Cải thiện việc suy diễn bộ quy tắc trong mô hình quy tắc liên kết lớp: ứng dụng vào lựa chọn phương thức giao thông

Springer Science and Business Media LLC - Tập 50 - Trang 63-106 - 2021
Jiajia Zhang1,2, Tao Feng2,3, Harry Timmermans2,4, Zhengkui Lin1
1School of Maritime Economics and Management, Dalian Maritime University, Dalian, China
2Urban Planning and Transportation Group, Eindhoven University of Technology, Eindhoven, Netherlands
3Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima, Japan
4College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Tóm tắt

Dự đoán lựa chọn phương thức giao thông là một thành phần quan trọng trong việc dự báo nhu cầu đi lại. Gần đây, các phương pháp học máy đã trở nên ngày càng phổ biến trong việc dự đoán lựa chọn phương thức giao thông. Quy tắc liên kết lớp (CARs) đã được áp dụng trong lựa chọn phương thức giao thông, nhưng việc áp dụng các quy tắc suy diễn để dự đoán vẫn là một thách thức lâu dài. Dựa trên CARs, bài báo này đề xuất một phương pháp gộp quy tắc mới, gọi là CARM, nhằm cải thiện độ chính xác dự đoán. Trong phương pháp đề xuất, trước tiên, CARs được suy diễn từ cây mẫu thường xuyên (FP-tree) dựa trên thuật toán phát triển mẫu thường xuyên (FP-growth). Tiếp theo, các quy tắc được cắt tỉa dựa trên khái niệm tỷ lệ lỗi bi quan. Cuối cùng, các quy tắc được gộp lại để tạo thành các quy tắc mới mà không làm tăng lỗi dự đoán. Sử dụng Khảo sát Du lịch Quốc gia Hà Lan năm 2015, hiệu suất của mô hình đề xuất được so sánh với hiệu suất của CARIG sử dụng thống kê thông tin thu được để tạo ra các quy tắc mới, quy tắc liên kết dựa trên lớp (CBA), cây quyết định (DT) và mô hình logit đa biến (MNL). Ngoài ra, mô hình đề xuất còn được đánh giá bằng bài kiểm tra kiểm tra chéo mười lần. Kết quả cho thấy độ chính xác của mô hình đề xuất đạt 91,1%, vượt trội hơn CARIG, CBA, DT và mô hình MNL.

Từ khóa

#lựa chọn phương thức giao thông #quy tắc liên kết lớp #học máy #độ chính xác dự đoán #suy diễn quy tắc

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