Yoshiki Ogawa

Promoting walking is crucial for sustainable development and fosters individual health and well-being. Therefore, comprehensive investigations of factors that make walking attractive are vital. Previous research has linked streetscapes at eye-level to walking preferences, which usually focuses on simple linear relationships, neglecting the complex non-linear dynamics. Additionally, the varied effects of streetscape factors across street segments and intersections and different street structures remain largely unexplored. To address these gaps, this study explores how eye-level streetscapes influence walking preferences in various street segments and intersections in Setagaya Ward, Tokyo. Using street view data, an image survey, and computer vision algorithms, we measured eye-level streetscape factors and walking preferences. The Extreme Gradient Boosting (XGBoost) model was then applied to analyze their non-linear relationships. This study identified key streetscape factors influencing walking preferences and uncovered non-linear trends within various factors, showcasing a variety of patterns, including upward, downward, and threshold effects. Moreover, our findings highlight the heterogeneity of the structural characteristics of street segments and intersections, which also impact the relationship between eye-level streetscapes and walking preferences. These insights can significantly inform decision-making in urban streetscape design, enhancing pedestrian perceptions.

The authors used a data-driven reinforcement learning model for the post-disaster rapid recovery of human mobility, considering human-mobility recovery rate, road connectivity, and travel cost as the recovery components, to generate the reward framework. Each component has relative importance with respect to the others. However, if the preference is different from the original one, the optimal policy may not always be identified. This limitation must be addressed to enhance the robustness and generalizability of the proposed deep Q-network model. Therefore, a set of optimal policies were identified over a predetermined preference space, and the underlying importance was evaluated by applying envelope multi-objective reinforcement learning. The agent used in this study could distinguish the importance of each damaged road based on a given relative preference and derive a road-recovery policy suitable for each criterion. Furthermore, the authors provided the guidelines for constructing the optimal road-management plan. Based on the generalized policy network, the government can access diverse restoration strategies and select the most appropriate one depending on the disaster situation.