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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

How to Increase Rail Ridership in Maryland: Direct Ridership Models for Policy Guidance
Chao Liu, Sevgi Erdoğan, Ting Ma, et al.
Journal of Urban Planning and Development (2016) Vol. 142, Iss. 4
Closed Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds
Chuan Ding, Xinyu Cao, Chao Liu
Journal of Transport Geography (2019) Vol. 77, pp. 70-78
Closed Access | Times Cited: 224
Examining the relationship between built environment and metro ridership at station-to-station level
Zuoxian Gan, Min Yang, Tao Feng, et al.
Transportation Research Part D Transport and Environment (2020) Vol. 82, pp. 102332-102332
Open Access | Times Cited: 185
Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China
Shaoying Li, Dijiang Lyu, Guanping Huang, et al.
Journal of Transport Geography (2020) Vol. 82, pp. 102631-102631
Closed Access | Times Cited: 155
Nonlinear, threshold and synergistic effects of first/last-mile facilities on metro ridership
Bozhezi Peng, Yi Zhang, Chaoyang Li, et al.
Transportation Research Part D Transport and Environment (2023) Vol. 121, pp. 103856-103856
Closed Access | Times Cited: 30
Nonlinear effects of built environment features on metro ridership: An integrated exploration with machine learning considering spatial heterogeneity
Mengyang Liu, Yuxuan Liu, Yu Ye
Sustainable Cities and Society (2023) Vol. 95, pp. 104613-104613
Closed Access | Times Cited: 28
Exploring the long-term threshold effects of density and diversity on metro ridership
Huanjie Zhu, Jiandong Peng, Qi Dai, et al.
Transportation Research Part D Transport and Environment (2024) Vol. 128, pp. 104101-104101
Closed Access | Times Cited: 9
The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system
Hiroyuki Iseki, Chao Liu, Gerrit Knaap
Transportation Research Part A Policy and Practice (2018) Vol. 116, pp. 635-649
Closed Access | Times Cited: 61
Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns
Jing Wang, Feng Wan, Chunjiao Dong, et al.
Journal of Transport Geography (2023) Vol. 109, pp. 103597-103597
Closed Access | Times Cited: 19
Exploring the Spatiotemporal Effects of the Built Environment on the Nonlinear Impacts of Metro Ridership: Evidence from Xi’an, China
Yafei Xi, Quanhua Hou, Yaqiong Duan, et al.
ISPRS International Journal of Geo-Information (2024) Vol. 13, Iss. 3, pp. 105-105
Open Access | Times Cited: 7
Exploring urban rail transit station-level ridership growth with network expansion
Shasha Liu, Enjian Yao, Binbin Li
Transportation Research Part D Transport and Environment (2018) Vol. 73, pp. 391-402
Closed Access | Times Cited: 47
Multi-city exploration of built environment and transit mode use: Comparison of Melbourne, Amsterdam and Boston
Laura Aston, Graham Currie, Md. Kamruzzaman, et al.
Journal of Transport Geography (2021) Vol. 95, pp. 103136-103136
Closed Access | Times Cited: 28
Changes in the determinants of travel demand for the Washington DC Metrorail system after COVID-19: Evidence from a replication study
Janna Chapman, Hiroyuki Iseki, Victor E. Irekponor, et al.
Case Studies on Transport Policy (2025), pp. 101394-101394
Closed Access
Investigating the Factors That Influence the Ridership of Light Rail Transit Systems Using Thematic Analysis of Academic Literature
Huseyin Ayan, MC Bell, Dilum Dissanayake
Future Transportation (2025) Vol. 5, Iss. 1, pp. 22-22
Open Access
An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership
Yuxin He, Yang Zhao, Kwok‐Leung Tsui
Transportation (2020) Vol. 48, Iss. 3, pp. 1185-1216
Closed Access | Times Cited: 30
Analyzing the Impacts of Land Use and Network Features on Passenger Flow Distribution at Urban Rail Stations from a Classification Perspective
Yuliang Guo, Zhenjun Zhu, Xiaohong Jiang, et al.
Sustainability (2024) Vol. 16, Iss. 9, pp. 3568-3568
Open Access | Times Cited: 3
Non-linear impact of the built environment on metro commuter flows before and after the COVID-19 outbreak: A case study in Guangzhou
Peng Zang, Hualong Qiu, Yun Yu, et al.
Applied Geography (2024) Vol. 168, pp. 103301-103301
Open Access | Times Cited: 3
Understanding the time-dependent effect of built environment attributes on station-level metro ridership uncertainty in Beijing: A big data analytic approach
Chuan Ding, Tiantian Liu, Baozhen Yao, et al.
Tunnelling and Underground Space Technology (2023) Vol. 137, pp. 105148-105148
Closed Access | Times Cited: 8
Ridership Prediction of Urban Rail Transit Stations Based on AFC and POI Data
Zhenjun Zhu, Yong Zhang, Shucheng Qiu, et al.
Journal of Transportation Engineering Part A Systems (2023) Vol. 149, Iss. 9
Closed Access | Times Cited: 8
Analysis of the relationship between metro ridership and built environment: A machine learning method considering combinational features
Linchao Li, Liangjian Zhong, Bin Ran, et al.
Tunnelling and Underground Space Technology (2023) Vol. 144, pp. 105564-105564
Closed Access | Times Cited: 8
Passenger Flow Scale Prediction of Urban Rail Transit Stations Based on Multilayer Perceptron (MLP)
Luzhou Lin, Yuezhe Gao, Bingxin Cao, et al.
Complexity (2023) Vol. 2023, pp. 1-19
Open Access | Times Cited: 7
Explaining and Predicting Station Demand Patterns Using Google Popular Times Data
Teethat Vongvanich, Wenzhe Sun, Jan‐Dirk Schmöcker
Data Science for Transportation (2023) Vol. 5, Iss. 2
Closed Access | Times Cited: 7
Geographically Modeling and Understanding Factors Influencing Transit Ridership: An Empirical Study of Shenzhen Metro
Yuxin He, Yang Zhao, Kwok‐Leung Tsui
Applied Sciences (2019) Vol. 9, Iss. 20, pp. 4217-4217
Open Access | Times Cited: 21
Neighbourhood effects on station-level transit use: Evidence from the Taipei metro
David Emanuel Andersson, Oliver Feng-Yeu Shyr, Jimmy C. Yang
Journal of Transport Geography (2021) Vol. 94, pp. 103127-103127
Closed Access | Times Cited: 16
Study design impacts on built environment and transit use research
Laura Aston, Graham Currie, Md. Kamruzzaman, et al.
Journal of Transport Geography (2019) Vol. 82, pp. 102625-102625
Open Access | Times Cited: 17
The influence of vicinity to stations, station characteristics and perceived safety on public transport mode choice: a case study from Copenhagen
Jesper Bláfoss Ingvardson, Otto Anker Nielsen
Public Transport (2021) Vol. 14, Iss. 2, pp. 459-480
Closed Access | Times Cited: 14
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