Short-term Traffic Volume Prediction for the Merging Roads by Artificial Neural Network

Abstract

The forecasting of merging road traffic volume is one of the critical issues for the main networks of traffic-congestion suffering cities. Artificial neural network (ANN) – used in many disciplines varying from economy to different engineering applications such as sales forecasting, industrial process control, customer research, data validation, risk management, target marketing and civil engineering – could be a promising solution to this issue. Providing a higher forecasting accuracy based on past traffic data, ANN has become very popular in transportation engineering for the last 30 years. In this paper, the main goal was to predict the short-term traffic volume of a connection road leading to one of Istanbul’s Bosphorous Bridge in Turkey by the three different implementations of ANN. These were Feed Forward Back Propagation (FFBP), Generalized Regression Neural Network (GRNN) and Radial Based Function (RBF). Then, obtained results were compared with each other and the result of Multi Linear Regression (MLR) method.

Keywords

• Artificial neural network
• traffic volume
• traffic flow
• short-term prediction

Tali Yollar için Kısa Vadeli Trafik Hacminin Yapay Sinir Ağlarıyla Belirlenmesi

Abstract

Ana arterlerinde trafik sıkışıklığı yaşanan şehirlerin ikincil derecedeki yollarında trafik hacim tahminlerinin yapılması kritik konulardan biridir. Ekonomiden farklı mühendislik uygulamalarına kadar birçok alanda (satış tahminleri, endüstriyel süreç kontrolü, müşteri araştırmaları, veri doğrulama, risk yönetimi, hedef pazarlama ve inşaat mühendisliği gibi) kullanılan yapay sinir ağı (YSA) bu konuda umut verici bir çözüm olabilir. Geçmiş trafik verilerine dayanarak daha yüksek bir tahmin doğruluğu sağlayan YSA, son 30 yıldır ulaştırma mühendisliği alanındaki uygulamalarda çok popüler hale geldi. Bu makaledeki temel amaç, İstanbul'un Boğaz Köprülerinden birine katılan bir bağlantı yolunun kısa dönem trafik hacmini YSA'nın üç farklı uygulamasıyla tahmin etmektir. Bunlar İleri Besleme Geri Yayılımı (FFBP), Genelleştirilmiş Regresyon Sinir Ağı (GRNN) ve Radyal Tabanlı Fonksiyon (RBF) idi. Daha sonra elde edilen sonuçlar birbirleriyle ve Çoklu Doğrusal Regresyon (MLR) yönteminin sonuçları ile karşılaştırıldı.

Keywords

• Yapay sinir ağı
• trafik hacmi
• trafik akışı
• kısa vadeli tahmin

References

1. [1] Ahmed, M. S. and Cook, A. R. 1979. Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Transp. Res. Rec. 722, 1–9.
2. [2] Aleksander, I. and Morton, H. 1990. An Introduction to Neural Computing. International Thomson Computer Press, doi: 10.1007/978-1-4471-0395-0_4.
3. [3] Altun, I. and Dundar, S. 2005. Yapay Sinir Aglari ile Trafik Akim Kontrolu, Proceedings of the Deprem Sempozyomu (in Turkish), pages: 1335-1344, 23-25 March 2005, Kocaeli, Turkey.
4. [4] Cigizoglu, K. 2009. Soft Computational Methods, Lecture Notes, pages: 35-47, Department of Hydraulic Engineering, Istanbul Technical University.
5. [5] Cheng, X., Lin, W., Liu. E., and Gu, D. 2010. Highway traffic incident detection based on BPNN, Procedia Engineering, Vol. 7, pages: 482–489, doi:10.1016/j.proeng.2010.11.080.
6. [6] Davis, G. A., Nihan, N. L., Hamed, M. M., and Jacobson, L. N. 1991. Adaptive forecasting of freeway traffic congestion. Transp. Res. Rec. 1287, 29–33.
7. [7] Dougherty, M. and Joint, M. 1992. A behavioural model of driver route choice using neural networks. International Conference on Artificial Intelligence Applications in Transportation Engineering, San Buenaventura, California, June 1992, pages: 99-110.
8. [8] Dougherty, M., Kirby, H., and Boyle, R. 1993. The use of neural networks to recognize and predict traffic congestion. Traffic Engineering & Control, June 1993, pages: 311-314.

9. [9] Dougherty, M. 1995. A review of neural networks applied to transport. Journal of Transportation Research Part C: Emerging Technologies, Volume 3, Issue 4, pages: 247-260, https://doi.org/10.1016/0968-090X(95)00009-8.
10. [10] Dougherty, M. S. and Cobbett, M. R. 1997. Short-term inter-urban traffic forecasts using neural networks. International Journal of Forecasting, Vol. 13, Issue 1, pages: 21-21, https://doi.org/10.1016/S0169-2070(96)00697-8.
11. [11] Florio, L. and Mussone, L. 1996. Neural-network models for classification and forecasting of freeway traffic flow stability. Control Engineering Practice, Vol. 4, Issue 2, Pages: 153-164, https://doi.org/10.1016/0967-0661(95)00221-9.
12. [12] Goves, C., North, R., Johnston, R., and Fletcher G. 2016. Short term traffic prediction on the UK motorway network using neural networks. Transportation Research Procedia, Vol. 13, pages: 184-195, doi:10.1016/j.trpro.2016.05.019.
13. [13] Hamed, M. M., Al-Masaeid, H. R., and Bani Said, Z. M. 1995. Short-Term Prediction of Traffic Volume in Urban Arterials. Journal of Transportation Engineering, Vol. 121, Issue 3, pages: 249–254, https://doi.org/10.1061/(ASCE)0733-947X(1995)121:3(249).
14. [14] Hunt, J. G. and Lyons, G. D. 1994. Modelling dual carriageway lane changing using neural networks. Transportation Research Part C: Emerging Technologies, Vol. 2, Issue 4, pages: 231–245, https://doi.org/10.1016/0968-090X(94)90012-4.
15. [15] Karim A. and Adeli, H. 2003. Radial Basis Function Neural Network for Work Zone Capacity and Queue Estimation. Journal of Transportation Engineering, Vol. 129, Issue 5, pages: 494-503, DOI:10.1061/(ASCE)0733-947X(2003)129:5(494).
16. [16] Karlaftis, M. G. and Vlahogianni, E. I. 2011. Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies, Vol. 19, Issue 3, pages: 387–399, https://doi.org/10.1016/j.trc.2010.10.004.
17. [17] Kirby, H. R., Watson, S. M., and Dougherty, M. S. 1997. Should we use neural networks or statistical models for short-term motorway traffic forecasting? International Journal of Forecasting, Vol. 13, Issue 1, pages: 43-50, https://doi.org/10.1016/S0169-2070(96)00699-1.
18. [18] Kumar, K., Parida, M., and Katiyar, V. K. 2013. Short term traffic flow prediction for a non urban highway using Artificial Neural Network. Procedia – Social and Behavioural Sciences, Vol. 104, Issue 2, pages: 755-764, https://doi.org/10.1016/j.sbspro.2013.11.170.
19. [19] Liu, M., Wang, R., Wu, J., and Kemp, R. 2005. "A Genetic-Algorithm-Based Neural Network Approach for Short-Term Traffic Flow Forecasting, Proceedings of Advances in Neural Networks - ISNN 2005, International Symposium on Neural Networks, Chongqing, China, May/June, pages: 965-970, https://doi.org/10.1007/11427469_152.
20. [20] Ritchie, S. G. and Cheu, R. L. 1993. Simulation of freeway incident detection using artificial neural networks. Transportation Research Part C: Emerging Technologies, Vol. 1, Issue 3, pages: 203-217, https://doi.org/10.1016/S0968-090X(13)80001-0.
21. [21] Rumelhart, D., Hinton, E., and Williams, R. 1986. Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, pages: 55-56, Bradford Books, Cambridge.
22. [22] Tsai, T. H. and Lee, C. K. 2003. An Artificial Neural Networks Approach to Forecast Short-Term Railway Passenger Demand. Journal of the Eastern Asia Society for Transportation Studies, Vol. 5, Pages: 221-235.
23. [23] Yang, H., Akiyama, T., and Sasaki, T. 1992. A neural network approach to the identification of real time origin-destination flows from traffic counts, International Conference on Artificial Intelligence Applications in Transportation Engineering, San Buenaventura, California, June 1992, pages: 253- 269.
24. [24] Yang, H., Kitamura, R., Jovanis, P. P., Vaughn, K. M., and Abdel-Aty M. A. 1993. Exploration of route choice behavior with advanced traveler information using neural network concepts. Transportation, Kluwer Academic Publisher, 20, No.2, pages: 199-223.
25. [25] Yu, X., Xiong, S., He, Y., Wong, W. E., and Zhao Y. 2016. Research on campus traffic congestion detection using BP neural network and Markov model. Journal of Information Security and Applications, Vol. 31, pages: 54-60, https://doi.org.10.1016/j.jisa.2016.08.003.
26. [26] Yun, S. Y., Namkoong, S., Rho, J. H., Shin, S. W., and Choi, J. U. 1998. A Performance evaluation of neural network models in traffic volume forecasting. Mathematical and Computer Modelling, Vol. 27, Issues 9-11, pages: 293-310, https://doi.org/10.1016/S0895-7177(98)00065-X.
27. [27] Zhu, J. Z., Cao, J. X., and Zhu, Y. 2014. Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections. Transportation Research Part C: Emerging Technologies, Vol. 47, Part 2, Pages: 139-154, https://doi.org/10.1016/j.trc.2014.06.011.
28. [28] Zurada, J. M. 1992. Introduction to Artificial Neural Systems, West Publishing Co., UK.
29. [29] Vlahogianni, E. I., Golias, J. C., and Karlaftis, M. G. 2004. Short-term traffic forecasting: Overview of objectives and methods. Transport Reviews, Vol. 24, Issue 5, pages: 533-557, https://doi.org/10.1080/0144164042000195072.