Crowdsourcing-based real-time urban traffic speed estimation: From trends to speeds

Author(s):  
Huiqi Hu ◽  
Guoliang Li ◽  
Zhifeng Bao ◽  
Yan Cui ◽  
Jianhua Feng
2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.


2020 ◽  
Vol 112 ◽  
pp. 136-152 ◽  
Author(s):  
Jingru Yu ◽  
Marc E.J. Stettler ◽  
Panagiotis Angeloudis ◽  
Simon Hu ◽  
Xiqun (Michael) Chen

2013 ◽  
Vol 35 ◽  
pp. 26-33 ◽  
Author(s):  
Chi-Hua Chen ◽  
Hsu-Chia Chang ◽  
Chun-Yun Su ◽  
Chi-Chun Lo ◽  
Hui-Fei Lin

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