Forecast of Logistics Demand Based on Risk Cognition

Abstract

The accurate prediction of road traffic determines the rationality of road design. Otherwise, the road capacity may be underused or insufficient to meet the logistics demand. Based on the regional demand theory and the analysis of regional logistics demand, this paper sets up a complete index system for road logistics demand, and combined gray model and backpropagation (BP) neural network into an integrated forecast model for logistical demand. To verify its performance, the proposed model, the gray model and the BP neural network were subjected to an empirical analysis. The results show that our model outperformed the contrastive models in forecast accuracy. The research results lay the basis for accurate prediction of logistics demand in road transport.