A Stacked Model Approach for Machine Learning-Based Traffic Prediction

Abstract

The application of technology for sensing, analysis, control, and communication within ground transportation is referred to as an intelligent transportation system. This system aims to enhance safety, mobility, and efficiency. Intelligent Transportation Systems (ITSs) are in the process of development and implementation, leading to improved accuracy in predicting traffic flow. The efficacy of traveler information systems, public transportation, and advanced traffic control is said to depend on these systems. In order to effectively manage and lessen traffic congestion, practical execution is essential, as evidenced by the expanding use of data in transportation management. By employing machine learning (ML), it is possible to construct predictive models that incorporate diverse data from numerous sources. Predicting traffic movement, reducing congestion, and identifying optimal routes that consume the least time or energy all require traffic prediction, which involves forecasting traffic volume and density. Traffic estimation and prediction systems have the potential to reduce travel times and enhance traffic conditions by enabling more efficient utilization of available capacity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.