Visibility is an important physical quantity that reflects the degree of atmospheric transparency, and is closely related to people's daily life and traffic travel.In this study, in order to make the estimation of visibility more flexible and efficient, three visibility estimation models are constructed and improved for different scenarios, and the respective applicability, advantages and disadvantages of the different models are analyzed.First, the visibility estimation is performed based on meteorological station observations, using correlation coefficient matrix and feature importance analysis to filter out the three variables of relative humidity, temperature and horizontal wind speed, and both day and night are considered to build a ternary cubic polynomial fitting model, which improves the overall fitting ability.Second, the deep learning model of visibility performs estimation based on images, and the scale invariant feature change method is used to extract the feature vector of key points of images, as the training of fully connected neural network model.Next, as the training data of the fully connected neural network model, the computational cost is reduced and the stability of the model is improved.Third, the inverse model of visibility estimation based on height highway images, according to the dark channel a priori theory and basic equation of visibility measurement, the atmospheric luminosity and transmittance are calculated, and the visibility of the monocular images is obtained based on the image distance information.The method does not require pre-set target and camera parameters, nor does it require training samples.The three visibility estimation models can be adapted to different scenarios, and can reduce the dependence on observation equipment.