Phát triển mô hình dự đoán hành vi lựa chọn vị trí đỗ xe trên đường ứng dụng mô hình máy vector hỗ trợ: nghiên cứu điển hình tại thành phố Hà Nội
Email:
tandang@utc.edu.vn
Từ khóa:
Hành vi, lựa chọn vị trí đỗ xe, đỗ xe trên đường, ùn tắc giao thông, SVM.
Tóm tắt
Với thực trạng thành phố Hà Nội đang thiếu hụt nghiêm trọng không gian đỗ xe và chịu áp lực giao thông ngày càng lớn, việc dự đoán hành vi lựa chọn vị trí đỗ xe của người lái xe ô tô có ý nghĩa rất quan trọng. Nghiên cứu này ứng dụng mô hình máy vector hỗ trợ (SVM) nhằm dự đoán hành vi lựa chọn vị trí đỗ xe trên đường tại Hà Nội. Dữ liệu để xây dựng mô hình được thu thập thông qua phỏng vấn trực tiếp người lái xe ô tô bằng bảng hỏi tại các khu vực khác nhau trong đô thị. Mô hình SVM được đánh giá thông qua các chỉ số đo lường hiệu suất. Bên cạnh đó, nghiên cứu đã kết hợp phân tích biểu đồ SHAP và biểu đồ phụ thuộc từng phần (PDP) để làm rõ vai trò và cơ chế tác động của biến đầu vào. Kết quả chỉ ra rằng mô hình SVM đạt được hiệu quả cao trong phân loại và dự đoán. Hành vi lựa chọn vị trí đỗ xe chủ yếu chịu chi phối bởi phương thức thu phí, cấp hạng đường phố, đặc điểm hè phố và nguy cơ ùn tắc giao thông, trong khi các yếu tố nhân khẩu học có ảnh hưởng hạn chế. Nghiên cứu đề xuất một số giải pháp và cơ sở khoa học trong quy hoạch, thiết kế và tổ chức hệ thống đỗ xe tại Hà Nội.Tài liệu tham khảo
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[35]. J. H. Friedman, Greedy function approximation: A gradient boosting machine, The Annals of Statistics, 29 (2001) 1189–1232. https://doi.org/10.1214/aos/1013203451
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[37]. X. Li, Parking choice behavior of urban village residents considering parking risk: An integrated modeling approach, Case Studies on Transport Policy, 15 (2024).
[2]. J.D. Hunt, S. Teply, A nested logit model of parking location choice, Transportation Research Part B: Methodological, 27 (1993) 253–265. https://doi.org/10.1016/0191-2615(93)90035-9
[3]. M. Chen, C. Hu, T. Chang, The research on optimal parking space choice model in parking lots, 3rd International Conference on Computer Research and Development, 2 (2011) 93–97. https://doi.org/10.1109/ICCRD.2011.5764091
[4]. K. Teknomo, K. Hokao, Parking behavior in central business district: A study case of Surabaya, Indonesia, EASTS Journal, 2 (1997) 551–570.
[5]. P. Coppola, Transportation Planning: State of the Art, 1st Edition, Springer US, Boston, MA, 2002.
[6]. Khaliq, P.J.H.J. Van Der Waerden, D. Janssens, A discrete choice approach to define individual parking choice behaviour for the ParkAgent model, 23rd International Conference on Urban Transport and the Environment, (2017) 493–502.
[7]. R.A. Waraich, K.W. Axhausen, Agent-based parking choice model, Transportation Research Record, 2319 (2012) 39–46. https://doi.org/10.3141/2319-05
[8]. L. Guo, S. Huang, J. Zhuang, A.W. Sadek, Modeling parking behavior under uncertainty: A static game theoretic versus a sequential neo-additive capacity modeling approach, Networks and Spatial Economics, 13 (2013) 327–350. https://doi.org/10.1007/s11067-012-9183-1
[9]. M. Ottomanelli, M. Dell’Orco, D. Sassanelli, Modelling parking choice behaviour using Possibility Theory, Transportation Planning and Technology, 34 (2011) 647–667.
[10]. E.M. Whitlock, Use of linear programming to evaluate alternative parking sites, Highway Research Record, 444 (1973).
[11]. Y. Asakura, M. Kashiwadani, Effects of parking availability information on system performance: A simulation model approach, Proceedings of VNIS'94 - 1994 Vehicle Navigation and Information Systems Conference, (1994) 251–254. https://doi.org/10.1109/VNIS.1994.396832
[12]. G. Antolín, A. Ibeas, B. Alonso, L. dell’Olio, Modelling parking behaviour considering users’ heterogeneities, Transport Policy, 67 (2018) 23–30.
[13]. X.Y. Ni, D. Sun, Agent-based modelling and simulation to assess the impact of parking reservation system, Journal of Advanced Transportation, 2017 (2017) 2576094.
[14]. C. Cortes, V. Vapnik, Support-vector networks, Machine Learning, 20 (1995) 273–297.
[15]. Y. Zhang, Y. Xie, Travel mode choice modeling with support vector machines, Transportation Research Record, 2076 (2008) 141–150. https://doi.org/10.3141/2076-16
[16]. B. Sun, B. B. Park, Route choice modeling with support vector machine, Transportation Research Procedia, 25 (2017) 1806-1814. https://doi.org/10.1016/j.trpro.2017.05.151
[17]. A. P. Alex, M. A. Thomas, R. Raj, Modelling of activity–travel pattern with support vector machine, European Transport, 82 (2021) 1–20. https://doi.org/10.48295/ET.2021.82.2
[18]. A. Dahiya, P. Mittal, Y. K. Sharma, U. K. Lilhore, S. Simaiya, E. Ghith, M. Tlija, Machine learning‐based prediction of parking space availability in IoT‐enabled smart parking management systems, Journal of Advanced Transportation, 2024 (2024). https://doi.org/10.1155/2024/8474973
[19]. Z. Zhao, Y. Zhang, Y. Zhang, A comparative study of parking occupancy prediction methods considering parking type and parking scale, Journal of Advanced Transportation, 2020 (2020). https://doi.org/10.1155/2020/5624586
[20]. C. Ma, X. Huang, J. Li, A review of research on urban parking prediction, Journal of Traffic and Transportation Engineering (English Edition), 11 (2024) 700–720.
[21]. S. Gao, M. Li, Y. Liang, J. Marks, Y. Kang, M. Li, Predicting the spatiotemporal legality of on-street parking using open data and machine learning, Annals of GIS, 25 (2019) 299–312.
[22]. Y. Sari, H. Suhud, A. R. Baskara, R. A. Pramunendar, I. F. Radam, Parking lots detection in static images using support vector machine based on genetic algorithm, International Journal of Intelligent Engineering and Systems, 14 (2021) 476–487. https://doi.org/10.22266/ijies2021.1231.42
[23]. P. Sattayhatewa, R.L. Smith Jr., Development of parking choice models for special events, Transportation Research Record, 1858 (2003) 31–38. https://doi.org/10.3141/1858-05
[24]. Y. Han, W. Huang, X. Wu, G. Yang, Parking location choice model in mixed residential and commercial land considering parking sharing policy, COTA International Conference of Transportation Professionals, (2017) 3543–3550. https://doi.org/10.1061/9780784480915.370
[25]. T. M. T. Truong, H. B. Ly, D. Lee, B. T. Pham, S. Derrible, Analyzing travel behavior in Hanoi using support vector machine, Transportation Planning and Technology, 44 (8) (2021).
[26]. R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2 (1995) 1137-1143.
[27]. M. Sivakumar, S. Parthasarathy, T. Padmapriya, Trade-off between training and testing ratio in machine learning for medical image processing, PeerJ Computer Science, 10 (2024).
[28]. D. S. Broomhead, D. Lowe, Multivariable functional interpolation and adaptive networks, Complex Systems, 2 (1988) 321–355.
[29]. T. M. Mitchell, Machine Learning, McGraw-Hill, New York, 1997.
[30]. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, 2001.
[31]. C. J. Van Rijsbergen, Information Retrieval, 2nd ed., Butterworths, London, 1979.
[32]. C. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press, Cambridge, 2008.
[33]. M. Sokolova, G. Lapalme, A systematic analysis of performance measures for classification tasks, Information Processing & Management, 45 (2009) 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
[34]. S. M. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, Proceedings of the 31st International Conference on Neural Information Processing Systems, (2017) 4765–4774.
[35]. J. H. Friedman, Greedy function approximation: A gradient boosting machine, The Annals of Statistics, 29 (2001) 1189–1232. https://doi.org/10.1214/aos/1013203451
[36]. S. B. Hassine, R. Mraihi, A. Lachiheb, E. Kooli, Modelling parking type choice behavior, International Journal of Transportation Science and Technology, 11 (2022) 653–664.
[37]. X. Li, Parking choice behavior of urban village residents considering parking risk: An integrated modeling approach, Case Studies on Transport Policy, 15 (2024).
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04/11/2025
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01/02/2026
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04/02/2026
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Kiểu trích dẫn
Lê Văn, C., Đặng Minh, T., & Bùi Xuân, C. (1771088400). Phát triển mô hình dự đoán hành vi lựa chọn vị trí đỗ xe trên đường ứng dụng mô hình máy vector hỗ trợ: nghiên cứu điển hình tại thành phố Hà Nội. Tạp Chí Khoa Học Giao Thông Vận Tải, 77(2), 166-180. https://doi.org/10.47869/tcsj.77.2.2
