Results 201 to 210 of about 8,907 (220)

Investigating the relationship between built environment and spatiotemporal heterogeneity of metro ridership

Travel Behaviour and Society
A growing body of research highlights the importance of understanding built environment factors influencing metro ridership to shape future transit strategies. However, previous traditional approaches relying on total or average ridership across distinct periods (peak hours and off-peak hours, weekdays and weekends) fail to capture the dynamic temporal
openaire   +2 more sources

Unraveling the relative contribution of TOD structural factors to metro ridership: A novel localized modeling approach with implications on spatial planning

Journal of Transport Geography, 2022
Shiliang Su, Bozhao Li, Mengjun Kang
exaly  

Examining the relationship between built environment and metro ridership at station-to-station level

Transportation Research, Part D: Transport and Environment, 2020
Tao Feng, Harry Timmermans
exaly  

Analyzing spatio-temporal impacts of extreme rainfall events on metro ridership characteristics

Physica A: Statistical Mechanics and Its Applications, 2021
Zihao Li, Yangyang Meng, Maohua Zhong
exaly  

Features Combination Selection for Metro Ridership Prediction

2025 6th International Conference on Computer Engineering and Application (ICCEA)
Yong Luo, Jianying Zheng, Xiang Wang, Zhongkui Zhu   +3 more
openaire   +1 more source

Impact of the Built Environment on Ridership Characteristics at Metro Stations

Journal of Transportation Engineering, Part A: Systems
Li Jiang, Shiyun Luo, Xisheng Hu, Kangkang Li, Said M. Easa, Yuanwen Lai   +5 more
openaire   +1 more source

Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction

IEEE Transactions on Intelligent Transportation Systems, 2019
Xiaolei Ma, Bowen Du, Chuan Ding
exaly  

Impact of Built Environment, Network Topology, and Accessibilityon Extreme Metro Ridership

This research proposes an interaction-aware framework to address three key gaps in metro ridership studies: single-factor dominance, neglect of extreme demand scenarios, and lack of circleline-based spatial differentiation. Using Guangzhou’s metro system, the framework integratesensemble machine learning with SHAP interaction analysis to examine how ...
openaire   +1 more source

Urban Metro Systems: Configurational Analysis of Antecedent Conditions Influencing High Metro Ridership

Journal of Urban Planning and Development
Zeyu Lin, Niaoer Yao, Chuangye Yu
openaire   +1 more source

Understanding the impact of built environment on metro ridership using open source in Shanghai

Cities, 2019
Dadi An, Xin Tong, Edwin H W Chan
exaly