是否觉得过时了？使用移动电话数据融合更新出行调查中的交通方式分布
Feel Old Yet? Updating Mode of Transportation Distributions from Travel Surveys using Data Fusion with Mobile Phone Data
AI总结：
针对传统交通数据过时的问题，提出利用移动电话数据融合更新交通方式分布的方法，通过应用使用轨迹推断难识别交通方式，经智利圣地亚哥案例验证有效并揭示交通模式变化。
摘要：
监测交通流量和评估快速城市交通规划干预措施所需的不同出行方式的最新信息往往缺失。交通系统通常依赖传统数据源，但这些数据源因数据延迟、收集频率低和成本高而提供过时的出行方式数据。为解决这一问题，我们提出一种方法，利用移动电话数据作为具有成本效益且丰富的地理空间信息源，以前所未有的时空分辨率捕捉当前人类移动模式。我们的方法利用移动电话应用使用轨迹，基于移动电话位置数据推断难以识别的交通方式（自行车和网约车/出租车服务）。通过数据融合和矩阵分解技术，我们将官方数据源（家庭调查和人口普查数据）与移动电话应用使用数据整合，从而重建官方数据并创建纳入应用使用数字足迹数据洞察的更新数据集。我们以智利圣地亚哥为案例研究展示该方法，成功推断出四种交通方式：公共交通、机动交通、主动交通（自行车）和出租车。分析显示2012年至2020年间交通模式发生显著变化：圣地亚哥各城市公共交通使用量减少，但最近新增地铁/铁路线路的地区除外，凸显这些基础设施改进增强了公共交通网络的韧性；此外，圣地亚哥机动交通总体增加，揭示推广城市可持续交通面临的持续挑战。我们通过将更新估计值与官方智能卡交易数据比较来验证研究结果。
英文摘要：
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 Transport systems typically rely on traditional data sources providing outdated mode-of-travel data due to their data latency, infrequent data collection and high cost.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.
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。
