Fusing Static and Roving Sensor Data for Detecting Highway Traffic Conditions in Real Time

With the aid of ubiquitous sensors/devices and pervasive networks, various types of multi-source data in the form of texts, videos, pictures, audios, etc. can be collected. They can be applied to detecting the traffic conditions. For highways, it has inevitable inherent defects in detecting traffic conditions by analyzing data from a single source or user participation. Therefore, the paper proposes a data fusion approach named Phecda. Phecda combines the signaling data of mobile phones with the data from static loop detectors. Phecda works in an unobtrusive way, it not only incorporates with the characteristics of traffic flows, but also includes the strategies of setting and optimizing parameters by learning historical data. Experiments are conducted with the large-scale real-world data as input. The experimental results show that the Phecda approach has high precisions and recalls in vehicle speed estimation. The corresponding Phecda system has been built and deployed in Fujian Province, China. It achieves the highway traffic monitoring with full road segment coverage at a very low cost.