neighbors in the graph by calculating the haversine distance between the OD point and all other nodes in the graph.

In each travel simulation run, we compute the shortest path (i.e., fastest route) using the Dijkstra’s algorithm<sup>78</sup> (minimizing total travel time along the network) and record the travel trajectory, time, and distance of the optimal route between each OD pair. We leverage the free-flow travel speed for all road types that are currently available from the OSM data set and assign it to the road segments in the network. During the simulation process, we calculate the average travel time = ∑ after the simulation run and track its change ������ over time. When ∆ reaches a threshold nn =0.01s<sup>10</sup> , the simulation<sup>�</sup> nn ii=1 ii tt tt nn<sup>tth</sup> runs are suspended (Fig. 5b). This approach achieves a stable or highly converged solution with reasonable<sup>�</sup> nn nn−1 ∆= tt −tt εε computational effort for large urban road networks. The mean travel time across all clusters is 18.25 min (1,095.46 s) with a maximum of 545.39 min (32,723.47 s or 9 hrs.), a minimum of 1.77 min (106 s), and 23.22 min (1,393.26 s) as the standard deviation.

In general, we find that the average travel time and distance increase with population less than proportionately (Supplementary Figure 3). Specifically, when population doubles, average travel time and distance increase by a factor of 1.35 and 1.38 respectively. This is globally aligned with the key findings from Angel and Blei<sup>79</sup> which demonstrate that when US cities double in population, travel time and distance only increase by a factor of 1.07 and 1.13 respectively. The authors explain the less than proportional increase as the result of the effects of spatial adjustments including increasing residential density, locational adjustments of residences and workplaces, and increasing commuting speeds brought about by transit system shifts on the average travel distance and time. This supports our OD sampling approach as a reasonable method to capture representative trips within settlement clusters.

Initial aggregated summaries of mean travel time at the country level reveal a discrepancy between developed and developing regions: countries in less developed (Tanzania, Mozambique, Zambia, Niger, Zimbabwe) and emerging economies (China, India, Philippines, Cambodia, Indonesia) have longer mean travel time compared to those in developed regions (United States, Canada, Sweden, Finland) (Fig. 5d). The standard deviation of mean travel time summarized at the country level shows a higher dispersion of average travel time values in developing countries, especially in East Asia, Southeast Asia, and Africa (Fig. 5e). Due to the discrepancies in the total number of settlement clusters in different countries (Fig. 4), country-level result summaries are more robust in countries with more clusters and less robust in countries with few clusters (e.g., African countries).

> 10 Results from sensitivity tests on show that the threshold value of 0.1 s or below yields the most consistent (variance of average travel time between 100 runs < 1s) across 100 sampled clusters. The chosen value of 0.01 s in relatively conservative. εε

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