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This paper presents the utilization of mobile phone data for transport models, with spatial modeling of the study region in geographical units that allows the integration of aggregated call detail records (CDR) with demographic data and other sources. The algorithm used for the estimation of the origin–destination matrices obtained a distribution of the number of trips compatible with those of a household survey conducted in 2013. With the use of a one-year dataset, two mobility patterns were identified in Rio de Janeiro: home–work and weekend trips. Changes in mobility patterns because of an important road modification were also detected, demonstrating that the use of CDR for urban planning and monitoring is a robust and low-cost option.

This study investigated the mobility patterns of favela residents based on an analysis of their daily movements derived from high-frequency mobile phone data. Daily movements were measured considering the distance traveled at different times of day over the course of more than two months. Potential trip purposes for the most frequently visited locations were inferred based on land use data from property taxes. The high volume of geocoded data from mobile phones enabled the analysis during multiple days, also covering weekends, usually not considered in traditional transport surveys. The mobile phone data indicated that, on average, favela residents go farther from home during business days and perform less out of home activities during weekends when compared with non-favela residents. Further, distinct patterns for favelas and non-favela residents were mapped, considering different geographical areas, revealing space and time mobility inequalities.

One of the most common applications of accessibility is in evaluating inequality in access to jobs. A vital factor to be incorporated by accessibility indicators when analyzing job accessibility is the competition for job positions by job seekers; otherwise, the results may be inaccurate or misleading. Despite efforts by researchers to develop accessibility measures that capture job competition, they fail to ensure that these measures are practical and easily interpretable and communicable, which in turn makes planners and policymakers continue to opt for more straightforward measures. In this paper we aim to fill this gap by providing a simple accessibility measure that accounts for competition effects, while remaining practical, intuitive, and highly communicable. The proposed indicator – Balancing Time – is applied to assess the inequality in job accessibility in 160 neighborhoods within the city of Rio de Janeiro, and the results are compared with the most popular indicator used in practice, the cumulative opportunities. The findings suggest that Balancing Time overcomes some of the limitations of cumulative opportunities and that it is a useful tool for planners, particularly in the cities with job opportunities concentrated in central areas. Given its simplicity, Balancing Time is especially relevant in the context of the Global South, where most transport agencies face data limitations and have low skilled technical staff.