Urban Traffic Flow Forecasting Based on Memory Time-Series Network

Abstract

Predicting urban traffic flow is significant to intelligent transportation systems. Urban traffic flow data is a type of time-series data, which collects the traffic flow of a road section or area. So in this paper, we treat urban traffic flow prediction as a time series problem. The traditional method to tackle traffic flow prediction is difficult, because of the complex influence factors and nonlinear dependencies. Recently, LSTM based network has been widely used to model long-term series, but the memory of LSTM is typically too small and is not enough to accurately remember facts from the past. In this paper, we using memory time-series network with additional memory mechanisms to address urban traffic prediction problems. Historical data were divided into longterm and short-term two parts, long-term historical data models the overall trend and short-term historical data takes into account recent changes. The experiment results on two urban traffic flow datasets demonstrate the model is effective and outperforms baselines.