Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data

• URL: http://arxiv.org/abs/2411.05842v1
• Date: Wed, 06 Nov 2024 15:13:40 GMT
• Title: Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data
• Authors: Yang He, Chengchuan An, Yuheng Jia, Jiachao Liu, Zhenbo Lu, Jingxin Xia,
• Abstract summary: We propose an efficient and robust low-rank model for precise oblique traffic speed state estimation. The proposed method achieves up to 12% improvement in Root Mean Square Error (RMSE) in the TSE scenarios. It runs more than 20 times faster than the state-of-the-art (SOTA) methods.
• Score: 19.01488741469791
• License:
• Abstract: Accurately estimating spatiotemporal traffic states on freeways is a significant challenge due to limited sensor deployment and potential data corruption. In this study, we propose an efficient and robust low-rank model for precise spatiotemporal traffic speed state estimation (TSE) using lowpenetration vehicle trajectory data. Leveraging traffic wave priors, an oblique grid-based matrix is first designed to transform the inherent dependencies of spatiotemporal traffic states into the algebraic low-rankness of a matrix. Then, with the enhanced traffic state low-rankness in the oblique matrix, a low-rank matrix completion method is tailored to explicitly capture spatiotemporal traffic propagation characteristics and precisely reconstruct traffic states. In addition, an anomaly-tolerant module based on a sparse matrix is developed to accommodate corrupted data input and thereby improve the TSE model robustness. Notably, driven by the understanding of traffic waves, the computational complexity of the proposed efficient method is only correlated with the problem size itself, not with dataset size and hyperparameter selection prevalent in existing studies. Extensive experiments demonstrate the effectiveness, robustness, and efficiency of the proposed model. The performance of the proposed method achieves up to a 12% improvement in Root Mean Squared Error (RMSE) in the TSE scenarios and an 18% improvement in RMSE in the robust TSE scenarios, and it runs more than 20 times faster than the state-of-the-art (SOTA) methods.

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