Potentialities of Data-Driven Nonparametric Regression in Urban Signalized Traffic Flow Forecasting

AbstractSingle-interval forecasting of traffic variables plays a key role in modern intelligent transportation systems (ITSs). Despite the achievements of advanced ITS forecasting in literature, forecast modeling of urban signalized traffic flow, which shows rapid-intensive fluctuations associated with the nonlinear and nonstationary behavior of temporal evolution, is still one of its big challenges. From the perspective of field experts, the mathematical complexity of an advanced model is also a renewal obstacle in practice. On the other hand, the accessibility of large volumes of historical data and the concurrent advanced data management systems used to access them provide data-driven nonparametric regression with a renewal opportunity in practice. In order to address these problems effectively, this paper proposes a k nearest neighbor nonparametric regression (KNN-NPR) forecasting methodology to be tested against vast quantities of real traffic volume data collected from urban signalized arterials. Th...

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

[2]  Brian Lee Smith,et al.  Meeting Real–Time Traffic Flow Forecasting Requirements with Imprecise Computations , 2003 .

[3]  Antony Stathopoulos,et al.  Temporal and Spatial Variations of Real-Time Traffic Data in Urban Areas , 2001 .

[4]  Shing Chung Josh Wong,et al.  Urban traffic flow prediction using a fuzzy-neural approach , 2002 .

[5]  Antony Stathopoulos,et al.  Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow , 2008, Comput. Aided Civ. Infrastructure Eng..

[6]  Rod E. Turochy.  Enhancing Short-Term Traffic Forecasting with Traffic Condition Information , 2006 .

[7]  J. Durbin,et al.  The Foreman Lecture: the State Space Approach to Time Series Analysis and its Potential for Official Statistics (with Discussion) , 2000 .

[8]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[9]  S. Yakowitz,et al.  Rainfall-runoff forecasting methods, old and new , 1987 .

[10]  Matthew G. Karlaftis,et al.  A multivariate state space approach for urban traffic flow modeling and prediction , 2003 .

[11]  Dongjoo Park,et al.  Dynamic multi-interval bus travel time prediction using bus transit data , 2010 .

[12]  Eleni I. Vlahogianni,et al.  Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach , 2005 .

[13]  S. Yakowitz.  NEAREST‐NEIGHBOUR METHODS FOR TIME SERIES ANALYSIS , 1987 .

[14]  Eleni I. Vlahogianni,et al.  Short‐term traffic forecasting: Overview of objectives and methods , 2004 .

[15]  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.

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

[17]  Eleni I. Vlahogianni,et al.  Statistical methods versus neural networks in transportation research: Differences, similarities and some insights , 2011 .

[18]  Eleni I. Vlahogianni,et al.  Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume , 2006 .

[19]  Dominique Guegan,et al.  Forecasting chaotic systems: The role of local Lyapunov exponents , 2007 .

[20]  Francis J Mulhern,et al.  A nearest neighbor model for forecasting market response , 1994 .

[21]  Stephen D. Clark,et al.  Traffic Prediction Using Multivariate Nonparametric Regression , 2003 .

[22]  Byoung-Jo Yoon,et al.  Dynamic near-term traffic flow prediction: system- oriented approach based on past experiences , 2012 .

[23]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[24]  ChangHyunho,et al.  Multiple time period imputation technique for multiple missing traffic variables: nonparametric regression approach , 2012 .

[25]  I Okutani,et al.  Dynamic prediction of traffic volume through Kalman Filtering , 1984 .

[26]  Gary A. Davis,et al.  Nonparametric Regression and Short‐Term Freeway Traffic Forecasting , 1991 .

[27]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[28]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[29]  Michael J Demetsky,et al.  Multiple-Interval Freeway Traffic Flow Forecasting , 1996 .

[30]  Joe Whittaker,et al.  TRACKING AND PREDICTING A NETWORK TRAFFIC PROCESS , 1997 .