Efficient self-organization of informal public transport networks

Human mobility underlies modern societies, and public transport is essential for social and economic participation as well as sustainable urban mobility by offering low-emission, space-efficient transport. In the Global North, public transport is typically centrally organized with fixed lines and schedules, providing predictability but often failing to compete with the flexibility of private motor vehicles, leading to lower modal shares. In contrast, developing countries in the Global South rely heavily on informal, publicly accessible transport, often privately operated and demand-driven (typically by minibuses), because centrally organized systems are unavailable or unaffordable for most. Research on informal systems has been limited by scarce data, leading to case-study scale insights rather than global comparisons. It remains insufficiently understood how routes in informal systems are organized and how efficient they are relative to formal systems. The study addresses this gap by analyzing over 7000 routes across 36 cities to quantify and compare structural efficiency between informal and formal public transport.

Prior literature highlights the role of public transport in sustainable mobility and its challenges competing with private cars in developed contexts. Informal transport in the Global South, including paratransit and minibuses, has been studied for its socio-economic impacts, flexibility, and complementarity to formal systems, but evidence has often been limited to specific cities or regions due to data scarcity. Recent initiatives (e.g., Digital Matatus and broader OpenStreetMap-based efforts) have begun mapping informal routes to improve accessibility and planning. Existing work notes that informal services operate with dynamic routes and stops, with drivers adjusting to demand and traffic, and networks often emerging organically and being licensed a posteriori. Despite concerns regarding vehicle conditions, overloading, reliability, and safety, informal services are a primary mobility mode for many and contribute to social equity. However, comparative, large-scale structural efficiency analyses of informal versus formal networks have been lacking, motivating the present study.

Data: The study analyzes GPS-tracked bus routes from OpenStreetMap for 36 medium-sized cities worldwide (balanced between formal and informal systems, primarily bus-based, excluding very large metro/rail-centric cities), totaling more than 7000 routes across 22 countries. Routes were categorized as informal or formal based on the OSM “operator” tag and contributor metadata. Population distribution was characterized using the Kontur population dataset (H3 hexagons at ~400 m resolution) to compute route densities and population-weighted measures. Only hexagons intersected by at least one route define city areas for analysis. Route representation: Each recorded trajectory is treated as an individual route. Multiple trajectories can share an ID (bus or line). Formal systems have a median ~1.4 routes per ID, informal ~2 per ID. Analyses use medians over routes to avoid over-representing repeatedly sampled routes. Detour profile: For a route of length L, the detour profile d_x quantifies relative detour for three non-overlapping segments of length ∆L≈L/3 located at positions x∈{0,0.5,1}. For each segment, the segment length l_x is compared to the shortest path s_x between the segment’s endpoints on the street network; the detour fraction is d_x=(l_x−s_x)/s_x (conceptually; implementation uses consistent normalization with respect to segment length). GPS resolution (~30 m) ensures microscopic geometry is captured. City-level profiles are the median of d_x across routes, evaluated at 50 points along normalized route position for visualization. Aggregate observables: Two observables summarize structure: detour heterogeneity ξ=(d_0−2d_0.5+d_1)/2 (captures convexity/concavity, i.e., variability of detours between ends and middle), and total detour D=d_0+d_0.5+d_1 (overall structural inefficiency). Mathematical constraints imply ξ≤D/2 when mid-segment detour is minimal; routes often cluster near this boundary, indicating low mid-route detours. Interconnectivity: The number of intermediate routes C between any two routes A and B is defined as the minimal number of other routes needed to transfer from A to B (C_AB=0 for direct transfer). A route-level average over B yields C_A; the city characteristic C is the median of C_A across routes. Robustness: Results are robust to changes in segment length (fixed or relative), and are not explained by numbers, lengths, or densities of routes. Population-weighted variants of ξ and D were also assessed.

• Informal routes adhere to relatively fixed service corridors despite dynamic operations; routes of the same ID show consistent paths with minor deviations. Aggregated networks organically cover city areas and reflect population densities.
• Structural efficiency: Cities with informal public transport consistently exhibit lower median detour heterogeneity ξ and lower median total detour D than cities with formal services. • Median across city medians (Table 1): ξ_informal=0.011 vs ξ_formal=0.070 (Mann–Whitney U p=0.0007). D_informal=0.12 vs D_formal=0.45 (p=0.00005). • Many cities cluster near the theoretical boundary ξ≈D/2, indicating very small mid-route detours across systems.
• Network interconnectivity is comparable: median number of intermediate routes C is statistically indistinguishable between categories: C_informal=0.670 vs C_formal=0.675 (p=0.48).
• Population-weighted analyses corroborate the same conclusions: informal services often have lower population-weighted detour heterogeneity and total detour.
• These efficiencies arise without the substantial subsidies typical in the Global North; informal services generally must remain profitable for operators.

The findings demonstrate that self-organized informal bus networks can produce route structures that are as efficient as, or more efficient than, centrally planned services. Lower detour heterogeneity and total detours imply more direct, uniform routes that can reduce travel times and perceived inconvenience, particularly for riders from peripheral areas who are often most affected by route detours. Comparable interconnectivity suggests that despite fewer detours, informal networks do not sacrifice the ability to transfer between routes. The results point to dynamic, competitive interactions among drivers and responsiveness to demand as potential mechanisms promoting efficient route structures in informal systems. However, structural route efficiency alone does not guarantee overall service quality: reliability, waiting times, operating hours, and safety strongly influence user experience and accessibility, and require temporal data to assess. The study thus reframes informal transport as a potentially efficient and equitable component of urban mobility, calling for integration of its strengths into broader planning rather than replacement by strictly formal systems.

This work provides a global, data-driven comparison of route structural efficiency across 36 cities, showing that informal public transport networks can self-organize into highly efficient, low-detour, and uniformly structured routes with interconnectivity on par with formal systems. These insights challenge prevailing assumptions that informal services are inherently inferior and support policies that incorporate effective elements of informality into sustainable public transport design. Future research should collect and analyze temporal and demand data (waiting times, reliability, operating times, safety) and develop models of route evolution across time scales—from driver trip decisions to network-level dynamics—to translate structural efficiency into comprehensive service quality and to inform equitable, low-carbon mobility strategies in both Global South and North contexts.

The analysis focuses on spatial route structure due to limited availability of temporal and operational data for informal services; it does not assess waiting times, reliability, frequency, operating hours, or safety. The dataset relies on OpenStreetMap GPS tracks and contributor metadata, which may introduce mapping or classification biases. The city sample targets medium-sized, bus-centric cities (excluding very large metro/rail-dominant cities), which may limit generalizability to different urban contexts. Despite robustness checks (segment length variations, population-weighted measures), conclusions pertain to structural properties and not full service performance.