From Detection to Decision: Linking Automated Traffic Extraction with Congestion Indicators

Traffic congestion is a persistent challenge in rapidly urbanizing regions, including Penang Island, Malaysia, where growing vehicle demand increasingly exceeds road capacity. Quantitative understanding of traffic dynamics is therefore essential for evidence-based traffic management and infrastructure planning. While previous studies have typically focused on either traffic detection, forecasting, or congestion assessment separately, limited attention has been given to integrating these components into a unified framework for continuous traffic-condition evaluation. This study integrates computer vision-based data extraction with statistical modeling to analyze and predict traffic behavior. Traffic volume is automatically obtained from video streams using a deep learning detection framework and subsequently processed for time-series modeling. Seasonal Autoregressive Integrated Moving Average (SARIMA) is employed to forecast traffic flow, while Ordinary Least Squares (OLS) regression is used to identify factors associated with congestion. Road performance is evaluated using the Volume-to-Capacity (V/C) ratio and Level of Service (LOS) indicators. The forecasting model captures daily and weekly traffic patterns with stable predictive performance across observation periods. Regression analysis indicates significant differences in traffic counts across road segments and vehicle types, while temporal traffic analysis identifies recurring peak-period traffic patterns. High V/C ratios consistently correspond to degraded LOS conditions, allowing identification of recurring bottleneck segments within the network. The combined framework demonstrates how automated sensing, statistical prediction, and engineering performance indicators can be jointly used to monitor and interpret urban traffic conditions. The approach provides a reproducible methodology for continuous congestion assessment and supports data-driven planning decisions in medium-sized urban road networks.