Parameter Tuning dalam Klasifikasi Load Factor pada Bus Rapid Transit (BRT)
Keywords:
transportasi, BRT, load factor, klasifikasi, KNN
Abstract
Public transport services are facing more challenges and some problems are gradually emerging with the increase in public transport users and varying travel demands. BRT as a type of public transportation in determining efficiency, service feasibility, and determining operational costs refers to the load factor. BRT bus load factor classification using KNN is proposed with parameter tuning to increase accuracy values. KNN algorithm with tuning parameters on 2 types of matrices (Minkowski and Euclidean) for BRT load factor classification for Transjatim corridor 1. The BRT load factor classification with the KNN algorithm increased by 7.81% by tuning parameters on the Euclidean matrix compared to the Minkowski matrix. The increase in accuracy is reflected in the confusion matrix with changes in increasing true negatives and decreasing false positives. Category 1 has a higher class than category 2 for boarding and alighting passengers. The classification presented can be a reference for Transjatim Corridor 1 Managers in determining efficiency, feasibility and operational costs.
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References
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[3] M. Chester, S. Pincetl, Z. Elizabeth, W. Eisenstein, and J. Matute, “Infrastructure and automobile shifts: Positioning transit to reduce life-cycle environmental impacts for urban sustainability goals,” Environmental Research Letters, vol. 8, no. 1, 2013, doi: 10.1088/1748-9326/8/1/015041.
[4] A. Olowosegun, D. Moyo, and D. Gopinath, “Multicriteria evaluation of the quality of service of informal public transport: An empirical evidence from Ibadan, Nigeria,” Case Stud Transp Policy, vol. 9, no. 4, 2021, doi: 10.1016/j.cstp.2021.08.002.
[5] Y. Sun and Y. Cui, “Evaluating the coordinated development of economic, social and environmental benefits of urban public transportation infrastructure: Case study of four Chinese autonomous municipalities,” Transp Policy (Oxf), vol. 66, 2018, doi: 10.1016/j.tranpol.2018.02.006.
[6] V. Varabuntoonvit, K. Boonyarith, P. Pakornkarn, P. Pichetwanit, and W. Sittipong, “Sustainable efficiency indicator for urban transportation: Case study of public bus and rapid transit systems in
[7] A. L. Kelkar and K. Kulkarni, “Route Profitability Prediction for Public Bus Transportation System,” in 2022 5th International Conference on Advances in Science and Technology (ICAST), IEEE, Dec. 2022, pp. 428–430. doi: 10.1109/ICAST55766.2022.10039610.
[8] T. Bubalo, M. Rajsman, and P. Škorput, “Methodological Approach for Evaluation and Improvement of Quality Transport Service in Public Road Passenger Transport,” Tehnicki vjesnik - Technical Gazette, vol. 29, no. 1, Feb. 2022, doi: 10.17559/TV-20201031104641.
[9] I. Moser et al., “A methodology for empirically evaluating passenger counting technologies in public transport,” in Australasian Transport Research Forum, ATRF 2019 - Proceedings, 2019. Accessed: Dec. 05, 2023. [Online]. Available: https://australasiantransportresearchforum.org.au/wp-content/uploads/2022/03/ATRF2019_resubmission_48.pdf
[10] S. C. Wirasinghe, L. Kattan, M. M. Rahman, J. Hubbell, R. Thilakaratne, and S. Anowar, “Bus rapid transit - a review,” International Journal of Urban Sciences, vol. 17, no. 1. 2013. doi: 10.1080/12265934.2013.777514.
[11] J. Singh, H. K. Chaudhary, and A. Malik, “A Case Study on Indore BRTS with Reference to Other Indian Cities,” Int J Res Appl Sci Eng Technol, vol. 10, no. 10, pp. 196–203, Oct. 2022, doi: 10.22214/ijraset.2022.46976.
[12] G. Tobias Portis, A. Mota dos Santos, and F. Gioppo Nunes, “Influência da Infraestrutura de Transportes sobre a Temperatura de Superfície em um corredor BRT, na cidade de Goiânia/GO,” Ateliê Geográfico, vol. 16, no. 1, pp. 143–164, Apr. 2022, doi: 10.5216/ag.v16i1.71849.
[13] J. Wang, Y. Chen, L. Mao, R. Yang, D. Li, and B. Chen, “Public Transport Passengers’ Classification and Path Choice Characteristics Analysis by Using C&RT Model in Beijing,” in IOP Conference Series: Materials Science and Engineering, 2019. doi: 10.1088/1757-899X/688/4/044018.
[14] P. Song, W. Song, A. Meng, and H. Li, “Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations,” Energies (Basel), vol. 17, no. 6, p. 1283, Mar. 2024, doi: 10.3390/en17061283.
[15] X. Shen and S. Feng, “How public transport subsidy policies in China affect the average passenger load factor of a bus line,” Research in Transportation Business & Management, vol. 36, p. 100526, Sep. 2020, doi: 10.1016/j.rtbm.2020.100526.
[16] B. Z. Malik, Z. ur Rehman, A. H. Khan, and W. Akram, “Investigating users’ travel behaviours and perceptions of single-corridor BRT: Lessons from Lahore,” J Transp Geogr, vol. 91, p. 102942, Feb. 2021, doi: 10.1016/j.jtrangeo.2020.102942.
[17] V. V. R. Akhil and B. Ramesh, “Feasibility Studies and Performance Evaluation of BRTS in Rajahmundry and Kakinada Corridor,” Int J Res Appl Sci Eng Technol, vol. 11, no. 4, pp. 4571–4577, Apr. 2023, doi: 10.22214/ijraset.2023.51072.
[18] R. Dantas, J. Dantas, C. Melo, and P. Maciel, “Performance evaluation in BRT systems: An analysis to predict the BRT systems planning,” Case Stud Transp Policy, vol. 9, no. 3, pp. 1141–1150, Sep. 2021, doi: 10.1016/j.cstp.2021.06.002.
[19] C. A. Sakurai, C. F. Fontana, B. De Carvalho Negrão, and A. G. Da Silva Andrade, “System Functionalities of Its for BRT,” International Journal of Computational and Applied Mathematics & Computer Science, vol. 2, pp. 18–25, Jun. 2022, doi: 10.37394/232028.2022.2.4.
[20] M. Admad, S. Saeed, A. Babar, and F. Ullah, “Socio-cultural and Economic Impacts of BRT Peshawar,” International Journal of Emerging Trends in Engineering Research, vol. 9, no. 6, pp. 759–764, Jun. 2021, doi: 10.30534/ijeter/2021/22962021.
[21] H. Thevadass et al., “A Review on BRT Scoring System for Malaysia Practice,” Civil Engineering and Architecture, vol. 9, no. 2, pp. 410–419, Mar. 2021, doi: 10.13189/cea.2021.090214.
[22] C. F. Fontana, C. A. Sakurai, C. L. Marte, J. R. Cardoso, and A. G. Da Silva Andrade, “Power Line Communication as Alternative for Data Communication Channel for BRT,” EARTH SCIENCES AND HUMAN CONSTRUCTIONS, vol. 2, pp. 60–67, Mar. 2022, doi: 10.37394/232024.2022.2.9.
[23] A. M. Santos and A. G. De Oliveira, “A importância da mobilidade urbana: implantação da primeira linha do Brt em Salvador / The importance of urban mobility: implementation of the first line of Brt in Salvador,” Brazilian Applied Science Review, vol. 5, no. 4, pp. 1951–1969, Aug. 2021, doi: 10.34115/basrv5n4-017.
[24] Hiroaki Inokuchi, “Combinations of Transportation Policies to Promote BRT Usage Using Artificial Society Model,” Journal of Traffic and Transportation Engineering, vol. 12, no. 1, Feb. 2024, doi: 10.17265/2328-2142/2024.01.001.
[25] S. C. Gupta and N. Goel, “Predictive Modeling and Analytics for Diabetes using Hyperparameter tuned Machine Learning Techniques,” Procedia Comput Sci, vol. 218, pp. 1257–1269, 2023, doi: https://doi.org/10.1016/j.procs.2023.01.104.
[26] D. Muhajir, M. Akbar, A. Bagaskara, and R. Vinarti, “Improving classification algorithm on education dataset using hyperparameter tuning,” Procedia Comput Sci, vol. 197, pp. 538–544, 2022, doi: https://doi.org/10.1016/j.procs.2021.12.171.
[27] A. Yazdinejad, A. Dehghantanha, R. M. Parizi, and G. Epiphaniou, “An optimized fuzzy deep learning model for data classification based on NSGA-II,” Neurocomputing, vol. 522, 2023, doi: 10.1016/j.neucom.2022.12.027.
[28] C. Singla and C. Jindal, “Comparison of Various Classification Models Using Machine Learning to Predict Mobile Phones Price Range,” in Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation, 2024. doi: 10.1002/9781119905233.ch17.
[29] S. Adhikary and S. Banerjee, “Introduction to Distributed Nearest Hash: On Further Optimizing Cloud Based Distributed kNN Variant,” in Procedia Computer Science, 2022. doi: 10.1016/j.procs.2023.01.135.
[30] A. Ali, M. Hamraz, N. Gul, D. M. Khan, S. Aldahmani, and Z. Khan, “A k nearest neighbour ensemble via extended neighbourhood rule and feature subsets,” Pattern Recognit, vol. 142, 2023, doi: 10.1016/j.patcog.2023.109641.
Published
2024-08-12
Section
Articles
Copyright (c) 2024 Joko Siswanto, Muhammad Iman Nur Hakim, Aninditya Anggari Nuryono (Author)
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