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Traffic Prediction Framework for OpenStreetMap Using Deep Learning Based Complex Event Processing and Open Traffic Cameras

Authors Piyush Yadav , Dipto Sarkar , Dhaval Salwala, Edward Curry

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Author Details

Piyush Yadav
  • Lero-SFI Irish Software Research Centre, Data Science Institute, National University of Ireland Galway, Ireland
Dipto Sarkar
  • Department of Geography, University College Cork, Ireland
Dhaval Salwala
  • Insight Centre for Data Analytics, National University of Ireland Galway, Ireland
Edward Curry
  • Insight Centre for Data Analytics, National University of Ireland Galway, Ireland

Funding

This work was supported by the Science Foundation Ireland grants SFI/13/RC/2094 and SFI/12/RC/2289_P2.

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Piyush Yadav, Dipto Sarkar, Dhaval Salwala, and Edward Curry. Traffic Prediction Framework for OpenStreetMap Using Deep Learning Based Complex Event Processing and Open Traffic Cameras. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 17:1-17:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.GIScience.2021.I.17

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.

Subject Classification

ACM Subject Classification
  • Information systems → Data streaming
  • Information systems → Geographic information systems
Keywords
  • Traffic Estimation
  • OpenStreetMap
  • Complex Event Processing
  • Traffic Cameras
  • Video Processing
  • Deep Learning

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