Research on space-time modelling and forecasting has focused on integrating space-time autocorrelation into statistical models to increase the accuracy of forecasting. These models include space-time autoregressive integrated moving average (STARIMA) and its various extensions. However, they are inadequate for the cases when the correlation between data is dynamic and heterogeneous, such as traffic network data. The aim of the paper is to integrate spatial and temporal autocorrelations of road traffic network by developing a novel space-time delay neural network (STDNN) model that capture the autocorrelation locally and dynamically. Validation of the space-time delay neural network is carried out using real data from London road traffic network with 22 links by comparing benchmark models such as Naive, ARIMA, and STARIMA models. Study results show that STDNN outperforms the Naive, ARIMA, and STARIMA models in prediction accuracy and has considerable advantages in travel time prediction.
['Jiaqiu Wang', 'Ioannis Tsapakis', 'Chen Zhong']
A space-time delay neural network model for travel time prediction