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

TL;DR

This paper presents a framework combining machine learning and deep learning to classify pedestrians, including disabled users, and predict their arrival times at signalized intersections using preprocessed LiDAR data, enhancing traffic safety.

Contribution

It introduces a novel systematic framework that accurately classifies vulnerable pedestrians and predicts their arrival times using advanced sensor data and machine learning techniques.

Findings

High classification accuracy for vulnerable pedestrians

Precise arrival time predictions at intersections

Effective use of preprocessed LiDAR data

Abstract

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.