INFRASTRUCTURE PROJECT DELIVERY IN RIYADH’S ROAD NETWORK
AI-ASSISTED OPTIMIZATION AND THE POLICY TRADE-OFFS OF URBAN COORDINATION
DOI:
https://doi.org/10.66838/sauc.6389Resumen
Coordinating cross-sector infrastructure works is a city-scale scheduling problem with direct implications for service continuity and neighborhood disruption. This study presents an AI-assisted spatiotemporal simulation using Tabu Search and a synthetic portfolio of 200 road-aligned projects on Riyadh's public road network. This approach eliminates all hard-overlaps (intersecting projects occurring simultaneously) while reducing proximity-overlaps (simultaneous projects within 1 km) by 76.4% relative to greedy coordination. However, reducing proximity overlaps increases delivery delays. Sensitivity analysis exposes a Pareto frontier between disruption reduction and delivery delay, showing that AI optimization alone is not sufficient and policymakers must determine acceptable trade-offs.
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