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DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
- Source :
- CIKM
- Publication Year :
- 2021
- Publisher :
- ACM, 2021.
-
Abstract
- Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot of attention in AI and Intelligent Transportation System community. The problem can be uniformly modeled with a 3D tensor (T, N, C), where T denotes the total time steps, N denotes the size of the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the channels of information. According to the specific modeling strategy, the state-of-the-art deep learning models can be divided into three categories: grid-based, graph-based, and multivariate time-series models. In this study, we first synthetically review the deep traffic models as well as the widely used datasets, then build a standard benchmark to comprehensively evaluate their performances with the same settings and metrics. Our study named DL-Traff is implemented with two most popular deep learning frameworks, i.e., TensorFlow and PyTorch, which is already publicly available as two GitHub repositories https://github.com/deepkashiwa20/DL-Traff-Grid and https://github.com/deepkashiwa20/DL-Traff-Graph. With DL-Traff, we hope to deliver a useful resource to researchers who are interested in spatiotemporal data analysis.<br />This paper has been accepted by CIKM 2021 Resource Track
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
business.industry
Computer science
Deep learning
Machine learning
computer.software_genre
Traffic prediction
Machine Learning (cs.LG)
Resource (project management)
Benchmark (computing)
Artificial intelligence
Car navigation systems
Internet of Things
business
Spatial domain
computer
Intelligent transportation system
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- CIKM
- Accession number :
- edsair.doi.dedup.....dc6243db9805b8208af5dbc4303eb6d5