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.

研究表明，使用轮椅的行人死亡率比整体人口的行人死亡率高 36％。使用机器学习和深度学习模型，结合先进的交通传感器，设计了一个系统架构，可以区分残障人士和正常行人，并预测到达下一个路口的时间。该方案表现出了较高的脆弱用户分类和到达时间预测准确性。