Predicting the least air polluted path using the neural network approach

Abstract

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.

Antisocial Behavior Identification from Twitter Feeds Using Traditional Machine Learning Algorithms and Deep Learning.

“…However, the impact can be mitigated if the individual's environment can be more positive. Some environmental factors that may trigger or lead to antisocial behavior are exposure to community violence, family dysfunction, and peer influence [8,27,28].…”

Parking Behaviour Analysis of Shared E-Bike Users Based on a Real-World Dataset - A Case Study in Dublin, Ireland

“…This was accomplished by implementing the Empirical Bayesian Kriging (EBK) model during the geospatial analysis phase. In a subsequent work [17], the authors extended their approach by incorporating convolutional neural networks to extract features and utilising long short-term memory models for capturing the sequential dependency of air quality. The results consistently demonstrated that although the predicted path might be longer than the shortest route, it effectively minimises the risk of pollution exposure.…”