A Self-Learning Short-Term Traffic Forecasting System

Abstract

A reliable and accurate short-term traffic forecasting system is crucial for the successful deployment of any intelligent transportation system. A lot of forecasting models have been developed in recent years but none of them could consistently outperform the others. In real-world applications, traffic forecasting accuracy can be affected by a lot of factors. Impacts of long-term changes to traffic patterns to short-term traffic forecasting are profound and this can easily make an existing forecasting system outdated. Therefore, it is very important for forecasting systems to detect long-term changes in traffic patterns and make updates accordingly. This paper presents a new forecasting mechanism, in which a dynamic hybrid approach is taken and self-learning ability is enhanced. Results of a case study show the proposed approach is feasible in enhancing the adaptability of traffic forecasting systems.