Difforecast: Image Generation Based Highway Traffic Forecasting with Diffusion Model
- Resource Type
- Conference
- Authors
- Chi, Pengnan; Ma, Xiaoliang
- Source
- 2023 IEEE International Conference on Big Data (BigData) Big Data (BigData), 2023 IEEE International Conference on. :608-615 Dec, 2023
- Subject
- Bioengineering
Computing and Processing
Geoscience
Robotics and Control Systems
Signal Processing and Analysis
Deep learning
Road transportation
Image synthesis
Time series analysis
Transforms
Predictive models
Traffic control
Diffusion model
traffic forecasting
generative model
image generation
- Language
Monitoring and forecasting of road traffic conditions is a common practice for real traffic information system, and is of vital importance to traffic management and control. While dynamic traffic patterns can be intuitively represented by space-time diagrams, this study proposes a new concept of space-time image (ST-image) to incorporate physical meanings of traffic state variables. We therefore transform the forecasting problem for time-series traffic states into a conditional image generation problem. We explore the inherent properties of the ST images from the perspectives of physical meaning and traffic dynamics. An innovative deep learning based architecture is designed to process the ST-image, and a diffusion model is trained to obtain traffic forecasts by generating the future ST-images based on the historical patterns.