This study performs an attribute-level analysis of the global and local path preferences of network travelers. To this end, a reward decomposition approach is proposed and integrated into a link-based recursive (Markovian) path choice model. The approach decomposes the instantaneous reward function associated with each state–action pair into the global utility, a function of attributes globally perceived from anywhere in the network, and the local utility, a function of attributes that are only locally perceived from the current state. Only the global utility then enters the value function of each state, representing the future expected utility toward the destination. This global–local path choice model with decomposed reward functions allows us to analyze to what extent and which attributes affect the global and local path choices of agents. The study applied the proposed model to the real pedestrian path choice observations in an urban street network where the green view index was extracted as a visual streetscape quality from Google Street View images. The result revealed that pedestrians locally perceive and react to the visual streetscape quality, rather than they have the pre-trip global perception on it. Furthermore, the simulation results using the estimated models suggested the importance of location selection of interventions when policy-related attributes are only locally perceived by travelers.

中文翻译：

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全局路径偏好和局部响应：存在视觉感知属性的网络路径选择分析的奖励分解方法

本研究对网络旅行者的全局和本地路径偏好进行属性级分析。为此，提出了奖励分解方法并将其集成到基于链接的递归（马尔可夫）路径选择模型中。该方法将与每个状态-动作对相关的瞬时奖励函数分解为全局效用（从网络中任何地方全局感知的属性的函数）和局部效用（仅从当前状态局部感知的属性的函数）。然后只有全局效用进入每个状态的价值函数，代表对目的地的未来预期效用。这种具有分解奖励函数的全局-局部路径选择模型使我们能够分析在多大程度上以及哪些属性影响智能体的全局和局部路径选择。该研究将所提出的模型应用于城市街道网络中的真实行人路径选择观察，其中绿色景观指数从谷歌街景图像中提取为视觉街景质量。结果表明，行人对视觉街景质量的感知和反应是局部的，而不是旅行前的全局感知。此外，使用估计模型的模拟结果表明，当旅行者仅在本地感知政策相关属性时，干预措施位置选择的重要性。