Research Article Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP) Luzhou Lin, 1,2 Yuezhe Gao , 3 Bingxin Cao, 3 Zifan Wang, 3 and Cai Jia 4,5 1 School of Economics, Peking University, Yiheyuan Rd. 5, 100871 Beijing, China 2 Quantutong Location Network Co., Ltd, No. 2, Liangshuihe 1st Street, Beijing Economic and Technological Development Zone, Beijing 100163, China 3 Beijing Key Laboratory of Trafc Engineering, Beijing University of Technology, Beijing 100124, China 4 School of Geography and Tourism, Anhui Normal University, Huajin Campus, South 189 Jiuhua Rd, Wuhu 241002, China 5 Engineering Technology Research Center of Resources Environment and GIS, Wuhu 241008, China Correspondence should be addressed to Yuezhe Gao; gyuezhe@163.com Received 11 November 2022; Revised 14 February 2023; Accepted 18 February 2023; Published 3 March 2023 Academic Editor: Lingzhong Guo Copyright © 2023 Luzhou Lin et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Accurately predicting passenger fow at rail stations is an efective way to reduce operation and maintenance costs, improve the quality of passenger travel while meeting future passenger travel demand. Te improvement of data acquisition capability allows fne-grained and large-scale built environment data to be extracted. Terefore, this paper focuses on investigating the relationship between the built environment around the station and the station passenger fow and discusses whether the built environment data can be applied to the station passenger fow prediction. Firstly, the evaluation system of station passenger fow infuencing factors is built based on multisource data. Te inner relationship between built environment factors and station passenger fow is investigated using the Pearson correlation analysis. Based on this, a multilayer perceptron (MLP)-based passenger fow prediction model was developed to predict the passenger fow at key stations. Te study results show that the built environment factors impact station passenger fow, and the MLP prediction model has better prediction accuracy and applicability. Te results of the study can be applied to predict the passenger fow scale of rail stations without historical passenger fow data and thus are also applicable to new rail stations. 1. Introduction With the rapid economic development, urban trans- portation demand is growing and motor vehicle ownership is increasing, but due to the limited urban construction area, the imbalance between transportation supply and demand can cause trafc congestion and safety problems in large cities, which reduces the quality of travel for residents. Big cities have chosen to vigorously develop rail transportation to solve the aforementioned problems. Urban rail transit has the shortcomings of high opera- tion and maintenance costs and cannot be developed in- defnitely. Usually, the passenger fow prediction work of existing stations utilizes the historical passenger fow data of existing stations. Once the nature of the land or population distribution around the station changes, the historical passenger fow data of the station does not play a decisive role in the future passenger fow scale prediction, so it is necessary to establish a direct relationship between built environment data and passenger fow. With the gradual maturity of the application of big data technology, the ac- quisition of refned and large-scale building attributes, population characteristics, and other data becomes possible, which provides new ideas for the station’s passenger fow prediction. Exploring the relationship between the built environment around the site and the passenger fow, and predicting the passenger fow of the site through the built environment data is the way to accurately grasp the scale of the site's passenger fow and maximize the reduction of operating costs. Hindawi Complexity Volume 2023, Article ID 1430449, 19 pages https://doi.org/10.1155/2023/1430449