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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.

Cite AsGet BibTex

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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References

  1. David Alm. Somebody’s Watching You: Ai Weiwei’s New York Installation Explores Surveillance in 2017, 2017. URL: https://bit.ly/2Um1hT5.
  2. Sheeraz A Alvi, Bilal Afzal, Ghalib A Shah, Luigi Atzori, and Waqar Mahmood. Internet of multimedia things: Vision and challenges. Ad Hoc Networks, 33:87-111, 2015. Google Scholar
  3. Jennings Anderson, Dipto Sarkar, and Leysia Palen. Corporate editors in the evolving landscape of openstreetmap. ISPRS International Journal of Geo-Information, 8(5):232, 2019. Google Scholar
  4. Waadt Andreas, Wang Shangbo, Jung Peter, et al. Traffic congestion estimation service exploiting mobile assisted positioning schemes in gsm networks. Procedia Earth and Planetary Science, 1(1):1385-1392, 2009. Google Scholar
  5. Asra Aslam and Edward Curry. Towards a generalized approach for deep neural network based event processing for the internet of multimedia things. IEEE Access, 6:25573-25587, 2018. Google Scholar
  6. Geoff Boeing. Osmnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65:126-139, 2017. Google Scholar
  7. John Canny. An algorithm to estimate mean traffic speed using uncalibrated cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698, 1986. Google Scholar
  8. Thurston Regional Planning Council. Appendix o level of service standard and measurements. URL: https://bit.ly/2z6yvzY.
  9. Gianpaolo Cugola and Alessandro Margara. Processing flows of information: From data stream to complex event processing. ACM Computing Surveys (CSUR), 44(3):15, 2012. Google Scholar
  10. Alan Demers, Johannes Gehrke, Mingsheng Hong, Mirek Riedewald, and Walker White. Towards expressive publish/subscribe systems. In International Conference on Extending Database Technology, pages 627-644. Springer, 2006. Google Scholar
  11. Mordechai Haklay. How good is volunteered geographical information? a comparative study of openstreetmap and ordnance survey datasets. Environment and planning B: Planning and design, 37(4):682-703, 2010. Google Scholar
  12. Alex Hern. Berlin artist uses 99 phones to trick google into traffic jam alert, 2020. URL: https://bit.ly/34ISrF4.
  13. Gorkem Kar, Shubham Jain, Marco Gruteser, Fan Bai, and Ramesh Govindan. Real-time traffic estimation at vehicular edge nodes. In Proceedings of the Second ACM/IEEE Symposium on Edge Computing, pages 1-13, 2017. Google Scholar
  14. George Kopsiaftis and Konstantinos Karantzalos. Vehicle detection and traffic density monitoring from very high resolution satellite video data. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 1881-1884. IEEE, 2015. Google Scholar
  15. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. nature, 521(7553):436-444, 2015. Google Scholar
  16. David G Lowe et al. Object recognition from local scale-invariant features. In iccv, volume 99, pages 1150-1157, 1999. Google Scholar
  17. Michael Lowry. Spatial interpolation of traffic counts based on origin-destination centrality. Journal of Transport Geography, 36:98-105, 2014. Google Scholar
  18. Pascal Neis, Dennis Zielstra, and Alexander Zipf. The street network evolution of crowdsourced maps: Openstreetmap in germany 2007-2011. Future Internet, 4(1):1-21, 2012. Google Scholar
  19. Fatih Porikli and Xiaokun Li. Traffic congestion estimation using hmm models without vehicle tracking. In IEEE Intelligent Vehicles Symposium, 2004, pages 188-193. IEEE, 2004. Google Scholar
  20. Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779-788, 2016. Google Scholar
  21. Narushige Shiode and Shino Shiode. Street-level spatial interpolation using network-based idw and ordinary kriging. Transactions in GIS, 15(4):457-477, 2011. Google Scholar
  22. NCSU Transport Tutorial Site. Traffic flow fundamentals: Flow, speed and density. URL: https://lost-contact.mit.edu/afs/eos.ncsu.edu/info/ce400_info/www2/flow1.html.
  23. Yongze Song, Xiangyu Wang, Graeme Wright, Dominique Thatcher, Peng Wu, and Pascal Felix. Traffic volume prediction with segment-based regression kriging and its implementation in assessing the impact of heavy vehicles. Ieee transactions on intelligent transportation systems, 20(1):232-243, 2018. Google Scholar
  24. Burkhard Stiller, Thomas Bocek, Fabio Hecht, Guilherme Machado, Peter Racz, and Martin Waldburger. Understanding and Managing Congestion For Transport for London. Technical report, Integrated Transport Planning Ltd., 2017. URL: http://content.tfl.gov.uk/understanding-and-managing-congestion-in-london.pdf.
  25. Unkown. Design manual for roads and bridges- cd 127 cross-sections and headrooms. Technical report, Department for Transport UK, 2020. URL: https://www.standardsforhighways.co.uk/ha/standards/.
  26. Website. How many cctv cameras in london? URL: https://caughtoncamera.net/news/how-many-cctv-cameras-in-london/.
  27. Nicolai Wojke, Alex Bewley, and Dietrich Paulus. Simple online and realtime tracking with a deep association metric. In 2017 IEEE international conference on image processing (ICIP), pages 3645-3649. IEEE, 2017. Google Scholar
  28. Piyush Yadav and Edward Curry. Vekg: Video event knowledge graph to represent video streams for complex event pattern matching. In 2019 First International Conference on Graph Computing (GC), pages 13-20. IEEE, 2019. Google Scholar
  29. Piyush Yadav and Edward Curry. Vidcep: Complex event processing framework to detect spatiotemporal patterns in video streams. In 2019 IEEE International Conference on Big Data (Big Data), pages 2513-2522. IEEE, 2019. Google Scholar
  30. Piyush Yadav, Dibya Prakash Das, and Edward Curry. State summarization of video streams for spatiotemporal query matching in complex event processing. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pages 81-88. IEEE, 2019. Google Scholar
  31. Haixiang Zou, Yang Yue, Qingquan Li, and Anthony GO Yeh. An improved distance metric for the interpolation of link-based traffic data using kriging: a case study of a large-scale urban road network. International Journal of Geographical Information Science, 26(4):667-689, 2012. Google Scholar
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