DOI: 10.17587/prin.16.347-357

Automation of Traffic Data Extraction and Processing using Map Services

N. D. Povaliaev, Postgraduate Student, nikita03565@gmail.com, A. Yu. Krylatov, Associate Professor, Head of Department, a.krylatov@spbu.ru, Saint Petersburg State University, Saint Petersburg, 199034, Russian Federation

Corresponding author: Nikita D. Povaliaev, Postgraduate Student, Saint Petersburg State University, Saint Petersburg, 199034, Russian Federation, E-mail: nikita03565@gmail.com

Traffic congestion analysis and traffic data extraction are key tasks for efficient traffic flow management in modern cities. This paper presents a system that combines automated data collection and processing from map services for subsequent analysis of road network graphs. The proposed method extracts information about traffic congestion using image processing of map services, which makes it independent of proprietary data sources. The approach is based on automated collection of map screenshots, parallel image processing and matching of the obtained information with road network graphs. At the processing stage, filtering by color ranges, selecting road segments and their mapping to the corresponding network segments are applied. Parallel execution of operations is used to increase efficiency, and validation and repeated query mechanisms are implemented to minimize errors. Experimental testing on the data of a large city showed that the method allows obtaining a detailed picture of traffic congestion, taking into account both temporal and spatial factors. The developed module can be used for traffic flow studies, traffic modeling and decision support in urban mobility. The main results are accurate identification of road segments with different degrees of congestion and their correct linking to the street network graph. The system has demonstrated its applicability for analyzing urban infrastructure and can be adapted to work with various map services, providing a wide range of opportunities for further research and practical use.

Keywords: road networks, data collection automation, map services, traffic congestion analysis, image processing, spatial data processing, traffic flow management, urban planning

pp. 347—357

For citation:
Povaliaev N. D., Krylatov A. Yu. Automation of Traffic Data Extraction and Processing using Map Services, Programmnaya Ingeneria, 2025, vol. 16, no. 7, pp. 347—357. DOI: 10.17587/prin.16.347-357 (in Russian).

References:

1. Small K. A., Verhoef E. T., Lindsey R. The Economics of Urban Transportation, 3rd ed. London: Routledge, 2024. DOI: 10.4324/9781315157375.
2. Boeing G. OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks, Computers, Environment and Urban Systems, 2017, vol. 65, pp. 126—139. DOI: 10.1016/j.compenvurbsys.2017.05.004.
3. Yadav P., Sarkar D., Salwala D., Curry E. Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic Cameras, LIPIcs. GIScience, 2021, vol. 177, pp. 17:1—17:17. DOI: 10.4230/LIPIcs.GIScience.2021.I.17.
4. Stanley A., Munir R. Vehicle Traffic Volume Counting in CCTV Video with YOLO Algorithm and Road HSV Color Model-based Segmentation System Development, 2021 15th International Conference on Telecommunication Systems, Services, and Applications (TSSA), Bali, Indonesia, IEEE, 2021, pp. 1—5. DOI: 10.1109/TSSA52866.2021.9768231.
5. Whardana A. K., Hadi Rentelinggi P., Deep Learning-Based Road Traffic Density Analysis and Monitoring Using Semantic Segmentation, J. Electr. Eng. Comput. Sci., 2024, vol. 9, no. 1, pp. 1—8. DOI: 10.54732/jeecs.v9i1.1.
6. Chen G.-W., Su Z.-J., Ik T.-U. Microscopic Traffic Information Collection Based on a Lightweight MTMC Tracking Network, 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, IEEE, Apr. 2024, pp. 1 — 6. DOI: 10.1109/WCNC57260.2024.10570539.
7. Argota Sanchez-Vaquerizo J. Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona's Traffic with Empirical Data, ISPRS International Journal of Geo-Information, 2021, vol. 11, no. 1, article 24, DOI: 10.3390/ijgi11010024.
8. Mostafi S., Elgazzar K. An Open Source Tool to Extract Traffic Data from Google Maps: Limitations and Challenges, 2021 International Symposium on Networks, Computers and Communications (ISNCC), Dubai, 2021, pp. 1—8. DOI: 10.1109/ISNCC52172.2021.9615680.
9. Hossain I., Nower N. Traffic Data Collection and Visualization Tool for Knowledge Discovery Using Google Maps, International Journal of Software Innovation, 2022, vol. 10, no. 1, pp. 1—12. DOI: 10.4018/IJSI.293270.
10. Rahman Md. M., Nower N. Attention based Deep Hybrid Networks for Traffic Flow Prediction using Google Maps Data, Proceedings of the 2023 8th International Conference on Machine Learning Technologies, Stockholm Sweden, ACM, 2023, pp. 74—81. DOI: 10.1145/3589883.3589894.
11. Jiao J., Wang H. Forecasting Traffic Speed during Daytime from Google Street View Images using Deep Learning, Transportation Research Record: Journal of the Transportation Research Board, 2023, vol. 2677, no. 12, pp. 743—753. DOI: 10.1177/03611981231169531.
12. Ensari E., Kobas B. Web scraping and mapping urban data to support urban design decisions, A/Z: ITU Journal of the Faculty of Architecture, 2018, vol. 15, no. 1, pp. 5—21. DOI: 10.5505/itujfa.2018.40360.
13. Boeing G., Higgs C., Liu S. et al. Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities, The Lancet Global Health, 2022, vol. 10, no. 6, pp. e907—e918. DOI: 10.1016/S2214-109X(22)00072-9.
14. Golze J., Feuerhake U., Sester M. Spatial Analysis of External Influences on Traffic Accidents Using Open Data, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022, vol. XLIII-B4-2022, pp. 125—132. DOI: 10.5194/isprs-archives-xliii-b4-2022-125-2022.
15. Shatalova N. V., Borodina O. V., Korol M. M. Application of machine learning methods in the management of transportation and logistics systems, Transport in Russia: Problems and Prospects — 2024, Proc. Int. Sci. and Pract. Conf, St. Petersburg, Russia, 2024, pp. 258—260 (in Russian).
16. Volik A. G., Yarutin S. A., Ugryumov I. S. et al. Integrated Traffic Management System for a Smart City Based on Machine Learning: Prospects for Efficient Urban Transport, Polythematic Network Electronic Scientific Journal of Kuban State Agrarian University, 2024, no. 199, pp. 229—239 (in Russian).
17. Lotfi C., Srinivasan S., Ertz M., Latrous I. Web Scraping Techniques and Applications: A Literature Review, SCRS Conference Proceedings on Intelligent Systems, 2021, pp. 381—394. DOI: 10.52458/978-93-91842-08-6-38.
18. Mitchell R., Web Scraping with Python: Collecting Data from the Modern Web, O'Reilly Media, 2015, 238 p.
19. Zhang T. Y., Suen C. Y. A fast parallel algorithm for thinning digital patterns, Commun. ACM, 1984, vol. 27, no. 3, pp. 236— 239. DOI: 10.1145/357994.358023.
20. Galamhos C., Matas J., Kittler J., Progressive probabilistic Hough transform for line detection, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), Fort Collins, CO, USA, IEEE Comput. Soc, 1999, pp. 554—560. DOI: 10.1109/CVPR.1999.786993.