| Summary: | 碩士 === 淡江大學 === 運輸管理學系碩士班 === 106 === The market segmentation method is often used to distinguish and model various groups of decision makers implementing trip decisions. The purpose of user segmentation is to classify decision makers of similar nature into the same group, such that the trip makers who belong to the same group have higher homogeneity within the same group but with more differences among other groups. This process of user segmentation is also referred to as market segmentation.
To implement the user segmentation, we need to use the market segmentation techniques. The segmentation techniques can be based on prior knowledge of the analyst under the assumption of differences between different groups, or applying mathematical/statistical methods. Unlike the past study using user segmentation techniques mainly to improve model accuracy, in this study, we focused on the grouping results of different user segmentation technique on both the mode choice/switch behavior and the associated disaggregate choice models.
The passenger car users of National Freeway No. 5 were selected as our demonstrate research subjects on their mode choice behavior. Three user segmentation techniques were implemented, including K-means, Random Forest and Latent Class Model. The results showed that the outcomes of grouping via the three methods were different, but the variables with grouping effect were consistent, which showed students and young people who aged 18-29 are generally distinct from other groups. In addition, such group with higher information awareness to the information of public transportation service stations and road networks, showed higher the proportion of willingness to transfer to public transport than that of others groups.
Besides the grouping results, we investigated the mode choice modeling effect in the commonly used Logit model where 80% of the data were randomly selected from the sample data for model estimation and 20% for prediction. Two models were further specified by two bases, named as “departure-unrestricted model” and “departure–restricted model” respectively, depending on the trip departure location for each user group. Estimation results showed the significant effect of the user segmentation. In addition, departure–restricted model had more significance variables than that with departure-unrestricted model.
Further, the calibration results show that non-young groups and those whose departure locations being not from Keelung, New Taipei City, Taipei and Taoyuan were more likely to have higher preference for passenger cars, and the more the number of cars available or travelling with family members will reduced their willingness to transfer to public transit. Reducing the time spent on public transit, includes three sections, before entering National Freeway No. 5, traveling on National Freeway No. 5 and after leaving National Freeway No. 5, will increase the respondents’ willingness to transfer to public transit. Besides, the young groups with higher potential to transfer to can further enhance their willingness by reducing the price of Freeway Schedule Bus Service.
Finally, the model verification were carried out with the remaining 20% of the data which weren’t used to calibrate the models. The results showed that the average prediction rate of the K-means-grouping models was approximately equal to that of the pooled model in the departure-unrestricted model, while the Random Forests-grouping models and the Latent Class Model models were both higher than the pooled model. In the departure-restricted model, the average prediction rate of the Random Forests-grouping models was about 2% lower than pooled model, while the K-means-grouping models and the Latent Class Model models were both higher than the pooled model.
As demonstrated by the findings of this study, comparing the difference of the transfer willingness ratio, the number of parameters with significant differences within the grouping-models, and the self-verification of average right of prediction rate, and average prediction switching rate, Random Forests and Latent Class Model performed better than K-means in this study.
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