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Displaying near-real-time traffic information is a useful feature of digitalnavigation maps. However, most commercial providers rely onprivacy-compromising measures such as deriving location information fromcellphones to estimate traffic. The lack of an open-source traffic estimationmethod using open data platforms is a bottleneck for building sophisticatednavigation services on top of OpenStreetMap (OSM). We propose a deeplearning-based Complex Event Processing (CEP) method that relies on publiclyavailable video camera streams for traffic estimation. The proposed frameworkperforms near-real-time object detection and objects property extraction acrosscamera clusters in parallel to derive multiple measures related to traffic withthe results visualized on OpenStreetMap. The estimation of object properties(e.g. vehicle speed, count, direction) provides multidimensional data that canbe leveraged to create metrics and visualization for congestion beyond commonlyused density-based measures. Our approach couples both flow and count measuresduring interpolation by considering each vehicle as a sample point and theirspeed as weight. We demonstrate multidimensional traffic metrics (e.g. flowrate, congestion estimation) over OSM by processing 22 traffic cameras fromLondon streets. The system achieves a near-real-time performance of 1.42seconds median latency and an average F-score of 0.80.

Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic Cameras