Visual techniques, such as density maps, can help to answer geolocated data-related questions, such as finding regions that concentrate most of the mobility flow during the day or the most common origin–destination pairs. Spreadsheets and statistics programs help producing tables and charts with aggregated information showing, e.g., the number of users of the public transportation system over the years or the number of women vs men that commute for work. While the above-mentioned OD survey is very comprehensive and accurate, urban planners need proper tools to analyze this large amount of multivariate data. Hence, the OD survey generates a large and multivariate dataset. Beyond a trip’s origin (O) and destination (D) data itself, the survey also gather a wide number of trip-related and socioeconomic aspects, or data attributes, such as transportation modes used, trip reasons, age, gender, and household income. The last OD survey (2017) shows that there are around 42 million trips over the 24 hours of a regular working day. This survey is performed by interviewing citizens about their life and commuting activities on a typical working day, resulting in a comprehensive panorama of the mobility behavior of the population over the SPMA. In the SPMA, every ten years since 1967, the São Paulo Metropolitan Company (Metrô), which manages the subway system in the city of São Paulo, conducts a travel study called Origin–Destination (OD) survey. Several data sources can be used for urban mobility analysis such as data captured with IoT devices such as traffic cameras, GPS tracking, bike-sharing systems, as well as censuses and trip surveys. Thus, the development of more efficient transportation systems is a critical issue that cities should tackle. For example, the traffic congestion in the São Paulo Metropolitan Area (SPMA) is estimated to affect 89% of work-related commuting trips Footnote 1, causing monetary losses of seven billion Brazilian reals ( ∼US$1.8 billion) every year. It directly affects people’s quality of life, causes prejudices to the environment, and has a high economical impact. Urban mobility is a great concern for citizens and governments. In the public administration realm, these data sources can be used to provide better services for citizens, to improve the management of urban infrastructure, or to reduce bureaucracy costs, supporting evidence-based policymaking. The Internet facilitates the gathering and the availability of a large variety of data sources, which allows citizens, researchers, companies, non-profit organizations, and public agents to perform analyses on their matters of interest regarding city-related issues. Governments around the world are providing open datasets about city resources, such as citizen surveys, data collected with the help of Internet of Things devices, accountability and transparency reports, and so on. The results show that bundling helps the identification and analysis of various mobility patterns for different data attributes, such as peak hours, social strata, and transportation modes. Our case study is based on the travel survey from the São Paulo Metropolitan Area, which is one of the most intense traffic areas in the world. In this paper, we augment a recent bundling technique to support multi-attribute trail datasets for the visual analysis of urban mobility. One approach to solve this problem is trail bundling, which groups motion trails that are spatially close in a simplified representation. Displaying the multiple attributes that these trajectories come with is an even larger challenge. Plotting raw trajectories on a map often causes data occlusion, impairing the visual analysis.
However, it is challenging to visualize huge amounts of data from mobility datasets. In addition, large, public datasets are made available on the Internet by open government programs, providing ways for citizens, NGOs, scientists, and public managers to perform a multitude of data analysis with the goal of better understanding the city dynamics to provide means for evidence-based public policymaking.
Internet-based technologies such as IoT, GPS-based systems, and cellular networks enable the collection of geolocated mobility data of millions of people in large metropolitan areas.
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