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Railway commuters to work place/school, who are the majority of railway users, often make stopover at various stations for eating or shopping on their way home. In recent years, the mobility of railway users has been diversified by expansion of railway network and redevelopment around railway stations. Therefore, we need to analyze such stopover behavior by commuters in order not only to improve safe and comfortableness of railway stations but also to activate the commercial activities in the vicinity of stations. In this paper, we constructed a multinomial logit model, which describes commuters' choice behavior of stopover-stations for eating or shopping on their way home from their workplace. More specifically, we considered commercial facilities in the vicinity of the stopover-station and the positional relationships between their home and workplace as explanatory variables. Analyzing the person trip data obtained in three different time (1988, 1998, and 2008) in Tokyo Metropolitan Area by the proposed model, we demonstrated the estimation accuracy of the model and the attractiveness of stopover-stations quantitatively. On the basis of the estimated parameters, we grasped the influence of each variable on the attractiveness of stopover-station in three different time. For instance, it is shown that transfer stations, stations located on a railway line of their way home, and stations surrounded by many commercial facilities were attractive for stopover. By contrast, the effects of resistance to the increase of travel time is getting larger and larger. Furthermore, we classified the samples of railway users by his/her personal attribute (gender, age, and the purpose of stopover), and estimated the stopover behavior model for each attribute. More concretely, we quantitatively analyzed the preference for choosing stopover-station, and obtained the new findings as follows: (1) Men prefer to make stopover at transfer stations on their way home, but women prefer stations which are surrounded by many commercial facilities with large total floor area. (2) Elderly people are reluctant to increase of travel time by stopover, and stations with high commercial potential are more attractive for young people. (3) People who make stopover for shopping tend to feel higher resistance to the increase in travel time by stopover. On the other hand, transit stations, which are convenient for meeting friends, are more attractive for people who make stopover for eating, socializing, or entertainment. Finally, as an application of the proposed model, we estimated the change of attractiveness of stopover-stations and the number of people who made stopover. Namely, assuming that the number of commercial facilities around the specific station increased, the attractiveness of the station and other adjacent stations on the same railway line tended to be decreased greatly. Additionally, assuming that the mutual railway line operation between different railway companies started, stations on the direct railway line became more attractive for stopover, but the attractiveness of stations on other railway lines were decreased. Through these examples of simulation, we demonstrated the possibilities of application of stopover behavior model proposed in this paper.
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