Developing a Twitter-based traffic event detection model using deep learning architectures
作者：
Highlights：
• Tweets are mapped into numerical feature vectors using word-embedding models.
• Tweets are classified into non-traffic, traffic incident, and traffic information.
• Classification task is performed using convolutional and recurrent neural networks.
• 51,100 tweets are collected, labeled, and publicly released for future research.
• Models’ superiority is demonstrated through several evaluation steps.
摘要
•Tweets are mapped into numerical feature vectors using word-embedding models.•Tweets are classified into non-traffic, traffic incident, and traffic information.•Classification task is performed using convolutional and recurrent neural networks.•51,100 tweets are collected, labeled, and publicly released for future research.•Models’ superiority is demonstrated through several evaluation steps.
论文关键词：Deep learning,Twitter,Recurrent and Convolutional Neural Networks,Traffic information systems
论文评审过程：Received 30 March 2018, Revised 9 October 2018, Accepted 10 October 2018, Available online 10 October 2018, Version of Record 18 October 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.10.017
