Simulation of Autoregressive Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) to Forecast Traffic Flow

Challiz D. Omorog


Modeling the unprecedented traffic flow data generated by Intelligent Transportation Systems can boost the innovation-capacity of the transportation management systems to drive informed decision-making. Thus, this paper attempts to simulate traffic forecasting techniques that can be adopted in the Philippines to make fact-based decisions into accurate and effective traffic management schemes. In this research, a schematic framework is introduced organized into three stages (Preprocessing, Model Identification and Estimation, and Model Checking) sequentially arranged to comprehensively estimate the best-appropriate model to forecast traffic flow using ARIMA and GARCH models. The Model Identification and Estimation is the conditional stage in the framework that pre-determines if hybrid modeling is necessary based on the given datasets. Various accuracy metrics are also used to find the “best†model and select the optimal values for ARIMA and GARCH models. The proposed framework is simulated in R Programming using the vehicular traffic flow datasets at North Avenue, EDSA northbound, Manila, Philippines. The resulting models, consist of the best fit ARIMA (1,1,3) and GARCH (1,2), are combined as the hybrid model and compared using its prediction results. Based on the visual simulation data, the prediction accuracy result of the ARIMA model outperforms the combined ARIMA-GARCH model given the actual data. Conclusively, the simulation performance provides proof to suggest that the forecasting models are timely tools to predict future traffic flow and aid in making better traffic inventions and schemes.


ARIMA; GARCH; hybrid algorithm; traffic prediction algorithm.

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A. Sumalee and H. W. Ho, “Smarter and more connected: Future intelligent transportation system,†IATSS Research, vol. 42, no. 2. Elsevier B.V., pp. 67–71, Jul. 2018, doi: 10.1016/j.iatssr.2018.05.005.

A. Atta, S. Abbas, M. A. Khan, G. Ahmed, and U. Farooq, “An adaptive approach: Smart traffic congestion control system,†J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 9, pp. 1012–1019, Nov. 2020, doi: 10.1016/j.jksuci.2018.10.011.

W. C. Tchuitcheu, C. Bobda, and M. J. H. Pantho, “Internet of smart-cameras for traffic lights optimization in smart cities,†arXiv, vol. 11. arXiv, p. 100207, Feb. 2020, doi: 10.1016/j.iot.2020.100207.

N. Chan Soe and T. Lai Lai Thein, “Haversine Formula and RPA Algorithm for Navigation System,†Int. J. Data Sci. Anal., vol. 6, no. 1, p. 32, Feb. 2020, doi: 10.11648/j.ijdsa.20200601.14.

M. Sarrab, S. Pulparambil, and M. Awadalla, “Development of an IoT based real-time traffic monitoring system for city governance,†Glob. Transitions, vol. 2, pp. 230–245, Jan. 2020, doi: 10.1016/j.glt.2020.09.004.

S. Felici-Castell, M. García-Pineda, J. Segura-Garcia, R. Fayos-Jordan, and J. Lopez-Ballester, “Adaptive live video streaming on low-cost wireless multihop networks for road traffic surveillance in smart cities,†Futur. Gener. Comput. Syst., vol. 115, pp. 741–755, Feb. 2021, doi: 10.1016/j.future.2020.10.010.

H. Shin, J. Jung, and Y. Koo, “Forecasting the Video Data Traffic of 5G Services in South Korea,†Technol. Forecast. Soc. Change, vol. 153, p. 119948, Apr. 2020, doi: 10.1016/j.techfore.2020.119948.

Y. Lian, G. Zhang, J. Lee, and H. Huang, “Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles,†Accid. Anal. Prev., vol. 146, p. 105711, Oct. 2020, doi: 10.1016/j.aap.2020.105711.

M. Rith, K. I. D. Z. Roquel, N. S. A. Lopez, A. M. Fillone, and J. B. M. M. Biona, “Towards more sustainable transport in Metro Manila: A case study of household vehicle ownership and energy consumption,†Transp. Res. Interdiscip. Perspect., vol. 6, p. 100163, Jul. 2020, doi: 10.1016/j.trip.2020.100163.

Y. Boquet, “Managing Metro Manila,†in Springer Geography, Springer, 2017, pp. 567–615.

S. Kaffash, A. T. Nguyen, and J. Zhu, “Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis,†Int. J. Prod. Econ., vol. 231, p. 107868, Jan. 2021, doi: 10.1016/j.ijpe.2020.107868.

I. Lana, J. Del Ser, M. Velez, and E. I. Vlahogianni, “Road Traffic Forecasting: Recent Advances and New Challenges,†IEEE Intelligent Transportation Systems Magazine, vol. 10, no. 2. Institute of Electrical and Electronics Engineers, pp. 93–109, Jun. 2018, doi: 10.1109/MITS.2018.2806634.

M. A. Ansari and T. Arundhathi, “Street Traffic Forecasting Ongoing Advances and New Challenges,†Int. J. Comput. Sci. Eng., vol. 7, no. 3, pp. 650–656, Mar. 2019, doi: 10.26438/ijcse/v7i3.650656.

M. Lippi, M. Bertini, and P. Frasconi, “Short-term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning,†IEEE Trans. Intell. Transp. Syst., vol. 14, no. 2, pp. 871–882, 2013, doi: 10.1109/TITS.2013.2247040.

X. Fu, W. Luo, C. Xu, X. Zhao, and F. J. Hwang, “Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit,†Math. Probl. Eng., vol. 2020, 2020, doi: 10.1155/2020/3697625.

S. R. Yaziz, N. A. Azizan, R. Zakaria, and M. H. Ahmad, “The Performance of Hybrid ARIMA-GARCH Modeling in Forecasting Gold Price,†20th Int. Congr. Model. Simulation, Adelaide, Aust., no. December, pp. 1–6, 2013.

L. Seymour, P. J. Brockwell, and R. A. Davis, Introduction to Time Series and Forecasting., vol. 92, no. 440. 1997.

R. Adhikari and R. K. Agrawal, “An Introductory Study on Time Series Modeling and Forecasting,†L. Lambert Acad. Publ. Ger., p. 67, Feb. 2013.

H. Zhang, X. Wang, J. Cao, M. Tang, and Y. Guo, “A hybrid short-term traffic flow forecasting model based on time series multifractal characteristics,†Appl. Intell., vol. 48, no. 8, pp. 2429–2440, Aug. 2018, doi: 10.1007/s10489-017-1095-9.

B. K. C. Chan, “Data analysis using R programming,†in Simultaneous Mass Transfer and Chemical Reactions in Engineering Science, Elsevier, 2020, pp. 39–60.

M. Guidolin and M. Pedio, “Single-Factor Conditionally Heteroskedastic Models, ARCH and GARCH,†in Essentials of Time Series for Financial Applications, Elsevier, 2018, pp. 151–228.

S. Yousefzadeh-Chabok, F. Ranjbar-taklimie, R. Malekpouri, and A. Razzaghi, “A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality,†PubMed Cent., no. 14, pp. 0–5, 2016, doi: 10.5812/atr.36570.Research.

A. Coghlan, A Little Book of R For Time Series Release 0.2. Cambridge, UK, 2018.

L. Pham, “Time Series Analysis with ARIMA – ARCH / GARCH model in R,†2013.



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