Enhancing transportation systems via deep learning: A survey. (February 2019)

• Record Type: Journal Article
• Authors: Wang, Yuan; Zhang, Dongxiang; Liu, Ying; Dai, Bo; Lee, Loo Hay
• Is Part Of: Transportation research. Volume 99(2019)
• Page Start: 144
• Page End: 163
• Publication Date: 2019-02
• Subjects: Deep learning -- Transportation systems -- Survey; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2018.12.004
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Survey how deep learning was applied in transportation systems. Present the technology evolving trend in multiple applications. Summarize the advantages and shortcomings of deep learning in ITS. Abstract: Machine learning (ML) plays the core function to intellectualize the transportation systems. Recent years have witnessed the advent and prevalence of deep learning which has provoked a storm in ITS (Intelligent Transportation Systems). Consequently, traditional ML models in many applications have been replaced by the new learning techniques and the landscape of ITS is being reshaped. Under such perspective, we provide a comprehensive survey that focuses on the utilization of deep learning models to enhance the intelligence level of transportation systems. By organizing multiple dozens of relevant works that were originally scattered here and there, this survey attempts to provide a clear picture of how various deep learning models have been applied in multiple transportation applications.