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Title	Hybrid Statistical And Machine Learning Methods For Road Traffic Prediction: A Review And Tutorial
ID_Doc	29829
Authors	Alsolami B.; Mehmood R.; Albeshri A.
Year	2020
Published	EAI/Springer Innovations in Communication and Computing
DOI	http://dx.doi.org/10.1007/978-3-030-13705-2_5
Abstract	Mobility is one of the major dimensions of smart city design and development. Transportation analysis and prediction play important parts in mobility research and development. Recent years have seen many new types of transportation data emerging, such as social media and Global Positioning System (GPS) data. These data contains hidden knowledge, which can be used in many applications to improve city operations; road traffic prediction is one aspect of this. Researchers have traditionally used single traffic flow prediction methods, which work well only under specific conditions. Some work has emerged in recent years on combining these methods into various hybrid methods. However, this work is in its infancy, and further investigations are required. More importantly, these hybrid methods have mostly been developed on stand-alone, nondistributed platforms, limiting the data and problem sizes that can be addressed, as well as the accuracy that can be achieved. This chapter gives a review of traffic flow prediction and modeling methods and discusses the limitations of each method. A review of the various types of transportation traffic data sources is provided. Notable big data analysis tools, including the Apache Spark platform, are described. Finally, we describe a hybrid method for road traffic prediction and provide a tutorial on the process of hybrid traffic flow prediction. The hybrid method is based on the autoregressive integrated moving average (ARIMA) and support vector machine (SVM) methods. © 2020, Springer Nature Switzerland AG.
Author Keywords	Apache Spark; ARIMA; Big Data; GPS Data; Hybrid Methods; Machine Learning; Statistical Analysis; Traffic Prediction

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