The commercial speed of buses is a crucial factor for route planning, service frequency, and the quality of urban mobility. This challenge proposes the creation of a data analytics tool that allows:
• Calculating speed by segment, hour, and type of day.
• Identifying routes and trips with the largest deviations from the planned schedule.
• Visualizing speed patterns throughout the day or by zone, highlighting critical locations and times.

In addition, a network optimization model could be developed with the aim of reducing delays and improving the efficiency of public transport.
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Understanding passenger mobility flows is essential for route planning and for adjusting transport supply, contributing to more sustainable urban mobility. Currently, the main difficulty for Carris Metropolitana lies in the absence of data regarding passengers’ exit stops.
This challenge proposes the development of a data analytics tool that allows:
• Visualizing and quantifying origin-destination flows.
• Calculating the overall OD matrix and segmenting it by hour and type of day.
• Estimating median travel times for each OD pair.
• Determining distances traveled.
• Measuring demand through the number of validations and distinct passengers.
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Sustainable urban mobility is one of the greatest challenges faced by contemporary cities. The collection and analysis of data on the behavior of users of different transport modes (pedestrians, cyclists, and vehicles) opens new opportunities to:
• Optimize public transport systems.
• Encourage the use of more sustainable modes of transport like public transport.
• Reduce urban congestion.
This challenge proposes the development of an innovative, citizen-centered solution that improves quality of life in cities. The solution should be scalable, allowing its application in other cities with similar problems and datasets. Data from a single mode of transport or a combination of multiple data sources may be used, enabling an integrated view of urban mobility.
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