Manuscript received on January 11, 2021. | Revised Manuscript received on April 16, 2021. | Manuscript published on May 30, 2021. | PP: 60-66 | Volume-10 Issue-1, May 2021. | Retrieval Number: 100.1/ijrte.E5232019521 | DOI: 10.35940/ijrte.E5232.0510121
Open Access | Ethics and Policies | Cite | Mendeley
© 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: The existing traffic administration policy is not worthy enough to tackle the density of movement in Bangladesh. This study proposes an advanced Internet of Things (IoT) based road traffic administration system to resolve the problem. All the smart lamp posts of road crossings handle four factors, i.e., number of cars, activation time, waiting time, and emergency signal of each lane. This research uses an automatic video processing method to count the number of cars on the road. In order to process the video mask, R-CNN is used, which is a combination of the faster R-CNN that performs object detection (class + bounding box), and Fully Convolutional Network (FCN) results into a pixel border. Modern statistical methods are also used, such as multiple regression analysis, cluster analysis, and factor analysis. For handling emergency traffic situations, a new activation function was proposed and named the RT activation function. Factor analysis with principal component analysis (PCA) allowed in reducing the number of variables from elevens to five. The linear regression explains 90.2% of the variance in the data. This research considers R, R-square, adjusted R-square with 0.950, 0.902, and 0.409, values respectively. The results analysis ensures that the performance of the proposed schema is good enough to apply in the road of Bangladesh.
Keywords: Traffic jam, IoT, traffic system, video processing.