Abstract Machine learning (ML) plays the core function to intellectualize the transportation
systems. Recent years have witnessed the advent and prevalence of deep learning which …

Non-predictive or inaccurate weather forecasting can severely impact the community of
users such as farmers. Numerical weather prediction models run in major weather …

Flood forecasting is an essential requirement in integrated water resource management.
This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood …

Short-term traffic forecasting based on deep learning methods, especially recurrent neural
networks (RNN), has received much attention in recent years. However, the potential of RNN …

The use of active noise control/cancelation (ANC) has increased because of the availability
of efficient circuits and computational power. However, most ANC systems are based on …

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due
to the time-varying traffic patterns and the complicated spatial dependencies on road …

Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery
reliability to determine the advent of failure and mitigate battery risk. The existing RUL …

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic
flow with big data. While existing DNN models can provide better performance than shallow …

Train dispatching (TD) is at the forefront of all rail operations that transport passengers or
goods. Recent technological advances and the explosion of digital data have introduced …

Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This
task is challenging due to the complex spatial and temporal correlations (eg, the constraints …