Building Sustainable Communities

A Fresh Approach to Evaluate Walkability Using Machine Learning

In the United States, city officials and developers have long relied on conventional traffic engineering and roadway design standards that prioritize vehicle movements, resulting in car-centric cities. However, the transportation paradigm is rapidly changing with a new focus on walkability and other active transportation modes. This shift requires new standards that prioritize walkability and promote livable communities.

To address this need, researchers developed an innovative methodology using a k-nearest neighborhood (KNN) machine learning algorithm that allows decision-makers to visualize how their planned development will function in terms of walkability. By utilizing existing developments among 220,740 U.S. Census Block Groups (CBG), the algorithm enables planners to identify the closest match to their development plan. By assessing the walkability of the closest match, planners can make informed decisions about their development before it is implemented.

The methodology was validated by testing four CBGs in New York City, which were intentionally excluded from the training data. The results identified the closest match to be four CBGs within the same vicinity. Following this, three different developments in Dubai were evaluated by identifying their closest matches within the CBGs, demonstrating the methodology’s potential for use in diverse urban contexts. The indicators used in the algorithm include intersection density, land use mix, proximity to public transit, and land use density, which are essential to ensure that the development is pedestrian-friendly and encourages active transportation.

Additionally, planners can adjust the walkability indicators of a proposed master plan development until their development matches and functions like their desired Census Block Group. This careful process of “what if” analysis using the tool ensures that the final plan meets the client’s vision.

As urbanization continues to rise, it is crucial for decision-makers to prioritize sustainable and walkable communities. The use of innovative methodologies, such as this machine learning algorithm, is a step in the right direction toward creating livable neighborhoods that prioritize active transportation. By taking a holistic approach to urban planning and engaging with local communities, we can create thriving cities that promote wellness and sustainability for all residents.

About The Author

Hamid Iravani is a Transportation Planning Director and a Parsons Fellow. He has 34 years of experience in international large-scale transportation planning projects. Hamid has authored numerous publications in peer-reviewed journals on New Urbanism, walkability, public transit, smart mobility, intersection operation, and the linkage between land use and transportation. His article “The Effect of New Urbanism on Public Health” was published in the book, “Urban Design and Human Flourishing, Creating Places that Enable People to Live Healthy and Fulfilling Lives.” Hamid’s recent accomplishment is a tool to optimize the locations of Electric Vehicle charging stations and a Multi-Criteria Decision Analysis program.

About The Author

Dr. Max Clark is the Parsons Chief Technology Officer and Parsons Fellow within the Europe and Middle East Business Unit. He has 20+ years of experience within the urban environment and leading large civil engineering projects. Max currently leads a number of initiatives associated with smart mobility, Artificial intelligence and innovation for Parsons within the Business Unit.

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