Chapter 4 examines the effects of BSS convenience on rental housing prices in Chiba and Shizuoka using BSS distance (BD) and the BSS availability index (BAI), which incorporates station proximity and bike availability. Falsification tests suggest omitted variable bias in generalized propensity score estimates, motivating a Double/debiased Machine Learning (DML) approach incorporating Moran eigenvectors as proxies for unobserved spatial confounders. In Chiba, including these eigenvectors reduces the estimated effect and renders it statistically insignificant, suggesting overestimation without spatial controls. Orthogonal Random Forest (ORF) analysis reveals that, within 1,500 m of railway stations in Chiba, a one-unit increase in BAI raises rents by approximately 1–3%, with the effect peaking around 1,200 m. No clear effect emerges in Shizuoka, where public transportation is less convenient and less widely used. These findings highlight variation across cities and areas, the importance of adequate bike availability alongside station proximity, and the need to capture and redistribute BSS-induced property value gains for broader societal benefit. 

Chapter 5 examines Chiba residents’ SMMS preferences using an online stated preference survey and a mixed logit model incorporating an error component. Absolute travel time coefficients are largest for e-scooter sharing, followed by e-moped sharing, bike sharing, walking, personal-size EV sharing, and bus. Respondents aged 60 or older tend to avoid bike and e-scooter sharing, while those earning below JPY 4 million annually are less likely to choose personal-size EV sharing. Health-oriented respondents prefer bike sharing; technology-oriented respondents prefer e-moped and personal-size EV sharing. Prior shared mobility experience encourages SMMS use. Simulations suggest that longer bus waits increase SMMS choice probabilities for trips exceeding 1 km. Median values of travel time (VoTT; JPY/min) are 28.16 for bus, 34.10 for bike sharing, 49.98 for e-scooter sharing, 49.04 for e-moped sharing, and 29.44 for personal-size EV sharing. Across studies, some SMMS VoTT estimates vary substantially, and their rankings differ. 

Chapter 6 proposes a framework for maximizing SMMS provider profit by optimizing station locations, capacities, and fleet size, applied to part of Chiba City. A transportation simulation captures stochastic multimodal choices, updates vehicle and parking availability every second, and reflects feedback among travelers’ choices. Adaptive Large Neighborhood Search (ALNS) combines strategic components through destroy and repair operators, using station and vehicle utilization to guide exploration. Results favor dense station deployment in the city center, raising concerns about suburban transport equity and central urban clutter. Scenario analyses show that subsidies for suburban station locations and publicly provided mobility hubs at suburban railway stations encourage suburban deployment and improve suburban accessibility while maintaining accessibility in the city center. These findings highlight the need to evaluate policy interventions using both social surplus and indirect benefits from improved access to potential activities.