Univariate Volatility-Based Models for Improving Quality of Travel Time Reliability Forecasting

The literature is rich in travel time prediction because of its importance in intelligent transportation systems. Despite the proliferation of advanced methodologies, modeling the uncertainty of traffic conditions is still a challenge, especially during congested situations. Travel time reliability associated with its time-dependent variation provides a way to measure the system performance and has received extensive attention in recent years. In practice, one of the measures for travel time reliability is the identification of prediction intervals (PIs). The PI measurement has many potential applications in the development of systems that aim at disseminating real-time traffic information to travelers. From a management point of view, the PIs forecast the unreliable traffic periods and enable the selection of proper strategies to avoid or release possible traffic congestion. The generalized autoregressive conditional heteroscedasticity (GARCH) model has proved to have the ability to model the uncertainties in the literature. However, the model has some drawbacks in traffic forecasting. To improve the quality of travel time reliability forecasting, this paper proposes two univariate volatility models and compares their performance in generating high-quantity PIs. Travel time data collected from automatic vehicle identification stations located along U.S. Highway 290 in Houston, Texas, are used to examine each model's performance in travel time reliability forecasting. Study results indicate that all three models give reasonable PIs that could be used to indicate the variability of future traffic conditions. The statistical analysis and forecasting results indicate that the proposed Glosten–Jagannathan–Runkle GARCH model outperforms the other two models in constructing better PIs.

[1]  Lily Elefteriadou,et al.  Travel time estimation on a freeway using Discrete Time Markov Chains , 2008 .

[2]  Chung-Cheng Lu,et al.  A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction , 2011 .

[3]  A. I. McLeod,et al.  DIAGNOSTIC CHECKING ARMA TIME SERIES MODELS USING SQUARED‐RESIDUAL AUTOCORRELATIONS , 1983 .

[4]  Haitham Al-Deek,et al.  Travel-Time Prediction for Freeway Corridors , 1999 .

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

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

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

[8]  H. V. van Zuylen,et al.  Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters , 2006 .

[9]  Carol Alexander,et al.  Practical financial econometrics , 2008 .

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

[11]  Eric R. Ziegel,et al.  Analysis of Financial Time Series , 2002, Technometrics.

[12]  Amir F. Atiya,et al.  Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances , 2011, IEEE Transactions on Neural Networks.

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

[14]  Alexander Skabardonis,et al.  Freeway Performance Measurement System: Operational Analysis Tool , 2002 .

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

[16]  Daniel B. Nelson.  CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[17]  Benjamin Coifman,et al.  Estimating travel times and vehicle trajectories on freeways using dual loop detectors , 2002 .

[18]  Saeid Nahavandi,et al.  A genetic algorithm-based method for improving quality of travel time prediction intervals , 2011 .

[19]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

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

[21]  Ruey S. Tsay,et al.  Analysis of Financial Time Series: Tsay/Analysis of Financial Time Series , 2005 .

[22]  Mascha C. van der Voort,et al.  Combining kohonen maps with arima time series models to forecast traffic flow , 1996 .

[23]  Natalia Ruiz Juri,et al.  Integrated Traffic Simulation–Statistical Analysis Framework for Online Prediction of Freeway Travel Time , 2007 .

[24]  Adrian Pagan,et al.  The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics , 1980 .

[25]  Dongjoo Park,et al.  Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks , 1998 .

[26]  D. T. Lee,et al.  Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.

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

[28]  Kevin N. Balke,et al.  A Guidebook for Effective Use of Incident Data at Texas Transportation Management Centers , 2009 .

[29]  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..