Promoting Matosinhos' transition to soft-mobility

Mobility is a major contributor to urban carbon emissions. With a demographics-based prediciton model as well as a prescreptive algorithm, we were able to find specific geographical zones with the highest potential for moving from personal vehicles to low carbon solutions. Besides, the python algorithm was able to suggest the number of both e-scooters and bikes to place in different zone to maximize carbon saved, which would save 3.5 times more carbon than what's currently happening!

• Client: CM Matosinhos
• Category: Mobility

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Optimizing Austin's waste management and collection

The world is becoming more and more conscious about the environmental problems associated with waste, but habits are hard to change. The city council of Austin, Texas, challenged us to improve the waste production and management in the region, and we created an integrated solution that not only identifies target zones with higher waste production and lower recycling rates but also forecasts future weekly waste productions. Moreover, we identified waste collection routes with suboptimal performance that, if optimized, would decrease CO2 emissions by up to 30%.

• Client: Urban AI
• Category: Waste

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Optimization of Soft-Mobility Drop-off Points in Porto

Knowing the alarming increase of traffic in Porto, we proposed a data-driven decision support system for optimization of soft-mobility drop-off zones in order to maximize the usage of these solutions by the population. Our method predicts scores combining factors that influence the usage of soft-mobility solutions and optimizes the distribution of drop-off sites by maximizing their potential usage. If this system, which includes an interactive user interface, is used, we expect an increase of at least 2.5% in the number of e-scooter trips.

• Client: Porto Digital
• Category: Mobility

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Predicting a safety score for women in Costa Rica

A Proof-of-Concept, in Python, of a navigation software for route planning in Costa Rica for traveling by foot, bike, and public transportation capable of not only identifying the safest routes in terms of street sexual harassment but also becoming a reporting tool for such crimes. Solution includes creation of sreet danger score, crime forecast and route optimization. Check our executive summary for this solution.

• Client: Urbanalytica
• Category: Security

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Identification of ecological dark corridors for bats in Bristol

Artificial Light at Night (ALAN) is a global phenomenon that poses a major threat to biodiversity. Bristol City Council challenged us to optimize Street Light Management to create dark ecological corridors that could have a positive impact in the wildlife, mainly bats. Our solution, in python, is able to increase bat activity by 10%, save about 185 tons of emitted CO2, as well as £25,000 in electricity. Check our executive summary for this solution.

• Client: Bristol City Council
• Category: Biodiversity

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• Client: Window
• Category: Photography

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