Dynamic factor model for network traffic state forecast

Tao Ma, Zhou Zhou and Constantinos Antoniou

Transportation Research Part B: Methodological, 2018, vol. 118, issue C, 281-317

Abstract: We propose a dynamic factor model to forecast traffic state for groups of locations. The model decomposes the grouped traffic time series into the latent common factor component and idiosyncratic component. It uses a few latent factor series to represent the comovement of the underlying dynamics of grouped traffic flows, and idiosyncratic component to represent location-specific traffic characteristics. The dynamic factor model is estimated by the maximum likelihood method via an iterative EM (expectation maximization) algorithm. The traffic state forecast for each location is a combination of the respective forecast from the common factor component and idiosyncratic component. The dynamic factor model exhibits four advantages. It provides an excellent way to (1) seamlessly incorporate spatial correlations among grouped traffic flows into forecast; (2) produce forecast simultaneously for group locations; (3) perform dimension reduction such that high-dimension grouped traffic time series can be modeled at a low-dimension space; (4) consider not only location-specific information but also global common dynamics in the forecast. Meanwhile, it also has capacity to accommodate typical characteristics of traffic flows including temporal correlation, seasonality, structural change in mean and/or covariance function, and cointegration. Forecast accuracy is significantly improved across highway network as well as urban road network in comparison with the Sparse VAR and ARIMA models. The proposed method is suitable for large-scale network traffic forecast in the context of big data environment. This research provides sufficient evidence that justified the importance and efficacy of spatial correlation for predictive accuracy and attempts to fill the gaps in literature of traffic forecast for groups of locations.

Keywords: Dynamic factor model; Latent factor; Dimension reduction; Sparse vector autoregressive; EM algorithm; Kalman filter; Network traffic state forecast; Spatial correlation; Forecast for groups of locations (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0191261517306604
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:transb:v:118:y:2018:i:c:p:281-317

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.trb.2018.10.018

Access Statistics for this article

Transportation Research Part B: Methodological is currently edited by Fred Mannering

More articles in Transportation Research Part B: Methodological from Elsevier
Bibliographic data for series maintained by Catherine Liu ().