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Multi-time-horizon Traffic Risk Prediction using Spatio-Temporal Urban Sensing Data Fusion

Having an effective and efficient model to predict traffic congestion using multi-sources data has challenged researchers for decades, especially when the number of data sources and data volume increase dramatically. In this research, we propose a new CNN-based approach that can absorb and wrap multi-sources data into a 2D/3D raster-image to predict traffic congestion. Thanks to the raster-image-based wrapping technique, the spatial-temporal correlation is conserved entirely. The proposed approach can (1) accurately predict traffic congestion over multi-scale areas at different time horizons, (2) additionally consider external factors that could affect traffic flows and might cause traffic congestion afterwards by using an immediate fusion strategy. Traffic congestion, precipitation data, posts on a social networking platform collected in Kobe city, Japan during the summer and fall of the two consecutive years are used to evaluate the proposed approach. The comparison to other methods working on the same topic is also conducted to confirm the advantage of the proposed approach. Last but not least, some insights regarding the consequences of spatial and temporal dimensions as well as external factors to different time windows are also discussed.