Regional dynamic distributions of PM2.5 can reflect the distributions and variation of the pollution concentration in the road networks. The research about dynamic distributions of PM2.5 is especially important to guarantee driver's safety and provide reference for transportation dispatchers. Traditional PM2.5 detecting equipment is not only expensive, but also limited by the geographical restrictions. Besides, the current detecting results about PM2.5 are very scattered and sparse, so it could also be explained that there is no full cover monitoring of the whole road networks. Therefore, this paper puts forward a visible sensing recognition linkage model based on the data collected from the current environmental detection system and traffic monitoring system. In this model, the authors use the visible recognition model and algorithms to get the identified data of the PM2.5 distributions by the first step. Then, they build big data structures for the PM2.5 distributions among the whole road networks. At the same time, the authors open unified decision support data ports to solve the compatibility problem of heterogeneous systems and the data sharing problems of the existing management system based on the new big distribution data structures. The authors use the video visibility detection model to analyze the surveillance video resources of the road networks and they also get the correlation between PM2.5 pollution particulates and the visibility of the regional road networks. The authors also successfully achieve the automatic early warning about the PM2.5 high-risk areas among road networks by developing the DSS (Decision Support System) based on the theoretical research.
|Publication status||Published - 8 Jan 2017|
|Event||Transportation Research Board 96th Annual Meeting - Washington DC, United States|
Duration: 8 Jan 2017 → 12 Jan 2017
|Conference||Transportation Research Board 96th Annual Meeting|
|Period||8/01/17 → 12/01/17|