Real-Time Prediction of Seasonal Heteroscedasticity in Vehicular Traffic Flow Series

Over the past decade, traffic heteroscedasticity has been investigated with the primary purpose of generating prediction intervals around point forecasts constructed usually by short-term traffic condition level forecasting models. However, despite considerable advancements, complete traffic patterns, in particular the seasonal effect, have not been adequately handled. Recently, an offline seasonal adjustment factor plus GARCH model was proposed in Shi et al. 2014 to model the seasonal heteroscedasticity in traffic flow series. However, this offline model cannot meet the real-time processing requirement proposed by real-world transportation management and control applications. Therefore, an online seasonal adjustment factors plus adaptive Kalman filter (OSAF+AKF) approach is proposed in this paper to predict in real time the seasonal heteroscedasticity in traffic flow series. In this approach, OSAF and AKF are combined within a cascading framework, and four types of online seasonal adjustment factors are developed considering the seasonal patterns in traffic flow series. Empirical results using real-world station-by-station traffic flow series showed that the proposed approach can generate workable prediction intervals in real time, indicating the acceptability of the proposed approach. In addition, compared with the offline model, the proposed online approach showed improved adaptability when traffic is highly volatile. These findings are important for developing real-time intelligent transportation system applications.

[1]  Yanru Zhang,et al.  Component GARCH Models to Account for Seasonal Patterns and Uncertainties in Travel-Time Prediction , 2015, IEEE Transactions on Intelligent Transportation Systems.

[2]  Billy M. Williams,et al.  MODELING AND FORECASTING VEHICULAR TRAFFIC FLOW AS A SEASONAL STOCHASTIC TIME SERIES PROCESS , 1999 .

[3]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

[4]  Antony Stathopoulos,et al.  Short-Term Prediction of Urban Traffic Variability: Stochastic Volatility Modeling Approach , 2010 .

[5]  Graham Currie,et al.  Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction , 2011, Eng. Appl. Artif. Intell..

[6]  Wanli Min,et al.  Real-time road traffic prediction with spatio-temporal correlations , 2011 .

[7]  Yanru Zhang,et al.  A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model , 2014 .

[8]  Yanru Zhang,et al.  Stochastic Volatility Modeling Approach that Accounts for Uncertainties in Travel Time Reliability Forecasting , 2014 .

[9]  Yiannis Kamarianakis,et al.  Modeling Traffic Volatility Dynamics in an Urban Network , 2005 .

[10]  Wei Huang,et al.  Modeling Seasonal Heteroscedasticity in Vehicular Traffic Condition Series Using a Seasonal Adjustment Approach , 2014 .

[11]  Hashem R Al-Masaeid,et al.  Short-Term Prediction of Traffic Volume in Urban Arterials , 1995 .

[12]  Paolo Frasconi,et al.  Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning , 2013, IEEE Transactions on Intelligent Transportation Systems.

[13]  Qiang Meng,et al.  Short-time traffic flow prediction with ARIMA-GARCH model , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[14]  Moshe Levin,et al.  ON FORECASTING FREEWAY OCCUPANCIES AND VOLUMES (ABRIDGMENT) , 1980 .

[15]  Man-Chun Tan,et al.  An Aggregation Approach to Short-Term Traffic Flow Prediction , 2009, IEEE Transactions on Intelligent Transportation Systems.

[16]  Jianhua Guo,et al.  Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification , 2014 .

[17]  Yanru Zhang,et al.  Univariate Volatility-Based Models for Improving Quality of Travel Time Reliability Forecasting , 2013 .

[18]  A. R. Cook,et al.  ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES , 1979 .

[19]  Antony Stathopoulos,et al.  Real-Time Traffic Volatility Forecasting in Urban Arterial Networks , 2006 .

[20]  Keemin Sohn,et al.  Statistical Model for Forecasting Link Travel Time Variability , 2009 .

[21]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[22]  Jianhua Guo,et al.  Adaptive Estimation and Prediction of Univariate Vehicular Traffic Condition Series , 2005 .

[23]  Jingxin Xia,et al.  Reliable Short-Term Traffic Flow Forecasting for Urban Roads , 2013 .

[24]  Billy M. Williams Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling , 2001 .

[25]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[26]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[27]  Y. Kamarianakis,et al.  Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches , 2003 .

[28]  Jianhua Guo,et al.  Real-Time Short-Term Traffic Speed Level Forecasting and Uncertainty Quantification Using Layered Kalman Filters , 2010 .

[29]  Chris Chatfield,et al.  Calculating Interval Forecasts , 1993 .

[30]  Brian Lee Smith,et al.  Data Collection Time Intervals for Stochastic Short-Term Traffic Flow Forecasting , 2007 .

[31]  Jianhua Guo,et al.  Integrated Heteroscedasticity Test for Vehicular Traffic Condition Series , 2012 .

[32]  Nancy L. Nihan,et al.  Use of the box and Jenkins time series technique in traffic forecasting , 1980 .

[33]  Pedro Sousa,et al.  Multi‐scale Internet traffic forecasting using neural networks and time series methods , 2010, Expert Syst. J. Knowl. Eng..

[34]  W. Fuller,et al.  Introduction to Statistical Time Series (2nd ed.) , 1997 .

[35]  Yiguang Liu,et al.  The Reliability of Travel Time Forecasting , 2010, IEEE Transactions on Intelligent Transportation Systems.