Citation

Kouskoulis G, Spyropoulou I, Antoniou C. Int. J. Transp. Sci. Technol. 2018; 7(4): 241-253.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.ijtst.2018.09.001

PMID

unavailable

Abstract

This paper presents a comparison and relative assessment of data driven techniques and conventional theoretical pedestrian simulation models. Data driven methods are applied for simulating phenomena without a priori knowledge of parameter connections, while indicating high modeling performance. In contrast, theoretical models rely on pedestrian kinematics principles and provide mathematical functions on model parameters. A comparison between locally weighted regression and a social force model in real data (collected by the authors within the framework of this research) suggests superior performance of the data driven model on modeling pedestrian movements. However, a more integrated comparative analysis should be conducted, to validate these preliminary observations. Additional contributions, presented in this research, include an algorithm for eliminating data noise, based on an Unscented Kalman filter and moving average extensions.

Language: en

Keywords

Data driven; Data noise reduction; Pedestrian modeling; Pedestrian tracking; Unscented Kalman filter