We propose a framework for predicting the effects of mobility introduction measures using a human-flow digital twin. This digital twin incorporates a multi-agent simulator that can represent how visitors choose destinations depending on factors such as their current location and the attractiveness of spots. We extract data on how visitors selected destinations with respect to measured pre-intervention human-flow data, inter-spot distances, spot attractiveness, and travel volumes, and use these data to train each agent's decision model of this simulator. The trained decision model is a function that takes a visitor's current state and surrounding environmental information as input and outputs which spot the visitor will move toward next. By expressing mobility introduction measures as changes to inter-point distances or to spot attractiveness, the framework can reproduce human flows with mobility introduction in the multi-agent simulator and thereby quantify effects such as changes in visitor counts and circulation. We evaluated the proposed method using human-flow data measured with and without introducing mobility within Wakayama Castle Park in Japan. When reproducing flows with mobility introduction using a multi-layer perceptron decision model, the cosine similarity of the spatial population distribution exceeded 0.7, confirming that the approach can replicate the flow changes caused by the mobility introduction.

翻译：我们提出了一种利用人流数字孪生预测移动性引入措施效果的框架。该数字孪生体集成了多智能体仿真器，能够根据游客当前位置和景点吸引力等因素，模拟其目的地选择行为。我们基于干预前实测的人流数据、景点间距、景点吸引力及出行流量等指标，提取游客目的地的选择特征，并以此训练仿真器中各智能体的决策模型。经训练后的决策模型作为函数，将游客当前状态及周围环境信息作为输入，输出其下一步将要前往的景点。通过将移动性引入措施转化为景点间距或吸引力的参数变化，该框架可在多智能体仿真器中复现引入移动性后的人流分布，进而量化游客数量变化及流动模式改变等效果。我们利用日本和歌山城公园在引入/未引入移动性两种场景下的人流实测数据对方法进行了验证。当采用多层感知器决策模型复现引入移动性后的流动模式时，空间人口分布的余弦相似度超过0.7，证实该方法能够准确还原移动性引入所导致的客流变化。