Predicting the least air polluted path using the neural network approach

Air pollution exposure during daily transportation is becoming a critical issue worldwide due to its adverse effect on human health. Predicting the least air polluted healthier path is the best alternative way to mitigate personal air pollution exposure risk. Computing the least polluted path for the current time might not be helpful for real-time applications. Therefore, we develop a routing algorithm based on a neural network-based CNN-LSTM-EBK (CLE), a temporal-spatial interpolation model. The proposed model predicts pollution levels at high temporal granularity. This paper introduces a weight function to compute air pollution concentration at the road network. It also predicts the least air polluted path among all possible paths from a source to a destination at different time granularity. The results show that the predicted path may be longer than the shortest route but minimize pollution exposure risk all the time, which proves its effectiveness during daily transportation.