  The mortality rate for pedestrians using wheelchairs was 36% higher than the
overall population pedestrian mortality rate. However, there is no data to
clarify the pedestrians' categories in both fatal and nonfatal accidents, since
police reports often do not keep a record of whether a victim was using a
wheelchair or has a disability. Currently, real-time detection of vulnerable
road users using advanced traffic sensors installed at the infrastructure side
has a great potential to significantly improve traffic safety at the
intersection. In this research, we develop a systematic framework with a
combination of machine learning and deep learning models to distinguish
disabled people from normal walk pedestrians and predict the time needed to
reach the next side of the intersection. The proposed framework shows high
performance both at vulnerable user classification and arrival time prediction
accuracy.
