Forecasting Geo-sensor data with participatory sensing based on dropout neural network

Nowadays, geosensor data, such as air quality and traffic flow, have become more and more essential in people's daily life. However, installing geosensors or hiring volunteers at every location and every time is so expensive. Some organizations may have only few facilities or limited budget to sense these data. Moreover, people usually tend to know the forecast instead of ongoing observations, but the number of sensors (or volunteers) will be a hurdle to make precise prediction. In this paper, we propose a novel concept to forecast geosensor data with participatory sensing. Given a limited number of sensors or volunteers, participatory sensing assumes each of them can observe and collect data at different locations and at different time. By aggregating these sparse data observations in the past time, we propose a neural network based approach to forecast the future geosensor data in any location of an urban area. The extensive experiments have been conducted with large-scale datasets of the air quality in three cities and the traffic of bike sharing systems in two cities. Experimental results show that our predictive model can precisely forecast the air quality and the bike rentle traffic as geosensor data.