@article{9345686,
title = {Measuring “Nigiwai” from Pedestrian Movement},
author = {Mohamed A. Abdelwahab, Shizuo Kaji, Maiya Hori, Shigeru Takano, Yutaka Arakawa, Rin-ichiro Taniguchi},
url = {https://youtu.be/Jd_vKuU4HXI},
doi = {10.1109/ACCESS.2021.3056698},
issn = {2169-3536},
year = {2021},
date = {2021-02-05},
journal = {IEEE Access},
volume = {9},
pages = {24859 - 24871},
abstract = {The analysis of the movement of people in a shopping area with the aim of improving marketing is an important research topic. Many conventional methods are dependent on the density of people in the area, which is easily estimated by counting the people entering or exiting the area. However, a high density does not always mean an increase in activity, as certain people are simply passing the area at a given time. The primary goal of this study was to introduce a set of indicators for measuring the bustle of the area, which we call “Nigiwai,” from pedestrian movement by using an analogy from classical kinematics. Such indicators can be used to measure the impact of promotional events and to optimize the design of the area. Our novel indicators were evaluated with simulated pedestrian scenarios and were demonstrated to distinguish shopping scenarios from those in which people move around without shopping successfully, even when the latter scenarios had much higher densities. The indicators were computed solely from the pedestrian trajectory, which can easily be obtained from ordinary sensors using deep learning-based techniques. As a demonstration with real data, we applied our method to a video of a street and provided a visualization of the indicators.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}