Bi-objective traffic count location model for mean and covariance of origin–destination estimation

Sun, Weiwei; Shao, Hu; Shen, Liang; Wu, Ting; Lam, William H. K.; Yao, Baozhen; Yu, Bin

doi:10.1016/j.eswa.2020.114554

Cited by 10 publications

(1 citation statement)

References 47 publications

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“…In the era of Intelligent Transportation Systems (ITS), the greater variety of data sources and advanced algorithms provides new opportunities for resolving this ageold problem more accurately and efficiently. The data for OD estimation can be derived from traffic counts [2], automatic vehicle identification [3][4][5], cellphone signaling [6,7], and floating vehicle tracks [8][9][10]. With these data, numerous novel and practical methods have been devised such as Probabilistic Tensor Factorization [11], Hierarchical Flow Network [12], Res3D [5], Path/Subpath-based Model [13,14], etc.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Wu,

et al. 2023

Applied Sciences

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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 46.98%, MAE by 45.40% and MAPE by 29.76%, in comparison to the state-of-the-art method with the best performance.

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