Up-to-date information on different modes of travel to monitor transport traffic and evaluate rapid urban transport planning interventions is often lacking. Transport systems typically rely on traditional data sources providing outdated mode-of-travel data due to their data latency, infrequent data collection and high cost. To address this issue, we propose a method that leverages mobile phone data as a cost-effective and rich source of geospatial information to capture current human mobility patterns at unprecedented spatiotemporal resolution. Our approach employs mobile phone application usage traces to infer modes of transportation that are challenging to identify (bikes and ride-hailing/taxi services) based on mobile phone location data. Using data fusion and matrix factorization techniques, we integrate official data sources (household surveys and census data) with mobile phone application usage data. This integration enables us to reconstruct the official data and create an updated dataset that incorporates insights from digital footprint data from application usage. We illustrate our method using a case study focused on Santiago, Chile successfully inferring four modes of transportation: mass-transit, motorised, active, and taxi. Our analysis revealed significant changes in transportation patterns between 2012 and 2020. We quantify a reduction in mass-transit usage across municipalities in Santiago, except where metro/rail lines have been more recently introduced, highlighting added resilience to the public transport network of these infrastructure enhancements. Additionally, we evidence an overall increase in motorised transport throughout Santiago, revealing persistent challenges in promoting urban sustainable transportation. We validate our findings comparing our updated estimates with official smart card transaction data.

翻译：及时获取不同出行方式的信息以监测交通流量并评估快速城市交通规划干预措施往往不足。交通系统通常依赖传统数据源，但由于其数据延迟、采集频率低及成本高昂，提供的是过时的出行方式数据。为解决这一问题，我们提出一种方法，利用手机数据作为成本效益高且富含地理空间信息的数据源，以前所未有的时空分辨率捕捉当前人类移动模式。我们的方法通过手机应用使用痕迹，推断基于手机定位数据难以识别的交通方式（自行车和网约车/出租车服务）。运用数据融合与矩阵分解技术，我们将官方数据源（家庭调查和人口普查数据）与手机应用使用数据整合。这种整合使我们能够重构官方数据，并创建融入应用使用数字足迹信息的更新数据集。我们以智利圣地亚哥为案例研究，成功推断出四种交通方式：公共交通、机动化交通、非机动化交通和出租车。分析揭示了2012年至2020年间交通模式的显著变化。我们量化了圣地亚哥各市公共交通使用率的下降，但地铁/铁路新线路覆盖区域除外，这凸显了这些基础设施改进对公共交通网络韧性的提升。此外，我们证实整个圣地亚哥机动化交通总体增加，揭示了促进城市可持续交通的持续挑战。我们通过将更新后的估算值与官方智能卡交易数据对比，验证了研究结果。