Hindawi Journal of Advanced Transportation Volume 2020, Article ID 8846715, 16 pages https://doi.org/10.1155/2020/8846715 Research Article Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques Yuedi Yang ,1 Jun Liu ,1 Pan Shang ,1 Xinyue Xu ,2 and Xuchao Chen 3 1School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China 2State Key Laboratory of Railway Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China 3Beijing Infrastructure Investment Co., LTD, Beijing 100044, China Correspondence should be addressed to Jun Liu;
[email protected] and Pan Shang;
[email protected] Received 30 March 2020; Revised 10 October 2020; Accepted 23 November 2020; Published 7 December 2020 Academic Editor: Jose E. Naranjo Copyright © 2020 Yuedi Yang et al. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. At present, the existing dynamic OD estimation methods in an urban rail transit network still need to be improved in the factors of the time-dependent characteristics of the system and the estimation accuracy of the results. *is study focuses on predicting the dynamic OD demand for a time of period in the future for an urban rail transit system. We propose a nonlinear programming model to predict the dynamic OD matrix based on historic automatic fare collection (AFC) data. *is model assigns the passenger flow to the hierarchical flow network, which can be calibrated by backpropagation of the first-order gradients and reassignment of the passenger flow with the updated weights between different layers.