Machine Learning and Deep Learning Approach in Traffic Flow Control and Prediction forTraffic Management System

Authors : Preeti Rekha Sahu

Volume/Issue : Volume 8 - 2023, Issue 9 - September

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Congested roads is a fundamental urban areas' issue which is primarily contributed from unexpected vehicular population growth and inefficient traffic operation and control in some mechanisms. The field of intelligent transportation systems (ITS) has developed quickly inrecent years. A cost-effective method for managing and planning smart public transportation.ITS enhances traffic safety, and mobility reduces the externalities that arise through all the transportation-related activities. (ITS) applications require the minimum human intervention and are utilized for advance route planning and traffic control systems. ITS applications areefficient in large scale traffic data collection both in time and space and several studies uselarge scale traffic data for developing efficient traffic operation programs for a city. Large scale data collection programs have several potential applications in solving transportation related problems by developing robust traffic flow prediction models. In recent times, researchersapply novel tools such as Machine Learning (ML) and Deep Learning (DL) to predict real-timetraffic. Real-time traffic prediction models are helpful for improved traffic control and efficienttraffic management system. Statistical models, ML and DL models are used for traffic signaldesign, que length analysis, and delay minimization for traffic stream in an urban network. Inessence, these models help in minimizing travel time for users and thus reduces travel cost.This paper's goal is to present a thorough grasp of the use of ML and DL approaches to improve traffic flow prediction models with recommendations for ITS application in smart cities. The findings from this research may be applied by smart city managers for developing efficienttraffic management programs in the cities in India and elsewhere.

Keywords : ITS, Machine Learning, Deep Learning, Traffic flow Control and Prediction.


Paper Submission Last Date
31 - December - 2023

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