A Scalable Automated System for Efficient Complete Street Data Collection and Maintenance

Urban complete street data collection is crucial for smart city initiatives, transportation planning, and infrastructure maintenance. Traditional data collection methods often lead to redundant coverage, increased fuel consumption, and higher labor costs. This paper presents a scalable system that integrates mobile apps for complete street data collection and infrastructure maintenance with an adaptive cluster-based routing optimization (ACRO) algorithm to minimize redundancy and maximize coverage efficiency. We evaluate the system through a realworld case study in Downtown Tacoma, Washington, comparing four scenarios: manual collection, app-assisted collection, manual driving with ACRO, and hybrid app-guided collection with ACRO. Results demonstrate that real-time visualization significantly reduces redundancy, while combining the ACRO algorithm with the data collection app improves both efficiency and coverage. This system has the potential to enhance urban mapping, transportation analytics, and city maintenance operations by reducing inefficiencies in complete street data collection and infrastructure management.