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。

使用预处理的 Lidar 数据进行人行分类和信号交叉口到达时间预测的框架

A Framework for Pedestrian Sub-classification and Arrival Time Prediction at Signalized Intersection Using Preprocessed Lidar Data

As the demand for vehicles continues to outpace construction of new roads, it
becomes imperative we implement strategies that improve utilization of existing
transport infrastructure. Traffic sensors form a crucial part of many such
strategies, giving us valuable insights into road utilization. However, due to
cost and lead time associated with installation and maintenance of traffic
sensors, municipalities and traffic authorities look toward cheaper and more
scalable alternatives. Due to their ubiquitous nature and wide global
deployment, cellular networks offer one such alternative. In this paper we
present a novel method for traffic flow estimation using standardized LTE/4G
radio frequency performance measurement counters. The problem is cast as a
supervised regression task using both classical and deep learning methods. We
further apply transfer learning to compensate that many locations lack traffic
sensor data that could be used for training. We show that our approach benefits
from applying transfer learning to generalize the solution not only in time but
also in space (i.e., various parts of the city). The results are very promising
and, unlike competing solutions, our approach utilizes aggregate LTE radio
frequency counter data that is inherently privacy-preserving, readily
available, and scales globally without any additional network impact.

本文介绍了一种使用 LTE/4G 射频性能测量计数器进行交通流量估计的新方法，使用经典和深度学习方法将问题视为监督回归任务。同时，应用传递学习来补偿很多地方缺乏交通传感器数据的情况。该方法不仅在时间上而且在空间上拓展解决方案，并利用具有隐私保护性、易于获取和全球可伸缩性的 LTE 射频计数器数据。