Vehicle Turn Pattern Counting and Short Term Forecasting Using Deep Learning for Urban Traffic Management System - Sundarakrishnan Narayanan; Sohan Varier; Tarun Bhupathi; Manaswini Simhadri Kavali; Mohana; P. Ramakanth Kumar; K. Sreelakshmi (IEEE Access)
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.