We propose a novel system leveraging deep learning-based methods to predict urban traffic
accidents and estimate their severity. The major challenge is the data imbalance problem in …

Traffic accidents pose a severe global public health issue, leading to 1.19 million fatalities
annually, with the greatest impact on individuals aged 5 to 29 years old. This paper …

The rapid development of urban air mobility (UAM) has emphasized the need for in‐flight
control and passenger safety management. Recently, with the significant spread of …

Accurately predicting the probability of crashes is crucial for preventing traffic crashes and
mitigating their impacts. However, the imbalance in crash data, irregular road network …

Road safety remains a critical global concern, with millions of crashes reported annually.
Understanding the safety of individual road junctions is vital, especially in areas prone to …

Existing research faces challenges in accurately predicting crashes due to the unreasonable
selection of spatial units, biased crash data collection, and insufficient integration of multi …

In large-scale traffic optimization, models based on Macroscopic Fundamental Diagram
(MFD) are recognized for their efficiency in broad analyses. However, they fail to reflect …

Accurate forecasting of solar irradiance and photovoltaic (PV) power generation is critical for
optimizing renewable energy integration and enhancing energy management systems. This …

In this study, we propose a new system that utilizes deep learning-based approaches and
data mining techniques to predict urban traffic accidents and identify their severity and risk …