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

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Spatiotemporal traffic forecasting: review and proposed directions

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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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Authors: Ermagun, A., & Levinson, D.
Affiliations: Northwestern University (US)
The University of Sydney (AU)
Corresponding author: Ermagun, Alireza
ORCID: Ermagun, Alireza
Levinson, David
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: 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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Spatiotemporal traffic forecasting: review and proposed directions

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