臺灣博碩士論文加值系統

當今大城市的城市軌道交通系統尖峰時刻列車的擁擠問題日益突出，北京地鐵在尖峰時刻已經出現運能嚴重不足現象，該現象已成為影響軌道交通運營安全和體驗的重要因素，因此本研究探討通過座位配置和尖離峰差別定價來緩解尖峰時刻地鐵客流過度集中。本研究除了考慮到傳統研究的尖離峰差別定價和擁擠定價，還加入了過去研究較少涉及到的尖峰列車座位配置和潛在變數對乘客行為選擇的影響。
本研究首先通過敘述性偏好法設計模擬情境來供地鐵乘客做出選擇，並且收集乘客的心理因素、出行習慣以及基本資料。再利用這些收集的資料放入個體選擇模式中進行分析。本研究首先利用多項羅吉特模式分析乘客的出行習慣和社經變數對於選擇行為的影響，但是該方法無法考慮到乘客心理因素。所以本研究再利用考慮到潛在變數的Hybrid選擇模式，加入就坐意圖、擁擠反應、自我意識、價格敏感以及時間靈活這五個潛在變數面向進行混合羅吉特分析。之後利用上述結果進行了彈性分析、敏感度分析以及願付價格分析，最後再通過政策分析研究票價、無座列車的政策效果以及對收益的影響。
研究結果顯示，地鐵通勤乘客會受到擁擠程度、票價以及時間轉移的影響，而不會受到離峰班距和有座列車班距的影響；通過Hybrid選擇模式可以發現，乘客的心理變數會影響到方案的選擇，並可以提升模型的適配度；通過彈性敏感度分析，可以發現差別定價可以將一部分尖峰乘客轉移到離峰；最後通過政策分析，可以發現尖離峰差別定價可以減少18%~34%的尖峰客流並增加地鐵公司的收益，而尖峰無座列車也具有較高的可行性。本研究的研究貢獻在於將座位配置和潛在變數納入考量，並且研究結果可供地鐵運營單位和相關部門進行參考。

The congestion of the urban rail transit system in peak hour is becoming more serious in big city, such as Beijing. And this phenomenon has become an important factor affecting the safety and experience of rail transit operation. This study not only uses price discrimination and crowding price, but also use seat configuration and potential variables which used seldom before to analysis passengers’ choice and to relieve the congestion in peak hour. We use stated preference method to collect data, and then use the discrete choice model and hybrid discrete choice model to analyze the passenger choice. The results show that the congestion, fares, and time change affect the subway commuter passengers, and not affected by departure density in off-peak hour and peak hour’s normal train. We also find that the passenger's psychological variables can affect the passenger’s choice and improve the model fitness. At last, the result shows that price discrimination can relieve the congestion in peak hour and rise the profit, and no seat train have high feasibility. The research contribution is to consider the seat configuration and potential variables, and the results can be used for subway operation department.

表目錄 iv
圖目錄 v
第一章 緒論 1
1.1 研究背景及動機 1
1.2 研究目的 7
1.3 研究範圍及對象 7
1.4 研究流程 8
第二章 文獻回顧 9
2.1 北京地鐵票價概況 9
2.1.1 北京地鐵車票種類 9
2.1.2 北京地鐵票價調整歷史 9
2.1.3 北京地鐵現行票價 10
2.2 軌道交通座位配置 11
2.3 軌道交通擁擠定價 13
2.4 運輸服務差別定價 18
2.4.1 運輸服務差別定價定義 18
2.4.2 運輸服務差別定價文獻 18
2.5 乘客心理因素相關研究 20
2.6 Hybrid選擇模式相關研究 21
2.7 小結 22
第三章 研究方法 23
3.1 敘述性偏好 24
3.1.1 敘述性偏好法基本介紹 24
3.1.2 敘述性偏好的優缺點 25
3.1.3 敘述性偏好之實驗設計與衡量方式 26
3.2 旅客行為個體選擇模式 27
3.2.1 多項羅吉特模式 28
3.2.2 混合羅吉特模式 31
3.2.3 Hybrid選擇模式 32
3.3 研究架構 35
第四章 研究模型與問卷設計 36
4.1 研究模型設計 36
4.2 方案和屬性水準值設計 37
4.2.1 制定屬性變數和水準值 37
4.2.2 直交設計 39
4.3 潛在變數設計 41
4.4 問卷設計和抽樣方法 43
4.4.1 問卷設計 43
4.4.2 抽樣方法 44
第五章 實證分析 45
5.1 敘述性統計分析 45
5.2 多項羅吉特分析 51
5.2.1 早間上班時段多項羅吉特分析 51
5.2.2 晚間下班時段多項羅吉特分析 56
5.3 Hybrid選擇模式分析 61
5.3.1 潛在變數分析 61
5.3.2 早間上班時段Hybrid混合羅吉特分析 68
5.3.3 晚間下班時段Hybrid混合羅吉特分析 71
5.4 彈性和敏感度分析 74
5.4.1 彈性分析 74
5.4.2 敏感度分析 77
5.5 願付價格分析 78
5.6 政策分析 79
5.6.1 票價政策分析 79
5.6.2 無座列車政策分析 81
5.6.3 擁擠改善和收益分析 82
5.6.4 小結 82
第六章 結論與建議 84
6.1 結論 84
6.2 研究貢獻 87
6.3 研究限制與建議 88
參考文獻 90
附錄：問卷設計 95

[1].Ben-Akiva, M., McFadden, D., Abe, M., Böckenholt, U., Bolduc, D., Gopinath, D., ... ＆ Steinberg, D. (1997). Modeling methods for discrete choice analysis. Marketing Letters, 8(3), 273-286.
[2].Ben-Akiva, M., McFadden, D., Train, K., Walker, J., Bhat, C., Bierlaire, M., ... ＆ Daly, A. (2002). Hybrid choice models: progress and challenges. Marketing Letters, 13(3), 163-175.
[3].Bianchi, R., Jara-Dı́az, S. R., ＆ Ortúzar, J. D. D. (1998). Modelling new pricing strategies for the Santiago Metro. Transport Policy, 5(4), 223-232.
[4].Burris, M. W., ＆ Pendyala, R. M. (2002). Discrete choice models of traveler participation in differential time of day pricing programs. Transport Policy, 9(3), 241-251.
[5].Cervero, R. (1985). Experiences with time-of-day transit pricing in the United States (No. 1039).
[6].Cheng, Y. H. (2010). High-speed rail in Taiwan: New experience and issues for future development. Transport policy, 17(2), 51-63.
[7].Creemers, L., Cools, M., Tormans, H., Lateur, P. J., Janssens, D., ＆ Wets, G. (2012). Identifying the determinants of light rail mode choice for medium-and long-distance trips: results from a stated preference study. Transportation Research Record: Journal of the Transportation Research Board, (2275), 30-38.
[8].Daly, A., Hess, S., Patruni, B., Potoglou, D., ＆ Rohr, C. (2012). Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour. Transportation, 39(2), 267-297.
[9].Dion, D. (2004). Personal control and coping with retail crowding. International Journal of Service Industry Management, 15(3), 250-263.
[10].Douglas, N., ＆ Karpouzis, G. (2006, October). Estimating the passenger cost of train overcrowding. In 29th Australian Transport Research Forum.
[11].Fielding, G. J. (1995). Congestion pricing and the future of transit. Journal of Transport Geography, 3(4), 239-246.
[12].Grove, S. J., ＆ Fisk, R. P. (1997). The impact of other customers on service experiences: a critical incident examination of “getting along. Journal of retailing, 73(1), 63-85.
[13].Haywood, Luke, and Martin Koning. The distribution of crowding costs in public transport: New evidence from Paris. Transportation Research Part A: Policy and Practice 77 (2015): 182-201.
[14].Henn, L., Douglas, N., ＆ Sloan, K. (2011). Surveying Sydney rail commuters’ willingness to change travel time.
[15].Hensher, D. A., Greene, W. H., ＆ Li, Z. (2011). Embedding risk attitude and decision weights in non-linear logit to accommodate time variability in the value of expected travel time savings. Transportation research part B: methodological, 45(7), 954-972.
[16].Holguín-Veras, J., Xu, N., Wang, Q., Ozbay, K., Zorrilla, J., ＆ Cetin, M. (2007). New Jersey Turnpike Time-of-Day Pricing Initiative's Behavioral Impacts: Observed Role of Travel Distance on Underlying Elasticities. Transportation Research Record: Journal of the Transportation Research Board.
[17].Hui, M. K., ＆ Tse, D. K. (1996). What to tell consumers in waits of different lengths: An integrative model of service evaluation. The Journal of Marketing, 81-90.
[18].Johansson, M. V., Heldt, T., ＆ Johansson, P. (2006). The effects of attitudes and personality traits on mode choice. Transportation Research Part A: Policy and Practice, 40(6), 507-525.
[19].Jung, S. Y., ＆ Yoo, K. E. (2016). A study on passengers' airport choice behavior using hybrid choice model: A case study of Seoul metropolitan area, South Korea. Journal of Air Transport Management, 57, 70-79.
[20].KFH Group. (2013). Transit capacity and quality of service manual.
[21].Kim, K. M., Hong, S. P., Ko, S. J., ＆ Kim, D. (2015). Does crowding affect the path choice of metro passengers?. Transportation Research Part A: Policy and Practice, 77, 292-304.
[22].Lam, W. H., Cheung, C. Y., ＆ Lam, C. F. (1999). A study of crowding effects at the Hong Kong light rail transit stations. Transportation Research Part A: Policy and Practice, 33(5), 401-415.
[23].Liu, Y., ＆ Charles, P. (2013). Spreading peak demand for urban rail transit through differential fare policy: a review of empirical evidence. In Australasian Transport Research Forum 2013 Proceedings.
[24].Lovely, M. E., ＆ Brand, D. (1982). Atlanta transit pricing study: moderating impact of fare increases on poor (No. 857).
[25].Lu, H., Fowkes, T., ＆ Wardman, M. (2008). Amending the incentive for strategic bias in stated preference studies: Case study in users' valuation of rolling stock. Transportation Research Record: Journal of the Transportation Research Board, (2049), 128-135.
[26].Luo, S., ＆ Gu, W. (2016). Attitudinal Market Segmentation for Transit Riders Using Factor Analysis. DEStech Transactions on Computer Science and Engineering, (icte).
[27].Machleit, K. A., Eroglu, S. A., ＆ Mantel, S. P. (2000). Perceived retail crowding and shopping satisfaction: what modifies this relationship?. Journal of consumer psychology, 9(1), 29-42.
[28].Mahudin, N. D. M., Cox, T., ＆ Griffiths, A. (2012). Measuring rail passenger crowding: Scale development and psychometric properties. Transportation research part F: traffic psychology and behaviour, 15(1), 38-51.
[29].Maister, D. H. (1984). The psychology of waiting lines. Harvard Business School.
[30].Marquis, M., ＆ Filiatrault, P. (2003). Public self‐consciousness disposition effect on reactions to waiting in line. Journal of Consumer Behaviour, 2(3), 212-231.
[31].Nuzzolo, A., Crisalli, U., ＆ Gangemi, F. (2000). A behavioural choice model for the evaluation of railway supply and pricing policies. Transportation Research Part A: Policy and Practice, 34(5), 395-404.
[32].Pepper, J., Spitz, G., ＆ Adler, T. (2003). Customer perspectives on multilevel coaches for increasing rail system capacity. Transportation Research Record: Journal of the Transportation Research Board, (1838), 19-29.
[33].Pruyn, A., ＆ Smidts, A. (1998). Effects of waiting on the satisfaction with the service: Beyond objective time measures. International journal of research in marketing, 15(4), 321-334.
[34].Shiftan, Y., Outwater, M. L., ＆ Zhou, Y. (2008). Transit market research using structural equation modeling and attitudinal market segmentation. Transport Policy, 15(3), 186-195.
[35].Smith, B., Olaru, D., Jabeen, F., ＆ Greaves, S. (2017). Electric vehicles adoption: Environmental enthusiast bias in discrete choice models. Transportation Research Part D: Transport and Environment, 51, 290-303.
[36].Taylor, S. (1994). Waiting for service: the relationship between delays and evaluations of service. The journal of marketing, 56-69.
[37].Taylor, S. (1995). The effects of filled waiting time and service provider control over the delay on evaluations of service. Journal of the Academy of Marketing Science, 23(1), 38-48.
[38].Temme, D., Paulssen, M., ＆ Dannewald, T. (2008). Incorporating latent variables into discrete choice models—a simultaneous estimation approach using SEM software. BuR-Business Research, 1(2), 220-237.
[39].Tirachini, A., Sun, L., Erath, A., ＆ Chakirov, A. (2016). Valuation of sitting and standing in metro trains using revealed preferences. Transport Policy, 47, 94-104.
[40].Wang, K. (2014). Analysis of Beijing Subway Passenger Flow Characteristics. Management Science and Engineering, 3, 51-56.
[41].Whelan, G., Batley, R., Shires, J., ＆ Wardman, M. (2008). Optimal fares regulation for Britain’s railways. Transportation Research Part E: Logistics and Transportation Review, 44(5), 807-819.
[42].Wardman, M., ＆ Murphy, P. (2015). Passengers’ valuations of train seating layout, position and occupancy. Transportation Research Part A: Policy and Practice, 74, 222-238.
[43].Zhang, Z., Fujii, H., ＆ Managi, S. (2014). How does commuting behavior change due to incentives? An empirical study of the Beijing Subway System. Transportation Research Part F: Traffic Psychology and Behaviour, 24, 17-26.
[44].任維廉, 胡凱傑, ＆ 崔妮臻. (2009). 知覺擁擠與公眾自我意識特質對顧客知覺等候經驗之影響-以國道客運台北總站爲例. 顧客滿意學刊, 5(1), 65-92.
[45].段良雄、劉慧燕(1996)，敘述性偏好之實驗設計與校估方法，運輸計劃季刊，25(1)，1-44.
[46].馮漢昌. (2008). 消費者對 3G 電信服務屬性偏好之研究. 成功大學電信管理研究所學位論文, 1-102.
[47].李奇(1992)，敘述性偏好模式與顯示性偏好模式比較之研究，國立成功大學交通管理科學研究所碩士論文.
[48].李香怡. (2004). 時間差別定價對臺北捷運乘客旅運行為之影響 ,國立交通大學運輸與物流管理學系博士論文.
[49].李子奈, ＆ 葉阿忠. (2000). 高等計量經濟學. 清華大學出版社有限公司.
[50].任維廉、崔妮臻、黃欣、劉柏廷、陳建元，「鐵路乘客等候經驗之知覺管理- 應用一個服務評估整合模式」，中華民國運輸學會第十九屆學術論文研討會，民國93年.
[51].施仲衡, 周慶瑞, ＆ 鄭曉薇. (2013). 地鐵設計規範. (GB50157-2013). 北京市規劃委員會.
[52].曾煥宗. (2016). 台灣高鐵休閒旅客出發時間選擇行為分析. 成功大學交通管理科學系學位論文, 1-103.
[53].趙鵬. (2000). SP 調查方法在交通預測中的應用. 北京交通大學學報, 24(6), 29-32