Intelligent transportation is an important physical foundation for smart cities, as well as a strong pillar to support the development of a country. Developing intelligent transportation has been recognized as an efficient way to enhance travel security, reduce energy consumption, and alleviate traffic congestion. Solving problems of intelligent transportation is a challenging task because the factors such as the multidiscipline nature and the complex traffic scenarios. Deep learning as an emerging machine learning technology is undergoing rapid development, and deep learning for intelligent transportations attracts increasing attention from both academia and industry. It provides important support for intelligent transportation in many aspects such as signal and image processing, traffic flow prediction, vehicle routing, traffic network optimization, traffic signal control, and automatic driving. Exploring the theory and application of deep learning technology in the context of intelligent transportation is timely and has real-world impact.
We invite researchers and practitioners to contribute their original research to this special issue, which aims to advance the field of deep learning in intelligent transportation and address the pressing challenges it faces. Topics of interest for submission include, but are not limited to:
- Deep learning for traffic flow prediction
- Deep learning for travel demand forecasting
- Deep learning for transportation pricing
- Deep learning for vehicle routing problem
- Deep learning for transportation network optimization
- Deep learning for automatic driving
We look forward to receiving your submissions and believe that your research can contribute significantly to the development of deep learning in intelligent transportation. If you have any inquiries or require further information, please feel free to contact us.
Sincerely,
Xiaofeng Xu & Qiang Shen
