Adaptive Traffic Light Control Based on Actual Condition Using Google Map API

Adi Sabwa Isti Besari Arkanuddin; Selo Sulistyo; Anugerah Galang Persada

doi:10.22146/ijitee.49964

Adaptive Traffic Light Control Based on Actual Condition Using Google Map API

https://doi.org/10.22146/ijitee.49964

Adi Sabwa Isti Besari Arkanuddin⁽¹⁾, Selo Sulistyo⁽²⁾, Anugerah Galang Persada^(3*)

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author

Abstract

Traffic congestion is one of the main problems in transportation sector and it causes a lot of drawbacks to public. The traffic light system is used to reduce the level of occurring traffic congestion. Generally, the available traffic light systems use a fixed time setting. This old traffic control system is no longer able to manage the ever-changing traffic conditions effectively and efficiently, causing a long queue of vehicles. To overcome this problem, a traffic light control system that can adapt to actual conditions of road density and can run automatically is offered. This system utilizes Google Map API as a road density data source. The result of this study is a traffic control system that can adjust the green light time duration based on the obtained density values and density trends, simulation of this adaptive system as well as simulation results analysis. A prototype of this adaptive control system was also produced in this study.

Keywords

Traffic Lights; Adaptive; Google Map API; Green Light Time Duration; Density

Full Text:

PDF

References

U. Arieza (2018) “Indonesia Penduduk Terbanyak Nomor 4 di Dunia, Siapa Juaranya? : Okezone Economy,” [Online], https://economy.okezone.com/read /2018/07/21/320/1925559/indonesia-penduduk-terbanyak-nomor-4-di-dunia-siapa-juaranya, access date: 08-Nov-2018.

J. Budiarto, S. Sulistyo, I.W. Mustika, and A. Infantono, “Road Density Prediction, Updated Methods of Turning Probabilities and Highway Capacities Manual for Achieving the Best Route,” 2014 International Conference on Electrical Engineering and Computer Science (ICEECS 2014), 2014, pp. 168-173.

E.E. Prasetiyo, O. Wahyunggoro, and S. Sulistyo, “Desain Pengatur Lampu Lalu Lintas Adaptif Dengan Kendali Logika Fuzzy,” Seminar Nasional Teknologi Informasi dan Multimedia 2015, 2015, pp. 3.7-1–6.

M. Tubaishat, Y. Shang, and H. Shi, “Adaptive Traffic Light Control with Wireless Sensor Networks,” 2007 4th IEEE Consumer Communications and Networking Conference, 2007, pp. 187–191.

S. Kwatirayo, J. Almhana, and Z. Liu, “Adaptive Traffic Light Control using VANET: A Case Study,” 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), 2013, pp. 752–757.

J. Li, Y. Zhang, and Y. Chen, “A Self-Adaptive Traffic Light Control System Based on Speed of Vehicles,” 2016 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2016, pp. 382–388.

S.M. Shinde, “Adaptive Traffic Light Control System,” 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), 2017, pp. 300–306

N. Rida, M. Ouadoud, A. Hasbi, and S. Chebli, “Adaptive Traffic Light Control System Using Wireless Sensors Networks,” 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), Marrakech, Morocco, 2018, pp. 552–556.

MnDOT Traffic Signal Timing and Coordination Manual, Minnesota Department of Transportation, 2017.

(2018) “View places, traffic, terrain, biking, and transit - Computer - Google Maps Help,” [Online], https://support.google.com/maps/ answer/3092439, access date: 17-Mar-2018.

J. Stevens (2013) “Joshua Stevens - Your Favorite Traffic Map is Lying to You,” [Online], http://www.joshuastevens.net/design/your-favorite-traffic-map-is-lying-to-you/, access date: 17-Mar-2018.

K.-L. Bang, “Final Report: Indonesian Highway Capacity Manual and Software (Kaji),” Directorate General Bina Marga, Directorate of Urban Road Development (Binkot), Indonesia, 3860, Feb. 1997.

DOI: https://doi.org/10.22146/ijitee.49964