Road transportation network planning and design based on the BP neural network

Road transportation network planning is an important way to ensure the rational layout and orderly and coordinated development of highways, and also the main basis for making medium and long-term plans for highway construction. This paper introduces the BP neural network theory to study the scale and grade structure in the road transportation network planning. It builds a road transportation network scale prediction model by combining the genetic algorithm with the BP neural network, establishes a road network grade structure optimization model using the multi-objective method and then takes the city of Dalian as an example for study. The research results show that the structural design and parameters of the model are reasonable. It is predicted that the total mileage of the road transport network in 2020 and 2025 will be 7500km and 8600km, respectively, and the grade structure of the road transport network is also optimized accordingly. This research expands and improves the existing road transportation network planning theory and provides reference and guidance for the construction of road transportation networks.