CQDFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation

Abstract

Featured Application: The methodology of this study enables real-time acquisition of dynamic traffic demand from the most basic data (traffic counts) in the field of transportation, which in turn can be applied to the fields involving traffic demand such as online urban traffic simulation, congestion management at urban intersections, short-term traffic flow prediction, urban layout planning, public transportation scheduling and the balance between supply and demand of shared mobility. Due to the inherent difficulty in direct observation of traffic demand (including generation, attraction, and assignment), the estimation of origin–destination (OD) poses a significant and intricate challenge in the realm of Intelligent Transportation Systems. As the state-of-the-art methods usually focus on a single traffic demand distribution, accurate estimation of OD in the face of diverse traffic demand and road structures remains a formidable task. To this end, this study proposes a novel model, Cyclic Quasi-Dynamic Transformers (CQDFormer), which leverages forward and backward neural networks for effective OD estimation and traffic assignment. The employment of quasi-dynamic assumption and self-attention mechanism enables CQDFormer to capture the diverse and non-linear characteristics inherent in traffic demand. We utilize calibrated simulations to generate traffic count-OD pairwise data. Additionally, we incorporate real prior matrices and traffic count data to mitigate the distributional shift between simulation and the reality. The proposed CQDFormer is examined using Simuation of Urban Mobility (SUMO), on a large-scale downtown area in Haikou, China, comprising 2328 roads and 1171 junctions. It is found that CQDFormer shows satisfied convergence performance, and achieves a reduction of RMSE by (Formula presented.), MAE by (Formula presented.) and MAPE by (Formula presented.), in comparison to the state-of-the-art method with the best performance.