Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

Authors:
Manoel Castro-Neto;Young-Seon Jeong;Myong-Kee Jeong;Lee D. Han
Affiliations:
Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA;Department of Industrial and Systems Engineering, Rutgers University, Knoxville, Piscataway, NJ 08854, USA;Department of Industrial and Systems Engineering, Rutgers University, Knoxville, Piscataway, NJ 08854, USA;Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Most literature on short-term traffic flow forecasting focused mainly on normal, or non-incident, conditions and, hence, limited their applicability when traffic flow forecasting is most needed, i.e., incident and atypical conditions. Accurate prediction of short-term traffic flow under atypical conditions, such as vehicular crashes, inclement weather, work zone, and holidays, is crucial to effective and proactive traffic management systems in the context of intelligent transportation systems (ITS) and, more specifically, dynamic traffic assignment (DTA). To this end, this paper presents an application of a supervised statistical learning technique called Online Support Vector machine for Regression, or OL-SVR, for the prediction of short-term freeway traffic flow under both typical and atypical conditions. The OL-SVR model is compared with three well-known prediction models including Gaussian maximum likelihood (GML), Holt exponential smoothing, and artificial neural net models. The resultant performance comparisons suggest that GML, which relies heavily on the recurring characteristics of day-to-day traffic, performs slightly better than other models under typical traffic conditions, as demonstrated by previous studies. Yet OL-SVR is the best performer under non-recurring atypical traffic conditions. It appears that for deployed ITS systems that gear toward timely response to real-world atypical and incident situations, OL-SVR may be a better tool than GML.