CrossGraphNet: a cross-spatiotemporal graph-based method for traffic speed reconstruction using remote sensing vehicle detection - Yan Zhang; Mei-Po Kwan; Jiannan Cai; Jianying Wang; Peifeng Ma (International Journal of Digital Earth)
Existing traffic flow modeling approaches typically rely on real-time observations, such as road sensors or GPS trajectories, which constrain their research scope and application scenarios. This study proposes a novel cross-spatiotemporal graph-based network method to rapidly reconstruct traffic flow speed based on remote sensing images. The method is designed to address the challenges of traffic modeling in the absence of ground observation data. Combining high-resolution remote sensing imagery, vehicle object detection, and graph modeling technology, our approach could handle the discontinuous spatiotemporal graph information. The method incorporates two key modules: a two-layer masked structure mechanism and a cross-spatiotemporal attention computation. This innovative design enables the model to continuously synthesize learning from discontinuous remote sensing images and sparse ground-based sensor data during pre-training, optimizing its parameters and improving prediction accuracy over time. Once pre-trained, the graph model can directly estimate street-level traffic flow speed based solely on remote sensing images. Our results demonstrate state-of-the-art performance (MSE=40.117, MAE=4.768, RMSE=6.334, RSE=0.228), outperforming previous graph-based and sequence-based models. This study showcases the potential of utilizing remote sensing techniques to reconstruct traffic speed in urbanizing regions. It can even be used in scenarios lacking sufficient ground stations and with discontinuous remote sensing data, and enables low-cost, large-scale, and multi-temporal traffic flow speed reconstruction.