A short-term traffic flow prediction network using filter-genetic feature selection method

Abstract Accurate selection of spatial-temporal features is key to the short-term traffic flow prediction model outputting higher quality results, which can effectively reduce the difficulty of constructing the prediction model. The spatial-temporal feature selection of most existing short-term traffic flow prediction models mainly relies on empirical knowledge methods and lacks interpretability. The proposed short-term traffic flow prediction network, named STFP-FGFS, utilizes a filter-genetic feature selection method to better explain the results of short-term traffic flow predictions. It consists of three stages: initial generation of temporal-spatial feature set, filtering, and feature optimization, as well as the predicted model. The initial spatial features are generated based on effective travel time, target time granularity, and vehicle type; that is, original spatial features are replaced by standardized spatial features. Four widely used feature selection methods for short-term traffic flow prediction are applied and compared, evaluating three experimental targets and four types of time granularity using four evaluation indexes. The results show that the STFP-FGFS proposed method has overall superior performance, good interpretability, and readability for selected spatial-temporal features.