Abstract
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.
Highlights
People spend their time within places, and moving from one place to another
We present the following contributions: (i) A processing pipeline that, given Data Detail Records (XDR) and auxiliary data commonly available, generates the distribution of mode(s) of transportation usage for commuting in a city; (ii) A case study that evaluates the proposed pipeline in a city with more than seven million inhabitants and a public transportation system designed for intermodality; and (iii) A discussion about the implications of using the proposed pipeline for transportation analysis, on the basis of its explainability
In prior work [19], we found that, for several values of k, the clusters determined by Negative Matrix Factorization (NMF) were of two types: urban areas delimited by contiguity, and transportation networks
Summary
People spend their time within places, and moving from one place to another. As Charles Montgomery says in his book, Happy City: “City life is as much about moving through landscapes as it is about being in them” [1]. Some trips are crucial in people’s lives, such as the trip from home to work, and vice versa. This recurrent activity, called commuting, has several effects in quality of life, both, positive and negative [2, 3]. For some people it is the least liked daily activity [4]. An understanding of commuting patterns would provide opportunities to improve quality of life at scale in a city. By understanding commuting, it would be possible to inform public-policy design, the planning of transportation networks, and correlate commuting to factors such as health, social habits, exposure to pollution, stress, among others
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