Revealing the hidden features in traffic prediction via entity embedding

Abstract

Models based on neural networks (NN) have been used widely and successfully in traffic prediction resulting in improved accuracy and efficiency in traffic flow, speed, passenger flow, and delay. Input data include continuous and discrete variables and these impact traffic changes both internally and externally. However, few studies have focused on discrete traffic-related variables in NN-based forecasting models. Inappropriate utilization of discrete variables may cause useful factors to become insignificant and lead to an inefficient forecasting model. In this paper, a NN-based model is used to predict traffic flow of a bike-sharing system in Suzhou, China. The model only uses external and discrete variables like weather, places of interest (POIs), and holiday periods. We applied both entity embedding and one-hot encoding for the data preprocessing of these variables. The results show that (1) Entity embedding can effectively increase the continuity of categorical variables and slightly improve the prediction efficiency for the NN model; and (2) The hidden relationship in variables can be identified through visual analysis, and the trained embedding vectors can also be used in traffic-related tasks.