Deep learning support for intelligent transportation systems,Transactions on Emerging Telecommunications Technologies

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Deep learning support for intelligent transportation systems
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-11-09 , DOI: 10.1002/ett.4169
J. Guerrero‐Ibañez ₁ , J. Contreras‐Castillo ₁ , S. Zeadally ₂

Affiliation

Intelligent Transportation Systems (ITS) help improve the ever‐increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in‐depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

中文翻译：

对智能交通系统的深度学习支持

智能交通系统（ITS）有助于改善城市交通中不断增长的车辆流量和交通效率，从而减少事故数量。通过连接到运输网络的所有数字设备生成的大量数据，可以创建数据集，从而使用深度学习方法对数据进行深入分析。此类方法可帮助预测交通性能，自动交通信号灯管理，车道检测以及识别车辆附近的物体，从而提高ITS的安全性和效率。我们讨论了实现ITS与深度学习方法之间的无缝集成以解决以下问题所需要解决的一些挑战，这些问题包括：（1）改善交通流量/运输物流，（2）预测货物运输的最佳路线，

更新日期：2020-11-09

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