, 22(10), 1–21. https://doi.org/10.3390/s22103813

Patel, R., & Pathak, M. (2018). Comparative Analysis of Search Algorithms. International Journal of Computer Applications, 179(50), 40–43. https://doi.org/10.5120/ijca2018917358

Perafan-Villota, J. C., Mondragon, O. H., & Mayor-Toro, W. M. (2022). Fast and Precise: Parallel Processing of Vehicle Traffic Videos Using Big Data Analytics. IEEE Transactions on Intelligent Transportation Systems, 23(8), 12064–12073. https://doi.org/10.1109/TITS.2021.3109625

Putra, B. C., Sctiyono, B., Sulistyaningrum, D. R., Soetrisno, & Mukhlash, I. (2018). Moving Vehicle Classification Using Pixel Quantity Based on Gaussian Mixture Models. 2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018, 254–257. https://doi.org/10.1109/CCOMS.2018.8463218

Rosita, Y. D., Rosyida, E. E., & Rudiyanto, M. A. (2019). Implementation of dijkstra algorithm and multi-criteria decision-making for optimal route distribution. Procedia Computer Science, 161, 378–385. https://doi.org/10.1016/j.procs.2019.11.136

Saputra, A. (2017). Implementasi Metode Hybrid MCDM Pada Sistem Pendukung Keputusan Pemilihan Pustakawan Berprestasi (Studi Kasus: Upt Perpustakaan Universitas Andalas). Prosiding Lokakarya Nasional Dokumentasi Dan Informasi, 271–285.

Satoinong, L., Shofwan Donny, M., Ray, N., Setia Budi Wibowo, L., Teknik Sipil, J., Teknik, F., Widya Kartika Surabaya, U., & Jl Sutorejo Prima Utara, I. I. (2019). Analisis Kinerja Dan Manajemen Lalu Lintas pada Bundaran ITS dan Bundaran Mulyosari Kota Surabaya. Ge-STRAM: Jurnal Perencanaan Dan Rekayasa Sipil , 2(1).

Sitanggang, R., & Saribanon, E. (2018). Faktor-Faktor Penyebab Kemacetan Di Dki Jakarta. Jurnal Manajemen Bisnis Transportasi Dan Logistik (JMBTL), 4(3), 289–296.

Sulistiyono. (2022). Kemacetan Kendaraan Pengguna BBM Fosil dan DampaknyaTerhadap Kerugian Ekonomi dan Lingkungan. Majalah Ilmiah Swara Pat, 12(2), 12–21.

Tasliyah Haramaini, Khairuddin Nasution, & Oris Krianto Sulaiman. (2018). Penerapan Metode Analytical Hierarchy Process (AHP) dalam Menentukan Tingkat Kemacetan Lalulintas di Kecamatan Medan Kota. Multitek Indonesia: Jurnal Ilmiah, 1, 1907–6223. http://journal.umpo.ac.id/index.php/multitek

Thomas L. Saaty. (2008). Decision Making with The Analytic Hierarchy Process. International Journal Services Sciences, 1(1).

Xing, Y., Ban, X., Liu, X., & Shen, Q. (2019). Large-scale traffic congestion prediction based on the symmetric extreme learning machine cluster fast learning method. Symmetry, 11(6), 1–19. https://doi.org/10.3390/sym11060730