Vehicle Turn Pattern Counting and Short Term Forecasting Using Deep Learning for Urban Traffic Management System
Urban traffic management has been facing increasing challenges due to the surge in number of vehicles and traffic congestion. As cities expand and population grows, efficient and accurate monitoring of vehicle counts is crucial for better traffic management. Hence, as part of the Bengaluru Mobility...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10830516/ |
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| author | Sundarakrishnan Narayanan Sohan Varier Tarun Bhupathi Manaswini Simhadri Kavali Mohana P. Ramakanth Kumar K. Sreelakshmi |
| author_facet | Sundarakrishnan Narayanan Sohan Varier Tarun Bhupathi Manaswini Simhadri Kavali Mohana P. Ramakanth Kumar K. Sreelakshmi |
| author_sort | Sundarakrishnan Narayanan |
| collection | DOAJ |
| description | Urban traffic management has been facing increasing challenges due to the surge in number of vehicles and traffic congestion. As cities expand and population grows, efficient and accurate monitoring of vehicle counts is crucial for better traffic management. Hence, as part of the Bengaluru Mobility Challenge 2024, organized by Bengaluru Traffic Police in collaboration with The Indian Institute of Science, we propose a solution to address the issue by developing a predictive model to estimate vehicle counts by turning pattern from traffic video footage. The dataset consists of traffic video footage of 23 different junctions around Bengaluru, on which 7 vehicle classes had to be detected, namely, Car, Truck, Bus, Two-Wheeler, Three-Wheeler, Light Commercial Vehicle and Bicycle. The proposed work focuses on two key objectives: counting vehicle turns over 30-minute clips and forecasting future vehicle turn counts by class for the next 30 minutes. A You Only Look Once (YOLOv8) and Auto-ARIMA based pipeline was deployed to address the challenge, which demonstrated robust detection capabilities, with an overall precision of 92.59% for vehicle detection. Building on this, we designed a custom vehicle counting algorithm that integrated the BoT-SORT tracker with dynamic counting boxes, accurately capturing vehicle movements and turn patterns in real-time and this integrated approach attained a best deviation of 20.79%. for turn pattern counting and 28.41% for forecasting. Furthermore, the system is scalable to accommodate any number of cameras and is capable of forecasting traffic over extended time frames, allowing it to be applied to a variety of urban traffic monitoring scenarios. These results highlight the effectiveness of our custom designed framework in real-world scenarios as a reliable model for applications needing high-precision detection and predictive analytics. |
| format | Article |
| id | doaj-art-b08f8f06ad384b56a09290b5b271f60d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b08f8f06ad384b56a09290b5b271f60d2025-01-21T00:01:45ZengIEEEIEEE Access2169-35362025-01-01138585859310.1109/ACCESS.2025.352688010830516Vehicle Turn Pattern Counting and Short Term Forecasting Using Deep Learning for Urban Traffic Management SystemSundarakrishnan Narayanan0https://orcid.org/0009-0000-3576-7867Sohan Varier1https://orcid.org/0009-0009-1546-7408Tarun Bhupathi2https://orcid.org/0009-0008-7323-4496Manaswini Simhadri Kavali3https://orcid.org/0009-0005-6792-523X Mohana4https://orcid.org/0000-0002-6642-2949P. Ramakanth Kumar5https://orcid.org/0000-0002-2811-1701K. Sreelakshmi6https://orcid.org/0000-0002-4475-7481Department of Computer Science and Engineering, R. V. College of Engineering, Bengaluru, IndiaDepartment of Computer Science and Engineering, R. V. College of Engineering, Bengaluru, IndiaDepartment of Computer Science and Engineering, R. V. College of Engineering, Bengaluru, IndiaDepartment of Computer Science and Engineering, R. V. College of Engineering, Bengaluru, IndiaDepartment of Computer Science and Engineering, R. V. College of Engineering, Bengaluru, IndiaDepartment of Computer Science and Engineering, R. V. College of Engineering, Bengaluru, IndiaDepartment of Electronics and Telecommunication Engineering, R. V. College of Engineering, Bengaluru, IndiaUrban traffic management has been facing increasing challenges due to the surge in number of vehicles and traffic congestion. As cities expand and population grows, efficient and accurate monitoring of vehicle counts is crucial for better traffic management. Hence, as part of the Bengaluru Mobility Challenge 2024, organized by Bengaluru Traffic Police in collaboration with The Indian Institute of Science, we propose a solution to address the issue by developing a predictive model to estimate vehicle counts by turning pattern from traffic video footage. The dataset consists of traffic video footage of 23 different junctions around Bengaluru, on which 7 vehicle classes had to be detected, namely, Car, Truck, Bus, Two-Wheeler, Three-Wheeler, Light Commercial Vehicle and Bicycle. The proposed work focuses on two key objectives: counting vehicle turns over 30-minute clips and forecasting future vehicle turn counts by class for the next 30 minutes. A You Only Look Once (YOLOv8) and Auto-ARIMA based pipeline was deployed to address the challenge, which demonstrated robust detection capabilities, with an overall precision of 92.59% for vehicle detection. Building on this, we designed a custom vehicle counting algorithm that integrated the BoT-SORT tracker with dynamic counting boxes, accurately capturing vehicle movements and turn patterns in real-time and this integrated approach attained a best deviation of 20.79%. for turn pattern counting and 28.41% for forecasting. Furthermore, the system is scalable to accommodate any number of cameras and is capable of forecasting traffic over extended time frames, allowing it to be applied to a variety of urban traffic monitoring scenarios. These results highlight the effectiveness of our custom designed framework in real-world scenarios as a reliable model for applications needing high-precision detection and predictive analytics.https://ieeexplore.ieee.org/document/10830516/Auto-ARIMAdeep learningobject trackingtime-series analysistraffic forecastingYOLOv8 |
| spellingShingle | Sundarakrishnan Narayanan Sohan Varier Tarun Bhupathi Manaswini Simhadri Kavali Mohana P. Ramakanth Kumar K. Sreelakshmi Vehicle Turn Pattern Counting and Short Term Forecasting Using Deep Learning for Urban Traffic Management System IEEE Access Auto-ARIMA deep learning object tracking time-series analysis traffic forecasting YOLOv8 |
| title | Vehicle Turn Pattern Counting and Short Term Forecasting Using Deep Learning for Urban Traffic Management System |
| title_full | Vehicle Turn Pattern Counting and Short Term Forecasting Using Deep Learning for Urban Traffic Management System |
| title_fullStr | Vehicle Turn Pattern Counting and Short Term Forecasting Using Deep Learning for Urban Traffic Management System |
| title_full_unstemmed | Vehicle Turn Pattern Counting and Short Term Forecasting Using Deep Learning for Urban Traffic Management System |
| title_short | Vehicle Turn Pattern Counting and Short Term Forecasting Using Deep Learning for Urban Traffic Management System |
| title_sort | vehicle turn pattern counting and short term forecasting using deep learning for urban traffic management system |
| topic | Auto-ARIMA deep learning object tracking time-series analysis traffic forecasting YOLOv8 |
| url | https://ieeexplore.ieee.org/document/10830516/ |
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