臺灣博碩士論文加值系統

本研究旨在探討受訪者工作日活動類型選擇與時間使用分析。根據過去文獻後，使用多重間斷連續極值模型作為估計方法，並在飽和參數有無異質下，建立活動時間的模型。模型估計探討社會經濟特性與活動特性對於活動類型選擇與時間使用之影響，透過飽和參數異質性，觀察活動類型的時間使用。本研究依據概似比指標、AIC與BIC績效指標作為模式評比指標，估計結果顯示具有飽和參數異質性的模式指定具有最佳的模式解釋能力。在對於活動類型選擇的影響變數方面，重要影響包括男性、年齡、個人月收入、家戶月收入、退休/家管、家庭人口數、接送需求、國小/國中子女數；而在活動時間使用的影響變數方面，女性、老年、職業狀況、接送需求等變數皆有顯著且正向影響。整體而言，在活動類型選擇與時間使用方面，女性會較男性花時間在購物時間，而60歲以上較其他年齡層花更多時間在購物活動，工作活動則是零售業、運輸業、餐飲業相較於其他職業則是花較多時間。在模型結果的意涵方面，女性與老年人會花較長時間在購物活動，故建議可針對這兩族群進行購物專區的規劃。

This research aims to investigate the analysis of activity type choice and time use on weekdays by the multiple discrete continuous extreme value model. Assuming the existence of satiation effects, this study estimated the model with the specification of socio-demographic and activity characteristics. This study compares those proposed models by criteria of likelihood ratio index, Akaike Information Criterion, and Bayes Information Criterion. The result shows that model with heterogeneous saturation effect owns a well explanatory power.The results indicate that the important variables affecting the choice of activity type include male, age, personal monthly income, household monthly income, retirement/ housewife , household population, pick-up demand, number of children in elementary/junior high school; and the important variables affecting time use include women, elderly, occupational status, pick-up demand. In the aspect of activity type choice and time use, women spend more time at shopping than men, and elderly spend more time shopping activities than other age groups. On the whole, the time use of work activity for occupational group of retail, transportation, and restaurant spend more time than other occupations. In terms of the model implication, women and elderly will spend more time on shopping activities, so it is recommended to plan for the shopping area for these two groups.

摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 研究範圍 4
1.4 研究流程 4
第二章 文獻回顧 6
2.1 活動時間使用分析 6
2.1.1 影響活動時間使用之因素 7
2.1.2 其他應用研究 10
2.2 多重間斷連續(MDC)模式 13
2.2.1 MDC活動類型選擇 14
2.2.2 MDC之應用 15
2.2.3 MDC應用於其他議題 18
第三章 研究方法 20
3.1 研究架構 20
3.2 MDC建模與估計 22
3.3 模式績效指標 26
第四章 研究樣本 28
4.1 樣本分布 28
4.2 活動分析 30
4.3 交叉分析 31
第五章 模式校估與分析 44
5.1 變數定義 44
5.2 常數基準模式 47
5.3 無異質飽和參數模式 49
5.4 有異質飽和參數模式 53
第六章 結論與建議 57
6.1 結論 57
6.2 建議 60
參考文獻 61
中文文獻 61
英文文獻 62

中文文獻
行政院主計總處（民106）。人力運用調查年報。（擷取自:https://www.dgbas.gov.tw/ct.asp?xItem=42117&ctNode=4987&mp=1,2018年12月）。
行政院主計總處（民107）。職業時間分配調查月報。（擷取自:https://www.dgbas.gov.tw/ct.asp?xItem=43604&ctNode=4987&mp=1,2019年3月）。
夏皓清（民102）。以活動觀點研究法探討平日、週末假日活動時間價值之研究（博士論文）。國立成功大學，臺南市。
陳佩棻（民85）。整合活動地點與運具選擇之運輸需求模式（碩士論文）。淡江大學，臺北市。
陳美惠（民93）。探討影響台灣地區有偶婦女時間分配之因素（碩士論文）。銘傳大學，臺北市。
曾譓升（民106）。國中生課餘時間使用分配之調查與分析（碩士論文）。國立虎尾科技大學，雲林縣。
楊昆城（民87）。探討台灣小家庭時間運用之決策（碩士論文）。淡江大學，臺北市。
劉均勵 (民107)。探討團體組成和連續支出之旅遊決策行為 (碩士論文)。國立台中科技大學，臺中市。
鍾易詩、陸冠宏與古雅涵（民107）。航空旅客機場活動類型與時間選擇行為分析。「中華民國運輸學會論文國際研討會」。

英文文獻
Akaike, H. (1974). A new look at the statistical model identification. In Selected Papers of Hirotugu Akaike (pp. 215-222). Springer, New York, NY.
Akaike, H., (1978), A Bayesian analysis of the minimum AIC procedure. Annals of the Institute of Statistical Mathematics, 30(1), 9-14
Astroza, S., Bhat, P. C., Bhat, C. R., Pendyala, R. M., & Garikapati, V. M. (2018). Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach. Journal of choice modelling, 28, 56-70.
Bernardo, C., Paleti, R., Hoklas, M., & Bhat, C. (2015). An empirical investigation into the time-use and activity patterns of dual-earner couples with and without young children. Transportation Research Part A: Policy and Practice, 76, 71-91.
Bhat, C. R. (1998). A model of post home-arrival activity participation behavior. Transportation Research Part B: Methodological, 32(6), 387-400.
Bhat, C. R. (2003). Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences. Transportation Research Part B: Methodological,37(9), 837-855.
Bhat, C. R. (2005). A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions. Transportation Research Part B: Methodological, 39(8), 679-707.
Bhat, C. R. (2008). The multiple discrete-continuous extreme value (MDCEV) model: role of utility function parameters, identification considerations, and model extensions. Transportation Research Part B: Methodological, 42(3), 274-303.
Bhat, C. R. (2018). A new flexible multiple discrete–continuous extreme value (MDCEV) choice model. Transportation Research Part B: Methodological, 110, 261-279.
Bhat, C. R., & Koppelman, F. S. (1999). Activity-based modeling of travel demand. In Handbook of transportation Science (pp. 35-61). Springer, Boston, MA.
Bhat, C. R., & Misra, R. (1999). Discretionary activity time allocation of individuals between in-home and out-of-home and between weekdays and weekends. Transportation, 26(2), 193-229.
Bhat, C. R., & Sen, S. (2006). Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model. Transportation Research Part B: Methodological, 40(1), 35-53.
Bhat, C. R., Astroza, S., Bhat, A. C., & Nagel, K. (2016). Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: Application to residential self-selection effects analysis in an activity time-use behavior model. Transportation Research Part B: Methodological, 91, 52-76.
Bhat, C. R., Sen, S., & Eluru, N. (2009). The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use. Transportation Research Part B: Methodological, 43(1), 1-18.
Calastri, C., Hess, S., Daly, A., & Carrasco, J. A. (2017). Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary data. Transportation Research Part A: Policy and Practice, 104, 1-20.
Calastri, C., Hess, S., Daly, A., Maness, M., Kowald, M., & Axhausen, K. (2017). Modelling contact mode and frequency of interactions with social network members using the multiple discrete–continuous extreme value model. Transportation Research Part C: Emerging Technologies, 76, 16-34.
Castro, M., Bhat, C. R., Pendyala, R. M., & Jara-Díaz, S. R. (2012). Accommodating multiple constraints in the multiple discrete–continuous extreme value (MDCEV) choice model. Transportation Research Part B: Methodological, 46(6), 729-743.
Garikapati, V. M., You, D., Pendyala, R. M., Vovsha, P. S., Livshits, V., & Jeon, K. (2014). Multiple discrete-continuous model of activity participation and time allocation for home-based work tours. Transportation Research Record, 2429(1), 90-98.
Golob, T. F., Bradley, M. A., & Polak, J. W. (1995). Travel and activity participation as influenced by car availability and use.
Gramm, W. L. (1975). Household utility maximization and the working wife. The American Economic Review, 65(1), 90-100.
Hurvich, C. M., & Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika, 76(2), 297-307.
Jara-Díaz, S., & Rosales-Salas, J. (2017). Beyond transport time: A review of time use modeling. Transportation Research Part A: Policy and Practice, 97, 209-230.
Jian, S., Rashidi, T. H., & Dixit, V. (2017). An analysis of carsharing vehicle choice and utilization patterns using multiple discrete-continuous extreme value (MDCEV) models. Transportation Research Part A: Policy and Practice, 103, 362-376.
Kato, H., & Matsumoto, M. (2009). Intra-household interaction in a nuclear family: A utility-maximizing approach. Transportation Research Part B: Methodological, 43(2), 191-203.
Kim, J., Allenby, G. M., & Rossi, P. E. (2002). Modeling consumer demand for variety. Marketing Science, 21(3), 229-250.
Kitamura, R., Yamamoto, T., Fujii, S., & Sampath, S. (1996). A discrete-continuous analysis of time allocation to two types of discretionary activities which accounts for unobserved heterogeneity. Transportation and traffic theory, 26, 431-453.
Kuppam, A. R., & Pendyala, R. M. (2001). A structural equations analysis of commuters'' activity and travel patterns. Transportation, 28(1), 33-54.
Lai, X., Lam, W. H., Su, J., & Fu, H. (2019). Modelling intra-household interactions in time-use and activity patterns of retired and dual-earner couples. Transportation Research Part A: Policy and Practice, 126, 172-194.
Liu, X., Usher, J. M., & Strawderman, L. (2014). An analysis of activity scheduling behavior of airport travelers. Computers & Industrial Engineering, 74, 208-218.
MARG (2018) R Codes for Travel Modeling [online]. Mobility Analytics Research Group. Available at:http://www.mobilityanalytics.org/r-codes-for-travel-modeling.html, Accessed [Date of Access].
McFadden, D. (1978). Modeling the choice of residential location. Transportation Research Record, (673).
Mensah, J. (1995). Journey to work and job search characteristics of the urban poor. Transportation, 22(1), 1-19.
Pas, E. I., & Koppelman, F. S. (1986). An examination of the determinants of day-to-day variability in individuals'' urban travel behavior. Transportation, 13(2), 183-200.
Pinjari, A. R. (2011). Generalized extreme value (GEV)-based error structures for multiple discrete-continuous choice models. Transportation Research Part B: Methodological, 45(3), 474-489.
Pinjari, A. R., & Bhat, C. (2010). A Multiple Discrete–Continuous Nested Extreme Value (MDCNEV) model: formulation and application to non-worker activity time-use and timing behavior on weekdays. Transportation Research Part B: Methodological, 44(4), 562-583.
Rajagopalan, B. S., & Srinivasan, K. K. (2008). Integrating household-level mode choice and modal expenditure decisions in a developing country: multiple discrete–continuous extreme value model. Transportation Research Record, 2076(1), 41-51.
Yamamoto, T., & Kitamura, R. (1999). An analysis of time allocation to in-home and out-of-home discretionary activities across working days and non-working days. Transportation, 26(2), 231-250.
Yu, B., Zhang, J., & Fujiwara, A. (2010). Analysis of Ownership and Usage of In-home Appliances and Vehicles based on the Multiple Discrete-Continuous Extreme Value (MDCEV) model.第41回土木學會土木計畫學研究發表會.