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 accessAbstract
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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“and it offers new methods for spatio-temporal road traffic forecasting-which do not always outperform other statistical methods [223] but may transfer well between areas [776].”
From Tackling climate change with machine learning (2023) · cites this paper as [223]
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Xu, Z., & Saleh, J. H. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107530 · Cited by 543
Traffic state estimation on highway: A comprehensive survey
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Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning
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A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
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Metadata
- Authors
- Ermagun, A., & Levinson, D.
- Affiliations
- Northwestern University (US)
- The University of Sydney (AU)
- Corresponding author
- Ermagun, Alireza
- 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
- 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
- green
- hdl.handle.net
- 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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