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

IEEE Open Journal of Intelligent Transportation Systems

Vasileia Papathanasopoulou¹  Ioanna Spyropoulou¹  Vassilis Gikas¹  Eleni Andrikopoulou¹  Harris Perakis¹ 

[1] School of Rural, Surveying and Geoinformatics Engineering (SRSE), National Technical University of Athens, Zografou, Greece;

关键词: Behavior classification;    distraction;    pedestrian speed prediction;    pedestrian trajectory prediction;    random forests;    GNSS;   

DOI  :  10.1109/OJITS.2022.3169700

来源: DOAJ

【 摘 要 】

Pedestrian modeling remains a formidable challenge in transportation science due to the complicated nature of pedestrian behavior and the irregular movement patterns. To this extent, accurate and reliable positioning technologies and techniques play a significant role in the pedestrian simulation studies. The objective of this research is to predict pedestrian movement in various perspectives utilizing historical trajectory data. The study features considered in this research are pedestrian class, speed and position. The ensemble of these features provides a thorough description of pedestrian movement prediction, whilst contributes to the context of pedestrian modeling and Intelligent Transportation Systems. More specifically, pedestrian movement is grouped into different classes considering gender, walking pace and distraction by employing random forest algorithms. Then, position and speed prediction is computed employing suitable data-driven methods, in particular, the locally weighted regression (LOESS method), taking into account the individual pedestrian’s profile. An LSTM-based (Long Short-Term Memory) model is also applied for comparison. The methodology is applied on pedestrian trajectory data that were collected in a controlled experiment undertaken at the Campus of the National Technical University of Athens (NTUA), Greece. Prediction of pedestrian’s movement is achieved, yielding satisfactory results.

【 授权许可】

Unknown