High-Resolution Large-Scale Urban Traffic Speed Estimation With Multi-Source Crowd Sensing Data

High-resolution large-scale urban traffic speed estimation is vital for intelligent traffic management and urban planning. However, single-source data from commonly used sources like cameras, loop detectors, or onboard devices exhibit limitations due to uneven distribution and significant noise, especially in large-scale urban areas. Consequently, existing approaches relying on these single-source data often yield low-resolution and biased estimations. In this study, we take the first attempt to leverage mobile pedestrian data and car navigation data for multi-source fusion, proposing a model to achieve high-resolution urban traffic speed estimation in large-scale areas. The key questions are how to obtain and utilize relatively static roadside pedestrian crowd sensing data to characterize the speed of moving vehicles, and how to design multi-source heterogeneous data fusion framework to improve the overall estimation performance. Specifically, a meta-learning-based matrix decomposition algorithm is first proposed to impute the missing values adaptively considering history speed data. After obtaining the imputed data, we utilize the self-view speed aggregation algorithm learning from complete spatial information to correct the imputed values. Subsequently, a multi-view speed aggregation algorithm is proposed to fuse multi-source data for tracking actual road conditions which improves road coverage. We evaluated our model with real-world datasets collected from more than 500,000 smartphones in Wenzhou, China. Experimental results show that the proposed model outperforms the state-of-the-art approaches by 7.48
$\%$
and 6.99
$\%$
in MAPE on missing data imputation and multi-source data fusion models, respectively.