When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges

被引:24
|
作者
Zhou, Xuan [1 ]
Ke, Ruimin [2 ]
Yang, Hao [3 ]
Liu, Chenxi [3 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Univ Texas El Paso, Dept Civil Engn, El Paso, TX 79968 USA
[3] Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 20期
关键词
intelligent transportation systems; sensing technology; edge computing; traffic data; DEEP LEARNING APPROACH; UNMANNED AERIAL VEHICLES; TRAFFIC FLOW PREDICTION; NEURAL-NETWORK; ROAD DETECTION; TRAVEL-TIME; DOMAIN ADAPTATION; VIDEO ANALYTICS; QUEUE LENGTH; DEMAND;
D O I
10.3390/app11209680
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The widespread use of mobile devices and sensors has motivated data-driven applications that can leverage the power of big data to benefit many aspects of our daily life, such as health, transportation, economy, and environment. Under the context of smart city, intelligent transportation systems (ITS), such as a main building block of modern cities and edge computing (EC), as an emerging computing service that targets addressing the limitations of cloud computing, have attracted increasing attention in the research community in recent years. It is well believed that the application of EC in ITS will have considerable benefits to transportation systems regarding efficiency, safety, and sustainability. Despite the growing trend in ITS and EC research, a big gap in the existing literature is identified: the intersection between these two promising directions has been far from well explored. In this paper, we focus on a critical part of ITS, i.e., sensing, and conducting a review on the recent advances in ITS sensing and EC applications in this field. The key challenges in ITS sensing and future directions with the integration of edge computing are discussed.
引用
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页数:28
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