Missing data are a universal data quality problem in many domains, leading to misleading
analysis and inaccurate decisions. Much research has been done to investigate the different …

In this paper, we present a data augmentation technique to reproduce crash data. The
dataset comprising crash and non-crash events are extremely imbalanced. For instance, the …

Real-time crash prediction is essential for proactive traffic safety management. However,
develo** an accurate prediction model is challenging as the traffic data of crash and non …

The prediction of traffic crashes is an essential topic in traffic safety research. Most of the
previous studies conducted experiments on real-time crash prediction of expressways or …

Autonomous vehicles (AVs) are emerging in the automobile industry with potential benefits
to reduce traffic congestion, improve mobility and accessibility, as well as safety. According …

Accurate solar photovoltaic (PV) generation forecast is critical to the reliable and economic
operation of a modern power system. In practice, due to various faulty issues in the sensor …

Real-time traffic crash prediction has been a major concern in the development of Collision
Avoidance Systems (CASs) along with other intelligent and resilient transportation …

The prediction of the likelihood of vehicle crashes constitutes an indispensable component
of freeway safety management. Due to data collection limitations, studies have used mainly …

Retrospective analyses of real-world clinical data face challenges owing to the absence of
some data elements. Historically, missing data was addressed by first classifying its …

Real-time crash risk prediction models aim to identify pre-crash conditions as part of active
traffic safety management. However, traditional models which were mainly developed …