Abstract: The Intelligent Transportation System is part of several smart city applications where it improves the processes of transportation and commutation. Its aims to organize traffic problems, mainly traffic jams. The road traffic flow prediction system has wide application in the city transportation and area management. In Some cities, it is very hardest task to manage traffic. But the prediction with reflection of some physical conditions of environment and weather like raining, thunder is found more effective. we Proposed a Road traffic flow prediction system model to predict the Road traffic flow with a duration interval of one hour up to 24 hours. The algorithms are used for research in the past, but there are not so many platforms found on which road traffic flow prediction has easy to use and access to public users. The system is Proposed to organize the problems Related with the historical and time series. Historical road Traffic data set was collected from an open source and various operations perform on it as per requirements. By using Machine learning algorithms, a system is designed, which gather the data from the roads using Vehicle detection sensors and stores into the database for future predictions. We also gathered the data of weather systems to get weather data. This road Traffic flow prediction system is developed to use the existing popular ML prediction algorithms that Support Vector Machine (SVM). After experiments, results were differentiated with the actual data to check the correctness of the algorithms. Support Vector Machine (SVM) helps to predict in short term road traffic flow prediction. But a shorter time interval Provides more accurate results.
Keywords: Traffic Prediction, Support vector machines, artificial neural network, Prediction, Jams
| DOI: 10.17148/IJARCCE.2022.11430