Modern cities dynamically face several challenges including digitalization, sustainability,
resilience and economic development. Urban planners and designers must develop urban …

With the rapid development of social economy, the traffic volume of urban roads has raised
significantly, which has led to increasingly serious urban traffic congestion problems, and …

With the increasing demand for intelligent transportation systems, short-term traffic flow
prediction has become an important research direction. The memory unit of a Long Short …

This paper presents the development and evaluation of short-term traffic prediction models
using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural …

Recently, different techniques have been applied to detect, predict, and reduce traffic
congestion to improve the quality of transportation system services. Deep learning (DL) is …

Long short-term memory (LSTM) models provide high predictive performance through their
ability to recognize longer sequences of time series data. More recently, bidirectional deep …

An accurate prediction of buildings' load demand is one of the most important issues in
smart grid and smart building applications. In this way, an important contribution is made to …

Short-term traffic speed prediction is a promising research topic in intelligent transportation
systems (ITSs), which also plays an important role in the real-time decision-making of traffic …

To solve the problem of urban traffic congestion is a major challenge to modern society.
Short-term urban traffic flow prediction is the key to realizing traffic control and vehicle …

Accurate forecasting of dengue cases number is urgently needed as an early warning
system to prevent future outbreaks. However, forecasting dengue fever cases with …