Learn, Assign, and Search: Real-Time Estimation of Dynamic Origin-Destination Flows Using Machine Learning Algorithms

Abstract

A common way to estimate dynamic origin–destination (O-D) flows is to establish and solve a bilevel optimization model. Though numerous efforts have been devoted to effectively and efficiently solving the model, challenges still exist because of the interdependence of jointly solving the upper level O-D estimation and lower level traffic assignment problems and the nonconvexity of the model. This paper presents an alternative framework for estimating dynamic O-D flows using machine learning algorithms. The framework consists of three major modules: a learner that learns the dynamic mapping patterns describing the relationship between prior O-D flows and observed link flows, an assigner that assigns a given O-D matrix to different links based on the learner, and a searcher that iteratively searches the optimal O-D solution using the assigner. A convolutional neural network is designed as the learner and trained as the assigner. Next, the algorithms to estimate a regular O-D matrix and real-time O-D flows are separately developed by using the assigner and two designed genetic algorithms built as the searcher. The framework was evaluated with a realistic network in the downtown area of Kunshan, China. The experimental studies show that the framework can achieve satisfactory estimation performances in real time. Meanwhile, it takes raw flow ranges as the prior inputs, making it robust in the case of lacking an accurate target O-D matrix.