中文文獻
1.王君如(2007)。活動停留對於運具選擇影響之研究-以新竹科學園區為例。國立交通大學運輸科技與管理學系,新竹市。
2.王慶瑞(2001)。運輸系統規劃):正揚出版。
3.交通部運輸研究所(2022),2022 年臺灣公路容量手冊。
4.交通部統計處(2021),109年民眾日常使用運具狀況調查 摘要分析。
5.行政院主計總處(2021),109 年人口及住宅普查初步統計結果提要分析。
6.何婉菁(2018)。交通指派方法的驗證與比較分析-以台北市為例。國立臺灣大學土木工程學研究所,台北市。
7.余修安(2010)。大型事件起迄矩陣推估之研究-以澳門格蘭披治大賽車為例。國立交通大學運輸科技與管理學系,新竹市。
8.周榮昌、邱靜淑、劉祐興(2010)。考量徵收機車停車費用與大眾運具服務品質對都市機車通勤者運具選擇行為之影響。中國土木水利工程學刊, 22(2),頁 215-223。
9.邱裕鈞等人(2012)。公路公共運輸發展政策推動效益之評估與回饋-運具選擇行為變動之分析及決策支援系統建置 (2/2),交通部運輸研究所。
10.胡大瀛(2008)。即時動態交通分析與預測模型 (DynaTAIWAN) 之實證分析與推廣 (1/3)(第1009701346冊):中華民國政府出版品。
11.張顥鐘(2002)。以敘述性偏好法探討迄點屬性對城際旅運者運具選擇行為之影響。國立成功大學都市計劃學系碩博士班,台南市。12.許真怡(2013)。台中市日間人口數量推估之研究。逢甲大學都市計畫與空間資訊學系,台中市。
13.陳韻竹(2005)。號誌路口車輛行進軌跡之模化與延滯時間之比較。國立交通大學運輸科技與管理學系,新竹市。
14.國家發展委員會國土區域離島發展處(2021),都市及區域發展統計彙編。
15.運輸經濟(2017):理論與實務,許書耕著,初版,臺北市:交通部運研所。
16.黃台生、陳敦基、黃世孟、梁世武(2009)。高雄都會區家戶旅次訪問調查與旅次特性分析報告書。高雄市政府交通局專題研究成果報告。取自 http://www. tbkc. gov. tw/image/03-09/98report. pdf。
17.楊志文、林佳宏、詹仕偉(2013)。探討運具分群與旅客區隔之運具選擇模式。中國土木水利工程學刊, 25(4),頁 283-292。
18.葉宜昀(2016)。模化不同旅次長度之運具選擇行為。國立交通大學運輸與物流管理學系,新竹市。
19.新竹縣政府(2009),現況交通特性調查與服務水準分析。
20.嘉義市政府交通處(2019)。嘉義市整體運輸規劃運輸需求模型建置計畫。取自https://traffic.chiayi.gov.tw/News_Content.aspx?n=4133&s=425645
21.澎湖縣政府(2021),澎湖縣國土計畫。
22.賴文泰(2011)。不同大眾運輸供給地區旅運者之大眾運具使用行為分析。運輸計劃季刊, 40(3),頁 287-308。
英文文獻
1.Al-Salih, W. Q., & Esztergár-Kiss, D. (2021). Linking mode choice with travel behavior by using logit model based on utility function. Sustainability, 13(8), 4332.
2.Amavi, A. A., Romero, J. P., Dominguez, A., dell’Olio, L., & Ibeas, A. (2014). Advanced trip generation/attraction models. Procedia-Social and Behavioral Sciences, 160, 430-439.
3.Barbosa, H., Barthelemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., . . . Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports, 734, 1-74.
4.Barthélemy, M. (2011). Spatial networks. Physics Reports, 499(1-3), 1-101.
5.Bautista-Hernández, D. A. (2022). Individual, household, and urban form determinants of trip chaining of non-work travel in México City. Journal of Transport Geography, 98, 103227.
6.Ben-Akiva, M. E., Lerman, S. R., & Lerman, S. R. (1985). Discrete choice analysis: theory and application to travel demand (Vol. 9): MIT press.
7.Cascetta, E., Pagliara, F., & Papola, A. (2007). Alternative approaches to trip distribution modelling: a retrospective review and suggestions for combining different approaches. Papers in regional Science, 86(4), 597-620.
8.Celik, H. M. (2010). Sample size needed for calibrating trip distribution and behavior of the gravity model. Journal of Transport Geography, 18(1), 183-190.
9.Chalumuri, R. S., Nath, R., & Errampalli, M. (2018). Development and evaluation of an integrated transportation system: A case study of Delhi. Paper presented at the Proceedings of the Institution of Civil Engineers-Transport.
10.Chang, J. S., Jung, D., Kim, J., & Kang, T. (2014). Comparative analysis of trip generation models: results using home-based work trips in the Seoul metropolitan area. Transportation Letters, 6(2), 78-88.
11.Currans, K. M., & Clifton, K. J. (2018). Exploring ITE’s Trip Generation Manual: Assessing age of data and land-use taxonomy in vehicle trip generation for transportation impact analyses. Transportation Research Part A: Policy and Practice, 118(C), 387-398.
12.Daisy, N. S., Liu, L., & Millward, H. (2020). Trip chaining propensity and tour mode choice of out-of-home workers: evidence from a mid-sized Canadian city. Transportation, 47(2), 763-792.
13.Elmorssy, M., & Onur, T. (2020). Modelling departure time, destination and travel mode choices by using generalized nested logit model: Discretionary trips. International Journal of Engineering, 33(2), 186-197.
14.Espino, R., Román, C., & De Ortúzar, J. D. (2006). Analysing demand for suburban trips: a mixed RP/SP model with latent variables and interaction effects. Transportation, 33(3), 241-261.
15.Evans, S. P. (1976). Derivation and analysis of some models for combining trip distribution and assignment. Transportation research, 10(1), 37-57.
16.Fiorello, D., Nökel, K., & Martino, A. (2018). The TRIMODE integrated model for Europe. Transportation Research Procedia, 31, 88-98.
17.Heyken Soares, P., Ahmed, L., Mao, Y., & Mumford, C. L. (2021). Public transport network optimisation in PTV Visum using selection hyper-heuristics. Public Transport, 13(1), 163-196.
18.Ho, C. Q., & Mulley, C. (2013). Multiple purposes at single destination: A key to a better understanding of the relationship between tour complexity and mode choice. Transportation Research Part A: Policy and Practice, 49, 206-219.
19.Isard, W. (1956). Location and space-economy.
20.Jacyna, M., Wasiak, M., Lewczuk, K., & Kłodawski, M. (2014). Simulation model of transport system of Poland as a tool for developing sustainable transport. Archives of Transport, 31(3), 23--35.
21.Jović, J., & Depolo, V. (2011). The role of trip generation models in sustainable transportation planning in South-East Europe. Transport, 26(1), 88-95.
22.Lee, D., Derrible, S., & Pereira, F. C. (2018). Comparison of four types of artificial neural network and a multinomial logit model for travel mode choice modeling. Transportation research record, 2672(49), 101-112.
23.Lenormand, M., Bassolas, A., & Ramasco, J. J. (2016). Systematic comparison of trip distribution laws and models. Journal of Transport Geography, 51, 158-169.
24.Mannering, F., & Winston, C. (1985). A dynamic empirical analysis of household vehicle ownership and utilization. The RAND Journal of Economics, 215-236.
25.Masoumi, H. (2022). Home-Based urban commute and Non-Commute trip generation in Less-Studied Contexts: Evidence from Cairo, Istanbul, and Tehran. Case Studies on Transport Policy, 10(1), 130-144.
26.Masucci, A. P., Serras, J., Johansson, A., & Batty, M. (2013). Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows. Physical Review E, 88(2), 022812.
27.McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of applied Econometrics, 15(5), 447-470.
28.Modi, K. B., Zala, L., Umrigar, F., & Desai, T. (2011). Transportation planning models: a review. Paper presented at the National Conference on Recent Trends in Engineering and Technology, Gujarat India.
29.PTV, A. (2022). PTV Visum 2022 Manual. PTV AG: Karlsruhe, Germany.
30.Rafiq, R., & McNally, M. G. (2022). A structural analysis of the work tour behavior of transit commuters. Transportation Research Part A: Policy and Practice, 160, 61-79.
31.Ramli, M., Runtulalo, D., Yatmar, H., & Mangessi, A. (2020). An estimation of origin-destination matrices for a public transport network in Makassar using macrosimulation visum. Paper presented at the IOP Conference Series: Materials Science and Engineering.
32.Schläpfer, M., Dong, L., O’Keeffe, K., Santi, P., Szell, M., Salat, H., . . . West, G. B. (2021). The universal visitation law of human mobility. Nature, 593(7860), 522-527.
33.Shamshiripour, A., Golshani, N., Shabanpour, R., & Mohammadian, A. (2019). Week-long mode choice behavior: dynamic random effects logit model. Transportation research record, 2673(10), 736-744.
34.Tian, Y., Sun, J., Chiu, Y.-C., & Chai, C. (2020). Sunsetting skim matrices. Journal of Transport and Land Use, 13(1), 413-428.
35.Vrtic, M., Fröhlich, P., Schüssler, N., Axhausen, K. W., Lohse, D., Schiller, C., & Teichert, H. (2007). Two-dimensionally constrained disaggregate trip generation, distribution and mode choice model: Theory and application for a Swiss national model. Transportation Research Part A: Policy and Practice, 41(9), 857-873.
36.Wardrop, J. G. (1952). Road paper. some theoretical aspects of road traffic research. Proceedings of the institution of civil engineers, 1(3), 325-362.
37.Zhao, X., Yan, X., Yu, A., & Van Hentenryck, P. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel behaviour and society, 20, 22-35.
網路資料
1.澎湖縣政府全球資訊網https://www.penghu.gov.tw/ch/home.jsp?id=5
2.澎湖縣政府民政處
3.Metropolitan Washington Council of Governments 官網 https://www.mwcog.org/transportation/data-and-tools/household-travel-survey/