Traffic Flow Prediction Using Genetic Algorithm-Optimized Wavelet Neural Networks (GA-WNN)

Urban traffic flow operations demonstrate high complexity, time-varying characteristics, and randomness. Real-time accurate traffic flow prediction serves as a critical component in intelligent transportation systems, particularly for advanced traffic management systems and traveler information systems. Based on the characteristics of traffic flow prediction, we propose a GA-WNN traffic prediction model that integrates genetic algorithms with wavelet neural networks. The genetic algorithm, which follows natural evolutionary principles, performs preliminary optimization training on the connection weights and scaling/translation parameters of the wavelet neural network. This approach partially replaces the gradient descent method used in wavelet framework neural networks for parameter optimization along a single gradient direction. The implementation typically involves encoding network parameters into chromosomes and applying selection, crossover, and mutation operations to evolve optimal solutions. This methodology overcomes the drawbacks of conventional gradient descent methods, which are prone to local minima and oscillation effects. Simulation experiments validate the effectiveness of the GA-WNN prediction model for short-term traffic flow forecasting.

Urban traffic flow operations are extremely complex, exhibiting both time-varying and random characteristics. In intelligent transportation systems, particularly in research concerning advanced traffic management systems and traveler information systems, real-time accurate traffic flow prediction is essential. To address this challenge, we propose a GA-WNN traffic prediction model based on genetic algorithm-optimized wavelet neural networks. The model employs genetic algorithms to optimize the connection weights and scaling/translation parameters of wavelet neural networks, serving as an alternative to traditional gradient descent methods. Key implementation steps include initializing population parameters, defining fitness functions based on prediction accuracy, and iteratively improving solutions through genetic operations. This approach effectively overcomes limitations such as susceptibility to local minima and oscillation effects commonly associated with gradient descent methods. Through simulation experiments, we have verified the effectiveness of the GA-WNN prediction model for short-term traffic flow forecasting.