Short Term Speed Variance Forecasting Using Linear Stochastic Modeling of Univariate Traffic Speed Series
Jianhua Guo
Research Associate
Brian L. Smith
Associate Professor
University of Virginia
Center for Transportation Studies
July 16, 2007
A Research Project
Report for the Intelligent Transportation Systems Implementation Center (ITS)
A U.S. DOT University Transportation Center
Dr. Brian L. Smith, PhD
Department of Civil Engineering
Email: bls2z@virginia.edu
Center for Transportation Studies at the University of Virginia produces outstanding transportation professionals, innovative research results and provides important public service. The Center for Transportation Studies is committed to academic excellence, multi-disciplinary research and to developing state-of-the-art facilities. Through a partnership with the Virginia Department of Transportation’s (VDOT) Research Council (VTRC), CTS faculty hold joint appointments, VTRC research scientists teach specialized courses, and graduate student work is supported through a Graduate Research Assistantship Program. CTS receives substantial financial support from two federal University Transportation Center Grants: the Mid-Atlantic Universities Transportation Center (MAUTC), and through the National ITS Implementation Research Center (ITS Center). Other related research activities of the faculty include funding through FHWA, NSF, US Department of Transportation, VDOT, other governmental agencies and private companies.
Disclaimer: The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation, University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof.
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UVACTS-15-17-10 |
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Short Term Speed Variance Forecasting Using Linear
Stochastic Modeling of Univariate Traffic Speed Series |
July 17, 2007 |
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Dr. Brian Smith, Jianhua Guo |
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Center for Transportation Studies |
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University of Virginia |
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PO Box 400742 Charlottesville, VA 22904-7472 |
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Office of University
Programs, Research Innovation and Technology Administration US Department of
Transportation 400 Seventh Street, SW Washington DC 20590-0001 |
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16. Abstract |
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17 Key Words |
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Speed Variance Forecasting, Univariate Traffic
Speed Series |
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1 Introduction
Intelligent transportation systems (ITS) offers the potential to address critical transportation needs. However, most ITS currently operates in a reactive mode. While this provides some level of benefit, “the full benefits of ITS cannot be realized without an ability to anticipate traffic conditions in the short-term (less than one hour into future).” (Smith and Oswald, 2003) Based on the anticipated traffic condition, proactive transportation management and comprehensive traveler information services are feasible. Therefore, traffic condition forecasting has been identified as one of the major challenges for ITS.
Considering the forecasting process as a state extrapolation process governed by certain regularity, the development of traffic condition forecasting methods demands a sound understanding of traffic condition dynamics. Volume, speed, and density (or occupancy for the widely-deployed inductive loop detectors) are three traffic variables that are most commonly used to characterize traffic conditions, and suitable traffic condition forecasting methods are expected to be built upon traffic condition dynamics in terms of these traffic variables.
Previous efforts addressing traffic flow forecasting at a higher aggregation interval, such as 15-minutes, indicate traffic conditions to be linear stochastic. The traffic flow forecasting methods can be roughly classified into nonlinear theory based methods and linear theory based methods. The former assumes traffic dynamics are nonlinear, and can be emulated through nonlinear operations. Typically, this category includes non-parametric regression, neural networks, kernel smoothing, and local linear regression. The latter assumes traffic dynamics are linear and can be emulated through linear operations. Typically, this category includes univariate Box-Jenkins approach, exponential smoothing, spectral analysis, and multivariate time series methods. Adaptive methods, such as Kalman filter and recursive least square, can be classified into linear methods due to their nature as sequential projection in linear space. Williams (1999), Smith et al. (2002), and Williams and Hoel (2003) showed that traffic flow forecasting method based on Seasonal Autoregressive Integrated Moving Average (SARIMA) process outperformed nonlinear theory based methods, supporting the adoption of SARIMA process to describe traffic flow dynamics. Guo (2005) further appended a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model to describe the conditional variance for forecasting confidence interval construction. In addition, recent research on traffic state transition summarized in Daganzo (2002) revealed that traffic dynamics could be modeled using simple first order continuum theory, suggesting a linear regularity in traffic dynamics.
Based on linear traffic dynamics, the traffic speed series is naturally expected to be described using line