A Framework for Pedestrian Sub-classification and Arrival Time Prediction at Signalized Intersection Using 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.

A Data-Driven Model for Pedestrian Behavior Classification and Trajectory Prediction

“…1. Methodology for pedestrian movement prediction and/or pedestrians), can result in poor positioning or even total inability to produce a navigation solution [34]- [35]. Therefore, appropriate tools should be utilized and appropriate measures need to be taken into account when collecting GNSS positioning data in order to minimize environmental effects.…”

“…The first one is simpler and is usually applied in smaller datasets, while the second one is more customized and can be applied when large datasets are available. In the first approach, the training period is defined as a fixed percentage of the available dataset size, for example 50% of the observations are used for training and the rest for testing [35]. In the second approach, the optimal training period is determined by the desired prediction accuracy [36].…”