Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles.
Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles.
复制标题
用于车联网交通预测的移动人群感知
DOI：
10.3390/s16010088
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发表时间：
2016-01-11
期刊：
影响因子：
--
通讯作者：
Zhou K
中科院分区：
文献类型：
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作者：
Wan J;Liu J;Shao Z;Vasilakos AV;Imran M;Zhou K
无线通信技术、移动云计算、汽车和智能终端技术的进步正在推动车载共享网络向车联网(IoV)范式的演进。这导致车辆路径问题从基于静态数据的计算转变为实时交通预测。本文首先从云计算与万物互联服务关系的角度对云辅助万物互联进行分类。然后，我们回顾了车辆到基础设施(V2I)和车辆到车辆(V2V)通信所使用的传统交通预测方法。在此基础上，我们提出了一种移动人群感知技术，以支持为希望避免拥堵的司机创建动态路线选择。通过实验对提出的方法进行了验证。最后，对可靠流量预测的前景进行了展望。
The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehiclead hocnetworks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction.