Abstract:
Smart traffic management systems forecast traffic. Missing data issues hinder urban road network traffic flow forecasting due to the widespread use of massive vehicle trajectory data. This paper investigates urban network-wide short-term traffic speed forecasting with missing link speed data using (i) a data recovery algorithm to impute missing speed data for the segment network with nonlinear spatial and temporal correlations and (ii) the GraphSAGE model to forecast spatially heterogeneous traffic speed within the road network. Traffic speed forecasting is affected by partially missing and recovered data. The proposed recovery algorithm outperforms benchmark methods in traffic speed information reconstruction in a case study of Hangzhou, China. The case study also shows that recovering data improves short-term speed forecasting accuracy and efficiency. The proposed methods address missing traffic data and urban road network forecasting issues.
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