Branch Road Traffic Flow Prediction Using Big Data, Neural Networks, and Genetic Algorithm Optimization Based on Main Road Data - Du, Zeyuan; Yu, Hongfei; Zeng, Xinlan; Dong, Yanglingyun; Wang, Yu (Transactions on Computer Science and Intelligent Systems Research, 2025)
The paper focuses on predicting electric load using a neural network prediction model for big data analysis. Accurate traffic flow estimation on branch roads, a crucial element for urban traffic management, is explored in the study. Traditional methods face challenges due to the high costs of equipment installation. The paper presents models that utilize main road data to indirectly estimate traffic flow, including linear and piecewise models. Additionally, the study integrates advanced techniques like genetic algorithms and Q-learning to optimize traffic signal scheduling and traffic flow forecasting. For complex intersections with multiple branch roads, the paper proposes a hybrid model combining constant, piecewise, and periodic traffic flow functions, validated through MATLAB and Python simulations. The results demonstrate that the developed models offer high accuracy in predicting traffic behavior, with a low RMSE of 2.51 and R² value of 0.9598. The models provide significant advantages in dynamic traffic environments and can be applied to optimize traffic signal control and congestion management.