Grey wolf and bald eagle search based hybrid algorithm for short term traffic prediction

Abstract

At present, traffic congestion is one of the critical challenges faced newlineby metropolitan cities across the globe. In a fast-moving world, transportation newlineconsumes most of the time and resources. Traffic congestion delays transit newlineand has some drastic indirect effects on time consumption, fuel consumption, newlineair, noise, and environmental pollution. To solve this global issue, it is newlinenecessary to execute a systematic well-organized solution. newlineProper analysis of traffic demand is needed to estimate traffic newlineconditions in the future. This analytic data can be used to optimize routes newlineopted by vehicles. More specifically, this road traffic data and its analysis newlinehave significant impact on the development of autonomous vehicles as we are newlinein the era of moving into smart transportation. newlineApplication of statistical models and machine learning algorithms newlinehave been proved successful in a wide range of applications. Traffic newlineprediction also becomes a thrust application for machine learning algorithms newlineto overcome hurdles faced by congestion. newlineA well-known prediction mechanism, Support Vector Regression newline(SVR), has been employed for short-term traffic forecasting. newline