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Cited by 231Traffic Prediction and Management TechniquesTransportation Planning and OptimizationTraffic control and management

The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.

Spatiotemporal traffic forecasting: review and proposed directions

Open access
Ermagun, Alireza, Levinson, David·2018·Transport Reviews
DOI 10.1080/01441647.2018.1442887
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Abstract

This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. We extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.

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Show all 42 references

Research works printed but not linked: 16

  1. 10.1080/10248070212011 · DOI
  2. Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review. London: Sage.
  3. Chandra S. R. (2009). Spatio-temporal analyses for prediction of traffic flow speed and occupancy on I-4 (PhD thesis). University of Central Florida Orlando Florida.
  4. Chen X.-Y. Pao H.-K. & Lee Y.-J. (2014). Efficient traffic speed forecasting based on massive heterogenous historical data. 2014 IEEE international conference on big data (Big Data) (pp. 10–17). Washington DC: IEEE. · DOI
  5. Cheng T. Wang J. Haworth J. Heydecker B. & Chow A. (2011). Modelling dynamic space-time autocorrelations of urban transport network. Proceedings of the 11th international conference on geocomputation 2011 University College London London pp. 215–210.
  6. Clark S. D. Dougherty M. S. & Kirby H. R. (1993). The use of neural networks and time series models for short term traffic forecasting: A comparative study. Transportation planning methods. Proceedings of seminar D held at the PTRC European transport highways and planning 21st summer annual meeting September 13–17 UMIST (Vol. P363) University of Manchester Institute of Science and Technology England.
  7. Dong C. Shao C. & Li X. (2009). Short-term traffic flow forecasting of road network based on spatial–temporal characteristics of traffic flow. 2009 WRI world congress on computer science and information engineering (Vol. 5 pp. 645–650). Xiamen China: IEEE. · DOI
  8. Head, L. (1995). Event—based short—term traffic flow prediction model. Transportation Research Record, 1510, 125–143.
  9. Hu C. Xie K. Song G. & Wu T. (2008). Hybrid process neural network based on spatio-temporal similarities for short-term traffic flow prediction. 2008 11th International IEEE conference on intelligent transportation systems (pp. 253–258). Beijing: IEEE. · DOI
  10. 10.1155/2016/9236156 · DOI
  11. Kamarianakis Y. Prastacos P. & Kotzinos D. (2004). Bivariate traffic relations: A space-time modeling approach. AGILE proceedings Crete Greece pp. 465–474.
  12. Min X. Hu J. Chen Q. Zhang T. & Zhang Y. (2009). Short-term traffic flow forecasting of urban network based on dynamic STARIMA model. 2009 12th International IEEE conference on intelligent transportation systems (pp. 1–6). St. Louis MO: IEEE. · DOI
  13. Min X. Hu J. & Zhang Z. (2010). Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model. 2010 13th International IEEE conference on intelligent transportation systems (ITSC) . Funchal Madeira Island: IEEE. pp. 1535–1540. · DOI
  14. Sun S. Zhang C. & Zhang Y. (2005). Traffic flow forecasting using a spatio-temporal Bayesian network predictor. International conference on artificial neural networks (pp. 273–278). Berlin: Springer. · DOI
  15. Van Grol R. Inaudi D. & Kroes E. (2000). On-line traffic condition forecasting using on-line measurements and a historical database. 7th World congress on intelligent transport systems Turin Italy pp. 6–9.
  16. Ye S. He Y. Hu J. & Zhang Z. (2008). Short-term traffic flow forecasting based on mars. Fifth international conference on fuzzy systems and knowledge discovery 2008 (FSKD'08) (Vol. 5 pp. 669–675). Jinan Shandong: IEEE. · DOI

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Metadata

Authors
Ermagun, A., & Levinson, D.
Affiliations
  • Northwestern University (US)
  • The University of Sydney (AU)
Corresponding author
Ermagun, Alireza
ORCID
Venue
Transport Reviews
Publisher
Informa UK Limited
Published
2018-03-06
Volume
38
Issue
6
Pages
786-814
ISSN
  • 0144-1647 (ISSN-L)
  • 1464-5327
DOI
10.1080/01441647.2018.1442887
OpenAlex
W2521007984
Cited by
231
References
42
Citations by year
2026: 23 · 2025: 22 · 2024: 22 · 2023: 29 · 2022: 41 · 2021: 34 · 2020: 27 · 2019: 22 · 2018: 6
FWCI
17.83
Type
journal article
Language
en
Open access
Locations
  • Transport Reviews (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • University of Minnesota Digital Conservancy (University of Minnesota) (submittedVersion)
  • Transportation Research Board 96th Annual MeetingTransportation Research Board
Topics
  • Social Sciences › Transportation › Traffic Prediction and Management Techniques
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
Keywords
short-term traffic forecasting, traffic congestion, forecast horizon

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