STANDARD Project Team

Dr. Jiaqiu Wang
Research Fellow

Space-Time Analysis Modelling in Transport Network

The purpose of the research is to analyze and model space-time correlation in transport network using space-time statistic model, and to improve prediction accuracy of travel time/speeds and traffic flow using the constructed model. The Space-Time Autoregressive Integrated Moving-Average(STARIMA) model family is a useful tool for modelling space-time processes that are stationary(or weak stationary) in space and time. It is proposed firstly by Martin and Oeppen (1975) and has gained widespread popularity in modelling multiple time series data that correspond to different spatial locations, which are known as space-time series. Currently, STARIMA models have been applied to model space-time dynamic processes in many domains, such as image analysis, transport, business and economics, and physical and environmental sciences. However, how to accommodate space-time association in transport network hasn’t been well recognized and examined in the previous traffic research. It hereby is essential to further study network space-time association. The study proposed space-time matrix to accommodate space-time association through considering spatial-temporal influence of upstream and downstream in STARIMA, DRNN, and STSVM models. Because STARIMA, DRNN and STSVM can accommodate dynamics in data, when the spatial autocorrelations are fully considered, they will become the models which unify space-time autocorrelations seamlessly and simultaneously.


Principal Investigator
Dr. Tao Cheng

Prof. Benjamin Heydecker

Research Fellows
Dr. Ioannis Tsapakis
Dr. Jiaqiu Wang

Research Students
Mr. Berk Anbaroglu
Mr. James Haworth
Mr. Ed Manley
Ms. Garavig Tanaksaranond

Industrial Partners at TfL
Mr. Andy Emmonds
Mr. Jonathan Turner
Mr. Alexandre Santacreu

alt: Research Poster
Sponsored By:     University College London   Transport For London   Engineering and Phyical Research Council