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Smart city article details

Title	Controlling Smart-City Traffic Using Machine Learning
ID_Doc	16058
Authors	Iram T.; Shamsi J.; Alvi U.; Rahman S.U.; Maaz M.
Year	2019
Published	Proceedings - 2019 International Conference on Frontiers of Information Technology, FIT 2019
DOI	http://dx.doi.org/10.1109/FIT47737.2019.00046
Abstract	Traffic situation in smart cities is getting worse day by day, which needs optimized control of traffic signals. This paper proposes a complete solution for real-time traffic signal control to reduce traffic congestion. For providing efficient solutions, we develop two machine-learning approaches, namely Edge based Traffic Light Control (ETLC) and Global Traffic Light Control (GTLC). The former approach is used to control traffic congestion for a smaller congested area and latter one provides the solution for the whole city. Both the approaches utilize real-time traffic data, generated from a vehicular traffic simulator, to solve the congestion problem in real-time. The ETLC approach identifies the most congested area through K-Means clustering and applies fuzzy logic for congestion removal. The GTLC approach identifies multiple congested areas using an occupancy threshold and reduces congestion. Our model is flexible to run each approach individually or integrate both the approaches to run sequentially. Comparison of the two approaches shows that the ETLC approach works better than GTLC approach for small areas. However, GTLC gives good results in peak congestions spanning larger areas. © 2019 IEEE.
Author Keywords	Big data; Cluster; ETLC; GTLC; Hybrid; Intersection; Machine learning; Smart city; Smart traffic; Spark; TraCI

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