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1 BI 2001 BI BI - - BI Key Words: behavioral intention method, travel demand forecasting, behavior-intention consistency behavioral intention 5 1 BI BI ) BI (Behavioral 3 Intention ) 4 [1] BI 6) BI 1

2 Step 1) Step 2) X (Verplanken & Aarts, ) ) (Gärling, et al., ) ) X BI Step 1) Step 2) Step 1) - - (Verplanken,et al., ) ) (Fishbein & Ajzen, ) ; Gärling & Fujii, ), ) ) (Gärling, et al., ) ) (Gärling & Fujii, 1999 ), ) ; Ajzen, ) ) (Gärling et al., 1998) 8) 3 4 BI 2 6) BI 2

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4 / DT ki i k SP DTS ki i k 3,885 1, DT ki 2, , ,001 1, ,422 4,337 2,124 6 FT ki DT ki 4.3% 4.3% FT ki 6 4.3%FAi QA QS QT k QS QS =(DS i DCS i )K i (3) i DCS i i DS i i DSiDCS i DSi DS i =PIIS i FS i (4) FSi i 6 PIIS i i SP PIISi SP PS i SP 4.3 SP FS i (2), (4), (6) PIIA i, PIIS i, PIIF ki 1.0 k BI 3 QT k QT k =(DTS ki DTki)K i (5) i DT i =PIIF ki FT ki (6) FT ki i k PIIF ki i k (4)(5) PIIF ki 4

5 (20.00%) (42.90%) (36.40%) (33.30%) (30.80%) (9.10%) (40.00%) (33.30%) (16.70%) (22.60%) (6.70%) (0.00%) (30.00%) (16.70%) (13.90%) (12.20%) (29.40%) (33.30%) (22.20%) (22.60%) BI , PIIA i 4 PIIS i 6 PIIF ki BI 4300 / 1/ / SP 14,000(/) SP 20 5

6 22.6% BI - - BI SP 100% [2] BI PIIA i PIIS i PIIF ki FT ki FA i FS i % % % % % 6 FT ki FA i FS i 6 - BI BI % 6

7 (7), (8), (9) / SP 3,885 1, BI 2, , , , ,422 2, n - FA i FS i FT ki FA i = FA # i PIIA i (7) FS i = FS # i PIIS i (8) -1, - FT ki = FT # ki PIIF ki (9) FA # i FS # i FT # ki FA i FS i FT ki (7), (8), (9) (2) (4) (6) (1), (2), (3) (7), (8), (9) ) [3] BI 1-1 7

8 * ** * ** ** * * * B C A B * (L(C), L(B), χ 2 [df], ρ, sample size) (-54.7, -43.6, 22.3[11],.20, 103) (-49.8, -40.1, 19.3[9],.20, 103) 3 A, B, C 2 A, B A * p <.10, ** p < [4]

9 BI BI 64% 49% 53% 25% 37% 27% (3, 0.05) (7, 0.34) (5, 0.04) (11, 0.26) (4, 0.05) (8, 0.23) 46% 42% 33% 15% 22% 16% (3, 0.06) (6, 0.29) (4, 0.04) (8, 0.18) (3, 0.03) (6, 0.16) 37% 31% 26% 11% 15% 13% (3, 0.05) (7, 0.26) (4, 0.03) (10, 0.15) (3, 0.02) (8, 0.12) BI BI - - BI P( ) P( ) = expv / (expv + 1) ( (= ) 0.91 (= ) - - (10), (11) BI BI (10), (11) - - 1) 9 V = (11

10 2) - 4 3) - [7] (10) (11), (10), (11) [5] (10), (11) vs. vs.vs. vs. vs. (10), (11) 3, 21 BI % % % ) [8] - 2 P P + (1-P)/2 [9] - BI - [6] BI ) SP BI BI (10), (11) 3 3) 2 - (11) 3 2 BI 10

11 BI (10), (11) BI - BI - BI BI (7), (8), (9) BI - 3 Verplanken et al. (1994) 18 (10), (11)) - 15 BI BI cognitive automaticity 4) % % % 11

12 [5] (10), (11) [10] 0.91 BI [6] [5] [1] Manski 1990 [7] 19 [8] 6) Step 2) [2] [9] P + (1-P)/ [10] [3] [4] t 1) No. 688/IV-53, pp , ) ) Fujii, S. and Gärling, T. (2003) Application of attitude theory for improved predictive accuracy of stated preference methods in travel demand analysis, Transport Research A: Policy & Practice, 37 (4), pp ) SP No. 723/IV58, pp.1-14, ) Fishbein, M., and Ajzen, I.: Belief, Attitude, Intention, and Behavior: 12

13 An Introduction to Theory and Research. Addison-Wesley, Reading, 14) MA, 1975., ) BI 15) Gärling, T. Jakobsson, C. Loukopoulos, P. and Fujii, S.: A Cost-Minimization Principle of Households Adaptation to Travel Demand Management Measures, presented at the TRIP research 7) Verplanken, B., & Aarts, H.: Habit, attitude and planned behaviour: conference: The Economic and Environmental Consequences of Is habit an empty construct or an interesting case of goal-directed Regulating Traffic, Hillerød, Denmark, 2003 automatic? European Review of Social Psychology, 10, , 16) Gärling, T. Jakobsson, C. Loukopoulos, P. and Fujii, S.: Roles of Information Technology in Households Adaptation of Private Car 8) Gärling, T., Gillholm, R., & Gärling, A.: Reintroducing attitude Use to Travel Demand Management Measures, presented in the theory in travel behavior research: The validity of an interactive workshop Behavioral Responses to ITS, Eindhoven The interview procedure to predict car use. Transportation, 25, , Netherlands, ) ) Verplanken, B., Aarts, H., Knippenberg, V.A. and Moonen, A.: ( ) Habit versus planned behavior: A field experiment, British Journal 18Verplanken, B. Aarts, H., Van Knienbertg, A., Van Knienberg, C.: of Social Psychology, 37, pp , Attitude versus general habit: Antecedents of travel mode choice, 10) Gärling, T. & S. Fujii: Structural Equation Modeling of Journal of Applied Social Psychology, 24, pp , Determinants of Implementation Intentions, Goteborg Psychological 19) Manski, C.F. (1990) The use of intentions data to predict behavior: A Reports, Goteborg University, Sweden, 4 (29), best-case analysis, Journal of American Statistical Association, 85 11) Gärling, T. and Fujii, S.: Structural equation modeling of (412), determinants of planning, Scandinavian Journal of Psychology, 43 20) in (1), 1-8, pp. 12) Gärling, T., Ettema, D., Gillholm, R., and Selart, M.:. Toward a 35-52, 2002 theory of the intention-behavior relationship with implications for the prediction of travel behavior, In D. Hensher, J. King & T. Oum (eds.). World transport research, Vol. 1, Travel behavior, Pergamon, Oxford, pp , ) Ajzen, I.: From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp ). Springer, Heidelberg, VALIDATION AND ELABORATION OF TRAVEL DEMAND FORECASTING BY BEHAVIORAL INTENTION METHOD Satoshi FUJII Travel demand of a new bus route opened in 2002 was forecasted in 2001 using the Behavioral Intention Method. In this paper, the predictive ability of the method was tested using actual travel demand and panel data from a survey implemented before and after the opening of the bus route. The tests indicated that behavioral-intention consistency used for the prediction was approximately compatible with the actual behavior-intention consistency. It was also indicated that there was a discrepancy between actual travel demand and predicted travel demand, but the discrepancy diminished by adjusting the numerical formulation used in the prediction. The behavior-intention consistency was then analyzed using regression analysis for the purpose of future travel demand forecasting using the Behavioral Intention Method. 13

第3章.DOC

第3章.DOC 000 Ben-Akiva and Lerman, 1985 1996 1996 4 1997 Banister, 1978; Verplanken et al., 1998 1 5 1996 3 () (I n ) 1 18 I n n P n (1) P n ( 1) = exp exp ( Vn 1 ) I n 1 ( V ) + exp µ ln exp ( V ) n1 + i= ni (3.1)