Prediction of Traffic Congestion Utilizing Machine Learning and Big Data Analysis for Smart Cities

Urban environments face traffic congestion today because of the increasing number of vehicles due to the rapid rise of the urban population and increased demands for transportation services. In this regard, the paper described a Traffic Congestion Prediction System (TCPS) that used Big Data Analytics to make efficient predictions by analysing large transportation data volumes. The proposed model involves different steps, such as acquiring, storing, preprocessing data, predicting, and visualising. The idea is to implement several algorithms for analysing the obtained information in order to improve prediction accuracy and optimise the process of decision-making related to traffic. Thus, the application of Big Data technologies and predictive analytics can provide significant advantages in terms of reducing congestion, selecting effective routes, lowering fuel consumption, and making travel more efficient. The proposed model is scalable and adaptive, and it may be applied for further implementations in smart cities. Traffic congestion has emerged as one of the biggest threats for modern urban societies owing to factors such as rapid urbanisation, increase in populations, and high densities of vehicles. Poor traffic management is associated with various problems including time wastage, more fuel usage, pollution, and economic inefficiencies. As such, the need for innovative, intelligent and analytical traffic management systems has emerged for sustainable urban living. This study proposed a Traffic Congestion Prediction System (TCPS) for effective traffic management using Big Data Analytics and transportation data sets. The TCPS framework consists of various layers such as acquisition of transportation data, storage, preprocessing, prediction, and visualisation modules to enable efficient decision-making through real-time analytics. It was established in the study that a combination of Big Data technologies and analytical processes allows for the identification of various patterns in traffic data obtained from different sources such as GPS technology, traffic sensors, CCTV monitoring systems, and mobile technologies. The use of such an approach will facilitate better traffic management.

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