Traffic State Spatial-Temporal Characteristic Analysis and Short-Term Forecasting Based on Manifold Similarity

Abstract

The study on the spatial-temporal characteristics of highway traffic flow is helpful to deeply understand the inherent evolution of highway traffic system and provide a theoretical basis for prediction and control of highway traffic flow. This paper makes an empirical analysis on the spatial-temporal characteristics of highway traffic flow using manifold similarity index and manifold learning technology. The time series of highway traffic flow is converted into the distance series containing manifold features to calculate the manifold distance between multi-section traffic flow data points, which are highly similar to spatial-temporal distribution of traffic flow speed parameters, and then, the levels calibration of traffic state is carried out according to the manifold distance, so as to reveal the distribution rule of spatial-temporal characteristics of highway traffic flow. Its prediction error is obviously lower than the traditional distance measurement method, which has higher accuracy. The research of this paper can provide new ideas and methods to reveal the highway traffic flow evolution and traffic state prediction.