Performance Analysis of Bandung City Traffic Flow Classification with Machine Learning and Kriging Interpolation
- Nuraena Ramdani Telkom University, Bandung, Indonesia
-
Sri Suryani Prasetyowati
*
Telkom University, Bandung,
Indonesia
- Yuliant Sibaroni Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
This research focuses on making classification maps using the Classification And Regression Trees (CART), Random Forest and Ordinary Kriging methods. The dataset used is data from the Area Traffic Control System (ATCS) of the Bandung City Transportation Agency and the Google Maps application in April 2022. After the dataset is obtained, then the data pre-processing process will be carried out then the CART and Random Forest classification learning models will be made, after the CART and Random Forest classification learning is complete. From the CART and Random Forest classification models, traffic congestion classification map will then be made using the ArcMap application with the Ordinary Kriging interpolation method. The results of the comparison of classification maps made with Ordinary Kriging interpolation with the Gaussian Model semivariogram in both methods, namely CART and Random Forest. With the CART method has an accuracy of up to 88% while the classification map made with the Random Forest method has an accuracy of up to 90%. This proves that in this study the Random Forest method is far superior in building classification maps compared to the CART method
Downloads
References
BPS Kota Bandung, “Jumlah Penduduk Kota Bandung,” Jumlah Penduduk Kota Bandung, 2020. https://bandungkota.bps.go.id/linkTableDinamis/view/id/9 (accessed Nov. 19, 2021).
E. Harahap, A. Suryadi, R. Ridwan, D. Darmawan, and R. Ceha, “Efektifitas Load Balancing Dalam Mengatasi Kemacetan Lalu Lintas,” Matematika, vol. 16, no. 2, pp. 1–7, 2017, doi: 10.29313/jmtm.v16i2.3665.
V. Effendy, “Sentiment Analysis on Twitter about the Use of City Public Transportation Using Support Vector Machine Method,” Int. J. Inf. Commun. Technol., vol. 2, no. 1, p. 57, 2016, doi: 10.21108/ijoict.2016.21.85.
M. Somantri, “Kemacetan dan Pandemi di Kota Bandung (24 Juni 2021),” 2021. https://kumparan.com/musliman-somantri-1614653043992639605/kemacetan-dan-pandemi-di-kota-bandung-1w0JT6DMtQq/full (accessed Nov. 19, 2021).
B. N. Mohapatra and P. P. Panda, “Machine learning applications to smart city,” Accent. Trans. Image Process. Comput. Vis., vol. 5, no. 14, pp. 1–6, 2019, doi: 10.19101/tipcv.2018.412004.
V. Nasteski, “An overview of the supervised machine learning methods,” Horizons.B, vol. 4, no. December 2017, pp. 51–62, 2017, doi: 10.20544/horizons.b.04.1.17.p05.
R. C. Chen, C. Dewi, S. W. Huang, and R. E. Caraka, “Selecting critical features for data classification based on machine learning methods,” J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537-020-00327-4.
R. Irmanita, Sri Suryani Prasetiyowati, and Yuliant Sibaroni, “Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 10–16, 2021, doi: 10.29207/resti.v5i1.2770.
I. Ahmad, M. Basheri, M. J. Iqbal, and A. Rahim, “Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection,” IEEE Access, vol. 6, pp. 33789–33795, 2018, doi: 10.1109/ACCESS.2018.2841987.
K. Nugroho, “Dasar Text Preprocessing dengan Python,” 2019. https://ksnugroho.medium.com/dasar-text-preprocessing-dengan-python-a4fa52608ffe (accessed Dec. 05, 2021).
Y. Liu and H. Wu, “Prediction of road traffic congestion based on random forest,” Proc. - 2017 10th Int. Symp. Comput. Intell. Des. Isc. 2017, vol. 2, pp. 361–364, 2018, doi: 10.1109/ISCID.2017.216.
L. Susiana, I. T. Utami, and J. Junaidi, “Penerapan Metode Boosting Pada Cart Untuk Mengklasifikasikan Korban Kecelakaan Lalu Lintas Di Kota Palu,” Nat. Sci. J. Sci. Technol., vol. 8, no. 2, pp. 106–109, 2019, doi: 10.22487/25411969.2019.v8.i2.13536.
N. Petrovska and A. Stevanovic, “Traffic Congestion Analysis Visualisation Tool,” IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC, vol. 2015-Octob, pp. 1489–1494, 2015, doi: 10.1109/ITSC.2015.243.
S. Ki, M. Keffeler, T. Atkison, and A. Hainen, “Using Time Series Forecasting for Adaptive Traffic Signal Control,” Proc. 13th Int. Conf. Data Min. DMIN’17, pp. 34–39, 2017.
T. Daniya, M. Geetha, and K. S. Kumar, “Classification and regression trees with gini index,” Adv. Math. Sci. J., vol. 9, no. 10, pp. 8237–8247, 2020, doi: 10.37418/amsj.9.10.53.
M. I. Ali and M. R. Abidin, “Pengaruh Kepadatan Penduduk Terhadap Intensitas Kemacetan Lalu Lintas Di Kecamatan Rappocini Makassar,” Pros. Semin. Nas. Lemb. Penelit. Univ. Negeri Makassar, pp. 68–73, 2019, [Online]. Available: http://eprints.unm.ac.id/17795/1/prosiding Pengaruh Kepadatan Penduduk.pdf
T. S. Tamir et al., “Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks,” IFAC-PapersOnLine, vol. 53, no. 5, pp. 512–517, 2020, doi: 10.1016/j.ifacol.2021.04.138.
N. Saif and M. Mussafi, “Penerapan Greedy Coloring Algorithm Pada Peta Kotamadya Yogyakarta Berbasis Four-Colour Theorem,” Kaunia Integr. Interconnect. Islam Sci., vol. 11, no. 1, pp. 19–26, 2015, [Online]. Available: http://ejournal.uin-suka.ac.id/saintek/kaunia/article/view/1078
L. E. Edison, “Analisis Dampak Kerugian Akibat Kemacetan Lalu Lintas Di Kota Makassar.,” 2017.
A. Aid, M. A. Khan, S. Abbas, G. Ahmad, and A. Fatimat, “Modelling smart road traffic congestion control system using machine learning techniques,” Neural Netw. World, vol. 29, no. 2, pp. 99–110, 2019, doi: 10.14311/NNW.2019.29.008.
N. Zafar and I. U. Haq, “Traffic congestion prediction based on Estimated Time of Arrival,” PLoS One, vol. 15, no. 12 December, pp. 1–19, 2020, doi: 10.1371/journal.pone.0238200.
T. G. Pham, M. Kappas, C. Van Huynh, and L. H. K. Nguyen, “Application of ordinary kriging and regression kriging method for soil properties mapping in hilly region of central Vietnam,” ISPRS Int. J. Geo-Information, vol. 8, no. 3, 2019, doi: 10.3390/ijgi8030147.
J. Song, C. Zhao, S. Zhong, T. A. S. Nielsen, and A. V. Prishchepov, “Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques,” Comput. Environ. Urban Syst., vol. 77, no. July, p. 101364, 2019, doi: 10.1016/j.compenvurbsys.2019.101364.
H. Hardiani, “Analisis Derajat Kejenuhan dan Biaya Kemacetan pada Ruas Jalan Utama di Kota Jambi,” J. Perspekt. Pembiayaan dan Pembang. Drh., vol. 2, no. 4, pp. 181–192, 2016, doi: 10.22437/ppd.v2i4.2614.
J. Gong and H. Kim, “RHSBoost: Improving classification performance in imbalance data,” Comput. Stat. Data Anal., vol. 111, no. xxxx, pp. 1–13, 2017, doi: 10.1016/j.csda.2017.01.005.
R. Mohammed, J. Rawashdeh, and M. Abdullah, “Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results,” 2020 11th Int. Conf. Inf. Commun. Syst. ICICS 2020, pp. 243–248, 2020, doi: 10.1109/ICICS49469.2020.239556.
S. S. Prasetiyowati, M. Imrona, I. Ummah, and Y. Sibaroni, “Prediction of public transportation occupation based on several crowd spots using ordinary kriging method,” J. Innov. Technol. Educ., vol. 3, no. January, pp. 93–104, 2016, doi: 10.12988/jite.2016.6723.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Performance Analysis of Bandung City Traffic Flow Classification with Machine Learning and Kriging Interpolation
Pages: 694-704
Copyright (c) 2022 Nuraena Ramdani, Sri Suryani Prasetyowati, Yuliant Sibaroni
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
