The Big Data Model for Urban Road Land Use Planning Is Based on a Neural Network Algorithm

Area of Science:

• Urban Planning
• Transportation Engineering
• Data Science

Background:

• Land use patterns drive traffic demand and infrastructure development.
• Traffic conditions significantly influence land use decisions, indicating a strong reciprocal relationship.
• Understanding this interaction is crucial for sustainable urban development.

Purpose of the Study:

• To analyze the complex interaction between transportation and land use at an urban grid level.
• To develop and validate a neural network model for simulating urban development phenomena.
• To provide insights for guiding the sustainable growth of cities and their transportation systems.

Main Methods:

• Utilized a neural network-based approach for urban grid-level analysis.
• Incorporated theoretical foundations, comparing road and resilient planning methodologies.
• Applied scenario analysis to future urban road development and introduced a multidimensional long and short-term memory (MDLSTM) network to model traffic's impact on land use, considering lags and potential transfers.

Main Results:

• The neural network model effectively simulated urban development laws.
• Case studies highlighted city-specific variations in transportation-land use dynamics.
• Larger cities with dominant industries and tertiary sectors exhibited higher land use potential and greater traffic influence.

Conclusions:

• The developed model accurately predicts land use changes influenced by traffic.
• Findings support tailored urban planning strategies based on city-specific industrial structures and traffic impacts.
• The research guides the development of resilient urban transportation and land use systems.