Nommon and Populus are working in Phase 2 of AI4Cities on Avenue, a solution aiming to predict the impact of shared mobility services on urban transport GHG emissions. This would support city decision makers in effectively integrating new mobility options. The tool differs from similar solutions by combining historical operators’ data with data on total mobility flows in cities.

Avenue puts the data it receives into an AI-based analytical engine which enables demand monitoring and based on it creates demand prediction models and GHG emission models, which could then be used for policy optimization. Based on these policy adjustments, new demand prediction models are created, giving city planners a good overview of the impact of their policy actions.

In Phase 2 of AI4Cities Nommon and Populus worked on developing the travel demand and GHG emission models, as well as the policy optimisation engine. In addition, they integrated these features into a user-friendly visualization tool. For their models, they collected data from Tallinn, Stavanger and Helsinki.

Avenue’s next steps will be to adapt its transport models to new technologies that could change mobility patterns, develop new use cases based on cities’ different regulatory frameworks, needs and priorities, extend to a regional scale taking into account shared mobility solutions with a broader territorial scopes, and add indicators related to air pollution and noise to its model