受限几何网络中的行人流量：大学校园的可达性熵测度与鲁棒性
Pedestrian fluxes in confined geometric networks: entropic measures and robustness of accessibility in a university campus
AI总结：
本研究利用Wi-Fi数据分析大学校园受限几何网络中的行人流量，通过熵和鲁棒性测度评估可达性，并识别亟需维护的道路区域。
摘要：
在讨论城市生活时，行人可达所有主要服务对于促进社会互动、倡导健康生活方式以及减少污染至关重要。这一点在像大学校园这样连贯的城市聚集体中尤为突出，因为此类区域集中了大量街道和社会设施。利用Wi-Fi数据，我们研究了代表大学校园路径的受限几何网络中的行人移动。我们估算了网络中每条弧段的拥挤程度，并识别了沿所有可能路径的行人流量，从而测量网络的熵和鲁棒性。具体而言，我们计算了通过使用Wi-Fi数据所获得的信息增益，并评估了在移除个别弧段后行人交通如何在网络内重新分布。我们的结果可用于促进对大学校园当前步行适宜性状况的调查研究，同时也可测试一套用于分析城市复杂网络的方法，从而有可能帮助我们识别亟需道路维护和改善的区域。
英文摘要：
When discussing urban life, pedestrian accessibility to all main services is crucial for fostering social interactions, promoting healthy lifestyles, and reducing pollution. This is especially relevant in coherent urban agglomerations like university campuses, which feature a high concentration of streets and social facilities. Using Wi-Fi data, we study pedestrian movements within a confined geometric network representing the pathways on a university campus. We estimate the level of crowding in each arc of the network and identify pedestrian flows along all possible paths, measuring the entropy and robustness of the network. In particular, we calculate the information gain achieved through the use of Wi-Fi data and we assess how pedestrian traffic redistributes within the network after the removal of individual arcs. Our results can be used to facilitate the investigation of the current state of walkability across the university campus while also testing a set of methods for analyzing urban complex networks, potentially allowing us to pinpoint areas in urgent need of road maintenance and enhancement.
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