Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques

Abstract

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. This 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. This 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. The proposed model can predict the time-varying OD matrix, the number of passengers departing at each time, and the travel time spent by passengers, of which the results are shown in the case study. Finally, the results indicate that the proposed model can effectively obtain a relatively accurate estimation result. The proposed model can integrate more traffic characteristics than traditional methods and provides an effective and hierarchical passenger flow estimation framework. This study can provide a rich set of passenger demand for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling.

Passenger Flow Path Prediction Based on Urban Rail Transit AFC Data: An Example of Chengdu, China

“…Te accuracy of the model prediction results can be fully guaranteed by simulating and testing the established station passenger fow or passenger fow OD prediction model using historical AFC data. For example, Guo et al [1], Tang et al [2] predicted station passenger fows and validated them using historical AFC data; Yang et al [3], Cao et al [4], and Yao et al [5] built an OD matrix prediction model and compared the prediction results with real data to verify their validity. However, people's travel patterns are heterogeneous [6] and may change over time.…”

“…On the one hand, based on the passenger fow characteristics, passenger fow distribution patterns [14], and passenger travel preferences [7], combined with the urban rail topology network [15], it is possible to build a generalized model to measure the OD matrix of urban rail passenger fow for the prediction of passenger point of interest (POI) [16]. For example, the improved LSTM algorithm [15,17,18] is a more widely used method for predicting the OD matrix, and there are also nonlinear models [3], HW-DMD [19], etc. On the other hand, in the transportation domain, a trip is generally described by an OD pair, and there are usually many paths between each OD pair that can be chosen by the traveler.…”

An Evaluation Method of Road Link Functionality Using Individual Trajectory Data and Weighted Network Analysis

“…Therefore, to identify the roles and functions of the roadways, it is significant to analyze not only the physical characteristics of the road (e.g., number of lanes, width, length, speed limit) but also how travel occurs on the road. Previous studies have used origin and destination (O-D) information [9][10][11], but traffic flow and network analysis using only O-D data have limitations in identifying detailed functionalities of roads. How much traffic occurs and ends on the road is also an essential characteristic.…”