Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches

Yiannis Kamarianakis, Poulicos Prastacos

Research output: Contribution to journalArticle

176 Scopus citations

Abstract

Several univariate and multivariate models have been proposed for performing short-term forecasting of traffic flow. Two different univariate [historical average and ARIMA (autoregressive integrated moving average)] and two multivariate [VARMA (vector autoregressive moving average) and STAREMA (space-time ARIMA)] models are presented and discussed. A comparison of the forecasting performance of these four models is undertaken with data sets from 25 loop detectors located in major arterials in the city of Athens, Greece. The variable under study is the relative velocity, which is the traffic volume divided by the road occupancy. Although the specification of the network's neighborhood structure for the STAREMA model was relatively simple and can be further refined, the results obtained indicate a comparable forecasting performance for the ARIMA, VARMA, and STAREMA models. The historical average model could not cope with the variability of the data sets at hand.

Original languageEnglish (US)
Pages (from-to)74-84
Number of pages11
JournalTransportation Research Record
Issue number1857
DOIs
StatePublished - 2003

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

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