People living in Urban Cities have a hard time commuting to and from work creating a vastly inefficient system in which most valuable productive working hours are spent navigating through traffic.
Cities collect traffic data frequently, and road attributes are well documented. However, there is no current process for identifying which roads can be improved and how to improve them.
Urban Developers do not have a way to plan out how different signs will lead to better use of public infrastructure and rely on trial and error to see how a potential change will impact travel times.
Private contractors often struggle with not only determining how to optimize roads but also with not having proper funding to do so. Residents are willing to fund infrastructure projects, but have no way to support them.
Our AI model inputs data on road features, such as lane count, signage, and signals, as well as the average traffic flow of the road. Our model then analyzes how each feature correlates with the road time and determines how adding or removing certain features can help improve traffic flow.
We allow city planners and contractors to interactively design their roads by adding or removing specific features. When the road is adjusted, we display the predicted change in traffic flow along with other key statistics, allowing planners to appropriately experiment solutions.
We allow local residents to contribute to infrastructure design. Often residents notice issues with roads more often than city planners, and by allowing them to test their own ideas and submit reports to the government, we make the process of road design much more efficient and impactful.
Often, infrastructure projects are taken on by private contractors, but they are only given projects instead of proposing projects. We allow contractors to design new road changes, send them to the government for approval, and share the changes with residents to collect funding through crowdsourcing.
