Predicting traffic congestion based on time series analysis
https://doi.org/10.37493/2307-910X.2023.2.5
Abstract
Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion.
Keywords
traffic forecasting,
Holt-Winter method,
ARIMA model,
intelligent transportation system,
time series forecasting
Supporting agencies: The research was supported by the Russian Science Foundation Grant No. 22-71-10046, https://rscf.ru/en/project/22-71-10046/
About the Authors
V. V. Lutsenko
North-Caucasus Federal University
Russian Federation
Russian Federation
Lutsenko Vladislav Viacheslavovich - post-graduate student, Department of computational
mathematics and cybernetics, Faculty of mathematics and computer science named after Professor N. I. Chervyakov,
355000, Stavropol
N. N. Kucherov
North-Caucasus Federal University
Russian Federation
Russian Federation
Kucherov Nikolay Nikolaevich – Senior Researcher, Educational and Scientific Center
"Computational Mathematics and Parallel Programming on Supercomputers", Faculty of
mathematics and computer science named after Professor N. I. Chervyakov,
355000, Stavropol
A. V. Gladkov
North-Caucasus Federal University
Russian Federation
Russian Federation
Gladkov Andrei Vladimirovich – Junior Researcher, Educational and Scientific Center
"Computational Mathematics and Parallel Programming on Supercomputers", Faculty of
mathematics and computer science named after Professor N. I. Chervyakov,
Stavropol, 355000
References
1. Batty M. Smart cities of the future // The European Physical Journal Special Topics. 2012. Vol. 214. P. 481-518.
2. Arena F., Pau G., Severino A. A review on IEEE 802.11 p for intelligent transportation systems // Journal of Sensor and Actuator Networks. 2020. Vol. 9. No. 2.
3. Allahar H. What are the Challenges of Building a Smart City? // Technology Innovation Management Review. 2020. Vol. 10. No. 9. P. 38-48.
4. Qu W. Short-term intersection traffic flow forecasting // Sustainability. 2020. Vol. 12. No. 19.
5. Vlahogianni E.I., Kar-laftis M.G., Golias J.C. Short-term traffic forecasting: Where we are and where we’re going // Transportation Research Part C: Emerging Technologies. 2014. Vol. 43. Part 1. P. 3-19.
6. Pavlyuk D. Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review // European Transport Research Review. 2019. Vol. 11. No. 1.
7. Kashyap A. A. Traffic flow prediction models–A review of deep learning techniques // Cogent Engineering. 2022. Vol. 9. No. 1.
8. Cai L. A sample-rebalanced outlier-rejected k-nearest neighbor regression model for short-term traffic flow forecasting // IEEE access. 2020. Vol. 8.
9. Sun H., Liu H., Xiao H., He R., Ran B. Short term traffic forecasting using the local linear regression model // Journal of Transportation Research Board. 2003. Vol. 1836. P. 143-150.
10. Mai T., Ghosh B., Wilson S. Short-term traffic flow forecasting using dynamic linear models // Irish Transport Research Network, 2011.
11. Ma T., Antoniou C., Toledo T. Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast // Transportation Research Part C: Emerging Technologies. 2020. Vol. 111. P. 352-372.
12. Ghosh B., Basu B., O’Mahony M. Bayesian time-series model for short-term traffic flow forecasting // Journal of transportation engineering. 2007. Vol. 133. No. 3. P. 180-189.
13. Çetiner B. G., Sari M., Borat O. A neural network based traffic-flow prediction model // Mathematical and Computational Applications. 2010. Vol. 15. No. 2. P. 269-278.
14. Yi H., Jung H. J., Bae S. Deep neural networks for traffic flow prediction // 2017 IEEE international conference on big data and smart computing (BigComp). – IEEE, 2017. P. 328- 331.
15. Guorong G., Yanping L. Traffic Flow Forecasting based on PCA and Wavelet Neural Network // Information Science and Management Engineering (ISME). 2010. Vol. 1. P. 158- 161.
16. Gardner Jr E. S. Exponential smoothing: The state of the art // Journal of forecasting. 1985. Vol. 4. No. 1. P. 1-28.
17. Tratar L. F., Strmčnik E. The comparison of Holt–Winters method and Multiple regression method: A case study // Energy. 2016. Vol. 109. P. 266-276.
18. Lin X., Huang Y. Short‐term high-speed traffic flow prediction based on ARIMAGARCH-M model // Wireless Personal Communications. 2021. Vol. 117. No. 4. P. 3421-3430.
Review
For citations:
Lutsenko V.V., Kucherov N.N., Gladkov A.V. Predicting traffic congestion based on time series analysis. Modern Science and Innovations. 2023;(2):50-58. https://doi.org/10.37493/2307-910X.2023.2.5
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