A space–time delay neural network model for travel time prediction. (June 2016)

• Record Type: Journal Article
• Authors: Wang, Jiaqiu; Tsapakis, Ioannis; Zhong, Chen
• Is Part Of: Engineering applications of artificial intelligence. Volume 52(2016:Apr.)
• Page Start: 145
• Page End: 160
• Publication Date: 2016-06
• Subjects: Space-time delay neural network -- Space-time autocorrelation -- London road traffic network -- Travel time prediction; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.02.012
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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 Naïve, ARIMA, and STARIMA models. Study results show that STDNN outperforms the Naïve, ARIMA, and STARIMA models in prediction accuracy and has considerable advantages in travel time prediction.