[1] Cui Qinghua, Xia Jingxin,. Time-varying confidence interval forecasting of travel timefor urban arterials using ARIMA-GARCH model [J]. Journal of Southeast University (English Edition), 2014, 30 (3): 358-362. [doi:10.3969/j.issn.1003-7985.2014.03.019]

Copy

Time-varying confidence interval forecasting of travel timefor urban arterials using ARIMA-GARCH model()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

30

Issue:

2014 3

Page:

358-362

Research Field:

Traffic and Transportation Engineering

Publishing date:

2014-09-30

Info

Title:

Time-varying confidence interval forecasting of travel timefor urban arterials using ARIMA-GARCH model

Author(s):

Cui Qinghua;  Xia Jingxin

Intelligent Transportation System Research Center, Southeast University, Nanjing 210096, China

Keywords:

confidence interval forecasting;  travel time;  autoregressive integrated moving average and generalized autoregressive conditional heteroskedasticity(ARIMA-GARCH);  conditional variance;  reliability

PACS:

U121

DOI:

10.3969/j.issn.1003-7985.2014.03.019

Abstract:

To improve the forecasting reliability of travel time, the time-varying confidence interval of travel time on arterials is forecasted using an autoregressive integrated moving average and generalized autoregressive conditional heteroskedasticity(ARIMA-GARCH)model. In which, the ARIMA model is used as the mean equation of the GARCH model to model the travel time levels and the GARCH model is used to model the conditional variances of travel time. The proposed method is validated and evaluated using actual traffic flow data collected from the traffic monitoring system of Kunshan city. The evaluation results show that, compared with the conventional ARIMA model, the proposed model cannot significantly improve the forecasting performance of travel time levels but has advantage in travel time volatility forecasting. The proposed model can well capture the travel time heteroskedasticity and forecast the time-varying confidence intervals of travel time which can better reflect the volatility of observed travel times than the fixed confidence interval provided by the ARIMA model.

References:

[1] Rice J, Van Zwet E. A simple and effective method for predicting travel times on freeways[J]. IEEE Transactions on Intelligent Transportation Systems, 2004, 5(3): 200-207.
[2] Du L, Peeta S, Kim Y H. An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks[J]. Transportation Research Part B: Methodological, 2012, 46(1): 235-252.
[3] Liu H, Van Zuylen H, Van Lint H, et al. Predicting urban arterial travel time with state-space neural networks and Kalman filters[J]. Transportation Research Record, 2006, 1968: 99-108.
[4] Yang M, Liu Y, You Z. The reliability of travel time forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(1): 162-171.
[5] Ahmed M S, Cook A R. Analysis of freeway traffic time-series data by using box-jenkins techniques [J]. Transportation Research Record, 1979, 722: 1-9.
[6] Myung J, Kim D K, Kho S Y, et al. Travel time prediction using k nearest neighbor method with combined data from vehicle detector system and automatic toll collection system[J]. Transportation Research Record, 2011, 2256: 51-59.
[7] Khosravi A, Mazloumi E, Nahavandi S, et al. Prediction intervals to account for uncertainties in travel time prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 537-547.
[8] Van Hinsbergen C P, Van Lint J W C, Van Zuylen H J. Bayesian committee of neural networks to predict travel times with confidence intervals[J]. Transportation Research Part C: Emerging Technologies, 2009, 17(5): 498-509.
[9] Chen X, Gong H, Wang J. BRT vehicle travel time prediction based on SVM and Kalman filter[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(4): 29-34.(in Chinese)
[10] Tsekeris T, Stathopoulos A. Real-time traffic volatility forecasting in urban arterial networks[J]. Transportation Research Record, 2006, 1964: 146-156.
[11] Taylor S J. Modelling financial time series[M]. Singapore:World Scientific Publishing, 2008.

Memo

Memo:

Biographies: Cui Qinghua(1988—), female, graduate; Xia Jingxin(corresponding author), male, doctor, associate professor, jingxinxia@yahoo.com.cn.
Foundation item: The National Natural Science Foundation of China(No.51108079).
Citation: Cui Qinghua, Xia Jingxin. Time-varying confidence interval forecasting of travel time for urban arterials using ARIMA-GARCH model[J].Journal of Southeast University(English Edition), 2014, 30(3):358-362.[doi:10.3969/j.issn.1003-7985.2014.03.019]

Common functions