By Pablo Cerdeira

Another major challenge for urban centers is to identify optimal models of public transport coverage. And for this, the use of massive data crossing information from various sources is critical. Often, in massive data usage projects, it is common for data generated for one purpose to have very relevant uses in other areas. In the case of Rio de Janeiro, the big data team used Global Positioning System (GPS) data of buses to produce analysis of overlapping lines that supported the work of rationalizing the public transport network of the city.

Based on insights presented with the massive use of data, the city of Rio de Janeiro reduced by 37% the circulating buses. The reduction was from 8,800 to 5,500 active vehicles at peak times, with improved coverage for the population. This impacts not only transit and greenhouse gas emissions but also future price updates on tariffs, which are calculated on the basis of system costs.

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