Enhancing User Circulation in the Design of a Prototype Metro- Line Station for Nigeria's Federal Capital Territory (FCT)

Adolphi et al. (2019); He, S. (2020). The impact of rail transport infrastructure on residential property rental values in Kubwa, Abuja. ResearchGate publication. Chang, Y., Zong, M., Dang, Y., & Wang, K. (2024). Multi-step passenger flow prediction for urban metro system based on spatial-temporal graph neural network. Applied Sciences, 14(18), 8121. Chatman, D., Song, Y., Levinson, D., et al. (2024). How do access and spatial dependency shape metro passenger flows? Transportation Research Part A. Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.). SAGE Publications. Du, Z., Yang, B., & Liu, J. (2017). Understanding the spatial and temporal activity patterns of subway mobility flows. ArXiv. K?rlang?ço?lu, C. (2015). Modeling passenger flows in public transport stations. International Journal of Human Sciences, 12(1), 1485–1501. Kittelson & Associates, Inc. (2013). Transit Capacity and Quality of Service Manual (3rd ed.). Transportation Research Board, National Academies Press. (Note: Although published in late 2013, it is still relevant for current transit studies.) Odeleye, J. A. (2012). Urban public transport in Nigeria: Principles, constraints and policy issues. Journal of Sustainable Development in Africa, 14(2), 184–201. (Included here for relevance to Nigerian context, though slightly older.) Oluwakoya, A. O., & Ogundipe, S. D. (2021). Spatiotemporal correlation between railway transport development and land use. Romanian Journal of Transport Infrastructure, 10(2). Tian, X., Zhang, C., & Zheng, B. (2020). Crowding prediction of in-situ metro passengers using smart card data. ArXiv. UN-Habitat. (2020). Planning Sustainable Cities: Global Report on Human Settlements. Earthscan. Wang, W., Wang, H., Liu, J., Liu, C., Wang, S., & Zhang, Y. (2024). Estimating rail transit passenger flow considering built environment factors: A case study in Shenzhen. Applied Sciences, 14(23), 10799. Wu, T., Li, M., & Zhou, Y. (2023). Exploring the spatial patterns of accessibility to metro services considering the locations of station entrances/exits. ISPRS International Journal of Geo- Information, 12, 18. Xie, P., Ma, M., Li, T., Ji, S., Du, S., Yu, Z., & Zhang, J. (2022). Spatio-temporal dynamic graph relation learning for urban metro flow prediction. ArXiv. Zhong, J., et al. (2018). Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data. Transportation Research Part D.