交通网络中导航复杂性的量化
Quantifying navigation complexity in transportation networks
摘要：
城市导航的复杂性随着城市面积的扩大而增加，产生了具有挑战性的交通问题，推动了许多关于网络可导航性的研究。然而，由于缺乏个人的流动性数据，大规模的实证分析的指路人的现实世界的导航是罕见的。在这里，我们使用来自中国三个主要城市的2.25亿次地铁出行，从信息的角度量化导航难度。我们的研究结果表明：1）人们保留少量重复使用的路线，2）由这些路线形成的子网络中的导航信息远小于全球网络中的理论值，这表明实际出行的决策成本显着小于先前研究中发现的理论上限。通过对不断增长的网络中的路由行为进行建模，我们表明，虽然全球网络变得难以导航，但子网络的可导航性可以得到提高。我们进一步提出了一个普遍的线性关系之间的经验和理论的搜索信息，这使得这两个指标来预测对方。我们的研究结果展示了大规模观测如何量化现实世界的导航行为，并有助于评估交通规划。
英文摘要：
The complexity of navigation in cities has increased with the expansion of urban areas, creating challenging transportation problems that drive many studies on the navigability of networks. However, due to the lack of individual mobility data, large-scale empirical analysis of the wayfinder's real-world navigation is rare. Here, using 225 million subway trips from three major cities in China, we quantify navigation difficulty from an information perspective. Our results reveal that 1) people conserve a small number of repeatedly used routes, and 2) the navigation information in the subnetworks formed by those routes is much smaller than the theoretical value in the global network, suggesting that the decision cost for actual trips is significantly smaller than the theoretical upper limit found in previous studies. By modeling routing behaviors in growing networks, we show that while the global network becomes difficult to navigate, navigability can be improved in subnetworks. We further present a universal linear relationship between the empirical and theoretical search information, which allows the two metrics to predict each other. Our findings demonstrate how large-scale observations can quantify real-world navigation behaviors and aid in evaluating transportation planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
