Real-Time Vehicular Traffic Analysis using Big Data Processing and IoT based Devices for Future Policy Predictions in Smart Transportation

Abstract

The primary objectives of his research work are “Node Congestion Management” and “Policy Settings”. The secondary objective is to assess the congestion nodes and construct a graph based output so as to analyze the trends in traffic flow of kerala state's Ernakulam district during 2 peak hours by considering first peak hour from 7.00 am to 8.30 am and second from 4.00 pm to 6.30 pm. The traffic management and public works department under state government of Kerala can obtain various insights related to congestion nodes. One important intelligence that this project provides is the change in congestion rate of various congestion points with respect to a single road infrastructure project. Even a road-hump or a speed breaker would affect the congestion rate at various congestion points far away. This automated analysis based on machine learning would save public funds by preventing unnecessary allocation of funds. Also this system justifies allocation of funds for various useful projects. The traffic monitoring helps in long term decision making especially while formulating transport policies and budgets. It also guides the law enforcement agencies to properly understand the variations of such traffic and properly take precautionary measures like installation of security cameras and other control measures.