An Intelligent Machine Learning Framework for Real-Time Traffic Prediction and Traffic Flow Op-timization in Smart Urban Transportation Systems
Authors-S.Gouthami, M. Amulya
Keyword-Real-Time Traffic Prediction, Traffic Flow Optimization, Machine Learning, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Intelligent Transportation Sys-tems, Smart City, Traffic Congestion Prediction, Cloud Computing, GPS-Based Traffic Moni-toring, Traffic Sensors, Route Optimization, Predictive Analytics, Urban Mobility.
Rapid urban expansion and the continuous increase in the number of vehicles have created sig-nificant challenges for transportation authorities in maintaining smooth traffic movement and minimizing congestion. Conventional traffic control systems generally operate using fixed signal timings and predefined traffic rules, making them less effective in responding to dynamic traffic conditions caused by accidents, weather changes, road maintenance, public events, or unex-pected increases in vehicle density. These limitations often result in prolonged travel times, ex-cessive fuel consumption, increased environmental pollution, and reduced efficiency of urban transportation networks. To address these issues, this research presents an intelligent machine learning-based framework for real-time traffic prediction and traffic flow optimization that com-bines multiple real-time data sources with predictive analytics to support efficient traffic man-agement. The rewritten work preserves the methodology, machine learning algorithms, and experimental findings presented in the base paper while providing completely original academic content. The proposed framework acquires traffic information from various heterogeneous sources, including GPS-enabled vehicles, roadside traffic sensors, weather information services, public transportation systems, and cloud-based traffic databases. These data sources continuous-ly provide updated information regarding vehicle movement, traffic density, road conditions, weather variations, and travel patterns. Before model development, the collected dataset under-goes a comprehensive preprocessing phase involving data cleaning, duplicate removal, missing value handling, noise filtering, outlier detection, feature transformation, and normalization. Ex-ploratory Data Analysis (EDA) and correlation analysis are further performed to identify the relationships among traffic variables and determine the most influential features affecting con-gestion levels. These preprocessing operations improve dataset quality, reduce inconsistencies, and enhance the learning capability of the predictive models.To estimate future traffic conditions, the proposed system implements three supervised machine learning algorithms: Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Random Forest constructs multiple decision trees to model complex traffic patterns and generate robust congestion predic-tions. Support Vector Machine performs accurate classification by identifying optimal decision boundaries between different traffic states, while K-Nearest Neighbor predicts traffic conditions by measuring the similarity between current observations and historical traffic patterns. Each algorithm is trained and evaluated independently using the same dataset to ensure a consistent and unbiased comparison of predictive performance. The trained models are then integrated with an intelligent traffic optimization module that dynamically recommends efficient travel routes and adaptive traffic signal strategies based on current congestion levels.
Doi-[https://doi.org/10.5281/zenodo.21679090]