[en] In this paper, daily traffic counts are explained and forecast by different modeling philosophies: an approach using autoregressive integrated moving average (ARIMA) models with explanatory variables (i.e., the ARIMAX model) and approaches using a seasonal autoregressive integrated moving average (SARIMA) model as well as a SARIMA model with explanatory variables (i.e., the SARIMAX model). Special emphasis is placed on the investigation of seasonality in daily traffic data and on the identification and comparison of holiday effects at different sites. To get insight into prior cyclic patterns in the daily traffic counts, spectral analysis provides the required framework to highlight periodicities in the data. The analyses use data from single inductive loop detectors, which were collected in 2003, 2004, and 2005. Four traffic count locations are investigated in this study: an upstream and a downstream traffic count site on a highway used extensively by commuters, and an upstream and a downstream traffic count site on a highway typically used for leisure travel. The different modeling techniques show that weekly cycles appear to determine the variation in daily traffic counts. The comparison between seasonal and holiday effects at different site locations reveals that both the ARIMAX and the SARIMAX modeling approaches are valid frameworks for identifying and quantifying possible influencing effects. The techniques yield the insight that holidays have a noticeable impact on highways extensively used by commuters, while having a more ambiguous impact on highways typically used for leisure travel. Future research challenges are the modeling of daily traffic counts on secondary roads and the simultaneous modeling of underlying reasons for travel and revealed traffic patterns.
Research Center/Unit :
Lepur : Centre de Recherche sur la Ville, le Territoire et le Milieu rural - ULiège LEMA - Local Environment Management and Analysis
Disciplines :
Civil engineering Special economic topics (health, labor, transportation...)
Park, B. Hybrid Neuron-Fuzzy Application in Short-Term Freeway Traffic Volume Forecasting. In Transportation Research Record: Journal of the Transportation Research Board, No.1802, Transportation Research Board of the National Academies, Washington, D. C., 2002, pp. 190-196.
Han, C., and S. Song. A Review of Some Main Models for Traffic Flow Forecasting. Proc. 2003 IEEE International Conference on Intelligent Transportation Systems. Vol.1, 2003, pp. 216-219.
Van Arem, B., H. R. Kirby, M. J. M. Van Der Vlist, and J. C. Whittaker. Recent Advances and Applications in the Field of Short-Term Traffic Forecasting. International Journal of Forecasting, Vol.13, No.1, 1997, pp. 1-12.
Ahmed, M. S., and A. R. Cook. Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques. In Transportation Research Record 772, TRB, National Research Council, Washington, D.C., 1979, pp. 1-9.
Nihan, N. L., and K. O. Holmesland. Use of the Box and Jenkins Time- Series Technique in Traffic Forecasting. Transportation, Vol.9, No.2, 1980, pp. 125-143.
Williams, B. M., and L. A. Hoel. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: A Theoretical Basis and Empirical Results. Journal of Transportation Engineering (ASCE), Vol.129, No.6, 2003, pp. 664-672.
Williams, B. M. Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling. In Transportation Research Record: Journal of the Transportation Research Board, No.1776, TRB, National Research Council, Washington, D.C., 2001, pp. 194-200.
Van Der Voort, M., M. Dougherty, and S. Watson. Combining Kohonen Maps with ARIMA Time-Series Models to Forecast Traffic Flow. Transportation Research Part C, Vol.4, No.5, 1996, pp. 307-318.
Stathopoulos, A., and M. G. Karlaftis. A Multivariate State Space Approach for Urban Traffic Flow Modeling and Prediction. Transportation Research Part C, Vol.11, No.2, 2003, pp. 121-135.
Kamarianakis, Y., and P. Prastacos. Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches. In Transportation Research Record: Journal of the Transportation Research Board, No.1857, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 74-84.
Okatuni, I., and Y. J. Stephanedes. Dynamic Prediction of Traffic Volume Through Kalman Filtering Theory. Transportation Research Part B, Vol.18, No.1, 1984, pp. 1-11.
Xie, Y., Y. Zhang, and Z. Ye. Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition. Computer- Aided Civil and Infrastructure Engineering, Vol.22, No.5, 2007, pp. 326-334.
Smith, B. L., and M. J. Demetsky. Short-Term Traffic Flow Prediction: Neural Network Approach. In Transportation Research Record 1453, TRB, National Research Council, Washington, D.C., 1994, pp. 98-104.
Yun, S.-Y., S. Namkoong, J.-H. Rho, S.-W. Shin, and J.-U. Choi. A Performance Evaluation of Neural-Network Models in Traffic Volume Forecasting. Mathematical and Computer Modeling, Vol.27, No.9, 1998, pp. 293-310.
Vlahogianni, E. I., M. G. Karlaftis, and J. C. Golias. Spatio-Temporal Short-Term Urban Traffic Volume Forecasting Using Genetically Optimized Modular Networks. Computer-Aided Civil and Infrastructure Engineering, Vol.22, No.5, 2007, pp. 317-325.
Chen, H., and S. Grant-Muller. Use of Sequential Learning for Short- Term Traffic Flow Forecasting. Transportation Research Part C, Vol.9, No.5, 2001, pp. 319-336.
Zheng, W., D.-H. Lee, and Q. Shi. Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural-Network Approach. Journal of Transportation Engineering, Vol.132, No.2, 2006, pp. 114-121.
Smith, B. L., B. M. Williams, and R. K. Oswald. Comparison of Parametric and Nonparametric Models for Traffic Flow Forecasting. Transportation Research Part C, Vol.10, No.4, 2002, pp. 303-321.
Danech-Pajouh, M., and M. Aron. ATHENA: A Method for Short-Term Inter-Urban Motorway Traffic Forecasting. Recherche Transports Sécurité-English Issue, No.6, 1991, pp. 11-16.
Lakhina, A., K. Papagiannaki, M. Crovella, C. Diot, E. D. Kolaczyk, and N. Taft. Structural Analysis of Network Traffic Flows. ACM SIGMETRICS Performance Evaluation Review, Vol.32, No.1, 2004, pp. 61-72.
Kim, T., H. Kim, C. Oh, and B. Son. Traffic Flow Forecasting Based on Pattern Recognition to Overcome Memoryless Property. Proc., 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07), 2007, pp. 1181-1186.
Li, L., W.-H. Lin, and H. Liu. Type-2 Fuzzy-Logic Approach for Short- Term Traffic Forecasting. Proc., 2006 IEEE International Conference on Intelligent Transportation Systems, Vol.153, No.1, 2006, pp. 33-40.
Zhang, Y., and Y. Xie. Forecasting of Short-Term Freeway Volume with v-Support Vector Machines. In Transportation Research Record: Journal of the Transportation Research Board, No.2024, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 92-99.
Cools, M., E. A. Moons, and G. Wets. Investigating Effect of Holidays on Daily Traffic Counts: Time-Series Approach. In Transportation Research Record: Journal of the Transportation Research Board, No.2019, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 22-31.
Van den Bossche, F., G. Wets, and T. Brijs. A Regression Model with ARMA Errors to Investigate the Frequency and Severity of Road Traffic Accidents. In 83rd Annual Meeting Compendium of Papers CD-ROM. Transportation Research Board of the National Academies, Washington, D.C., 2004.
Maerivoet, S. Modeling Traffic on Motorways: State-of-the-Art, Numerical Data Analysis, and Dynamic Traffic Assignment, PhD thesis. Department of Electrical Engineering ESAT-SCD (SISTA), Katholieke Universiteit, Leuven, Belgium, 2006.
Neter, J., M. H. Kutner, C. J. Nachtsheim, and W. Wasserman. Applied Linear Statistical Models. WCB/McGraw-Hill, Burr Ridge, Ill., 1996.
Shumway, R. H., and D. S. Stoffer. Time-Series Analysis and Its Applications. Springer, New York, 2000.
Yaffee, R. A., and M. McGee. Introduction to Time-Series Analysis and Forecasting, with Applications of SAS and SPSS. Academic Press, San Diego, Calif., 2000.
Brocklebank, J. C., and D. A. Dickey. SAS for Forecasting Time Series, 2nd ed. SAS Institute, Cary, N.C., 2003.
Tian, J., and G. Fernandez. Seasonal Trend Analysis of Monthly Water Quality Data. Proc., 7th Annual Western Users of SAS Software Regional Users Group, 1999, pp. 229-234.
SAS Institute, Inc. SAS/ETS 9.1 User's Guide. SAS Institute Inc., Cary, N.C., 2004, pp. 363-485.
Makridakis, S., S. Wheelwright, and R. Hyndman. Forecasting: Methods and Applications, 3rd ed. John Wiley and Sons, Inc., New York, 1998.
Wets, G., K. Vanhoof, T. Arentze, and H. Timmermans. Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms. In Transportation Research Record: Journal of the Transportation Research Board, No.1718, TRB, National Research Council, Washington, D.C., 2000, pp. 1-9.
Akaike, H. A. New Look at the Statistical Model Identification. IEEE Transaction on Automatic Control, Vol.19, 1974, pp. 716-723.
Pankratz, A. Forecasting with Dynamic Regression Models. John Wiley and Sons, Inc., New York, 1991.
Ljung, G., and G. Box. On a Measure of Lack of Fit in Time Series Models. Biometrika, Vol.65, No.2, 1978, pp. 297-303.
Cools, M., E. Moons, and G. Wets. Travel Time Expenditure in Flanders: Towards a Better Understanding of Travel Behavior. Proc. 10th International Conference on Computers in Urban Planning and Urban Management (CUPUM). (CD-ROM), Iguassu Falls, Brazil, 2007.