Varieties of Navigational Effects Caused by Sound Contents

Fukuda, Nana; Ohno, Ryuzo; Nasu, Satoshi

doi:10.3130/aija.79.2653

Cited by 4 publications

(2 citation statements)

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“…[11][12][13][14] These studies mainly aim to apply actual measurement results to the estimation of behavioral models, [15][16][17][18][19] improvement of pedestrian simulations, 15,20,21 marketing, 22 security, 23 and mobility support for disabled individuals. [24][25][26][27][28] Research classified under Category (2), on an architectural scale involves those on fire prevention equipment, 29 design guidelines for corridor spaces, [30][31][32] and exhibitions and mobility in museums. 33 Studies on a city scale in the aforementioned category involve those on the relationships between children's activity areas and the characteristics of school districts, 34 those that clarified the relationship between walking in housing complexes and waterside spaces, 35,36 and those that classified street spaces according to walking speed features.…”

Section: Summary Of Previous Researchmentioning

confidence: 99%

“…Studies classified under Category (1) focus on topics such as behaviors, for example avoidance and interaction of individual pedestrians in various scales from laboratories/living rooms to urban spaces, 3–7 spatial perception and behavioral characteristics of visually impaired people, 8,9 and behavioral characteristics of pedestrian groups 10 and crowds 11–14 . These studies mainly aim to apply actual measurement results to the estimation of behavioral models, 15–19 improvement of pedestrian simulations, 15,20,21 marketing, 22 security, 23 and mobility support for disabled individuals 24–28 …”

Section: Introductionmentioning

confidence: 99%

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Proposal of a classification method for public space usage based on pedestrian trajectory data

MASUMURA

Sadohara

2023

Japan Architectural Review

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Herein, we propose a machine learning method based on pedestrian trajectory data to classify public space usage states and discriminate unknown usage states. Assuming that there are several frequent patterns in the usage state of public spaces, each element of the data set representing the usage state of public spaces can be classified into several clusters. Each cluster is defined as a “type” of usage state. They were classified into usage state “types” via principal component analysis and x‐means clustering. We employed data from the detection and recording of pedestrian trajectories by six 3D laser (LiDAR) sensors, conducted by the authors during the summer of 2019 in a public space in Yokohama, Japan. In the training phase, we defined “types” of usage states based on the data obtained for 3 weeks. In the test phase, the “type” of usage state was determined for the data of other periods. Consequently, 16 types that appeared at specific times and days were identified, and 1.1% of the test data were determined to be “new usage states,” which were not found in the training data. This method helps understand long‐term and complex variations in public space utilization patterns.

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