Geospatial data to images: a deep-learning framework for traffic forecasting
Open accessAbstract
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).
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Related initiatives in sustainable transportation have been advanced by applying ML, CV, and DSS utilizing ANN, DT, NC, SVM, FL, and time series models.
“Related initiatives have been advanced by applying ML, CV, and DSS utilizing ANN, DT, NC, SVM, FL, and time series models. Key studies in this area include those by Jiang and Zhang [187] and Liyanage et al. [101].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [187]
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- Authors
- Jiang, W., & Zhang, L.
- Affiliations
- Tsinghua University (CN)
- Venue
- Tsinghua Science and Technology
- Publisher
- Tsinghua University Press
- Published
- 2019-02
- Volume
- 24
- Issue
- 1
- Pages
- 52-64
- ISSN
- 1007-0214 (ISSN-L)
- OpenAlex
- W2899619587
- Cited by
- 176
- References
- 58
- Citations by year
- 2026: 2 · 2025: 22 · 2024: 27 · 2023: 33 · 2022: 35 · 2021: 34 · 2020: 20 · 2019: 3
- FWCI
- 11.42
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Tsinghua Science & Technology (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Physical Sciences › Environmental Science › Environmental Engineering › Air Quality Monitoring and Forecasting
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Keywords
- traffic prediction, geospatial data, convolutional neural networks, ResNet, deep learning framework
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