Open Access
Issue
E3S Web Conf.
Volume 476, 2024
The 4th Aceh International Symposium on Civil Engineering (AISCE 2023)
Article Number 01023
Number of page(s) 10
DOI https://doi.org/10.1051/e3sconf/202447601023
Published online 17 January 2024
  1. M. Aghaabbasi, Z.A. Shekari, M.Z. Shah, O. Olakunle, D.J. Armaghani, and M. Moeinaddini, Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques, Transportation Research Part A: Policy and Practice, 136, pp. 262–281 (2020). [CrossRef] [Google Scholar]
  2. Google, Temasek, and Bain, e-Conomy SEA 2022. 2022. [Google Scholar]
  3. S. Saleh, F. Apriandy, S. Sugiarto, L. Lulusi, and A. Salmannur, Investigating the determinants of travel mode choice across age classes in Langsa, Indonesia utilizing logit model, Journal of Applied Engineering Science, 20, 2, pp. 511–522 (2022). [CrossRef] [Google Scholar]
  4. S. Sugiarto, L. Lulusi, M. Isya, F. Apriandy, and F. Ramadhan, Understanding Household’s Travel Costs Budget Frontier in Banda Aceh, Indonesia, Communications - Scientific letters of the University of Zilina, 23, 2, pp. A116–A124 (2021). [CrossRef] [Google Scholar]
  5. L. Lulusi et al., Travel Cost Budget and Ability of Urban Bus Users to Pay Considering the Income Classes in Indonesia, Transactions on Transport Sciences, 12, 1, pp. 19–24 (2021). [CrossRef] [Google Scholar]
  6. F. Apriandy, S. Sugiarto, Y. Darma, R. Anggraini, J. Fisaini, and I. Bahrumy, An exploratory analysis of factors that encourage students to choose sustainable modes in travelling to schools: evidence from Victoria, Australia, Transportation Planning and Technology, pp. 1–30 (2023). [Google Scholar]
  7. D. Lee, S. Derrible, and F.C. Pereira, Comparison of Four Types of Artificial Neural Network and a Multinomial Logit Model for Travel Mode Choice Modeling, Transportation Research Record: Journal of the Transportation Research Board, 2672, 49, pp. 101–112 (2018). [CrossRef] [Google Scholar]
  8. M.Z. Irawan, F.F. Bastarianto, S. Sugiarto, and M.R.F. Amrozi, Measuring the perceived need for motorcycle-based ride-hailing services on trip characteristics among university students in Yogyakarta, Indonesia, Travel Behaviour and Society, 24, pp. 303–312 (2021). [CrossRef] [Google Scholar]
  9. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd Edition ed. New Jersey: Prentice Hall, 1999. [Google Scholar]
  10. F. Apriandy, S. Sugiarto, S.M. Saleh, and L. Lulusi, Model Estimasi Bangkitan Pergerakan Moda Laut Menggunakan Metode Regresi Linier Dan Random Forest, Jurnal Arsip Rekayasa Sipil dan Perencanaan, 4, 4, pp. 197–204 (2021). [CrossRef] [Google Scholar]
  11. S.M. Saleh, Lulusi, F. Apriandy, J. Fisiani, A. Salmannur, and R. Faisal, Trip generation and attraction model and forecasting using machine learning methods, IOP Conference Series: Materials Science and Engineering, 1087, 1, (2021). [Google Scholar]
  12. M.G. Karlaftis and E.I. Vlahogianni, Statistical methods versus neural networks in transportation research: Differences, similarities and some insights, Transportation Research Part C: Emerging Technologies, 19, 3, pp. 387–399 (2011). [CrossRef] [Google Scholar]
  13. L. Cheng, X. Chen, J. De Vos, X. Lai, and F. Witlox, Applying a random forest method approach to model travel mode choice behavior, Travel Behaviour and Society, 14, pp. 1–10 (2019). [CrossRef] [Google Scholar]
  14. J. Hagenauer and M. Helbich, A comparative study of machine learning classifiers for modeling travel mode choice, Expert Systems with Applications, 78, pp. 273–282 (2017). [CrossRef] [Google Scholar]
  15. IBM. (2020, 14 Desember). Neural Networks. Available: https://www.ibm.com/cloud/leam/neural-networks [Google Scholar]
  16. F. Pedregosa et al., Scikit-learn: Machine learning in Python, the Journal of machine Learning research, 12, pp. 2825–2830 (2011). [Google Scholar]

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