This paper proposes a possible methodology for detecting and mitigating traffic congestion.
This method is carried out using a custom-designed traffic scenario model. The model is fully …

Spatiotemporal data from urban road traffic are pivotal for intelligent transportation systems
and urban planning. Nonetheless, missing data in traffic datasets is a common challenge …

The energy market operates in a highly deregulated and competitive environment, where
electricity price plays a crucial role. Forecasting electricity prices presents a significant …

In response to the insufficient consideration of spatio-temporal dependencies and traffic
pattern similarity in traffic flow prediction methods based on federated learning, as well as …

Travel time reliability affects the behavior of passengers in private or public transportation
and can be seen as an important factor in the context of freight transportation. The main …

This study investigates the effectiveness of data augmentation techniques like noise
creation, scaling, shifting, and Grey models (GMs) for improving prediction model …

Central Java Province has experienced increased movement due to economic, social, and
cultural developments. This increased activity has caused transportation problems …

抄録 'Traffic peak hour'is an arguably unclear yet widely used concept in urban transport
studies. It is common to see the term 'peak hour'used interchangeably with 'peak period' …

The topic of this paper is power system power consumption prediction based on BP neural
network and LSTM neural network. In power systems, accurate prediction of power …

According to statistics, congestion is becoming a great challenge for metropolitan areas, and
in congested traffic regimes, prediction of travel time is necessary both for travelers and …