A Generic Betweenness Centrality-Based Approach for Urban Street Network Reallocation to Mitigate Climate Change Impacts
This study proposes a generic, data-driven framework utilizing edge betweenness centrality algorithms to reallocate urban street networks for climate change mitigation, effectively balancing space optimization for green infrastructure with traffic redistribution to peripheral zones across diverse city networks.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Cities are breathing harder than they used to. As global temperatures rise, the concrete and asphalt that cover our urban landscapes trap heat, creating "urban heat islands" where temperatures can soar significantly higher than in the surrounding countryside. This trapped heat, combined with the sheer volume of vehicles, makes cities increasingly uncomfortable and dangerous during extreme weather. With more than four billion people now living in urban areas, the pressure to adapt is mounting. One of the most promising ways to cool a city is to turn its hard surfaces into green spaces, but finding room for new parks or tree-lined avenues is difficult when every inch of land is already occupied by buildings, roads, or infrastructure.
The solution, researchers suggest, might lie in the streets themselves. In many cities, the network of roads and lanes takes up between 20 and 30 percent of the total urban surface. If even a fraction of this space could be reclaimed for vegetation or active mobility like walking and cycling, it could significantly lower temperatures and improve air quality. However, simply closing off roads is risky; it can paralyze a city, leaving neighborhoods cut off or forcing traffic into chaotic bottlenecks. The challenge is to find a way to remove lanes without breaking the city's connectivity. This is where the work of Supharoek Chattanachot and his colleagues at the University of Le Havre Normandy and the University of Luxembourg comes in. They have developed a method to identify exactly which streets can be safely repurposed, using a mathematical concept called "betweenness centrality" to map out the flow of the city without needing to know the specific traffic patterns of every single driver.
The researchers approached the problem by treating the city's street network as a giant map of connections, where every intersection is a point and every lane is a line connecting them. They focused on a specific property of these lines: how often a particular street segment is used as a shortcut between two other points. In their model, a street with a high score on this measure is a critical artery, a route that many people rely on to get from A to B quickly. A street with a low score is more of a local connector, used less frequently for long-distance travel. The team realized that by analyzing these scores, they could make two very different kinds of decisions about how to reshape a city.
The first approach they tested was designed to maximize the amount of space that could be freed up while keeping travel times as short as possible. They applied an algorithm that looked for the streets with the lowest scores—the ones that were least essential for the overall flow of the network. By systematically removing these low-priority lanes and converting them into green spaces or pedestrian zones, they found that it was possible to reclaim a massive amount of road space. In tests using real street data from 48 different cities around the world, the method showed that in most cases, more than 95 percent of the two-way lanes could be removed or converted without disconnecting the city. The result was a network that remained fully connected, with the total distance people needed to travel increasing only slightly. This suggests that cities have far more "spare" road capacity than they realize, capacity that can be repurposed without causing gridlock.
The second approach took a different goal: instead of just saving space, the researchers wanted to move the city's traffic congestion away from the hot, crowded centers and push it toward the cooler, less dense outskirts. In many cities, the downtown area is the hottest part, suffering the most from the heat island effect, yet it is also where the most traffic is concentrated. The team hypothesized that if they could identify the busiest, most central routes and remove them, the traffic would naturally be forced to reroute through the periphery. To do this, they used an algorithm that targeted the streets with the highest scores—the most critical arteries. By removing these high-traffic lanes from the city center, the traffic flow was effectively redistributed to the edges of the city.
The results of this second strategy were striking. When they applied this method to the city of Troyes in France, for example, the cluster of the most important, high-traffic streets shifted noticeably away from the city center. The researchers developed new ways to measure this shift, calculating how far the "center of gravity" of the busiest streets moved and how much more spread out the traffic became. In most of the 48 cities they tested, the algorithm successfully moved the heaviest traffic loads away from the core, potentially cooling the downtown areas by reducing the number of cars idling there and freeing up space for trees and plants. However, the study also noted that this didn't work perfectly for every city; in places with very specific, clustered layouts, the traffic sometimes ended up concentrating in new, unintended spots, showing that no single rule fits every urban shape.
The researchers are careful to point out that their work is a theoretical guide, not a final blueprint for construction. They acknowledge that real-world city planning involves factors their computer models cannot see, such as historic landmarks, local laws, or the specific needs of a neighborhood. A street might be mathematically "removable" but politically or culturally untouchable. Nevertheless, the study provides a powerful starting point for planners. It offers a data-driven way to ask, "What if we closed this street?" and get an immediate answer about how the rest of the city would react. By using these algorithms, city officials can identify which lanes are truly expendable and which are vital, allowing them to make informed decisions about where to plant trees, build bike lanes, or create parks.
Ultimately, the paper suggests that the fight against climate change in cities does not always require building new infrastructure or finding empty land. Sometimes, the solution is already there, hidden in the existing grid of asphalt. By understanding the hidden logic of how a city moves, planners can gently nudge the flow of traffic, reclaiming space for nature and cooling the urban environment. The study confirms that with the right tools, cities can be reshaped to be more resilient and livable, turning the very roads that contribute to the heat problem into the solution for a cooler future.
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