  Displaying near-real-time traffic information is a useful feature of digital
navigation maps. However, most commercial providers rely on
privacy-compromising measures such as deriving location information from
cellphones to estimate traffic. The lack of an open-source traffic estimation
method using open data platforms is a bottleneck for building sophisticated
navigation services on top of OpenStreetMap (OSM). We propose a deep
learning-based Complex Event Processing (CEP) method that relies on publicly
available video camera streams for traffic estimation. The proposed framework
performs near-real-time object detection and objects property extraction across
camera clusters in parallel to derive multiple measures related to traffic with
the results visualized on OpenStreetMap. The estimation of object properties
(e.g. vehicle speed, count, direction) provides multidimensional data that can
be leveraged to create metrics and visualization for congestion beyond commonly
used density-based measures. Our approach couples both flow and count measures
during interpolation by considering each vehicle as a sample point and their
speed as weight. We demonstrate multidimensional traffic metrics (e.g. flow
rate, congestion estimation) over OSM by processing 22 traffic cameras from
London streets. The system achieves a near-real-time performance of 1.42
seconds median latency and an average F-score of 0.80.
