Complex Event Analysis for Traffic Risk Prediction based on 3D-CNN with Multi-sources Urban Sensing Data

Predictive analytics are concerned as a type of complex event processing where a complex event can be predicted by utilizing insights extracted from a set of related events. This paper introduces a new complex event analysis for traffic risk prediction using 3D-CNN and a set of related events detected from multi-sources urban sensing data (e.g., congestion, traffic accident, precipitation). The contribution of this paper involves (1) the spatio-temporal information of multi-sources urban sensing data is reserved and wrapped into 3D raster images towards being able to leverage recent developments of 3D-CNN to conduct predictive analytics, (2) the imbalanced data problem which could severely affect the performance of deep learning models is tackled by straightening curved geographic chains, (3) traffic risks can be predicted well in both short-term and medium-term time horizons, and (4) The influence of related events detected from extra factors on a complex event can be explained explicitly. The proposed method is evaluated on the real dataset collected in Kobe, Japan during 2014 and 2015. The comparisons to baseline methods such as historical average and 2D-CNN show the advantage of the proposed method as well.