Multi-Agent Based Automation of Pavement Management with Embodied Carbon Optimization

This workflow automates the core bottleneck of sustainable pavement management: manually correlating condition surveys, traffic models, and Life Cycle Assessment (LCA) databases to evaluate carbon-cost tradeoffs. For a DOT or large municipality, it replaces weeks of spreadsheet analysis with a system that continuously ingests PMS data, triggers multi-objective optimization, and outputs prioritized, carbon-aware maintenance plans. The operational upside is direct: reducing embodied carbon in capital programs by 15-25% while protecting asset condition, and cutting planning cycle time by over 60%.

Implementation integrates with legacy Pavement Management Systems (like StreetSaver or dTIMS), material supplier portals, and financial systems via API and ETL layers. The orchestrator, built on LangGraph or similar, manages state across specialized agents for strategy generation, carbon calculation, and financial modeling. Critical controls include human-in-the-loop approval gates for final recommendations, confidence scoring on LCA data, and exception routing for data gaps or edge-case scenarios. Observability is built into the workflow, providing a full audit trail of decision rationale for sustainability reporting and funding compliance.

This workflow automates the complex trade-off analysis between pavement performance, budget, and embodied carbon—a manual, spreadsheet-heavy bottleneck for infrastructure teams. It ingests condition surveys from drones, traffic volumes from ITS, and material LCA data from databases like Athena or EC3. The operational upside comes from reducing analysis cycles from weeks to hours, enabling data-driven decisions that lower long-term carbon liability and defer capital-intensive reconstructions through optimized preservation scheduling.

Implementation integrates with legacy Pavement Management Systems (PMS) like StreetSaver or dTIMS and ERP platforms via API. The orchestrator, built on LangGraph, manages state, handles exceptions like missing LCA data, and routes scenarios for human review via a dashboard. Controls include approval gates for high-cost strategies, confidence scoring on model outputs, and full audit trails for carbon credit verification and regulatory compliance, ensuring the system is both autonomous and governable.

Phase 1 establishes the data backbone, ingesting and normalizing condition surveys from drones, traffic data from ITS, material inventories from ERP systems like SAP, and embodied carbon factors from LCA databases. This creates a unified asset register, eliminating manual data aggregation and enabling reliable baseline analysis. The focus is on data quality controls and exception routing for missing or anomalous inputs before any optimization logic is applied.

Phase 2 deploys the core orchestration logic using a framework like LangGraph. Specialized agents score pavement segments, generate carbon-optimized strategy options (e.g., cold-in-place recycling vs. mill-and-fill), and route recommendations through budget and policy approval gates. The final phase integrates the output with pavement management systems (PMS) and CMMS like Cityworks, triggering work orders and updating the digital twin. Continuous monitoring via the data pipeline allows for recalibration, creating a closed-loop system that measurably reduces lifecycle cost and carbon.

This workflow automates the high-labor, data-intensive process of evaluating pavement condition, traffic forecasts, and material options to recommend maintenance strategies that minimize lifecycle carbon and cost. It eliminates weeks of manual data fusion from pavement management systems (PMS), LCA databases, and traffic models. The operational upside comes from reducing planning cycles, lowering long-term carbon liability, and improving capital allocation for DOTs and asset managers, directly impacting sustainability KPIs and maintenance budgets.

Implementation requires integrating agents with enterprise PMS like dTIMS or StreetSaver, LCA databases (e.g., Athena), and financial systems. The orchestrator, built on LangGraph, manages data validation, triggers specialized analysis agents, and routes outputs. Critical controls include confidence scoring on model recommendations, mandatory human review for high-cost or novel strategies, and a full audit trail of all data inputs, agent decisions, and overrides. Rollout is phased, starting with a pilot corridor to validate agent logic and exception handling before scaling to the full network.

This workflow automates the complex trade-off between pavement performance, maintenance cost, and embodied carbon. It ingests data from pavement management systems (PMS), traffic sensors, and material LCA databases to evaluate thousands of maintenance scenarios. The operational upside comes from reducing manual analysis cycles by 60-80%, enabling data-driven decisions that lower total lifecycle cost and carbon footprint. Implementation requires robust data validation and integration with legacy systems like Oracle or SAP for asset registers and procurement.

Implementation centers on a LangGraph-based orchestrator that coordinates specialized agents for condition analysis, carbon calculation, and cost modeling. The architecture must include exception routing for data gaps, human review gates for high-cost strategies, and observability dashboards tracking carbon savings versus baseline. Rollout requires phased integration with existing PMS (like dTIMS or StreetSaver) and supplier EPD platforms, ensuring the workflow delivers auditable, permit-ready sustainability documentation alongside operational plans.