Abstract
A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting.
Highlights
Spatiotemporal traffic forecasting is based on advanced models that utilise traffic flow information both in spatial and temporal dimensions
We consider traffic forecasting methodologies that usually do not rely on the availability of any information at the time period (t + 1); we continue with spatiotemporal network (STN) matrices as defined above for simpler formulations
4 Conclusions Spatiotemporal traffic forecasting is an emerging field in the scientific literature, and correct identification of the spatiotemporal structure plays an important role in this research area
Summary
This paper reviews studies that empirically utilise spatiotemporal traffic flow forecasting models, paying special attention to applied feature selection and extraction (FSE) methods. Four main questions for this review are: Which FSE methods are applied for spatiotemporal structure identification in empirical traffic forecasting studies? The same authors [2] suggested the identification of spatiotemporal relationships as an important research direction in traffic flow forecasting Haworth, in another related review [3], evaluated different types of spatiotemporal structures and covered 39 publications. Selection of spatiotemporal FSE methods is closely related to the utilised forecasting model, its topology, and the size of an analysed road network, and these characteristics are part of the main focus of this review. We present a review of applied methodologies based on utilised FSE methods to discover potential gaps in the literature. We summarise the current state of the reviewed area and propose several future research directions
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