Point Based Forecasting Model of Vehicle Queue with Extreme Learning Machine Method and Correlation Analysis

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Author(s)

Kasliono 1,* Suprapto 2 Faizal Makhrus 2

1. Master of Computer Science, Universitas Gadjah Mada, Yogyakarta, Indonesia

2. Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta, Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2021.03.02

Received: 19 Nov. 2020 / Revised: 10 Jan. 2021 / Accepted: 19 Feb. 2021 / Published: 8 Jun. 2021

Index Terms

Forecasting, Correlation, Traffic, Vehicle queue, Extreme leaning machine (ELM), Neural network, Correlation coefficient

Abstract

Traffic is a medium to move from one point to another. Therefore, the role of traffic is very important to support vehicle mobility. If congestion occurs, mobility will be hampered so that it gives influence to other sectors such as financial, air pollution and traffic violations. This study aims to create a model to predict vehicle queue at the traffic lights when its status is red. The prediction is conducted by using Neural Network with Extreme Learning Machine method to predict the length of the vehicle queue, and Correlation Analysis was used to measure the correlation between the connected roads. The conducted experiments use data of the length of the vehicle queue at the traffic lights which was obtained from DISHUB (Transportation Bureau) DI Yogyakarta. Several experiments were carried out to determine the optimum prediction model of vehicle queue length. The experiments found that the optimum model had an average MAPE value of 15.5882% and an average Running Time of 5.2226 seconds.

Cite This Paper

Kasliono, Suprapto, Faizal Makhrus, "Point Based Forecasting Model of Vehicle Queue with Extreme Learning Machine Method and Correlation Analysis", International Journal of Intelligent Systems and Applications(IJISA), Vol.13, No.3, pp.11-22, 2021. DOI:10.5815/ijisa.2021.03.02

Reference

[1]I. G. A. Wibawa, L. Arida, and A. Rahning, “Deteksi Kemacetan Lalu Lintas Jalan Raya Menggunakan Metode Moving Object Detection,” no. November, pp. 2–4, 2016.
[2]I. dkk Santoso, Manajemen Lalu-Lintas Perkotaan. Bandung: Badan Penerbit ITB, 1997.
[3]G. Huang, Q. Zhu, and C. Siew, “Extreme Learning Machine : A New Learning Scheme of Feedforward Neural Networks,” IEEE Int. Jt. Conf. Neural Networks, vol. 2, pp. 985–990, 2004, doi: 10.1109/IJCNN.2004.1380068.
[4]U. Mahdiyah, M. I. Irawan, and E. M. Imah, “Study Comparison Backpropogation, Support Vector Machine, and Extreme Learning Machine for Bioinformatics Data,” J. Ilmu Komput. dan Inf. (Journal Comput. Sci. Information), vol. 1, pp. 53–59, 2015, [Online]. Available: http://dx.doi.org/10.21609/jiki.v8i1.284.
[5]R. E. Walpole and R. H. Myers, Ilmu Peluang dan Statistika untuk Insinyur dan Ilmuan (Terjemahan RK Sembiring), Ed. 4. Bandung: Penerbit ITB, 1995.
[6]R. E. Walpole, Pengantar Statistika (Alihbahasa: Ir. Bambang Sumantri), Ed. 3. Jakarta: PT Gramedia Pustaka Utama, 1995.
[7]Surjandy, F. Anindra, H. Soeparno, and T. A. Napitupulu, “CCTV traffic congestion analysis at Pejompongan using case based reasoning,” 2018 Int. Conf. Inf. Commun. Technol. ICOIACT 2018, vol. 2018-Janua, pp. 861–865, 2018, doi: 10.1109/ICOIACT.2018.8350807.
[8]R. Chairan and A. Martiningtyas, “Sistem Lampu Lalu Lintas Dengan Video Processing Sebagai Pendeteksi Kepadatan Lalu Lintas,” Universitas Gadjah Mada, 2017.
[9]S. Mahatmaputra, E. Permata, and William, “Deteksi Kemacetan Lalu Lintas Melalui Kamera Menggunakan Pin Hole Algorithm,” Comtech, vol. 2, no. 2, pp. 821–834, 2011.
[10]I. G. A. Wibawa, L. Arida, and A. Rahning, “Deteksi Kemacetan Lalu Lintas Jalan Raya Menggunakan Metode Moving Object Detection,” Semin. Nas. SAINSTEK 2016, no. November, pp. 2–4, 2016.
[11]I. H. Setiadi and Y. H. P, “Perancangan Sistem Pendeteksi Kepadatan Lalu Lintas Menggunakan Image Processing Dengan Metode Background Subtraction Pada Sikomolintas,” Perpust. UNIKOM, pp. 1–8, 2017, [Online]. Available: https://elib.unikom.ac.id/gdl.php?mod=browse&op=read&id=jbptunikompp-gdl-indrahadis-37151.
[12]A. R. Wibowo and I. Soesanti, “Analisis data time series dan VCR kepadatan lalu lintas ( studi kasus : Jalan Adisucipto depan Ambarukmo Plaza ),” Elinvo, vol. 2, Nomor 2, pp. 131–137, 2015.
[13]T. B. Atmojo, R. Pulungan, and H. Syahputra, “Pengembangan Model Peramalan Permintaan Kebutuhan Reseller Menggunakan Extreme Learning Machine dalam Konteks Intelligent Warehouse Management System(IWMS),” Semin. Nas. Inform. 2013, vol. 2013, no. semnasIF, pp. 258–263, 2013.
[14]J. J. Siang, Jaringan Syaraf Tiruan dan Pemrogramannya Menggunakan Matlab. Yogyakarta: Andi Offset, 2005.
[15]S. Haykin, Neural Networks : A Comprehensive Foundation, Second. Delhi: Pearson Education, 2005.
[16]G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 17, no. 1, pp. 489–501, 2006, doi: 10.1007/s10462-013-9405-z.
[17]G. Bin Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: A survey,” Int. J. Mach. Learn. Cybern., vol. 2, no. 2, pp. 107–122, 2011, doi: 10.1007/s13042-011-0019-y.
[18]G. Huang and L. Chen, “Convex incremental extreme learning machine,” vol. 70, pp. 3056–3062, 2007, doi: 10.1016/j.neucom.2007.02.009.
[19]G. Huang and L. Chen, “Author ’ s personal copy Enhanced random search based incremental extreme learning machine Author ’ s personal copy,” vol. 71, pp. 3460–3468, 2008, doi: 10.1016/j.neucom.2007.10.008.
[20]G. Huang, S. Member, H. Zhou, X. Ding, and R. Zhang, “Extreme Learning Machine for Regression and Multiclass Classification,” vol. 42, no. 2, pp. 513–529, 2012.
[21]G. Bin Huang, X. Ding, and H. Zhou, “Optimization method based extreme learning machine for classification,” Neurocomputing, vol. 74, no. 1–3, pp. 155–163, 2010, doi: 10.1016/j.neucom.2010.02.019.
[22]M. A. A. Albadr and S. Tiun, “Extreme learning machine: A review,” Int. J. Appl. Eng. Res., vol. 12, no. 14, pp. 4610–4623, 2017.
[23]Anoop Kumar Patel, Sanjay Kumar Jain, " Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach", International Journal of Image, Graphics and Signal Processing, Vol.11, No.11, pp. 18-28, 2019.
[24]R. Singh and S. Balasundaram, “Application of Extreme Learning Machine Method for Time Series Analysis,” Proc. World Acad. Sci., vol. 26, no. Part 1, pp. 361–367, 2007, doi: 10.1148/radiology.138.2.7455105.
[25]A. Anbarasa Pandian, R. Balasubramanian,"Analysis on Shape Image Retrieval Using DNN and ELM Classifiers for MRI Brain Tumor Images", International Journal of Information Engineering and Electronic Business, Vol.8, No.4, pp.63-72, 2016.
[26]R. Macausland, “University of Puget Sound MATH 420 : Advanced Topics in Linear Algebra The Moore-Penrose Inverse and Least Squares,” 2014.
[27]P. A. G. T. Ag, “The Moore-Penrose Pseudoinverse,” vol. 1, no. 1, pp. 1–5, 1972.
[28]H. Stadtler, “Supply chain management and advanced planning - Basics, overview and challenges,” Eur. J. Oper. Res., vol. 163, no. 3, pp. 575–588, 2005, doi: 10.1016/j.ejor.2004.03.001.
[29]I. N. Pujawan, Supply Chain Management, Edisi Kedu. Surabaya: Guna Widya, 2010.
[30]P. M. Swamidass, Ed., “MAPE (mean absolute percentage error)MEAN ABSOLUTE PERCENTAGE ERROR (MAPE),” in Encyclopedia of Production and Manufacturing Management, Boston, MA: Springer US, 2000, p. 462.
[31]U. Khair, H. Fahmi, S. Al Hakim, and R. Rahim, “Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error,” 2017.
[32]Fahirah, Lily Wulandari, " The Implementation of Least Square Method on the Palm Shells Sales Forecasting Application", International Journal of Information Engineering and Electronic Business, Vol.12, No.5, pp. 1-13, 2020.
[33]S. Makridakis, S. C. Wheelwright, and V. E. McGEE, Metode dan Aplikasi Peramalan (Terjemahan: Ir. Untung Sus Andriyanto, M.Sc.), Ed. 2. Jakarta: Erlangga, 1995.
[34]D. G. of H. D. of U. R. Directorate General Bina Marga, “Highway Capacity Manual Project ( Hcm ),” vol. 1, no. I, p. 564, 1997, doi: 10.1021/acsami.7b07816.
[35]B. D. Sinulingga, Pembangunan Kota Tinjauan Regional dan Lokal. Bandung: Penerbit ITB, 1999.
[36]O. Z. Tamin, Perencanaan dan Pemodelan Transportasi, Ed. Ke 2. Bandung: Penerbit ITB, 2000.
[37]M. D. Meyer and E. J. Miller, Urban Transportation Planning: A Decision-Oriented Approach. New York: McGraw-Hill, 1984.