Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen
This paper aims to analyze the influence mechanism of built environment factors on passenger flow by predicting the passenger flow of Shenzhen rail transit in the morning peak hour. Based on the classification of built environment factors into socio-economic variables, built environment variables, a...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/23/10799 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850261440506626048 |
|---|---|
| author | Wenjing Wang Haiyan Wang Jun Liu Chengfa Liu Shipeng Wang Yong Zhang |
| author_facet | Wenjing Wang Haiyan Wang Jun Liu Chengfa Liu Shipeng Wang Yong Zhang |
| author_sort | Wenjing Wang |
| collection | DOAJ |
| description | This paper aims to analyze the influence mechanism of built environment factors on passenger flow by predicting the passenger flow of Shenzhen rail transit in the morning peak hour. Based on the classification of built environment factors into socio-economic variables, built environment variables, and station characteristics variables, eight lines and one hundred sixty-six stations in Shenzhen Railway Transportation are taken as research objects. Based on the automatic fare collection (AFC) system data and the POI data of AMAP, the multiple regression model (OLS) and the geographically weighted regression (GWR) model based on the least squares method are established, respectively. The results show that the average house price is significantly negatively correlated with passenger flow. The GWR model considering the house price factor has a high prediction accuracy, revealing the spatial characteristics of the built-up environment in the administrative districts of Shenzhen, which has shifted from the industrial structure in the east to the commercial and residential structure in the west. This paper provides a theoretical basis for the synergistic planning of house price regulation and rail transportation in Shenzhen, which helps to develop effective management and planning strategies. |
| format | Article |
| id | doaj-art-e01057ca3cd54be38f1da0cb85a2e41a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e01057ca3cd54be38f1da0cb85a2e41a2025-08-20T01:55:26ZengMDPI AGApplied Sciences2076-34172024-11-0114231079910.3390/app142310799Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in ShenzhenWenjing Wang0Haiyan Wang1Jun Liu2Chengfa Liu3Shipeng Wang4Yong Zhang5School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaInstitute of Transport Management, Guangdong City Technician College, Guangzhou 510520, ChinaProduction Management Center, Shenzhen Metro Operation Group Co., Ltd., Shenzhen 518000, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, ChinaShenzhen Research Institute, Northwestern Polytechnical University, Shenzhen 518063, ChinaThis paper aims to analyze the influence mechanism of built environment factors on passenger flow by predicting the passenger flow of Shenzhen rail transit in the morning peak hour. Based on the classification of built environment factors into socio-economic variables, built environment variables, and station characteristics variables, eight lines and one hundred sixty-six stations in Shenzhen Railway Transportation are taken as research objects. Based on the automatic fare collection (AFC) system data and the POI data of AMAP, the multiple regression model (OLS) and the geographically weighted regression (GWR) model based on the least squares method are established, respectively. The results show that the average house price is significantly negatively correlated with passenger flow. The GWR model considering the house price factor has a high prediction accuracy, revealing the spatial characteristics of the built-up environment in the administrative districts of Shenzhen, which has shifted from the industrial structure in the east to the commercial and residential structure in the west. This paper provides a theoretical basis for the synergistic planning of house price regulation and rail transportation in Shenzhen, which helps to develop effective management and planning strategies.https://www.mdpi.com/2076-3417/14/23/10799rail transitspatial heterogeneitypassenger flow forecastbuilt environment factorordinary least squares (OLS) regression modelgeographically weighted regression (GWR) model |
| spellingShingle | Wenjing Wang Haiyan Wang Jun Liu Chengfa Liu Shipeng Wang Yong Zhang Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen Applied Sciences rail transit spatial heterogeneity passenger flow forecast built environment factor ordinary least squares (OLS) regression model geographically weighted regression (GWR) model |
| title | Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen |
| title_full | Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen |
| title_fullStr | Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen |
| title_full_unstemmed | Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen |
| title_short | Estimating Rail Transit Passenger Flow Considering Built Environment Factors: A Case Study in Shenzhen |
| title_sort | estimating rail transit passenger flow considering built environment factors a case study in shenzhen |
| topic | rail transit spatial heterogeneity passenger flow forecast built environment factor ordinary least squares (OLS) regression model geographically weighted regression (GWR) model |
| url | https://www.mdpi.com/2076-3417/14/23/10799 |
| work_keys_str_mv | AT wenjingwang estimatingrailtransitpassengerflowconsideringbuiltenvironmentfactorsacasestudyinshenzhen AT haiyanwang estimatingrailtransitpassengerflowconsideringbuiltenvironmentfactorsacasestudyinshenzhen AT junliu estimatingrailtransitpassengerflowconsideringbuiltenvironmentfactorsacasestudyinshenzhen AT chengfaliu estimatingrailtransitpassengerflowconsideringbuiltenvironmentfactorsacasestudyinshenzhen AT shipengwang estimatingrailtransitpassengerflowconsideringbuiltenvironmentfactorsacasestudyinshenzhen AT yongzhang estimatingrailtransitpassengerflowconsideringbuiltenvironmentfactorsacasestudyinshenzhen |