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研究生: 陳亭聿
Chen, Ting-Yu
論文名稱: 應用迴歸分析探究林口新市鎮捷運站周邊建成環境特性對捷運通勤流量的影響
The Effect of Station-level Built Environment on Metro Commuting Ridership in LinKou New Town by Using Multiple Regression Analysis
指導教授: 吳秉昇
Wu, Bing-Sheng
口試委員: 丁志堅
Ding, Tsu-Jen
陳哲銘
Chen, Che-Ming
吳秉昇
Wu, Bing-Sheng
口試日期: 2023/07/06
學位類別: 碩士
Master
系所名稱: 地理學系
Department of Geography
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 114
中文關鍵詞: 建成環境5D構面都會通勤桃園捷運地理資訊系統普通最小平方法
英文關鍵詞: 5Ds of Built Environment, Urban Commuting, Taoyuan Metro, Geographic Information System, Ordinary Least Squares
DOI URL: http://doi.org/10.6345/NTNU202300772
論文種類: 學術論文
相關次數: 點閱:133下載:56
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  • 多數國家主要以興建都市軌道運輸如捷運,並提出相關策略提升搭乘量作為緩解交通壅塞的方式。其中,策略之一為透過整合交通運輸與土地利用的規劃,進而引導或鼓勵民眾增加使用大眾運輸的機會。然而,針對林口新市鎮的交通壅塞問題,仍未有研究以土地利用相關的建成因素探討其與捷運流量的關係。因此,本研究試圖調查林口新市鎮內三個捷運站之服務區域的土地利用,藉由過往研究提出的建成環境4D架構,分別為土地利用之密度、多樣性、設計與至運輸場站的距離,以界定建成環境變數,探究其對早高峰捷運通勤流量的影響為何。
    本研究使用桃園捷運運量資料與公車電子票證資料獲取捷運進站流量與轉乘捷運流量,並使用開放資料取得捷運站與轉乘人流來源地之公車站的行人服務區域內相關的建成環境數據,透過多元迴歸分析建立桃捷三站整體與分站的OLS模型,藉此探討整體的建成環境之影響因素,以及各站的建成環境特性。
    研究結果表明,密度與多樣性構面無法顯著提升早高峰的捷運流量;設計構面之十字路口數和人行道面積則對早高峰流量有顯著正向影響;至運輸場站的距離之構面反映公車轉乘服務對桃捷A8站和桃捷A9站的重要性。有關各站建成環境特性的部分,由於桃捷A7站周邊土地正處開發階段,屬於具發展潛力的捷運站,可多關注街道的連通性與步行空間的規劃。直達車停靠與鄰近醫院的桃捷A8站,具有通勤和醫療旅次的流量特性,屬多功能複合型的捷運站,可著重設計構面進行改善評估。在桃捷A9站則是特別發現到,密度之住宅土地面積和通勤人口數與該站流量呈顯著正向關係,顯示桃捷A9站屬於較典型通勤功能的捷運站,可將住宅用地與街道連通性作為規劃考量重點。上述的研究成果,可提供未來林口新市鎮發展大眾運輸相關的建成環境開發作為參考,以鼓勵通勤者於早高峰時段搭乘捷運。

    The development of mass rapid transit (MRT) in urban areas has become an important public transit policy in many countries because it helps the increase of ridership and alleviates traffic congestion. Many research focus on the integration of transportation facilities and land-use types to examine how the transit-oriented development encourages commuters to use public transportation for daily commuting. However, none of the existing studies discusses if constructions of the built environment at MRT stations could attract more ridership of MRT and ease traffic congestion in Linkou New Town, Taipei. Therefore, this study attempts to investigate how land-use types interact with the built environment around metro stations in Linkou New Town from the 4D perspectives, which are density, diversity, design, and distance. A variety of variables are categorized to the four groups and statistical analysis, including Ordinary least squares (OLS) analysis, is conducted to further represent how certain key variables play an essential role in the changes of ridership at each metro station. To highlight impacts of built environment, this study specifically focuses on the transit of commuting during morning peak hours.
    The traffic data collecting the boardings and intermodal transit trips which transferred between metro and bus were summed up from the Taoyuan Metro ridership data and the smart card data by Taipei Metro. Moreover, bus ridership data is used to measure the built environment within MRT and pedestrian catchment area (PCA) of bus stations. The study developed multiple regression models to analyze the relationship between transit ridership and the built environment, and explore the characteristics of the built environment at A7, A8 and A9 stations of Taoyuan Metro.
    The analytical results that the relationship between ridership and the built environment reflects different patterns under the 4D perspectives. First of all, there is no significantly positive impact on morning peak-hour boarding ridership with factors under the density and diversity dimensions. Secondly, the intersections and the sidewalk area within a station’s PCA are positively associated with morning peak-hour boarding ridership from the aspect of design. Lastly, the relationship between transit ridership and distance to transit variable reflects significant and positive signals and delivers the message that multi-modal transit is important to the analysis of built environment and ridership. Regarding to the characteristics of the built environment in the respective transit station areas, the surrounding environment of A7 station is still developing, because an vacant area near the A7 station is planned and constructed soon. To attract more people using A7 station, street connectivity and larger pedestrian space should be seriously considered. A8 station is a transit stop for express trains to Downtown Taipei, and an important medical center, Linkou Chang Gung Memorial Hospital, is nearby. Therefore, commuting and medical trips are the two main transit purposes at A8 station. These characteristics reflect that A8 station serves as the multi-functional MRT station. As a result, the design of footpaths, such as width and length, should be the mendotary task for the improvement of A8 station. Residential areas and the number of commuters within the PCA of A9 station are positively related to morning peak-hour boarding ridership. It implies that A9 station is the station with commuting functions. Planners can prioritize factors such as residential land and street connectivity to make more residents feel comfortable taking public transport. In summary, the analysis of built environment and ridership of MRT in Linkou New Town not only quantifies the influence of various physical factors on the use of MRT stations, but also provides some novel suggestions for the future planning of the built environment at MRT stations. The ultimate goal of this study is to help the development of better transit facilities and encourage more people willing to take public transport.

    謝辭 i 摘要 ii Abstract iii 目錄 v 表目錄 vi 圖目錄 vii 第一章 緒論 1 第二章 文獻回顧 5 第一節 旅運行為 5 第二節 建成環境對旅運行為的影響 8 第三節 直接式運量模型 17 第三章 研究方法與設計 25 第一節 研究範圍 25 第二節 研究設計 31 第四章 研究分析與討論 47 第一節 樣本資料 47 第二節 實證分析 57 第三節 分析結果與討論 72 第四節 建成環境規劃之建議 84 第五章 結論 93 參考文獻 99

    內政部(2016)。變更林口特定區計畫(第四次通盤檢討)書。
    內政部(2019)。變更林口特定區(配合「改善庶民生活行動方案—機場捷運沿線站區周邊土地開發—A7站區開發案興辦事業計畫」)(第二階段)案書。
    內政部統計處(2022)。臺北市與新北市鄉鎮市區人口統計。取自社會經濟資料服務平台https://segis.moi.gov.tw/STAT/Web/Platform/QueryInterface/STAT_QueryInterface.aspx?Type=0
    白仁德、劉人華(2014)。大眾運輸導向建成環境特性對捷運運量影響之研究-以臺北捷運為實證對象。建築與規劃學報,15(2/3),111–128。
    交通部(2022)a。新北市區與桃園市區公車路線站牌資料。取自交通部數據匯流平臺https://ticp.motc.gov.tw/ConvergeProj/index
    交通部(2022)b。新北市與桃園市公車電子票證資料。取自交通部數據匯流平臺https://ticp.motc.gov.tw/ConvergeProj/index
    交通部高速公路局(2022)。111年1月至12月國道易壅塞路段彙整表。取自政府資料開放平臺。https://data.gov.tw/dataset/33191
    吳欣芳(2013)。乘客搭乘高雄捷運滿意度影響因素之分析。國立中山大學經濟學研究所碩士論文。
    李婉鈺(2016)。新市鎮發展成果與期待差距之觀察與理論分析—以林口新市鎮為例。中國文化大學建築及都市設計學系博士論文。
    邱皓政(2019)。量化研究與統計分析: SPSS 與 R 資料分析範例解析。五南圖書出版股份有限公司。
    林楨家、吳建彤、方若庭(2011)。建成環境對捷連轉乘運具選擇的影響:臺北捷連南港線之實證研究。運輸計劃季刊,40(4),335–366。
    林楨家、施亭伃(2007)。大眾運輸導向發展之建成環境對捷運運量之影響─臺北捷運系統之實證研究。運輸計畫季刊,36(4),451–476。
    林聖偉(2005)。需求反應運輸服務需求分析之研究-以醫療運輸為例。淡江大學運輸管理學系碩士論文。
    胡竣詠(2007)。林口特定區的地方發展。國立臺灣師範地理學系碩士論文。
    紀秉宏(2010)。高齡者醫療旅次運具選擇之研究。國立交通大學交通運輸研究所碩士論文。
    陳怡靜(2014)。影響捷運運量因素之探討-以高雄捷運為例。國立中山大學經濟學研究所碩士論文。
    郭昌儒(2015)。探勘交通統計大數據(Big Data)-高速公路易壅塞路段概況分析。主計月刊,710,78–85。
    桃園大眾捷運股份有限公司(2022)。桃園機場捷運A7體育大學站、A8長庚醫院站、A9林口站之每日分時進出站人次統計。
    楊徨仁(2020)。場站尺度建成環境探討台北捷運運量影響因素之研究。國立成功大學都市計劃學系碩士論文。
    新北市民政局(2022)。112年12月林口區人口統計資料。
    劉丁維(2009)。台北捷運系統服務品質、乘客滿意度與忠誠度關係之研究。國立台北大學都市計劃研究所碩士論文。
    劉永祥(2020)。桃園機場捷運通車前後對場站周邊土地使用發展及旅運行為影響之研究。國立政治大學地政學系碩士論文。
    賴進貴、葉高華、王韋力(2004)。土地利用變遷與空間相依性之探討以臺北盆地聚落變遷為例。臺灣地理資訊學刊,1,29–40。
    蘇振維、張瓊文、呂蕙美、張舜淵、林嘉宏、王世俠、黃琬雯、黃莉芬、林昱嫻、李雅文(2013)。國道1號五楊高架對林口交流道周邊車流影響分析。交通部運輸研究所。
    英文文獻
    Aditjandra, P. T., Mulley, C., and Nelson, J. D. (2013). The influence of neighbourhood design on travel behaviour: Empirical evidence from North East England. Transport Policy, 26, 54–65. https://doi.org/10.1016/j.tranpol.2012.05.011
    An, D., Tong, X., Liu, K., and Chan, E. H. W. (2019). Understanding the impact of built environment on metro ridership using open source in Shanghai. Cities, 93(May), 177–187. https://doi.org/10.1016/j.cities.2019.05.013
    An, R., Wu, Z., Tong, Z., Qin, S., Zhu, Y., and Liu, Y. (2022). How the built environment promotes public transportation in Wuhan: A multiscale geographically weighted regression analysis. Travel Behaviour and Society, 29, 186–199. https://doi.org/10.1016/j.tbs.2022.06.011
    Andersson, D. E., Shyr, O. F., and Yang, J. (2021). Neighbourhood effects on station-level transit use: Evidence from the Taipei metro. Journal of Transport Geography, 94, 103127. https://doi.org/10.1016/j.jtrangeo.2021.103127
    Aston, L., Currie, G., Delbosc, A., Kamruzzaman, M., and Teller, D. (2021). Exploring built environment impacts on transit use–an updated meta-analysis. Transport Reviews, 41(1), 73–96. https://doi.org/10.1080/01441647.2020.1806941
    Bagley, M. N., and Mokhtarian, P. L. (2002). The impact of residential neighborhood type on travel behavior: A structural equations modeling approach. Annals of Regional Science, 36(2), 279–297. https://doi.org/10.1007/s001680200083
    Bhat, C. R., and Guo, J. Y. (2007). A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transportation Research Part B: Methodological, 41, 506–526. https://doi.org/10.1016/j.trb.2005.12.005
    Boarnet, M. G. (2011). A broader context for land use and travel behavior, and a research agenda. Journal of the American Planning Association, 77(3), 197–213. https://doi.org/10.1080/01944363.2011.593483
    Brunsdon, C., Fotheringham, A. S., and Charlton, M. (1999). Some Notes on Parametric Significance Tests for Geographically Weighted Regression. Journal of Regional Science, 39(3), 497–524. https://doi.org/10.1111/0022-4146.00146
    Brunsdon, C., Fotheringham, A. S., and Charlton, M. E. (1996). Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.4135/9781412939591.n478
    Cao, X. (Jason), Mokhtarian, P. L., and Handy, S. L. (2009). The relationship between the built environment and nonwork travel: A case study of Northern California. Transportation Research Part A: Policy and Practice, 43(5), 548–559. https://doi.org/10.1016/j.tra.2009.02.001
    Cao, X., Mokhtarian, P. L., and Handy, S. L. (2009). Examining the impacts of residential self-selection on travel behaviour: A focus on empirical findings. In Transport Reviews (Vol. 29, Issue 3). https://doi.org/10.1080/01441640802539195
    Cardozo, O. D., García-Palomares, J. C., and Gutiérrez, J. (2012). Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography, 34, 548–558. https://doi.org/10.1016/j.apgeog.2012.01.005
    Caset, F., Blainey, S., Derudder, B., Boussauw, K., and Witlox, F. (2020). Integrating node-place and trip end models to explore drivers of rail ridership in Flanders, Belgium. Journal of Transport Geography, 87, 102796. https://doi.org/10.1016/j.jtrangeo.2020.102796
    Cervero, R. (2001). Walk-and-Ride: Factors Influencing Pedestrian Access to Transit. Journal of Public Transportation, 3(4), 1–23. https://doi.org/10.5038/2375-0901.3.4.1
    Cervero, R. (2006). Alternative Approaches to Modeling the Travel-Demand Impacts of Smart Growth. Journal of the American Planning Associarion, 72(3), 285–296.
    Cervero, R., and Day, J. (2008). Residential Relocation and Commuting Behavior in Shanghai, China: The Case for Transit Oriented Development. https://doi.org/10.11436/mssj.15.250
    Cervero, R., and Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219. https://doi.org/10.1016/S1361-9209(97)00009-6
    Cervero, R., Sarmiento, O. L., Jacoby, E., Gomez, L. F., and Neiman, A. (2009). Influences of built environments on walking and cycling: Lessons from Bogotá. International Journal of Sustainable Transportation, 3(4), 203–226. https://doi.org/10.1080/15568310802178314
    Chakour, V., and Eluru, N. (2016). Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal. Journal of Transport Geography, 51, 205–217. https://doi.org/10.1016/j.jtrangeo.2016.01.007
    Chan, E. T. H., Schwanen, T., and Banister, D. (2021). The role of perceived environment, neighbourhood characteristics, and attitudes in walking behaviour: evidence from a rapidly developing city in China. Transportation, 48, 431–454. https://doi.org/10.1007/s11116-019-10062-2
    Chan, S., and Miranda-Moreno, L. (2013). A station-level ridership model for the metro network in Montreal, Quebec. Canadian Journal of Civil Engineering, 40(3), 254–262. https://doi.org/10.1139/cjce-2011-0432
    Chen, E., Ye, Z., Wang, C., and Zhang, W. (2019). Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data. Cities, 95, 102359. https://doi.org/10.1016/j.cities.2019.05.028
    Chen, E., Ye, Z., and Wu, H. (2021). Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity. Transportation Research Part D: Transport and Environment, 90, 102677. https://doi.org/10.1016/j.trd.2020.102677
    Chen, L., Lu, Y., Liu, Y., Yang, L., Peng, M., and Liu, Y. (2022). Association between built environment characteristics and metro usage at station level with a big data approach. Travel Behaviour and Society, 28, 38–49. https://doi.org/10.1016/j.tbs.2022.02.007
    Chen, Q., Zhao, S., and Higuchi, G. (2018). Analysis on the Influence Factors of Passenger By Using Small Sample Size of Subway Stations. Journal of Architecture and Planning (Transactions of AIJ), 83(747), 907–916. https://doi.org/10.3130/aija.83.907
    Choi, J., Lee, Y. J., Kim, T., and Sohn, K. (2012). An analysis of Metro ridership at the station-to-station level in Seoul. Transportation, 39(3), 705–722. https://doi.org/10.1007/s11116-011-9368-3
    Choi, Y., Guhathakurta, S., Journal, S., Use, L., and Choi, Y. (2020). Linked references are available on JSTOR for this article : Do people walk more in transit-accessible places ? 13(1), 343–365.
    Chu, X. (2004). Ridership models at the stop level. University of South Florida. Center for Urban Transportation Research.
    Dieleman, F. M., Dijst, M., and Burghouwt, G. (2002). Urban form and travel behaviour: Micro-level household attributes and residential context. Urban Studies, 39(3), 507–527. https://doi.org/10.1080/00420980220112801
    Ding, C., Cao, X., and Liu, C. (2019). How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds. Journal of Transport Geography, 77, 70–78. https://doi.org/10.1016/j.jtrangeo.2019.04.011
    Du, M., Cheng, L., Li, X., and Yang, J. (2020). Factors affecting the travel mode choice of the urban elderly in healthcare activity: comparison between core area and suburban area. Sustainable Cities and Society, 52, 101868. https://doi.org/10.1016/j.scs.2019.101868
    Eldeeb, G., Mohamed, M., and Páez, A. (2021). Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice. Journal of Transport Geography, 96, 103158. https://doi.org/10.1016/j.jtrangeo.2021.103158
    Estupiñán, N., and Rodríguez, D. A. (2008). The relationship between urban form and station boardings for Bogotá’s BRT. Transportation Research Part A: Policy and Practice, 42(2), 296–306. https://doi.org/10.1016/j.tra.2007.10.006
    Ewing, R., and Cervero, R. (2010). Travel and the built environment:A Meta-Analysis. Journal of the American Planning Association, 76(3), 265–294. https://doi.org/10.1080/01944361003766766
    Ewing, R., and Cervero, R. (2017). “Does Compact Development Make People Drive Less?” The Answer Is Yes. Journal of the American Planning Association, 83(1), 19–25. https://doi.org/10.1080/01944363.2016.1245112
    Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. In Contemporary Sociology. Reading, MA: Addison-Wesley. https://doi.org/10.2307/2065853
    Fotheringham, A. S., Charlton, M. E., and Brunsdon, C. (1998). Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A, 30(11), 1905–1927. https://doi.org/10.1068/a301905
    Fujii, S., and Gärling, T. (2003). Applications of attitude theory for improved predictive accuracy of stated preference methods in the travel demand analysis. Transportation Research Part A: Policy and Practice, 37(4), 389–402. https://doi.org/10.1016/S0965-8564(02)00032-0
    Gan, Z., Feng, T., Yang, M., Timmermans, H., and Luo, J. (2019). Analysis of Metro Station Ridership Considering Spatial Heterogeneity. Chinese Geographical Science, 29(6), 1065–1077. https://doi.org/10.1007/s11769-019-1065-8
    Gan, Z., Yang, M., Feng, T., and Timmermans, H. J. P. (2020). Examining the relationship between built environment and metro ridership at station-to-station level. Transportation Research Part D: Transport and Environment, 82(April), 102332. https://doi.org/10.1016/j.trd.2020.102332
    Gan, Z., Yang, M., Zeng, Q., and Timmermans, H. J. P. (2021). Associations between built environment, perceived walkability/bikeability and metro transfer patterns. Transportation Research Part A: Policy and Practice, 153, 171–187. https://doi.org/10.1016/j.tra.2021.09.007
    Gehlert, T., Dziekan, K., and Gärling, T. (2013). Psychology of sustainable travel behavior. Transportation Research Part A: Policy and Practice, 48, 19–24. https://doi.org/10.1016/j.tra.2012.10.001
    Goulias, K. G., Davis, A. W., and McBride, E. C. (2020). Introduction and the genome of travel behavior. In Mapping the Travel Behavior Genome. Elsevier Inc. https://doi.org/10.1016/B978-0-12-817340-4.00001-2
    Guerra, E., Caudillo, C., Monkkonen, P., and Montejano, J. (2018). Urban form, transit supply, and travel behavior in Latin America: Evidence from Mexico’s 100 largest urban areas. Transport Policy, 69, 98–105. https://doi.org/10.1016/j.tranpol.2018.06.001
    Guo, R., and Huang, Z. (2020). Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan, China. Journal of Advanced Transportation, 2020. https://doi.org/10.1155/2020/7538508
    Guo, Y., and He, S. Y. (2021). Perceived built environment and dockless bikeshare as a feeder mode of metro. Transportation Research Part D: Transport and Environment, 92, 102693. https://doi.org/10.1016/j.trd.2020.102693
    Gutiérrez, J., Cardozo, O. D., and García-Palomares, J. C. (2011). Transit ridership forecasting at station level: An approach based on distance-decay weighted regression. Journal of Transport Geography, 19, 1081–1092. https://doi.org/10.1016/j.jtrangeo.2011.05.004
    Handy, S. (1993). Regional versus local accessibility. Transportation Research Record, 1400(234), 58–66.
    Handy, Susan, Cao, X., and Mokhtarian, P. (2005). Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transportation Research Part D: Transport and Environment, 10, 427–444. https://doi.org/10.1016/j.trd.2005.05.002
    Harbering, M., and Schlüter, J. (2020). Determinants of transport mode choice in metropolitan areas the case of the metropolitan area of the Valley of Mexico. Journal of Transport Geography, 87, 102766. https://doi.org/10.1016/j.jtrangeo.2020.102766
    He, Y., Zhao, Y., and Tsui, K. L. (2019). Geographically modeling and understanding factors influencing Transit Ridership: An empirical study of Shenzhen Metro. Applied Sciences (Switzerland), 9(20), 4217. https://doi.org/10.3390/app9204217
    Hong, J., Shen, Q., and Zhang, L. (2014). How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales. Transportation, 41(3), 419–440. https://doi.org/10.1007/s11116-013-9462-9
    Hsiao, S., Lu, J., Sterling, J., and Weatherford, M. (1997). Use of Geographic Information System for Analysis of Transit Pedestrian Access. Transportation Research Record, 1604(1), 50–59. https://doi.org/10.3141/1604-07
    Huang, J., Chen, S., Xu, Q., Chen, Y., and Hu, J. (2022). Relationship between built environment characteristics of TOD and subway ridership: A causal inference and regression analysis of the Beijing subway. Journal of Rail Transport Planning and Management, 24. https://doi.org/10.1016/j.jrtpm.2022.100341
    Ibraeva, A., VanWee, B., Correia, G. H. de A., and Pais Antunes, A. (2021). Longitudinal macro-analysis of car-use changes resulting from a TOD-type project: The case of Metro do Porto (Portugal). Journal of Transport Geography, 92, 103036. https://doi.org/10.1016/j.jtrangeo.2021.103036
    Jiang, Y., Christopher Zegras, P., and Mehndiratta, S. (2012a). Walk the line: Station context, corridor type and bus rapid transit walk access in Jinan, China. Journal of Transport Geography, 20, 1–14. https://doi.org/10.1016/j.jtrangeo.2011.09.007
    Jiang, Y., Christopher Zegras, P., and Mehndiratta, S. (2012b). Walk the line: Station context, corridor type and bus rapid transit walk access in Jinan, China. Journal of Transport Geography, 20(1), 1–14. https://doi.org/10.1016/j.jtrangeo.2011.09.007
    Jiang, Y., Gu, P., Chen, Y., He, D., and Mao, Q. (2017). Influence of land use and street characteristics on car ownership and use: Evidence from Jinan, China. Transportation Research Part D: Transport and Environment, 52, 518–534. https://doi.org/10.1016/j.trd.2016.08.030
    Jones, W. B., Cassady, C. R., and Bowden Jr, R. O. (2000). Developing a standard definition of intermodal transportation. Transp. LJ, 27, 345.
    Jun, M. J., Choi, K., Jeong, J. E., Kwon, K. H., and Kim, H. J. (2015). Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul. Journal of Transport Geography, 48, 30–40. https://doi.org/10.1016/j.jtrangeo.2015.08.002
    Kitamura, R., Mokhtarian, P. L., and Laidet, L. (1997). A micro-analysis of land use and travel in five neighborhoods in the San Impacts of travel-based multitasking on mode choice and value of time View project Travel satisfaction View project. Transportation, 24, 125–158. https://www.researchgate.net/publication/263523300
    Kuby, M., Barranda, A., and Upchurch, C. (2004). Factors influencing light-rail station boardings in the United States. Transportation Research Part A: Policy and Practice, 38(3), 223–247. https://doi.org/10.1016/j.tra.2003.10.006
    Li, L., Gao, T., Yu, L., and Zhang, Y. (2021). Applying an integrated approach to metro station satisfaction evaluation: A case study in Shanghai, China. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2021.10.004
    Li, M., Zou, M., and Li, H. (2019). Urban travel behavior study based on data fusion model. In Y.Wang andZ.Zeng (Eds.), Data-Driven Solutions to Transportation Problems. Elsevier Inc. https://doi.org/10.1016/B978-0-12-817026-7.00005-9
    Li, S., Lyu, D., Huang, G., Zhang, X., Gao, F., Chen, Y., and Liu, X. (2020). Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China. Journal of Transport Geography, 82, 102631. https://doi.org/10.1016/j.jtrangeo.2019.102631
    Li, S., Lyu, D., Liu, X., Tan, Z., Gao, F., Huang, G., and Wu, Z. (2020). The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou. Cities, 99, 102580. https://doi.org/10.1016/j.cities.2019.102580
    Li, X. Y., Sinniah, G. K., and Li, R. (2022). Identify impacting factor for urban rail ridership from built environment spatial heterogeneity. Case Studies on Transport Policy, 10, 1159–1171. https://doi.org/10.1016/j.cstp.2022.04.003
    Limtanakool, N., Dijst, M., and Schwanen, T. (2006). The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium- and longer-distance trips. Journal of Transport Geography, 14(5), 327–341. https://doi.org/10.1016/j.jtrangeo.2005.06.004
    Lin, T., Wang, D., and Guan, X. (2017). The built environment, travel attitude, and travel behavior: Residential self-selection or residential determination? Journal of Transport Geography, 65, 111–122. https://doi.org/10.1016/j.jtrangeo.2017.10.004
    Liu, C., Ma, T., Erdogan, S., and Ducca, F. W. (2016). How to Increase Rail Ridership in Maryland: Direct Ridership Models for Policy Guidance. Journal of Urban Planning and Development, 142. https://doi.org/10.1061/(asce)up.1943-5444.0000340
    Liu, Y., Yang, D., Timmermans, H. J. P., and deVries, B. (2020). Analysis of the impact of street-scale built environment design near metro stations on pedestrian and cyclist road segment choice: A stated choice experiment. Journal of Transport Geography, 82, 102570. https://doi.org/10.1016/j.jtrangeo.2019.102570
    Loo, B. P. Y., Chen, C., and Chan, E. T. H. (2010). Rail-based transit-oriented development: Lessons from New York City and Hong Kong. Landscape and Urban Planning, 97(3), 202–212. https://doi.org/10.1016/j.landurbplan.2010.06.002
    Ma, J., Liu, Z., and Chai, Y. (2015). The impact of urban form on CO2 emission from work and non-work trips: The case of Beijing, China. Habitat International, 47, 1–10. https://doi.org/10.1016/j.habitatint.2014.12.007
    Ma, L., Dill, J., and Mohr, C. (2014). The objective versus the perceived environment: what matters for bicycling? Transportation, 41(6), 1135–1152. https://doi.org/10.1007/s11116-014-9520-y
    Ma, X., Zhang, J., Ding, C., and Wang, Y. (2018). A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Computers, Environment and Urban Systems, 70, 113–124. https://doi.org/10.1016/j.compenvurbsys.2018.03.001
    Maier, G. (2015). Forecasting Ridership Impacts of Transit Oriented Development At Marta Rail Stations. Georgia Institute of Technology.
    McNally, M. G. (2007). The Four-Step Model. In D. A.Hensher andK. J.Button (Eds.), Handbook of Transport Modelling (Vol. 1, pp. 35-53.). Emerald Group Publishing Limited.
    Michael, J., and Gavilanes, R. (2020). Low sample size and regression: A Monte Carlo approach Low sample size and regression: A Monte Carlo approach. Journal of Applied Economic Sciences, 67, 22–44. https://mpra.ub.uni-muenchen.de/97017/
    Næss, P. (2014). Tempest in a teapot: The exaggerated problem of transport-related residential self-selection as a source of error in empirical studies. Journal of Transport and Land Use, 7(3), 57–79. https://doi.org/10.5198/jtlu.v7i3.491
    Nielsen, T. A. S., and Skov-Petersen, H. (2018). Bikeability – Urban structures supporting cycling. Effects of local, urban and regional scale urban form factors on cycling from home and workplace locations in Denmark. Journal of Transport Geography, 69, 36–44. https://doi.org/10.1016/j.jtrangeo.2018.04.015
    Páez, A. (2006). Exploring contextual variations in land use and transport analysis using a probit model with geographical weights. Journal of Transport Geography, 14(3), 167–176. https://doi.org/10.1016/j.jtrangeo.2005.11.002
    Páez, A., Uchida, T., and Miyamoto, K. (2002). A general framework for estimation and inference of geographically weighted regression models: 2. Spatial association and model specification tests. Environment and Planning A, 34(5), 883–904. https://doi.org/10.1068/a34133
    Pan, H., Li, J., Shen, Q., and Shi, C. (2017). What determines rail transit passenger volume? Implications for transit oriented development planning. Transportation Research Part D: Transport and Environment, 57, 52–63. https://doi.org/10.1016/j.trd.2017.09.016
    Park, K., Ewing, R., Scheer, B. C., and Tian, G. (2018). The impacts of built environment characteristics of rail station areas on household travel behavior. Cities, 74, 277–283. https://doi.org/10.1016/j.cities.2017.12.015
    Rahman, M., and Sciara, G.-C. (2022). Travel attitudes, the built environment and travel behavior relationships: Causal insights from social psychology theories. Transport Policy. https://doi.org/10.1016/j.tranpol.2022.04.012
    Ramos-Santiago, L. E., and Brown, J. (2016). A comparative assessment of the factors associated with station-level streetcar versus light rail transit ridership in the United States. Urban Studies, 53(5), 915–935. https://doi.org/10.1177/0042098015571057
    Rietveld, P. (2000). Non-motorised modes in transport systems: A multimodal chain perspective for The Netherlands. Transportation Research Part D: Transport and Environment, 5(1), 31–36. https://doi.org/10.1016/S1361-9209(99)00022-X
    Ryan, S., and Frank, L. (2009). Pedestrian Environments and Transit Ridership. Journal of Public Transportation, 12(1), 39–57. https://doi.org/10.5038/2375-0901.12.1.3
    Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x
    Shao, Q., Zhang, W., Cao, X., Yang, J., and Yin, J. (2020). Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning. Journal of Transport Geography, 89, 102878. https://doi.org/10.1016/j.jtrangeo.2020.102878
    Singh, Y. J., Lukman, A., Flacke, J., Zuidgeest, M., and VanMaarseveen, M. F. A. M. (2017). Measuring TOD around transit nodes - Towards TOD policy. Transport Policy, 56, 96–111. https://doi.org/10.1016/j.tranpol.2017.03.013
    Singhal, A., Kamga, C., and Yazici, A. (2014). Impact of weather on urban transit ridership. Transportation Research Part A: Policy and Practice, 69, 379–391. https://doi.org/10.1016/j.tra.2014.09.008
    Singleton, P. A. (2013). A Theory of Travel Decision-Making with Applications for Modeling Active Travel Demand. Portland State University.
    Sohn, K., and Shim, H. (2010). Factors generating boardings at Metro stations in the Seoul metropolitan area. Cities, 27(5), 358–368. https://doi.org/10.1016/j.cities.2010.05.001
    Spears, S., Houston, D., and Boarnet, M. G. (2013). Illuminating the unseen in transit use: A framework for examining the effect of attitudes and perceptions on travel behavior. Transportation Research Part A: Policy and Practice, 58, 40–53. https://doi.org/10.1016/j.tra.2013.10.011
    Speed, R. (1994). Regression type techniques and small samples: A guide to good practice. Journal of Marketing Management, 10(1–3), 89–104. https://doi.org/10.1080/0267257X.1994.9964262
    Stead, D., and Marshall, S. (2001). The Relationships between Urban Form and Travel Patterns . An International Review and Evaluation. European Journal of Transport and Infrastructure Research, 1(2).
    Su, S., Zhao, C., Zhou, H., Li, B., and Kang, M. (2022). 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, 100, 103308. https://doi.org/10.1016/j.jtrangeo.2022.103308
    Sun, B., Ermagun, A., and Dan, B. (2017). Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai. Transportation Research Part D: Transport and Environment, 52, 441–453. https://doi.org/10.1016/j.trd.2016.06.001
    Sung, H., Choi, K., Lee, S., and Cheon, S. H. (2014). Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership. Journal of Transport Geography, 36, 134–140. https://doi.org/10.1016/j.jtrangeo.2014.03.013
    Sung, H., and Oh, J. T. (2011). Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea. Cities, 28(1), 70–82. https://doi.org/10.1016/j.cities.2010.09.004
    Taylor, B. D., Miller, D., Iseki, H., and Fink, C. (2009). Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas. Transportation Research Part A: Policy and Practice, 43(1), 60–77. https://doi.org/10.1016/j.tra.2008.06.007
    Tu, W., Cao, R., Yue, Y., Zhou, B., Li, Q., and Li, Q. (2018). Spatial variations in urban public ridership derived from GPS trajectories and smart card data. Journal of Transport Geography, 69, 45–57. https://doi.org/10.1016/j.jtrangeo.2018.04.013
    vanAcker, V., vanWee, B., and Witlox, F. (2010). When transport geography meets social psychology: Toward a conceptual model of travel behaviour. Transport Reviews, 30(2), 219–240. https://doi.org/10.1080/01441640902943453
    VanDyck, D., Cerin, E., Conway, T. L., DeBourdeaudhuij, I., Owen, N., Kerr, J., Cardon, G., Frank, L. D., Saelens, B. E., and Sallis, J. F. (2012). Perceived neighborhood environmental attributes associated with adults’ transport-related walking and cycling: Findings from the USA, Australia and Belgium. International Journal of Behavioral Nutrition and Physical Activity, 9, 1–14. https://doi.org/10.1186/1479-5868-9-70
    VanWee, B. (2002). Land use and transport: Research and policy challenges. Journal of Transport Geography, 10(4), 259–271. https://doi.org/10.1016/S0966-6923(02)00041-8
    Vergel-Tovar, C. E., and Rodriguez, D. A. (2018). The ridership performance of the built environment for BRT systems: Evidence from Latin America. Journal of Transport Geography, 73, 172–184. https://doi.org/10.1016/j.jtrangeo.2018.06.018
    Walters, G., and Cervero, R. (2003). Forecasting transit demand in a fast growing corridor: The direct-ridership model approach. Fehrs and Peers Associates.
    Wang, D., and Zhou, M. (2017). The built environment and travel behavior in urban China: A literature review. Transportation Research Part D: Transport and Environment, 52, 574–585. https://doi.org/10.1016/j.trd.2016.10.031
    Wang, K., and Woo, M. (2017). The relationship between transit rich neighborhoods and transit ridership: Evidence from the decentralization of poverty. Applied Geography, 86, 183–196. https://doi.org/10.1016/j.apgeog.2017.07.004
    Wang, X., Shao, C., Yin, C., and Dong, C. (2021). Exploring the effects of the built environment on commuting mode choice in neighborhoods near public transit stations: evidence from China. Transportation Planning and Technology, 44(1), 111–127. https://doi.org/10.1080/03081060.2020.1851453
    Wolf, L. J., Oshan, T. M., and Fotheringham, A. S. (2018). Single and Multiscale Models of Process Spatial Heterogeneity. Geographical Analysis, 50(3), 223–246. https://doi.org/10.1111/gean.12147
    Yang, H., Li, C., Li, X., Huo, J., Wen, Y., Sexton, E. G. P., and Liu, Y. (2021). Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit. Journal of Advanced Transportation, 2021. https://doi.org/10.1155/2021/7345807
    Yang, H., Lu, X., Cherry, C., Liu, X., and Li, Y. (2017). Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression. Journal of Transport Geography, 64, 184–194. https://doi.org/10.1016/j.jtrangeo.2017.09.007
    Yang, L., Ding, C., Ju, Y., and Yu, B. (2021). Driving as a commuting travel mode choice of car owners in urban China: Roles of the built environment. Cities, 112, 103114. https://doi.org/10.1016/j.cities.2021.103114
    Zhang, M. (2004). The Role of Land Use in Travel Mode Choice:Evidence from Boston and Hong Kong. Journal of the American Planning Association, 70(3), 344–360.
    Zhao, J., Deng, W., Song, Y., and Zhu, Y. (2013). What influences Metro station ridership in China? Insights from Nanjing. Cities, 35, 114–124. https://doi.org/10.1016/j.cities.2013.07.002
    Zhu, Y., Chen, F., Wang, Z., and Deng, J. (2019). Spatio-temporal analysis of rail station ridership determinants in the built environment. Transportation, 46, 2269–2289. https://doi.org/10.1007/s11116-018-9928-x

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