Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather
Wensong Zhang, Ronghan Yao, Xiaojing Du, Jinsong Ye
Abstract
Three hybrid deep spatio-temporal models are proposed to accurately predict traffic flow under normal conditions, on holidays and under adverse weather. Each of the proposed models consists of the global and target parts, and fuses the weather and traffic flow data obtained from the target and upstream sections. The convolutional neural network (CNN), and the gated recurrent unit (GRU) and convolutional long short-term memory (ConvLSTM) neural networks are selected to analyze the spatio-temporal characteristics of traffic flow data. Then, the three proposed models are verified using three actual cases, including traffic flow prediction under normal conditions, on holidays and under adverse weather. Moreover, the characteristics of traffic flow data on the Independence Day and Thanksgiving Day, and the patterns of traffic flow data under heavy rain and strong wind are discussed. The experimental results show that: the three models usually perform better than the existing models under all the situations; different holidays and different types of adverse weather have various impacts on the characteristics of traffic volume and speed data.