Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends

By Matthew Veres, Medhat A. Moussa. IEEE transactions on intelligent transportation systems (Print), 2020

328 citations recorded by the source corpus.

Transportation systems operate in a domain that is anything but simple. Many exhibit both spatial and temporal characteristics, at varying scales, under varying conditions brought on by external sources such as social events, holidays, and the weather. Yet, modeling the interplay of factors, devising generalized representations, and subsequently using them to solve a particular problem can be a challenging task. These situations represent only a fraction of the difficulties faced by modern intelligent transportation systems (ITS). In this paper, we present a survey that highlights the role modeling techniques within the realm of deep learning have played within ITS. We focus on how practitioners have formulated problems to address these various challenges, and outline both architectural and problem-specific considerations used to develop solutions. We hope this survey can help to serve as a bridge between the machine learning and transportation communities, shedding light on new domains and considerations in the future.

Subfields: Building and Construction, Ocean Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/851a63972fc756cf97d3c8a4d686632c495b0889

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Nizar Bouguila
2. Saeed Rahmani
3. Asiye Baghbani
4. Yasin Yilmaz
5. Zachary Patterson
6. Ammar Haydari

In Ocean Engineering

1. Yasin Yilmaz
2. Ammar Haydari

In Transportation

1. Nizar Bouguila
2. Saeed Rahmani
3. Asiye Baghbani
4. Yasin Yilmaz
5. Zachary Patterson
6. Ammar Haydari

Citations in the corpus

• Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, background): “Machine learning, and its more recent variant, deep learning algorithms, have turned out to be popular and practical solutions for many real-world applications and have proved to perform satisfactorily in the domain of intelligent transportation systems [1], [2], [3].”
• Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey (2020, background): “highlights the trends and challenges of deep learning applications in ITS [9].”