A Self-Learning Short-Term Traffic Forecasting System

Jiasong Zhu and Anthony Gar-On Yeh
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Jiasong Zhu: Department of Transportation Engineering, Faculty of Civil Engineering, Shenzhen University, Nanhai Road 3688, Shenzhen, Guangdong, China
Anthony Gar-On Yeh: Department of Urban Planning and Design, The University of Hong Kong, Pokfulam Road, Hong Kong SAR

Environment and Planning B, 2012, vol. 39, issue 3, 471-485

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traffic patterns to short-term traffic forecasting are profound and this can easily make an existing forecasting system outdated.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.

Keywords: self-learning; traffic forecasting (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:39:y:2012:i:3:p:471-485

DOI: 10.1068/b36174

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