| 研究生: |
林桓平 Lin, Huan-Ping |
|---|---|
| 論文名稱: |
旅客尖峰時刻搭乘大眾捷運系統路徑選擇行為之研究 Exploring Passengers’ Path Choice Behavior at Peak Time for Mass Rapid Transit Systems |
| 指導教授: |
鄭永祥
Cheng, Yung-Hsiang |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 交通管理科學系 Department of Transportation and Communication Management Science |
| 論文出版年: | 2015 |
| 畢業學年度: | 103 |
| 語文別: | 中文 |
| 論文頁數: | 77 |
| 中文關鍵詞: | 個體選擇模式 、潛在變數選擇模式 、路線選擇行為 、大眾捷運尖峰時刻 |
| 外文關鍵詞: | Discrete choice model, Hybrid discrete choice model, Route choice behavior, MRT peak time |
| 相關次數: | 點閱:166 下載:7 |
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大眾捷運系統隨著城市的擴張與發展,路網與路線也越來越複雜,搭乘人數也越來越多,捷運已經成為城市公共旅運與日常通勤運輸不可或缺的一環。然而伴隨著快速的成長,捷運系統浮現日漸嚴重的人流問題,尖峰時段人流過度集中於捷運中心轉運站與核心路線造成了強烈的擁擠。因此本研究主要探討旅客在規畫搭乘捷運路線時的選擇行為和影響的因素,由個體差異來瞭解旅客的路線選擇偏好,除了傳統上的共生變數如價格、時間外,納入了感知的因素,如可靠度、舒適度、習慣或是過往經驗等等,並設定不同特性的路線讓旅客做選擇,分析旅客在相同起訖點選擇不同路線的原因和特性,其結果可提供經營者在決策、行銷或政策上的參考
本研究透過混合羅吉特模式(Mixed Logit Model)與潛在變數選擇模式(Hybrid Discrete Model, HDCM)分析潛在變數與共生變數來解釋個體異質性與對於路線的不同偏好,並利用彈性與敏感度分析來討論旅客對於路線共生變數的影響程度。研究結果發現旅客較偏好搭乘較為容易的路線,且發現旅客對於方便性和舒適性有較為明顯的偏好差異性,而敏感度分析與彈性分析也顯示透過價格的改變會促使旅客流量往網路線性質較相近的路線做轉移,結果證實了旅客在捷運的路線選擇偏好會受到心理變因的影響。
With the expansion of urban development, not only MRT network and route are more and more complex but also more and more passengers. MRT has been a necessary and important part of developing urban transportation and daily commuting. However, we can see the flow bubble to the surface with MRT rapid growth. Flow of passengers over concentrate in station of core and main line cause serious congestion in the peak time. This study is aim at factors of route choice behavior, and use differences in personality to understand what passengers' preference of route. Therefore, In addition to the traditional variables, for example, travel time and price, included perceived factors such as reliability, comfort, habits or past experience, etc., and set the different characteristics of the route allows visitors to choose and thereby analysis that passengers choose different route causes and characteristics. The result of study can provide a reference in decision-making, marketing or policies for operator.
This study focus on MRT passengers’ route choice behavior, and explores the issue of taste heterogeneity by applying the mixed logit model (ML). In addition, the latent variable choice model (LVCM) is adopted to capture the impact of latent variable on passengers’ route choice behavior in this study. We also use elasticity analysis and sensitivity analysis method to measure the impact of symbiotic variables on the passengers. It is found out that passengers prefer to take easier route and choose simple transfer, furthermore, it is more obvious differences in preferences for passengers on the convenience and comfort. Elasticity analysis and sensitivity analysis method show that passengers will switch the route to another one which have similar nature. The results confirmed the passengers in transit route preference will be affected by travel perception.
中文
1.李鈺雯(民84)。都市幹道動態旅行時間推估與交通偵測設施佈設準則之研究,淡江大學運輸管理學系碩士論文。
2.張有恆(民79)。大眾捷運系統之設計與營運管理,台北市:黎明文化出版。
3.馮正民、黃承傳等(民80年6月)。都會區運輸網路規劃原則之研究。經建會住宅及都市發展處委託:交通大學交通運輸研究所執行。
英文
1.Abdel-Aty, M. A., Kitamura, R., & Jovanis, P. P. (1995). Investigating effect of travel time variability on route choice using repeated-measurement stated preference data. Transportation Research Record, (1493), 39-45.
2.Abdel-Aty, M., & Huang, Y. (2004). Exploratory spatial analysis of expressway ramps and its effect on route choice. Journal of Transportation Engineering, 130(1), 104-112.
3.Abkowitz, M., Josef, R., Tozzi, J., & Driscoll, M. K. (1987). Operational feasibility of timed transfer in transit systems. Journal of transportation engineering, 113(2), 168-177.
4.Asakura, Y. (1999). Evaluation of network reliability using stochastic user equilibrium. Journal of Advanced Transportation, 33(2), 147-158.
5.Ben-Akiva, M., & Bolduc, D. (1996). Multinomial probit with a logit kernel and a general parametric specification of the covariance structure. Dʹepartement d'ʹeconomique, Universitʹe laval with Department of Civil and Environmental Engineering, Massachusetts Institute of Technology.
6.Ben-Akiva, M., McFadden, D., Gärling, T., Gopinath, D., Walker, J., Bolduc, D., ... & Rao, V. (1999). Extended framework for modeling choice behavior. Marketing letters, 10(3), 187-203.
7.Ben-Akiva, M., McFadden, D., Train, K., Walker, J., Bhat, C., Bierlaire, M., & Munizaga, M. A. (2002). Hybrid choice models: progress and challenges. Marketing Letters, 13(3), 163-175.
8.Bekhor, S., & Albert, G. (2014). Accounting for sensation seeking in route choice behavior with travel time information. Transportation research part F: traffic psychology and behaviour, 22, 39-49.
9.Bhat, C. R. (1997). An endogenous segmentation mode choice model with an application to intercity travel. Transportation science, 31(1), 34-48.
10.Bogers, E., Viti, F., & Hoogendoorn, S. (2005). Joint modeling of advanced travel information service, habit, and learning impacts on route choice by laboratory simulator experiments. Transportation Research Record: Journal of the Transportation Research Board, (1926), 189-197.
11.Ceapa, I., Smith, C., & Capra, L. (2012, August). Avoiding the crowds: understanding tube station congestion patterns from trip data. In Proceedings of the ACM SIGKDD international workshop on urban computing (pp. 134-141). ACM.
12.Ceder, A. V. I. S. H. A. I. (2007). Public Transit Planning and Operation: Theory. Modelling and Practice: London: Elsevier.
13.Choocharukul, K. & Sriroongvikrai, K. (2013). Multivariate Analysis of Customer Satisfaction: A Case Study of Bangkok’s Mass Rapid Transit (MRT) Passengers. Journal of the Eastern Asia Society for Transportation Studies, 10(0), 1258-1269.
14.Clever, R. (1997). PART 3: Intermodal: Integrated Timed Transfer: A European Perspective. Transportation Research Record: Journal of the Transportation Research Board, (1571), 107-115.
15.de Lapparent, M., & Koning, M. (2015). Analyzing time sensitivity to discomfort in the Paris subway: an interval data model approach. Transportation, 1-21.
16.Feng, T., Arentze, T., & Timmermans, H. (2013). Capturing preference heterogeneity of truck drivers’ route choice behavior with context effects using a latent class model. EJTIR, 13(4), 259-273.
17.Gopimatj, D. A. (1997). Modeling heterogeneity in discrete choice processes: Application to travel demand. Transportation Research Part A, 1(31), 86.
18.Grison, E., Gyselinck, V., & Burkhardt, J. M. (2014, April). Exploring factors related to users’ experience of public transport: users profiles and route choice. In Transport Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment.
19.Guihaire, V., & Hao, J. K. (2008). Transit network design and scheduling: A global review. Transportation Research Part A: Policy and Practice, 42(10), 1251-1273.
20.Guo, Z., & Wilson, N. H. (2004). Assessment of the Transfer Penalty for Transit Trips Geographic Information System-Based Disaggregate Modeling Approach. Transportation Research Record: Journal of the Transportation Research Board, 1872(1), 10-18.
21.Guo, Z., & Wilson, N. H. (2007). Modeling effects of transit system transfers on travel behavior: case of commuter rail and subway in Downtown Boston, Massachusetts. Transportation Research Record: Journal of the Transportation Research Board, 2006(1), 11-20.
22.Guo, Z., & Ferreira, J. (2008). Pedestrian environments, transit path choice, and transfer penalties: understanding land-use impacts on transit travel. Environment and Planning B Planning and Design, 35(3), 461.
23.Guo, Z., & Wilson, N. H. (2011). Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground. Transportation Research Part A: Policy and Practice, 45(2), 91-104.
24.Habib, K. M. N., Kattan, L., & Islam, M. (2011). Model of personal attitudes towards transit service quality. Journal of Advanced Transportation, 45(4), 271-285.
25.Hine, J., & Scott, J. (2000). Seamless, accessible travel: users’ views of the public transport journey and interchange. Transport Policy, 7(3), 217-226.
26.Kwan, M. P., & Kotsev, A. (2015). Gender differences in commute time and accessibility in Sofia, Bulgaria: a study using 3D geovisualisation. The Geographical Journal, 181(1), 83-96.
27.Lam, S. H., & Xie, F. (2002). Transit path-choice models that use revealed preference and stated preference data. Transportation Research Record: Journal of the Transportation Research Board, 1799(1), 58-65.
28.Lathia, N., & Capra, L. (2011, September). How smart is your smartcard?: measuring travel behaviours, perceptions, and incentives. In Proceedings of the 13th international conference on Ubiquitous computing (pp. 291-300). ACM.
29.Lee, J. Y., Lam, W. H., & Wong, S. C. (2001, August). Pedestrian simulation model for Hong Kong underground stations. In Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE (pp. 554-558). IEEE.
30.Leurent, F. (2009). On seat congestion, passenger comfort and route choice in urban transit: a network equilibrium assignment model with application to Paris. In Annual Meeting of the Transportation Research Board Session Transit Capacity and Quality of Service (pp. TRB-09). TRB.
31.Litman, T. (2008). Valuing transit service quality improvements. Journal of Public transportation, 11(2), 3.
32.McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior.
33.McFadden, D. (1986). The choice theory approach to market research. Marketing science, 5(4), 275-297.
34.McFadden, D., & Train, K. (2000). Mixed MNL models for discrete response. Journal of applied Econometrics, 15(5), 447-470.
35.Morikawa, T., Ben-Akiva, M., & McFadden, D. (2002). Discrete Choice Models Incorporating Revealed Preferences and Psychometric Data. Econometric Models in Marketing, 29.
36.New York Metropolitan Transportation Council. (1998). The 1997/1998 Regional Travel – Household Interview Survey. Retrieved November 9, 2014, from the New York Metropolitan Transportation Council Web site:
http://www.nymtc.org
37.Nicolaidis, G. C. (1975). Quantification of the comport variable. Transportation Research, 9(1), 55-66.
38.Nielsen, O. A. (2000). A stochastic transit assignment model considering differences in passengers utility functions. Transportation Research Part B: Methodological, 34(5), 377-402.
39.Ortuzar, J. D. D., & Willumsen, L. G. (2004). Introduction in modeling transport. John Wiley, West Sussex.
40.Papinski, D., Scott, D. M., & Doherty, S. T. (2009). Exploring the route choice decision-making process: A comparison of planned and observed routes obtained using person-based GPS. Transportation research part F: traffic psychology and behaviour, 12(4), 347-358.
41.Paulssen, M., Temme, D., Vij, A., & Walker, J. L. (2014). Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice. Transportation, 41(4), 873-888.
42.Peng, J., Zhi-cai, J., & Lin-jie, G. (2014). Application of the Expanded Theory of Planned Behavior in Intercity Travel Behavior. Discrete Dynamics in Nature and Society, 2014.
43.Prashker, J. N. (1979). Mode choice models with perceived reliability measures. Transportation Engineering Journal, 105(3), 251-262.
44.Prato, C. G. (2011). Latent factors and route choice behaviour.
45.Prud'homme, R., Koning, M., Lenormand, L., & Fehr, A. (2012). Public transport congestion costs: The case of the Paris subway. Transport Policy, 21, 101-109.
46.Ramming, M. S. (2001). Network knowledge and route choice (Doctoral dissertation, Massachusetts Institute of Technology).
47.Raveau, S., Guo, Z., Muñoz, J. C., & Wilson, N. H. (2014). A behavioural comparison of route choice on metro networks: Time, transfers, crowding, topology and socio-demographics. Transportation Research Part A: Policy and Practice, 66, 185-195.
48.Rundmo, T., & Hale, A. R. (2003). Managers’ attitudes towards safety and accident prevention. Safety science, 41(7), 557-574.
49.Shafahi, Y., & Khani, A. (2010). A practical model for transfer optimization in a transit network: Model formulations and solutions. Transportation Research Part A: Policy and Practice, 44(6), 377-389.
50.Steer Davies and Gleave, 1998. The Seamless Public Transport Journey. Report for London Docklands Development Corporation, London, UK.
51.Stokols, D. (1972). On the distinction between density and crowding: some implications for future research. Psychological review, 79(3), 275.
52.Sun, Y., & Xu, R. (2012). Rail Transit Travel Time Reliability and Estimation of Passenger Route Choice Behavior: Analysis Using Automatic Fare Collection Data. Transportation Research Record: Journal of the Transportation Research Board, (2275), 58-67.
53.Train, K. E. (2009). Discrete choice methods with simulation. Cambridge university press.
54.Transport for London (TfL), 2001. London Area Travel Survey (LATS), London, UK.
55.U.S. Federal Transit Administration (FTA), 1996. Passenger Transfer System Review: A Synthesis of Transit Practice. TCRP Synthesis 19, Washington, DC.
56.Vuchic, V. R., Clarke, R., & Molinero, A. M. (1981). Timed transfer system planning, design and operation (No. UMTA-PA-11-0021-82-2).
57.Vuchic, V. R. (2007). Urban transit systems and technology. John Wiley & Sons.
58.Wardman, M. (1998). The value of travel time: a review of British evidence. Journal of transport economics and policy, 285-316.
59.Wen, C. H., & Lai, S. C. (2010). Latent class models of international air carrier choice. Transportation Research Part E: Logistics and Transportation Review, 46(2), 211-221.
60.Wen, C. H., Wang, W. C., & Fu, C. (2012). Latent class nested logit model for analyzing high-speed rail access mode choice. Transportation Research Part E: Logistics and Transportation Review, 48(2), 545-554.
61.Westaby, J. D. (2005). Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior. Organizational Behavior and Human Decision Processes, 98(2), 97-120.
62.Xu, J., Ning, Y., Wei, H., Xie, W., Guo, J., Jia, L., & Qin, Y. (2014). Route Choice in Subway Station during Morning Peak Hours: A Case of Guangzhou Subway. Discrete Dynamics in Nature and Society, 501, 151434.Yanez, M. F., Raveau, S., & Ortúzar, J. D. D. (2010). Inclusion of latent variables in mixed logit models: modelling and forecasting. Transportation Research Part A: Policy and Practice, 44(9), 744-753.
63.Zhang, Y. S., Yao, E. J., & Yang, Y. (2013). A route-planning algorithm based on subway passenger routes choice behavior analysis. In Proceedings of the Second International Conference on Transportation Information and Safety. Wuhan, China: American Society of Civil Engineers (pp. 1972-1978).
64.Zheng, Y., Guo, W., Zhang, Y., & Hu, J. (2014). A generalized comfort function of subway systems based on a nested logit model. Tsinghua Science and Technology, 19(3), 300-306.
65.Zhong, M., Shi, C., Tu, X., Fu, T., & He, L. (2008). Study of the human evacuation simulation of metro fire safety analysis in China. Journal of Loss Prevention in the Process Industries, 21(3), 287-298.
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