Traffic prediction framework for OpenStreetMap using deep learning based complex event processing and open traffic cameras
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Date
2020-09-25Author
Yadav, Piyush
Sarkar, Dipto
Salwala, Dhaval
Curry, Edward
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Yadav, Piyush, Sarkar, Dipto, Salwala, Dhaval, & Curry, Edward. (2020). Traffic prediction framework for OpenStreetMap using deep learning based complex event processing and open traffic cameras. Paper presented at the 11th International Conference on Geographic Information Science (GIScience 2021)-Part I, Poznań, Poland, 15-18 September.
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Abstract
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.