INTEGRATED SMART TRAFFIC CONTROL SYSTEM MENUJU PEKANBARU SEBAGAI SMART CITY

Reny Medikawati Taufiq, Sunanto Sunanto, Yoze Rizki

Abstract


Abstract: Pekanbaru still using conventional traffic light control system. Pekanbaru as the capital of Riau Province is predicted  udergo the  increased of urban population by 54.5% in 2025. It is important for Pekanbaru to immediately implement smart and efficient traffic management system, so that traffic congestion can be resolved quickly. This research paper provides a design solution for smart traffic light management (Smart Traffic Control System), based on object detection technology that uses deep learning to detect the number and type of vehicles. The number of vehicle is the basis for determining the green light timer automatically. The Smart Traffic Control System (STCS) is integrated with a web based geographic information system (smart map) that can display the current condition  (picture, the number of vehicle, congestion level) of each STCS location. This integrated system has been tested on a traffic light prototype, using a mini computer and a miniature vehicle. This integrated system is able to detect 9 out of 12 vehicles, and able to send data regularly to the smart map.

 

Keywords: deep learning; smart mobility; smart traffic control system

 

Abstrak: Pengaturan lampu lalu lintas di Kota Pekanbaru masih dilakukan secara  konvensional. Pekanbaru sebagai ibukota Provinsi Riau diprediksikan akan mengalami peningkatan jumlah penduduk  perkotaan sebesar 54,5% pada tahun 2025. Dengan melihat predikisi ini, penting bagi kota Pekanbaru untuk segera memiliki tata kelola lalu lintas yang cerdas dan efisien agar kemacetan dapat ditanggulangi dengan cepat. Penelitian ini memberikan rancangan solusi untuk tata kelola  lampu lalu lintas cerdas (Smart Traffic Control System), berbasis teknologi object detection  yang menggunakan deep learning untuk mendeteksi jumlah dan jenis kendaraan. Jumlah kendaraan menjadi dasar penentuan timer lampu hijau secara otomatis. Smart Traffic Control System (STCS) terintegrasi dengan sistem informasi geografis berbasis web (smart map) yang secara kontinu menerima informasi kepadatan (gambar terkini, jumlah kendaraan, level kepadatan), kemudian menampilkannya diatas peta Kota Pekanbaru. Solusi sistem terintegrasi ini telah diujikan pada sebuah prototipe lampu lalu lintas, menggunakan komputer mini  dan  miniatur kendaraan. Sistem terintegrasi ini mampu mendeteksi 9 dari 12 kendaraan, dan mampu mengirimkan data secara berkala kepada smart map.

 

Kata kunci: deep learning; smart mobility; smart traffic control system


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References


S. Dirks, M. Keeling, and J. Dencik, “How Smart is Your City? Helping Cities Measure Progress,” IBM Glob. Bus. Serv., 2009.

BAPPENAS, “Proyeksi Penduduk Indonesia 20020-2025 (Publikasi bersama oleh BPS, BAPPENAS, dan UNFPA Indonesia),” p. 398, 2008.

F. R. Harahap, “Dampak Urbanisasi Bagi Perkembangan Kota Di Indonesia,” Society, vol. 1, no. 1, pp. 35–45, 2013.

D. Washburn and U. Sindhu, “Helping CIOs Understand ‘Smart City’ Initiatives,” 2010.

Q. Hidayati, “Kendali Lampu Lalu Lintas dengan Deteksi Kendaraan Menggunakan Metode Blob Detection,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 6, no. 2, 2017.

S. Saravanan, “Implementation of efficient automatic traffic surveillance using digital image processing,” 2014 IEEE Int. Conf. Comput. Intell. Comput. Res. IEEE ICCIC 2014, pp. 3–6, 2014.

P. Maheshwari, D. Suneja, P. Singh, and Y. Mutneja, “Smart traffic optimization using image processing,” Proc. 2015 IEEE 3rd Int. Conf. MOOCs, Innov. Technol. Educ. MITE 2015, pp. 1–4, 2015.

B. Ghazal, K. Elkhatib, K. Chahine, and M. Kherfan, “Smart traffic light control system,” 2016 3rd Int. Conf. Electr. Electron. Comput. Eng. their Appl. EECEA 2016, no. April, pp. 140–145, 2016.

M. B. Natafgi, M. Osman, A. S. Haidar, and L. Hamandi, “Smart Traffic Light System Using Machine Learning,” 2018 IEEE Int. Multidiscip. Conf. Eng. Technol. IMCET 2018, pp. 1–6, 2018.

Y. Nie, “Intelligent traffic lights based on MATLAB,” AIP Conf. Proc., vol. 1955, no. April 2018, 2018.

S. C. Ng and C. P. Kwok, “An intelligent traffic light system using object detection and evolutionary algorithm for alleviating traffic congestion in hong kong,” Int. J. Comput. Intell. Syst., vol. 13, no. 1, pp. 802–809, 2020.

A. N. Rombe, L. F. Aksara, and L. Surimi, “Analisis Perbandingan Real Time Streaming Protocol (Rtsp) Dan Hypertext Transfer Protocol (Http) Pada Layanan Live Video Streaming,” semanTIK, vol. 5, no. 1, pp. 149–156, 2019.

“Model optimization,” TensorFlow Lite guide. [Online]. Available:https://www.tensorflow.org/lite/performance/model_optimization. [Accessed: 27-Oct-2020].

APTA, “Selection of Cameras, Digital Recording Systems, Digital High-Speed Networks and Trainlines for Use in Transit-Related CCTV Systems,” CCTV Stand. Work. Gr., no. June, pp. 1–48, 2011.




DOI: https://doi.org/10.33330/jurteksi.v7i1.942

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