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Open Access

Geospatial Data to Images: A Deep-Learning Framework for Traffic Forecasting

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Department of Electronic Engineering and Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China.
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Abstract

Traffic forecasting has been an active research field in recent decades, and with the development of deep-learning technologies, researchers are trying to utilize deep learning to achieve tremendous improvements in traffic forecasting, as it has been seen in other research areas, such as speech recognition and image classification. In this study, we summarize recent works in which deep-learning methods were applied for geospatial data-based traffic forecasting problems. Based on the insights from previous works, we further propose a deep-learning framework, which transforms geospatial data to images, and then utilizes the state-of-the-art deep-learning methodologies such as Convolutional Neural Network (CNN) and residual networks. To demonstrate the simplicity and effectiveness of our framework, we present a formulation of the New York taxi pick-up/drop-off forecasting problem, and show that our framework significantly outperforms traditional methods, including Historical Average (HA) and AutoRegressive Integrated Moving Average (ARIMA).

Keywords

geospatial datadeep learningconvolutional neural networkresidual networktraffic forecasting

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Tsinghua Science and Technology
Volume 24 Issue 1,
February 2019
Pages 52-64
DOI: 10.26599/TST.2018.9010033
Cite this article:
Jiang W, Zhang L. Geospatial Data to Images: A Deep-Learning Framework for Traffic Forecasting. Tsinghua Science and Technology, 2019, 24(1): 52-64. https://doi.org/10.26599/TST.2018.9010033

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Received: 05 July 2017
Accepted: 28 September 2017
Published: 08 November 2018
© The author(s) 2019
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