Enhancing Accessibility of Rural Populations through Vehicle-based Services

TL;DR

The paper addresses rural accessibility gaps in Sub-Saharan Africa by enabling mobile, vehicle-based public services (healthcare, education, energy, and water) through a GIS-driven, demand-aware clustering approach that places service stops within a 5 km catchment. It introduces a data-light methodology with an Accessibility Scaling Factor $A_i = d_i / d_{max}$ and a k-means clustering workflow to derive road-aligned vehicle stops, without requiring preselected candidate locations. In a case study in Bekoji, Ethiopia, 45 vehicle stops can address a substantial portion of addressable demand across services (e.g., Energy ~58%, Water ~62%, Education ~61%, ANC ~39%), with robust performance against boundary changes and superior coverage compared to random stop placements. The approach provides decision-makers with a transparent visualization of trade-offs between investment and service coverage, supports spatial-justice-oriented prioritization, and offers a scalable framework for planning mobile public-service delivery in rural SSA.

Abstract

Improving access to essential public services like healthcare and education is crucial for human development, particularly in rural Sub-Saharan Africa. However, limited reliable transportation and sparse public facilities present significant challenges. Mobile facilities like mobile clinics offer a cost-effective solution to enhance spatial accessibility for the rural population.Public authorities require detailed demand distribution data to allocate resources efficiently and maximize the impact of mobile facilities. This includes determining optimal vehicle service stop locations and estimating operational costs. Our integrated approach utilizes GIS data and an accessibility scaling factor to assess spatial accessibility for rural populations. We tailor demand structures to account for remote and underserved populations. To reduce average travel distances to 5 km, we apply a clustering algorithm and optimize vehicle service stop locations. In a case study in rural Ethiopia, focusing on four key public services, our analysis demonstrates that mobile facilities can address 39-62\% of unmet demand, even in areas with widely dispersed populations. This approach aids decision-makers, including fleet operators, policymakers, and public authorities in Sub-Saharan Africa, during project evaluation and planning for mobile facilities. By enhancing spatial accessibility and optimizing resource allocation, our methodology contributes to the effective delivery of essential public services to underserved populations.

Figures (10)

• Figure 1: Basic principle of a vehicle-based service (VbS) system
• Figure 2: Overview of the proposed methodology
• Figure 3: Overview three step vehicle service stop allocation model
• Figure 4: Results for the demand analysis with red = high demand and yellow = low demand. Upper left: education, upper right: water, lower left: energy, lower right: ANC
• Figure 5: The resulting 45 final vehicle service stops (= red points with yellow 5 km catchment area) for the case study in Bekoji