本研究針對不同型式之即時交通資訊提供下國道高速公路旅運者路徑移轉行為進行研究。研究中透過實驗設計提供四種型式即時交通資訊之情境，並針對休息站上之旅運者進行訪問以蒐集所需之敘述性偏好資料。在本研究中定義四種不同型式之即時交通資訊，分別為質的訊息、量的訊息、質的導引以及量的導引。研究中應用有限理性法則(亦即熟知之無異區間)架構在四種不同型式即時交通資訊提供下高速公路旅運者路徑移轉行為模式。而在無異區間中整合兩個決策變數：旅行時間以及旅行成本。由於國道高速公路未來將實施電子收費(ETC)，以設置於匝道上之感應器代替原本設置於主線車道上之收費站，因此，在實施電子收費後旅行成本將轉為以行駛距離長短來計算。本研究更進一步建立兩種不同之移轉機制(行駛路徑與習慣路徑相比較、行駛路徑與最佳路徑相比較)，探討在不同型式即時交通資訊提供下高速公路旅運者路徑移轉之行為。
本研究結果顯示，旅行時間與旅行成本皆為影響旅運者路徑移轉之主要變數。而在四種即時交通資訊型式中，以量化型式的即時交通資訊、具有導引建議之即時交通資訊較容易改變旅運者路徑移轉行為。另外，社經變數中以性別、年齡以及個人每月收入為顯著影響移轉行為之變數。

This study investigates freeways travelers’ route switching behavior with the provision of different types of real-time traffic information. Different scenarios with four types of real-time information are designed and provided to the travelers randomly selected in the rest areas to obtain the stated preference data. Four types of real-time information defined in this paper are qualitative, quantitative, qualitative with guidance instruction, and quantitative with guidance instruction. Bounded rationality framework, also known as indifference band approach, is applied to modeling freeways travelers’ route switching behavior with the provision of different types of real-time traffic information. There are two important variables, travel time and travel cost, included in the indifference band. Since the freeways will adopt the ETC (Electronic Toll Collection) systems in the near future, toll stations will be located on ramps instead of within ramps on freeways. Therefore, travel cost need to be modified to be linearly proportional to the distance travelers traveled after the implementation of ETC systems. In this study, two route switching rules, travelers’ current routes compared to habitual and best routes, are investigated and compared to further provide valuable insights into freeways travelers’ route switching behavior with the provision of different types of real-time traffic information.
The empirical results indicate that travel time and travel cost are the major factors influencing travelers’ route switching behavior. Quantitative traffic information and real-time traffic information with guidance instruction could be easier influencing switching behavior than other types of real-time information. Gender, age and income per month are the significant variables which are influencing switching behavior.

誌謝………………………………………………………………. I
中文摘要…………………………………………………………. III
英文摘要…………………………………………………………. IV
目錄………………………………………………………………. V
圖目錄……………………………………………………………. VII
表目錄……………………………………………………………. VIII
第一章 緒論..……………………………………………….... 1
1.1 研究動機與背景…………………………………………….. 1
1.2 研究目的…………………………………………………….. 3
1.3 研究範圍與對象…………………………………………….. 4
1.4 研究方法…………………………………………………….. 5
1.5 研究內容……………………………………………………… 6
1.6 研究流程…………………………………………………….. 7
第二章 文獻回顧…………………………………………………. 9
2.1 國內用路人路線選擇行為相關研究……………………….. 9
2.2 國外用路人路線選擇行為相關研究……………………….. 11
2.3 有限理性法則於交通領域上之運用……………………….. 16
2.4 小結………………………………………………………….. 18
第三章 模式理論與架構…………………………………….... 20
3.1 有限理性法則……………………………………………….. 20
3.2 一般化旅行成本之有限理性法則………………………….. 21
3.3 一般化旅行成本之多項普羅比模式推導…………………. 25
3.4 多項普羅比模式校估程序…………………………………. 31
第四章 問卷設計與資料分析………………………………….. 33
4.1 調查計畫…………………………………………………….. 33
4.1.1 研究範圍與對象…………………………………….. 33
4.1.2 抽樣方式、時間及地點……………………………. 35
4.2 問卷設計及內容………………………………………...... 36
4.2.1 社會經濟特性………………………………….….. 36
4.2.2 旅次特性資料………………………………………… 37
4.2.3 路徑移轉實驗設計…………………………………… 38
4.3 調查資料整理與分析………………………………………… 44
4.3.1 社會經濟特性分析…………………………………… 44
4.3.2 旅次特性分析………………………………………… 48
4.3.3 路徑移轉實驗設計分析……………………………… 55
第五章 模式校估結果…………………………………………… 59
5.1 有限理性法則多項普羅比模式校估變數方向預期………… 59
5.2 有限理性法則多項普羅比模式校估結果…………………… 64
5.2.1移轉機制I之多項普羅比模式校估結果…………………… 64
5.2.2移轉機制II之多項普羅比模式校估結果………………… 68
5.3 不同移轉機制下有限理性法則
多項普羅比模式校估結果比較……………………………… 72
5.3 小結…………………………………………………………… 76
第六章 結論與建議……………………………………………… 78
6.1 結論…………………………………………………………… 78
6.2 建議…………………………………………………………… 80
參考文獻…………………………………………………………… 82
附錄一……………………………………………………………… 89
附錄二……………………………………………………………… 97
附錄三……………………………………………………………… 98

1.交通部統計處，「臺灣地區旅運時間價值調查報告」，民國85年。
2.胡大瀛，「即時性資訊對路網車流分佈之影響」，中華民國運輸學會第十二屆學術論文研討會論文集，民國86年。
3.趙祖佑，「模糊理論應用於通勤駕駛者出發時間路徑決策行為之研究」，淡江大學運輸管理學系運輸科學碩士論文，民國87年。
4.張禎誼，「模糊理論應用於逐日動態通勤行為之研究」，淡江大學運輸管理學系運輸科學碩士論文，民國87年。
5.黃運貴，「世界道路發展新趨勢-智慧型運輸系統(ITS)」出國考察報告，交通部運輸研究所，民國87 年7 月。
6.魏慶地，「行前交通逐日動態模擬之研究」，國立台灣大學土木工程研究所博士論文，民國87年。
7.吳淑順，「應用模糊纇神經網路於動態路徑選擇之研究」，淡江大學運輸管理學系運輸科學碩士論文，民國88年。
8.黃燦煌，「即時資訊狀況下駕駛人路線選擇行為之分析」，國立交通大學運輸研究所博士論文，民國89年6月。
9.廖曉萍，「自小客車通勤者出發時間與路徑選擇動態行為之研究」，淡江大學土木工程學系碩士論文，民國85年。
10.廖苑伶，「模糊效用模型於動態路徑選擇行為之研究」，淡江大學運輸管理學系運輸科學碩士論文，民國89年。
11.陳士邦，「車內導引資訊影響下之逐點動態路徑選擇行為」，淡江大學運輸管理學系運輸科學碩士論文，民國89年。
12.周榮昌、陳志成、翁美娟，「即時交通資訊對高速公路使用者路線移轉之影響」，運輸學刊第15卷第1期，中華民國92年3月。
13.許鳳升，「不同交通資訊來源對城際通勤者路線選擇行為影響」，國立交通大學運輸研究所論文，中華民國90年5月。
14.陳科宏、周榮昌、劉祐興，「即時交通資訊對高速公路用路人路線移轉之影響」，中華民國運輸學會第十八屆論文研討會，中華民國92年12月。
15.張貴貞，「駕駛者在不同路網型態下途中路徑選擇行為之研究」，淡江大學運輸管理學系運輸科學碩士論文，民國92年。
16.葉謀銓，「應用啟發式解法求解多項普羅比模式之最大概似估計值」，中華民國運輸學會第十八屆論文研討會，中華民國92年12月。
17.葉謀銓，「多項普羅比模式參數校估之研究」，國立暨南國際大學土木工程學系碩士論文，民國92年。
18.「高速公路智慧化之整體規劃」，台灣區國道高速公路局，民國90 年2 月。
19.「台灣地區智慧型運輸系統綱要計畫」，交通部，民國90 年1 月。
20.「高速公路電子收費系統建置及營運」案，交通部台灣區國道高速公路局，民國92年3月。
21.「高速公路匝道收費系統通行費率之研擬與試算」，交通部運研所，民國91年3月。
22.http://www.its-taiwan.org.tw，中華智慧型運輸系統協會。
23.http://www.freeway.gov.tw/，交通部台灣區國道高速公路局全球資訊網。
24.「汽車駕駛者對資訊可變標誌需求調查」，淡江大學運輸管理學系，民國92年。
25.Abdel-Aty, M. A., Kitamura, R. and Jovanis, P. P., “Using stated preference data for studying the effect of advanced traffic information on drivers’ route choice,” Transportation Research Part C, Vol. 5, pp. 39-50, 1997a.
26.Abdel-Aty, M., Abdalla, P.E.F., ” Modeling compliance with pre-trip advised route and en-route choice with repeated observations,” submitted for possible publication in Transportation Research Part A, 2003.
27.Adler, J. L. and Michael G. M., “A structural model with discrete-choice variables for predicting enroute behavior under ATIS,” Preprint No. 940902, Transportation Research Board, 73rd Annual Meeting, Washington, D. C., 1994.
28.Adler, J. L. and Victor J. B., “Toward the design of intelligent traveler information systems,” Transportation Research Part C, Vol. 6, pp. 157-172, 1998.
29.Adler, J. L., Blue, V. J., Wu, T. L., “Assessing network and driver benefits from bi-objective in-vehicle route guidance,” Presented in the 78th TRB Annual Meeting, TRB 1999, 1999.
30.Adler, J. L., ”Investigating the learning effects of route guidance and traffic advisories on route choice behavior,” Transportation Research Part C, Vol. 9, pp. 1-14, 2001.
31.Al-Deek, H. M., Ishak, S. S. and Radwan, A. E., “Impact of traffic diversion with ATIS on traveler’s safety,” Computers ind. Engng Vol. 34, No.2, pp. 547-558, 1998.
32.Benson, P. G., “Motorist attributes about content of variable message sign,” Transportation Research Record 1550, Transportation Research Board, Washington, D. C., pp. 48-57, 1996.
33.Busch, F., “Incident detection,” In M. Papageorgiou (Ed.), Concise Enoyclopedia of Traffic and System, pp. 219-225, 1987.
34.Catchpole, J., Hancock, A., and Cairney, P., “Driver comprehension of formats for presenting traffic information on dynamics signs,” Research Report ARR 269, ARRB Transport Research, Vermont South, Victory, Australia, 1995.
35.Chen, P. S. T., Srinivasan, K. K., and Mahamassani, H. S., “Effect of information quality on compliance behavior of commuters under real-time traffic information,” Transportation Research Record 1676, Paper No. 99-1207, pp53-60, 1999.
36.Cambridge Systematic Ins., 1990.
37.Daganze, C. F. and Sheffi, Y., “Multinomial probit with time series data: unifying state dependence and serial correlation models.” Environment and Planning A 14, pp. 1377-1388, 1982.
38.Dia, H., “An agent-based approach to modelling driver route choice behaviour under the influence of real-time information,” Transportation Research Part C, pp. 331-349, 2002.
39.E. Hato, M. Taniguchi, Y. Sugie, M. Kuwahara, H. Morita, “Incorporating an information acquisition process into a route choice model with multiple information sources,” Transportation Research Part C, pp. 109-129, 1999.
40.Emmerink, R. H. M., Verhoef, E. T., Nijkamp, P., Rietveld P., “Information in road network with multiple origin-destination Paris,” Tinbergen Institute, Amsterdam-Rotterdam, TI 5-95-1198, 1995a.
41.Emmerink, R. H. M., Axhausen, K. W., Rietveld, P., “Effects of information in road transport network with recurrent congestion,” Transportation 22, pp. 21-53, 1995b.
42.Emmerink, R. H. M., Nijkamp, P., Rietveld, P., “Perception and uncertainty in stochastic network equilibrium models: an alternative approach,” Tinbergen Institute, Amsterdam-Rotterdam, TI 5-95-1198, 1995c.
43.Emmerink, R. H. M., Axhausen, K. W., Nijkamp, P., and Rietveld, P., “The potential of information provision in a simulated road transport network with non-recurrent congestion,” Transportation Research Part C, Vol. 3, pp. 293-309, 1995d.
44.Emmerink, R. H. M., Erik, T. V., Peter, N., Piet, R., “Information provision in road transport with elastic demand,” Journal of Transport Economics and Policy30 (2), pp.117-136, 1996a.
45.Emmerink, R. H. M., Erik, T. V., Peter, N., Piet, R., “Endogenising demand for information in road transport,” Annals of Regional Science 30 (2), pp. 201-222, 1996b.
46.Emmerink, R. H. M., Peter N., Rietveld, P. and Ommeren, J. N. V., “Variable message signs and radio traffic information: An integrated empirial analysis of driver’s route choice behaviour,” Transportation Research Part A, pp. 135-153, 1996.
47.Jou, R. C., Lam, S. H., Wneg, M. C. and Chen, C. C., ”Real time traffic information and ITS impacts on route switching behavior of expressway drivers,” Journal of Advanced Transportation, forthcoming 2004.
48.Khattak, A., Koppelman, F. and Schofer, J., “Stated preference for investigating commuters’ diversion propensity,” Transportation, Vol. 20, No. 2, pp.107-127, 1993a.
49.Khattak, A., Schofer, J., Koppelman, “Commuters’ enroute diversion and return decisions: analysis and implications for advanced traveler information systems,” Transportation Research Part A, Vol. 27, pp.101-111, 1993b.
50.K. Chatterjee, N.B. Hounsell, P.E. Firmin, P.W. Bonsall, “Driver response to variable message sign information in London,” Transportation Research Part C, pp. 149-169, 2002.
51.King, G.. F., ”Driver performance in highway tasks,” Transportation Research Record, 1093, 1-11, 1986.
52.King, G. F. and Mast, T. M., “Excess travel: causes, extent and consequences,” Transportation Research Record, 1111, 126-134, 1987.
53.Koppelman, F.S. and Pas. E. I., “Travel-choice behavior: models of perceptions, feelings, preference, and choice”, Transportation Research Record 765, pp. 26-33, 1980.
54.Lai, K. H. and Wong, W. G., “SP approach toward driver comprehension if message formats on VMS,” Journal of Transportation Engineering, pp. 221-227, May/June 2000.
55.Lam, S. H. and Mahmassani, H. S., “Multinomial probit model estimation: computational procedures and applications,” Ph.D. Thesis, The university of Texas at Austin, USA, 1991.
56.Lerman, S. R. and Manski, C. F., “On the use of simulated of frequencies to approximate choice probabilities. Structural analysis of discrete data with econometric applications, MIT Press, Cambridge, Massachusetts, pp. 305-319, 1981.
57.Levinson, D., “The value of advanced traveler information system for route choice,” Transportation Research Part C, Vol. 11, pp. 75-87, 2003.
58.Liu, Y. H. and Mahmassani, H. S., “Global maximum likelihood estimation procedure for multinomial probit (MNP) model parameters,” Transportation Part B, Vol. 34, pp. 419-449, 2000.
59.Mahmassani, H. S. and Chang, G. L., “On boundedly-rational user equilibrium in transportation systems,” Transportation Science, 21(2), pp. 88-89, 1987.
60.Mahmassani, H. S. and Jayakrishnan, R., “System performance and user response under real-time information in a congested traffic corridor,” Transportation Research Part A, Vol. 25A, pp. 293-307, 1991.
61.Mahmassani, H. S., “Dynamic models of commuter behavior: experimental investigation and application to the analysis of planned traffic disruptions,” Transportation Research Part A, Vol. 24A, No. 6, pp. 465-484, 1990.
62.Madanat, S., Yang, C. Y. and Yen, Y-M, “Analysis of stated route diversion intensions under ATIS using latent variable modeling,” Preprint No. 950049, Transportation Research Board, 74th Annual Meeting, Washington, D.C., 1995.
63.Mahmassani, H. S. and Jou, R. C., ”Bounded rationality in commuter decision dynamics: incorporating trip chaining in departure time and route switching decisions.” Theoretical Foundations of Travel Choice Modeling (T. Garling, T. Laitila and K. Westin, eds.), Pergamon Press, pp. 201-229, 1998.
64.Mahmassani, H. S., Liu, Y. S., “Dynamics of commuting decision behaviour under advanced traveler information systems,” Transportation Research Part C, Vol. 7, pp. 91-107, 1999.
65.Ng, L., Barfield, W., and Mannering, F., “A survey-based methodology to determine information requirements for advanced traveler information systems,” Transportation Research Part C, Vol. 3, pp. 113-127, 1995.
66.Ramsay, E. D., Catchpole, J. E., and Luk. J. Y. K., “Evaluation of VicRoads’ drive time system,” Research Report ARR 305, ARRN Transportation Research, Vermont South, Victory, Australia, 1997.
67.Simon, H., “A behaviour model of rational choice, ”Quarterly Journal of Economics, Vol. 69, pp. 99-118, 1955.
68.Simon, H., “Rational choice and structure of the environment,” Quarterly Journal of Economics, Vol. 73, pp. 129-138, 1956.
69.Srinivasan, K. K., Mahmassani, H. S., “Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: a dynamic kernel logit formulation,” Transportation Research Part B, Vol. 37, pp. 793-814, 2003.
70.Srinivasan, K. K., Krishnamurthy, A., “Investigating the role of mixed real-time information strategies in network performance,” Transportation Research Board, 83rd Annual Meeting, Washington, D.C., 2004.
71.Wardman, M., Bonsall, P. W., and Shires, J. D., “Driver response to variable message signs: a stated preference investigation,” Transportation Research Part C, Vol. 5, pp. 389-405, 1997.