Cities are growing at a fast rate, and transportation networks need to adapt accordingly. To design, plan, and manage transportation networks, domain experts need data that reflect how people move from one place to another, at what times, for what purpose, and in what mode(s) of transportation. However, traditional data collection methods are not cost-effective or timely. For instance, travel surveys are very expensive, collected every ten years, a period of time that does not cope with quick city changes, and using a relatively small sample of people. In this paper, we propose an algorithmic pipeline to infer the distribution of mode of transportation usage in a city, using mobile phone network data. Our pipeline is based on a Topic-Supervised Non-Negative Matrix Factorization model, using a Weak-Labeling strategy on user trajectories with data obtained from open datasets, such as GTFS and OpenStreetMap. As a case study, we show results for the city of Santiago, Chile, which has a sophisticated intermodal public transportation system. Importantly, our pipeline delivers coherent results that are explainable, with interpretable parameters at each step. Finally, we discuss the potential applications and implications of such a system in transportation and urban planning.

中文翻译：

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使用手机数据推断运输方式

城市发展迅速，交通网络需要相应地适应。在设计，规划和管理运输网络时，领域专家需要能够反映人们如何从一个地方到另一个地方，在什么时间，什么目的，以哪种运输方式移动的数据。但是，传统的数据收集方法不具有成本效益或不及时。例如，旅行调查非常昂贵，每十年收集一次，这一时间段无法应对快速的城市变化，并且使用的样本量相对较小。在本文中，我们提出了一种算法管道，利用手机网络数据来推断城市中交通使用方式的分布。我们的管道基于主题监督的非负矩阵分解模型，根据从开放数据集（例如GTFS和OpenStreetMap）获得的数据，对用户轨迹使用弱标签策略。作为案例研究，我们显示了智利圣地亚哥市的结果，该市拥有完善的多式联运公共交通系统。重要的是，我们的管道提供了可解释的连贯结果，每一步都有可解释的参数。最后，我们讨论了该系统在交通运输和城市规划中的潜在应用和含义。