A STUDY ON THE PEDESTRIAN OCCUPIED TERRITORY IN THE CROSSING FLOW : The analysis with pedestrian territory model

Takayanagi, Hideaki; Sano, Takeshi; Watanabe, Hitoshi

doi:10.3130/aija.66.185_5

Cited by 14 publications

(5 citation statements)

References 2 publications

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“…Furthermore, according to a report [5] in architectural engineering, human stress significantly increases when the people walk at intersections of multiple pedestrian flows. Such intersections should be categorized and detected to find comfortable routes.…”

Section: Definition Of Congestion Categoriesmentioning

confidence: 95%

Detecting smoothness of pedestrian flows by participatory sensing with mobile phones

Nishimura

Higuchi

Yamaguchi

et al. 2014

Proceedings of the 2014 ACM International Symposium on Wearable Computers

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In this paper, we propose a novel system for estimating crowd density and smoothness of pedestrian flows in public space by participatory sensing with mobile phones. By analyzing walking motion of the pedestrians and ambient sound in the environment that can be monitored by accelerometers and microphones in off-the-shelf smartphones, our system classifies the current situation at each area into four categories that well represent the crowd behavior. Through field experiments using Android smartphones, we show that our system can recognize the current situation with accuracy of 60-78%.

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Section: Definition Of Congestion Categoriesmentioning

confidence: 95%

Detecting smoothness of pedestrian flows by participatory sensing with mobile phones

Nishimura

Higuchi

Yamaguchi

et al. 2014

Proceedings of the 2014 ACM International Symposium on Wearable Computers

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“…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][4][5][6][7] spatial perception and behavioral characteristics of visually impaired people, 8,9 and behavioral characteristics of pedestrian groups 10 and crowds. [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.…”

Section: Summary Of Previous Researchmentioning

confidence: 99%

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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“…Methods using a camera for estimating the number of passengers have been proposed [ 14 , 15 , 16 ]. Song et al proposed a system for counting passersby using video data obtained from surveillance cameras [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

Estimating Congestion in a Fixed-Route Bus by Using BLE Signals

Kanamitsu

Taya

Tachibana

et al. 2022

Sensors

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Information on congestion of buses, which are one of the major public transportation modes, can be very useful in light of the current COVID-19 pandemic. Because it is unrealistic to manually monitor the number of riders on all buses in operation, a system that can automatically monitor congestion is necessary. The main goal of this paper’s work is to automatically estimate the congestion level on a bus route with acceptable performance. For practical operation, it is necessary to design a system that does not infringe on the privacy of passengers and ensures the safety of passengers and the installation sites. In this paper, we propose a congestion estimation system that protects passengers’ privacy and reduces the installation cost by using Bluetooth low-energy (BLE) signals as sensing data. The proposed system consists of (1) a sensing mechanism that acquires BLE signals emitted from passengers’ mobile terminals in the bus and (2) a mechanism that estimates the degree of congestion in the bus from the data obtained by the sensing mechanism. To evaluate the effectiveness of the proposed system, we conducted a data collection experiment on an actual bus route in cooperation with Nara Kotsu Co., Ltd. The results showed that the proposed system could estimate the number of passengers with a mean absolute error of 2.49 passengers (error rate of 38.8%)

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A STUDY ON THE PEDESTRIAN OCCUPIED TERRITORY IN THE CROSSING FLOW : The analysis with pedestrian territory model

Cited by 14 publications

References 2 publications

Detecting smoothness of pedestrian flows by participatory sensing with mobile phones

Detecting smoothness of pedestrian flows by participatory sensing with mobile phones

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

Estimating Congestion in a Fixed-Route Bus by Using BLE Signals

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