A Data-Driven Decision-Support Framework for Municipal Road-Watering Operations: Environmental Priority Scoring and Route Optimization

Abstract

Dust lifted from road surfaces by passing vehicles is a recurring source of urban particulate-matter exposure. Municipal road-watering operations must allocate limited vehicle time and water capacity across road segments with different service priorities while accounting for refilling, accessibility, and repeated-service requirements. This study proposes an integrated decision-support framework for daily road-watering and applies it to Dongjak-gu, Seoul, Republic of Korea. The framework combines data-driven urgency scoring based on floating population, traffic volume, and PM10/PM2.5 hazard-day frequencies with a selective multi-vehicle, multi-trip arc-routing model and heuristic solution procedures. Its distinguishing formulation feature is a completion-conditioned reward: a road segment contributes its priority score only when all required watering passes are completed within the planning horizon. The case study includes 2213 candidate road segments covering approximately 208 km. Over 30 runs, the proposed genetic algorithm (GA) improves the priority-weighted objective by 35.24% relative to the Phase I constructive solution (mean Z=127.13, 95% CI [125.88, 128.39]). The practice-inspired route-corridor baseline attains a higher aggregate objective through broader contiguous coverage, whereas the GA concentrates service more strongly on higher-urgency roads, completing 67.3% of C4 segments compared with 21.6% under the baseline. Under the same decoder-call budget, adaptive large neighborhood search achieves a higher mean objective value than the GA (130.16 vs. 127.13). These results position the framework as a decision-support architecture for comparing feasible service-allocation strategies rather than as a claim of GA dominance.