Social Cost Optimization Model and Empirical Evaluation of Intervention Effects on Ugandan Road Pavements

An important prerequisite for economic growth is the efficient and safe delivery of goods and services. In many developing countries, the most popular delivery mode for goods is road transportation. Because of the poor condition and congestion on some roads, a significant amount of productive time is lost during travel, a huge cost that hinders economic development. For instance, in Kampala, Uganda, an estimated 40% of rush-hour journeys are spent at a standstill due to narrow, dilapidated roads. In addition, pavement management has been fragmented with uncoordinated decisions taken for interlinked road aspects; for example, the inadequacy in road capacity (congestion) has been managed separately by planners, whereas durability (structural failure) has been handled by engineers despite the close links between these aspects. This study looks at three fundamental considerations for road travel, i.e., safety, condition, and capacity. The study builds a social cost model to evaluate intervention (condition improvement and capacity increase) choice for multiple road sections simultaneously by optimizing social costs after setting safety limits. The social cost model contains a travel time function that incorporates a condition term in the original Bureau of Public Roads function. The model defines social cost as a summation of travel and intervention costs incurred by the society and proposes an evaluation framework that combines both capacity and durability aspects. To show the applicability of the model, an empirical study was carried out on Ugandan road pavements.

This study uses road data to build an asset management model applied to determine the intervention choice for multiple road sections concurrently by optimizing social costs incurred by society. Social cost is defined as the summation of the cost of using roads (travel time) and the cost of improving road infrastructure (intervention). The model innovatively combines condition improvement (e.g., patching) and capacity increase (e.g., increasing number of lanes) in a joint decision framework to facilitate socially optimum intervention choice after minimum safety levels have been met. The model was empirically applied to Ugandan roads. The empirical result showed the contribution to the decrease in travel time on account of condition improvement and capacity increase. Optimal intervention strategies were proposed with consideration given to the impact of change in the safety limit and the total budget to the social cost; which suggested that there may be no need to arbitrarily increase intervention budgets. This study can be applied to other infrastructure such as bridges and tunnels and can also be adapted to optimally decide effective and efficient management choices for other facilities, such as pipelines and building and road furniture, that have component parts and many possible interventions during their life time.