Space-time modeling of traffic flow

Yiannis Kamarianakis, Poulicos Prastacos

Research output: Contribution to journalArticlepeer-review

242 Scopus citations


This paper discusses the application of space-time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space-time process are incorporated in the STARIMA model through the use of weighting matrices estimated on the basis of the distances among the various locations where data are collected. These matrices distinguish the space-time approach from the vector autoregressive moving average (VARMA) methodology and enable the model builders to control the number of the parameters that have to be estimated. The proposed models can be used for short-term forecasting of space-time stationary traffic-flow processes and for assessing the impact of traffic-flow changes on other parts of the network. The three-stage iterative space-time model building procedure is illustrated using 7.5 min average traffic flow data for a set of 25 loop-detectors located at roads that direct to the centre of the city of Athens, Greece. Data for two months with different traffic-flow characteristics are modelled in order to determine the stability of the parameter estimation.

Original languageEnglish (US)
Pages (from-to)119-133
Number of pages15
JournalComputers and Geosciences
Issue number2
StatePublished - Mar 2005
Externally publishedYes


  • Network
  • Time series

ASJC Scopus subject areas

  • Information Systems
  • Computers in Earth Sciences


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