Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks now enables large-scale, dynamic analyses of how people navigate cities. This study uses eXtended Detail Records (XDRs) derived from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. First, we identify residential and work locations and model commuting routes using the R5 multimodal routing engine, which combines public transport and walking. To explore spatial patterns, we apply a bivariate spatial clustering analysis (LISA) alongside regression techniques to identify distinct commuting behaviors and their alignment with vulnerable population groups. Our findings reveal that average commuting times remain consistent across socioeconomic groups. However, despite residing in areas with greater opportunity density, higher-income populations do not consistently experience shorter commuting times. This highlights a disconnect between spatial proximity to opportunities and actual travel experience. Our analysis reveals significant disparities between sociodemographic groups, particularly regarding the distribution of indigenous populations and gender. Overall, the findings of our study suggest that commuting and accessibility inequalities in Santiago are closely linked to broader social and demographic structures.

翻译：传统的城市可达性度量通常依赖于静态模型或调查数据。然而，来自移动网络的位置信息使得对人们如何穿行城市进行大规模动态分析成为可能。本研究利用源自手机活动的扩展详单记录（XDRs）来分析智利圣地亚哥的通勤模式与可达性不平等。首先，我们识别居住和工作地点，并使用结合公共交通与步行的R5多模式路径规划引擎对通勤路线进行建模。为探索空间模式，我们应用双变量空间聚类分析（LISA）及回归技术，以识别不同的通勤行为及其与脆弱人口群体的关联。我们的研究结果表明，平均通勤时间在不同社会经济群体间保持稳定。然而，尽管高收入人群居住在机会密度更高的区域，他们并未持续经历更短的通勤时间。这凸显了机会的空间邻近性与实际出行体验之间的脱节。我们的分析揭示了社会人口群体间的显著差异，特别是在原住民人口分布和性别方面。总体而言，我们的研究结果表明，圣地亚哥的通勤与可达性不平等与更广泛的社会和人口结构密切相关。