1. Estimation of switching models from revealed preferences and stated intentions Ben-Akiva, Moshe, and Takayuki Morikawa. "Estimation of switching models from revealed preferences and stated intentions." Transportation Research Part A: General 24.6 (1990): 485-495. 1 Claudio Feliciani 37-147293 Fujita Kazuyuki 37-146044 He Le 37-146059 Wang Yanjun 37-146920

2. Paper’s Objective: Relation with our project: Find an efficient method to predict switching behavior. In particular analyze the case in which a new metro line is opened and try to predict its share by combining revealed preferences (RP) and stated intentions (SI) data. This paper deals with the opening of a new metro line in Yokohama in march 1985 and predicting the market share of that line. In our project we are considering the introduction of a bus line during the Tokyo Olympics and we are trying to find out the optimal parameters’ combination (cost, time,…). 1. Introduction 2

3. Revealed Preference (RP) theory (by Paul Samuelson, 1938): Stated Intentions (or Preferences) (SI): Someone’s preferences can be revealed by analyzing his/her behavior under different circumstances. Preferences are directly obtained by asking people what they would prefer and analyzing the results. Pro: people behavior is analyzed in natural environment. Contra: for behaviors not implicit in the data analyzed, making forecasts may result difficult. Pro: options to be analyzed are directly used in the survey. Contra: responses may yield significant bias as decision-making protocol generating SI may differ from the one actually used. 3 1. Introduction

4. Revealed Preference model (actual travel behavior – process generating RP data): Stated Intentions model (consider alternative not currently available, switch or not?): Random utility function: Choice indicator: 4 2.1. Theory; combining RP and SI models Utility function: Stated intention response:

5. 5 2.1. Theory; combining RP and SI models Combining random components: Log-likelihood function: binary choice probability (binary logit used here)

6. Surveyed in order to investigate the ridership of a new subway line, which opened in March, 1985 in Yokohama, Japan. The before survey (3 month before opening); questions: Regularly used transit route from home to work/school; Alternative transit route; Intentions of using the new subway line. The after survey (6 months after opening); questions: Route from home to work using the new subway line; Route for the same trip without using the new subway line; Satisfaction level with the service of both routes. 6 3.1. Description of the data

7. 7 3.2. Before data summary Before survey: 564 respondents 70% valid However, for data analysis only individuals whose principal commuting mode is rail for both regular and alternative route selected: 107 respondents

8. 8 3.2.1. Estimation with RP data – Theory

9. 9 Bus dummy (busdum) = Bike dummy (bikedum) = Car dummy (cardum) = Walk time (walkt) = walking time (in minutes) Access in-vehicle time (accivt) = in-vehicle time of access trip (minutes) Number of transfers (xfern) = number of transfers “Base” access mode is “walk”. Regular and alternative routes use rail commuter mode: Total travel time or cost had no significant coefficient; Commuting cost usually covered by employer. Socioeconomic variables had non-significant coefficients. 3.2.1. Estimation with RP data – Explanatory variables

10. 10 3.2.2. Estimation with SI data

11. 11 3.2.3. Estimation with RP + SI data Revealed Preference Model specifications: Stated Intentions Model specifications:

12. 12 3.2.3. Estimation with RP + SI data For the SI model, choice probability is given by (scale parameter is used here): Remember that we assumed:

13. 13 3.2.3. Estimation with RP + SI data Log-likelihood function for the RP + SI model is given by:

14. RP modelSI modelRP+SI model Bus dummy-2.99 (-2.99)-1.57 (-1.78)-2.54 (-2.81) Bike dummy-5.77 (-4.37)-2.89 (-3.21)-5.73 (-4.55) Car dummy-8.48 (-5.84)-8.81 (-6.21) Walk time-0.335 (-3.59)-0.238 (-3.31)-0.365 (-4.27) Access in-vehicle time0.00769 (0.16)-0.0856 (-2.22)-0.0630 (-1.63) Number of transfers-1.12 (-2.73)-1.32 (-2.65)-1.69 (-4.31) Subway route constant0.974 (1.67)1.22 (2.25) μ0.559 (3.21) L(0)-113.51-97.28-210.80 -74.14-59.76-135.77 0.3470.3860.356 0.2940.3240.318 N107 14 3.2.4. Before data results

15. 15 3.2.5. Statistical test

16. 16 3.3. After data summary After survey: 1201 respondents 80.7% valid However, by selecting only rail users: 428 observations From those, by selecting subway route users: 254 respondents. Ambiguity in the way question was asked may have caused significant errors.

17. After modelBefore+After model Bus dummy-0.399 (-1.02)-2.00 (-2.53) Bike dummy (Before)-5.42 (-4.80) Bike dummy (After)0.228 (0.41)0.0733 (0.04) Car dummy-1.79 (-1.06)-8.37 (-6.16) Walk time-0.0768 (-5.09)-0.327 (-4.40) Access in-vehicle time (Before)-0.0657 (-1.78) Access in-vehicle time (After)-0.0754 (-3.87)-0.275 (-3.47) Number of transfers-0.942 (-3.89)-2.03 (-5.41) Subway route constant (Before)1.45 (2.94) Subway route constant (After)-0.112 (-0.36)-0.917 (-1.41) μ1 μ2 0.561 (3.39) 0.313 (3.88) L(0)-296.67-507.47 -257.92-395.66 0.1310.220 0.1070.197 N428535 17 3.3.1. After data results

18. 18 3.4. Prediction test Market share of the subway route given by: To obtain the “observed” market share after data used. Prediction of the market share made by using before data. Log-likelihood computed to check for goodness-of-fit.

19. ModelLog-likelihoodPredicted share of subway route (observed share = 59.35) After model-260.5959.35% RP model-367.6469.70% SI model-365.9081.97% SI model*-298.6368.22% RP+SI combined model-443.8582.89% RP+SI combined model*-355.7168.68% 19 3.4.1. Prediction test – Results SI bias adjusted (*) obtained by omitting the subway constant. May lead to an overstatement of the subway route usage. Correction of the bias required to reduce overstatement. After data reveled that captive travelers do not use subway for different reasons. Unfamiliarity, disliking or habitual usage of non-subway routes. Bias adjusted SI model had closest prediction and best goodness-of-fit. If over overestimation can be corrected, SI data can lead to good prediction.

20. Combining the stated intention data with the RP data increased the accuracy of parameter estimates of the model; a statistical test showed that the model for the stated intention data, if properly scaled, had the same coefficients as the RP model; the stated intention data contained more random noise; and the utility threshold value for switching routes estimated from the stated intentions was negative, implying that the respondents overstated their switching to the new alternative. 20 4. Conclusions

21. 21 End of the presentation. Thank you for your attention. Questions?