Traffic Congestion Detection using Whale Optimization Algorithm and Multi-Support Vector Machine
Bodanapu Sony1, Ankit Chakravarti2, M. Madhusudhan Reddy3
1Bodanapu Sony, Assistant Professor, Department of Civil Engineering, KL University, GNIT, IBP, RR Guntur District (A.P), India.
2Dr. Ankit Chakravarti, Department of Civil Engineering, KL University, Guntur District (A.P), India.
3M. Madhusudhan Reddy, Research Scholar, Department of Civil Engineering, KL University, Guntur District (A.P), India.
Manuscript received on 03 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript Published on 28 May 2019 | PP: 589-593 | Volume-7 Issue-6C2 April 2019 | Retrieval Number: F11070476C219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Currently, urban traffic is an evolving issue that is closely related to economic factors and population growth. Several developing and developed countries are seeing increases in vehicle growth per each passing year. So, traffic flow congestion forecasting is one of the important aspects in intelligent transport system that aims to improve mobility, influence travel behavior, and save energy. In this research paper, an effective system; Whale Optimization Algorithm (WOA) and Multi-Support Vector Machine (MSVM) classifier was proposed in order to improve the performance of road traffic congestion detection. Here, the proposed system was experimented by using a simulated dataset, which was collected for beibei tunnel (4.2 km long road in yuwu free-way). The resulting section confirms that the proposed system enhanced the classification accuracy in road traffic congestion detection up-to 9.3% related to other existing systems.
Keywords: Data Normalization; Multi Support Vector Machine; Traffic Congestion Detection; Whale Optimization Algorithm.
Scope of the Article: Algorithm Engineering