Estimating Dynamic Distribution Condition of Pedestrian Concentration on an Urban Scale
Publication: Journal of Urban Planning and Development
Volume 146, Issue 4
Abstract
The distribution of pedestrians in urban space reflects the status of urban spatial planning to some extent. The reasonable prediction of pedestrian concentration is of great significance to the evaluation of urban vitality, urban comfort, and urban spatial layout planning. In this paper, a method for predicting pedestrian concentration is proposed, which can estimate pedestrian concentration in a whole city without being limited to a specific intersection or city node. According to the characteristics of three kinds of transportation accessibility based on space syntax and commercial vitality index, a dynamic distribution estimation model of pedestrian concentration is proposed. Taking Xi’an city of China as a case study, through multiple linear regression (MLR), a support vector regression (SVR) algorithm, and random forest (RF) algorithm, the pedestrian concentration in five periods of a day was predicted and analyzed, and the spatial and temporal characteristics of crowd distribution are comprehensively described. The results show that the dynamic distribution model of pedestrian concentration constructed by RF is superior to the MLR and SVR, and its average prediction accuracy can reach 93.86%.
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Acknowledgments
Financial support provided by the Special Fund for Basic Scientific Research of Central Colleges provided by Chang’an University (Grant Nos. 300102328402 and 300102320201) is acknowledged. This research was supported by Shaanxi Provincial Natural Science Foundation of China (Grant No. 2016JM5052) and by Xi’an Science and Technology Bureau Project Funding (2019218514GXRC021CG022-GXYD21.3).
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Published In
Journal of Urban Planning and Development
Volume 146 • Issue 4 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Feb 19, 2020
Accepted: Jul 2, 2020
Published online: Sep 21, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 21, 2021
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