Development of Linear Programming Model for Effective Budget Allocation for Road Accident Reduction in Road Management

Ajayeoba, A. O.; Ogunniyi, S. F.; Raheem, W. A. · 2025 · Crossref

DOI: 10.36108/laujoces/5202.51.0140

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Summary

This study addresses the critical challenge of optimizing budget allocation for road safety improvements in Nigeria, specifically within Oyo State. Motivated by the high incidence of road traffic accidents and the associated economic and social burdens, the research aims to develop a predictive model that maximizes accident reduction and economic benefits under limited financial resources. The authors argue that while cost-benefit analyses are common, they often fail to effectively handle complex budget allocation constraints, necessitating a more robust optimization approach. The researchers developed a Mixed Integer Linear Programming (MILP) model to determine the optimal selection of safety countermeasures. The model’s objective function maximizes the total discounted economic benefits derived from crash reductions, categorized by severity (fatal, serious, minor injury, and property damage only). Constraints included total budget limits, restrictions on the number of capital and maintenance interventions, location-specific suitability, and logical dependencies between measures. Secondary data regarding historical crash frequencies, infrastructure conditions, and countermeasure costs were sourced from government platforms and the Crash Modification Factor clearinghouse. The model was applied to the Oyo-Ogbomoso route as a case study, utilizing a relative urgency score to prioritize locations based on crash history and traffic volume. Sensitivity analyses were conducted to evaluate performance across varying budget levels (₦750 million to ₦3 billion) and under mandatory or urgency-based constraints. The results demonstrate significant economic returns on investment. Under an unconstrained scenario with a ₦2.15 billion investment, the model projected ₦293.76 billion in economic benefits, yielding a benefit-cost ratio of approximately 136. When constrained to a ₦1.5 billion budget, the model selected three specific interventions costing ₦1.42 billion, which generated ₦179.7 billion in benefits. Even with a reduced budget of ₦654.9 million, the model identified a single intervention yielding ₦64.4 billion in benefits. The analysis revealed that maintenance measures and lower-cost capital projects, such as pavement markings and driver education, provided higher returns under tight budget constraints, whereas capital-intensive projects like road dualization were less efficient unless mandated. Sensitivity analysis confirmed that increased budgets correlate with higher benefits and greater crash reduction. The significance of this work lies in providing a data-driven framework for strategic resource allocation in road management. The model enables policymakers to prioritize countermeasures based on crash severity and cost-effectiveness, ensuring fair and efficient use of limited funds. The findings highlight that substantial economic benefits can be achieved even with modest investments, particularly when focusing on high-impact, lower-cost interventions. This approach supports evidence-based decision-making, offering a replicable tool for improving road safety outcomes in developing nations facing similar resource constraints.

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