tsinghua-fib-lab/KSTDiff-Urban-flow-generation
Official implementation of "Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusion"(SIGSPATIAL'23)
This project helps urban planners, transportation analysts, and city developers generate realistic urban traffic flow patterns. By using real-world city data as input, it can produce simulated future or hypothetical traffic movements within cities like NYC, Washington D.C., and Baltimore. This tool is for professionals who need to model and understand how people move through a city.
No commits in the last 6 months.
Use this if you need to create synthetic yet realistic urban traffic flow data for planning, simulation, or analysis purposes.
Not ideal if you need to analyze real-time traffic or predict specific short-term traffic congestion, as this focuses on generating general patterns.
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25
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5
Language
Python
License
MIT
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Last pushed
Jan 24, 2024
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