CoolWalks for active mobility in urban street networks

Abstract

Walking is the most sustainable form of urban mobility, but is compromised by uncomfortable or unhealthy sun exposure, which is an increasing problem due to global warming. Shade from buildings can provide cooling and protection for pedestrians, but the extent of this potential benefit is unknown. Here we explore the potential for shaded walking, using building footprints and street networks from both synthetic and real cities. We introduce a route choice model with a sun avoidance parameter $α$ and define the CoolWalkability metric to measure opportunities for walking in shade. We derive analytically that on a regular grid with constant building heights, CoolWalkability is independent of $α$, and that the grid provides no CoolWalkability benefit for shade-seeking individuals compared to the shortest path. However, variations in street geometry and building heights create such benefits. We further uncover that the potential for shaded routing differs between grid-like and irregular street networks, forms local clusters, and is sensitive to the mapped network geometry. Our research identifies the limitations and potential of shade for cool, active travel, and is a first step towards a rigorous understanding of shade provision for sustainable mobility in cities.

Figures (6)

• Figure 1: Higher sun avoidance $\alpha$ implies choice of routes that are not physically shortest but that minimize experienced length.A: Example of three different links connecting two nodes in the street network, from shortest and least shaded (1, blue) to longest and most shaded (3, green). B: The choice of which link to take depends on the pedestrian's sun avoidance $\alpha$. Increasing sun avoidance increases the experienced length because walking in the sun becomes less tolerable. At the threshold between different regimes, for example 1 (blue) to 2 (orange), the experienced length of the shaded link 2 becomes shorter than the experienced length of sunny link 1, implying a preference for link 2 despite longer physical length (dashed orange line). C,D: Generalization from links to shortest paths with an example of five routes.
• Figure 2: Study area definition. We use a subgraph of the Manhattan street network centered around Times Square; similar subgraphs are extracted from the centers of Barcelona and Valencia. Each considered source node $i \in V_{src}$ (orange) lies within 800m of the center and has a set of reachable nodes $V_{dst}(i)$, here highlighted in blue for one example node $i$ (red). We limit the number of reachable nodes to a maximum distance of 800m on the street network, to effectively capture the local structure of the city. To avoid edge effects caused by cutting the network from the full road network, we include all nodes within 1600m of the center. The nodes used in our analysis are therefore the source nodes together with all the nodes in the padding-area (grey).
• Figure 3: Comparing CoolWalkability of Manhattan with a synthetic grid shows similarities but also crucial differences. A: We introduce the diurnal Coolwalkability profile (here shown for 2023-06-21, and $\alpha=1.5$). It shows two characteristic dips for Manhattan (green) due to the two Manhattanhenge events MH1 and MH2 at 11:05 and 13:25, respectively, where the sun is aligned with the grid. The dips are also present in the synthetic grid (grey), but less pointed. B: We introduce the diurnal CoolWalkability phase portrait. It shows CoolWalkability versus shadow fraction, as functions of time, revealing larger differences. Manhattan's portrait (green) is relatively smooth due to slight imperfections in its grid structure and heterogeneous building heights, while the grid's portrait (grey) jumps discontinuously due to its perfect symmetries and constant building heights. The grid's analytical solution of Manhattanhenges (black crosses) fits well with the numerical simulation. C: Illustration of the two Manhattanhenge events MH1 and MH2 for Manhattan and the synthetic grid. Grey polygons denote building footprints, black polygons their shadows, orange arrows the direction of sun rays.
• Figure 4: Disentangling the effects of building height distribution and street geometry on Coolwalkability. Multiple curves shown in diurnal profiles correspond to different sun avoidance values $\alpha$ -- the curves mostly overlap, showing independence of Coolwalkability from $\alpha$, as proven analytically for the grid. All diurnal profiles shown for 2023-06-21. A: Left: Zoom into Manhattan's building footprints. Right: diurnal Coolwalkability profile. B: Left: Zoom into Manhattan's building footprints at constant height, set as the average 71m of empirical building heights. Right: The corresponding diurnal Coolwalkability profile. Due to loss of building height heterogeneity, the Manhattanhenge dips are slightly less pointy. C: Right: Zoom into the grid with empirical building footprints taken from Manhattan. Left: the corresponding diurnal Coolwalkability profile. Keeping building height heterogeneity but changing from empirical street network to grid implies only slight differences in diurnal Coolwalkability. D: Right: Zoom into the grid's building footprints at constant height, set as the average 71m of empirical building heights. Left: the corresponding diurnal Coolwalkability profile. Due to loss of building height heterogeneity and the change to a grid, the Manhattanhenge dips are considerably broader and less pointy.
• Figure 5: Spatial clustering by CoolWalkability leads to areas with different profiles. From top to bottom, we study the cities Manhattan, Barcelona, Valencia, and the random null model (Poisson-Voronoi). Left column: Clustering local Coolwalkability of each node in the street network leads to spatial clusters of similar CoolWalk potential. Middle column: The diurnal profiles of these clusters display high variations within each city and between different cities. In particular, the more organic, least grid-like areas (red curves) display the highest potential. K: The null model shows the baseline of small variation. Right column: the distributions of the time average of each diurnal profile within each cluster illustrate the large potential differences in empirical street networks. L: These differences are negligible in the null model.