Carto is working on one of the 10 mobility solutions in Ai4Cities’ Phase 2. Its aim is to design urban emission scenarios which can help optimise shared and active mobility modes in urban planning. The solution is applying Location Intelligence and Spatial Data Science to help policy makers reduce emissions when planning urban mobility solutions, and to help citizens choose more eco-friendly routes.

The solution collects data related to climate, infrastructure, traffic and demographics, which it then feeds into its AI technology, enabling the creation of a traffic simulator engine, an emissions scenario engine, an optimization engine and a routing engine. These models can then be used to quantify the impact of urban planning measures on road emissions, optimise the locations of micro-mobility charging stations, and create a gamification tool to find the most eco-friendly route.

The solution quantifies the impact of the urban planning measures on CO2 emissions by combining a macroscopic traffic model with a pollutant emission model. It then simulates the traffic flow and road emissions per pollutant by selecting a number of different parameters at three different city levels. It can look at the entire city, a specific area or a specific road network. Urban planners can also use these insights to plan a priority network for different modes of micro-mobility.

To help the citizens choose more eco-friendly routes it is developing a mobile app. By selecting a starting point, destination and date, users can discover the emissions of their road trip and be able to make more environmentally friendly choices.