The rise in traffic congestion has become a significant concern in the urban city environment.
The conventional traffic control systems with inefficient human resources management fail to …

Nowadays, many cities have problems with traffic congestion at certain peak hours, which
produces more pollution, noise and stress for citizens. Neural networks (NN) and machine …

Feature selection becomes predominant and quite prominent in the case of datasets that are
contained with a higher number of variables. RF (Random Forest) has emerged as a robust …

With the aggravation of traffic congestion, traffic flow data (TFD) prediction is very important
for traffic managers to control traffic congestion and for traffic participants to plan their trips …

Urban groundwater influences a wide range of processes in the natural world, including
climatic, geological, geomorphic, biogeochemical, ecotoxicological, hydrological, and …

Abstract Machine Learning (ML) algorithms can analyze large amounts of traffic data, learn
from patterns and past behaviors, and provide insights into the current and future traffic flow …

Building data-driven intelligent transportation is a significant task for establishing data-
centric smart cities, and exceptionally efficient and accurate traffic flow prediction (TFP) is a …

This study proposes a short-term traffic flow prediction framework. The vector autoregression
(VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model …

Traffic stream determining is an essential part of the intelligent transportation management
system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting …

Short-term metro passenger flow forecasting is an essential component of intelligent
transportation systems (ITS) and can be applied to optimize the passenger flow organization …