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Project Title
Inference of commuting mode using mobile phone network data
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Internal ID
11180913
Principal Investigator
Graells Garrido, Eduardo

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Now showing 1 - 7 of 7
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Publication

Toward Characterizing Cities with Social Media Images Using Activity Recognition, Topic Modeling and Visualization

2020 , OPITZ OPITZ, DANIELA PIERINA , Eduardo Graells-garrido , Ignacio Pérez-Messina

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Publication

Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data

2020 , Eduardo Graells-garrido , Irene Meta , Feliu Serra-Buriel , Patricio Reyes , Fernando M. Cucchietti

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Toward An Interdisciplinary Methodology to Solve New (Old) Transportation Problems

2020 , Eduardo Graells-garrido , Vanessa Peña-Araya

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Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility

2020 , Ignacio Pérez-Messina , Eduardo Graells-Garrido , María Jesús Lobo , Christophe Hurter

Pervasive data have become a key source of information for mobility and transportation analyses. However, as a secondary source, it has a different methodological origin than travel survey data, usually relying on unsupervised algorithms, and so it requires to be assessed as a dataset. This assessment is challenging, because, in general, there is not a benchmark dataset or a ground truth scenario available, as travel surveys only represent a partial view of the phenomenon and suffer from their own biases. For this critical task, which involves urban planners and data scientists, we study the design space of the visualization of cross-origin, multivariate flow datasets. For this purpose, we introduce the Modalflow system, which incorporates and adapts different visualization techniques in a notebook-like setting, presenting novel visual encodings and interactions for flows with modal partition into scatterplots, flow maps, origin-destination matrices, and ternary plots. Using this system, we extract general insights on visual analysis of pervasive and survey data for urban mobility and assess a mobile phone network dataset for one metropolitan area.

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Tweets on the Go: Gender Differences in Transport Perception and Its Discussion on Social Media

2020 , Paula Vasquez-Henriquez , Eduardo Graells-Garrido , Diego Caro

People often base their mobility decisions on subjective aspects of travel experience, such as time perception, space usage, and safety. It is well recognized that different groups within a population will react differently to the same trip, however, current data collection methods might not consider the multi dimensional aspects of travel perception, which could lead to overlooking the needs of large population groups. In this paper, we propose to measure several aspects of the travel experience from the social media platform Twitter, with a focus on differences with respect to gender. We analyzed more than 400,000 tweets from 100,000 users about transportation from Santiago, Chile. Our main findings show that both genders express themselves differently, as women write about their emotions regarding travel (both, positive and negative feelings), that men express themselves using slang, making it difficult to interpret emotion. The strongest difference is related to harassment, not only on transportation, but also on the public space. Since these aspects are usually omitted from travel surveys, our work provides evidence on how Twitter allows the measurement of aspects of the transportation system in a city that have been studied in qualitative terms, complementing surveys with emotional and safety aspects that are as relevant as those traditionally measured.

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A city of cities: Measuring how 15-minutes urban accessibility shapes human mobility in Barcelona

2021 , Eduardo Graells-Garrido , Feliu Serra-Burriel , Francisco Rowe , Fernando M. Cucchietti , Patricio Reyes , Wenjia Zhang

As cities expand, human mobility has become a central focus of urban planning and policy making to make cities more inclusive and sustainable. Initiatives such as the “15-minutes city” have been put in place to shift the attention from monocentric city configurations to polycentric structures, increasing the availability and diversity of local urban amenities. Ultimately they expect to increase local walkability and increase mobility within residential areas. While we know how urban amenities influence human mobility at the city level, little is known about spatial variations in this relationship. Here, we use mobile phone, census, and volunteered geographical data to measure geographic variations in the relationship between origin-destination flows and local urban accessibility in Barcelona. Using a Negative Binomial Geographically Weighted Regression model, we show that, globally, people tend to visit neighborhoods with better access to education and retail. Locally, these and other features change in sign and magnitude through the different neighborhoods of the city in ways that are not explained by administrative boundaries, and that provide deeper insights regarding urban characteristics such as rental prices. In conclusion, our work suggests that the qualities of a 15-minutes city can be measured at scale, delivering actionable insights on the polycentric structure of cities, and how people use and access this structure.

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Adoption-Driven Data Science for Transportation Planning: Methodology, Case Study, and Lessons Learned

2020 , Eduardo Graells-Garrido , Vanessa Peña-Araya , BRAVO CELEDÓN, MARÍA LORETO

The rising availability of digital traces provides a fertile ground for data-driven solutions to problems in cities. However, even though a massive data set analyzed with data science methods may provide a powerful and cost-effective solution to a problem, its adoption by relevant stakeholders is not guaranteed due to adoption barriers such as lack of interpretability and interoperability. In this context, this paper proposes a methodology toward bridging two disciplines, data science and transportation, to identify, understand, and solve transportation planning problems with data-driven solutions that are suitable for adoption by urban planners and policy makers. The methodology is defined by four steps where people from both disciplines go from algorithm and model definition to the development of a potentially adoptable solution with evaluated outputs. We describe how this methodology was applied to define a model to infer commuting trips with mode of transportation from mobile phone data, and we report the lessons learned during the process.

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