Centram Centre for Applied Artificial Intelligence

AI ASSISTED ANALYTICS AND AUTOMATION COURSE FOR ROAD INFRASTURE DEVELOPMENT

Course overview

This course is designed for professionals working in road agencies, transport ministries, public works, planning authorities, highways/asset managers, pavement engineers, traffic authorities, design & procurement teams, environmental and social safeguards units, field inspectors, contractors, and the data/IT teams that support them.

Target audience

 Road/transport planners, asset managers, pavement engineers, traffic engineers, GIS/remote sensing teams, construction supervision, procurement officers, M&E and safeguards staff, data scientists/engineers supporting road infrastructure.
Learning outcomes

  1. By course end participants will be able to:
  2. Design auditable data pipelines that combine satellite, UAV, LiDAR, field inspection, traffic/ITS, contract and financial data for planning, design, construction supervision and asset management.
  3. Build and validate models for route selection, pavement condition indexing, predictive maintenance, traffic modelling, safety hotspot detection, drainage/flood risk, and lifecycle cost forecasting.
  4. Operationalise near‑real‑time monitoring and automated workflows (pothole detection → work orders; sensor alerts → maintenance scheduling; automated contract progress verification) with human‑in‑the‑loop controls and auditable logs.
  5. Embed governance and safeguards: land acquisition consent/FPIC where required, environmental and social safeguards, procurement transparency, contractor performance monitoring, and model risk management.
  6. Deploy MLOps and digital twin practices for production systems supporting long‑term asset management and resilience planning.

Course outline

Introduction: road sector goals, stakeholders & use cases

  1. Objectives: Map national road infrastructure objectives (connectivity, economic growth, maintenance, safety, climate resilience) to analytics & automation opportunities.
  2.  Topics: Road classes and stakeholders (national/regional/local), typical workflows (planning → design → procurement → construction → maintenance), procurement models (design‑build, PPP), KPIs (pavement condition index, AADT, travel time reliability).
  3. Lab: Problem scoping — select a national priority (e.g., reduce backlog of preventive maintenance by X%) and define measurable KPIs, data needs and evaluation plan.

Data governance, rights, procurement & safeguards

  1. Objectives: Define governance that covers data sharing, sensitive geolocations, procurement transparency and safeguards for affected communities.
  2. Topics: Land acquisition/FPIC, ESIA/ESMP integration, anonymisation of user/vehicle data, procurement compliance (evidence for payments/milestones), retention & provenance, secure sharing across agencies.
  3. Lab: Draft a data‑sharing & access matrix for road project data (alignment, surveys, social assessments, contractor reporting) with provenance tagging and redaction rules.

Ingestion & integration: satellite, UAV, LiDAR, field surveys & ITS

  1. Objectives: Ingest and normalise diverse sources: Sentinel/Landsat/Planet, UAV photogrammetry, mobile LiDAR, field distress surveys, pavement test data (FWD), weigh‑in‑motion (WIM), ITS/traffic sensors, GPS probe data.
  2. Topics: Georeferencing, orthorectification, point cloud processing, time alignment, sensor calibration, data schemas for asset registers, linking contract milestones and payments.
  3. Tools: PDAL, OpenDroneMap, QGIS, PostGIS, Google Earth Engine, GDAL, Python ETL.
  4. Lab: Build an ETL that ingests UAV orthomosaics, mobile LiDAR strip and field distress logs into a canonical spatial asset register with provenance.

Road network mapping, clearance & baseline condition mapping

  1. Objectives: Produce up‑to‑date road network maps, surface classification (paved/unpaved), and baseline pavement condition mapping with uncertainty.
  2. Topics: Semantic segmentation for surface type, lane/shoulder detection, pothole/crack detection via CV, network topology corrections, accuracy assessment and sample‑based validation.
  3. Tools: OpenCV, TensorFlow/PyTorch (UNet/Mask R‑CNN), OSM tooling, GeoPandas.
  4. Lab: Generate a road surface map from high‑resolution imagery/UAV and a pothole/crack detection model; validate via field samples.

Pavement condition assessment & predictive maintenance

  1. Objectives: Estimate pavement condition indices (PCI, IRI), model deterioration and predict optimal maintenance interventions across network.
  2. Topics: PCI/IRI derivation from mobile data, FWD response interpretation, survival/deterioration models, life‑cycle cost analysis, optimization of maintenance scheduling under budget constraints.
  3. Tools: scikit‑learn, XGBoost, time‑to‑event models, optimization (OR‑Tools), Pavement Management Systems (PMS) integration patterns.
  4. Lab: Calibrate an IRI/PCI predictive model using mobile survey + FWD samples, produce network‑level deterioration forecasts and an optimal maintenance plan under budget scenarios.

Structural health, geotechnical and drainage risk analytics

  1.  Objectives: Assess subgrade/structure risk (embankment stability, landslide, scour) and integrate drainage/flood risk into design and maintenance planning.
  2. Topics: Remote sensing for terrain and slope change (InSAR), soil suitability proxies, culvert and bridge scour risk, hydrological modelling for road design, resilience to extreme weather.
  3. Tools: InSAR (SNAP/GAMMA overview), hydrological models, DEM processing, Python geospatial libs.
  4. Lab: Use DEM/time‑series EO to map slope stability hotspots and integrate with road network to prioritise geotechnical investigations.

Traffic modelling, demand forecasting & road safety analytics

  1.  Objectives: Model traffic flows, forecast demand (AADT, OD matrices), and identify safety hotspots and interventions using crash analytics.
  2. Topics: Probe/GPS data fusion for speed/travel time, traffic assignment (static/dynamic), microscopic/macroscopic simulation (SUMO/MATSim), crash data analysis (hotspot detection, severity mapping), Safe System approaches.
  3. Tools: SUMO, MATSim, PySAL, spatial statistics, crash analysis tools.
  4. Lab: Build an OD estimation and traffic simulation for a corridor and run safety counterfactuals (e.g., speed limit, median barrier) to estimate crash reduction.

Construction monitoring, progress verification & quality assurance

  1. Objectives: Use remote sensing, UAVs and automated analytics to verify contractor progress, quality and compliance with contracts.
  2. Topics: Progress monitoring using time‑series imagery, earthworks volume estimation (DTM differencing), automated QA for pavement layer thickness (where sensors available), flagging noncompliance and payment‑linked verifications.
  3. Tools: Photogrammetry, change detection scripts, point cloud comparison (PDAL), digital elevation differencing.
  4.  Lab: Implement a pipeline to monitor earthworks volumes from periodic UAVs and generate a contractor progress report tied to milestone payments.

 Intelligent Transport Systems (ITS), IoT & edge analytics

  1. Objectives: Integrate ITS sensors (traffic cameras, WIM, environmental sensors) with edge analytics for real‑time incident detection and maintenance triggers.
  2. Topics: Computer vision for vehicle counts/violation detection, WIM data analytics for pavement loading, NB‑IoT/LoRaWAN sensor design, edge deployment patterns and low‑bandwidth reporting.
  3. Tools: OpenCV, TensorFlow Lite, MQTT/Edge frameworks, Kafka for streaming.
  4. Lab: Build an edge prototype for automated vehicle counting from roadside camera, stream counts to central store and trigger maintenance alerts when thresholds exceeded.

Costing, lifecycle modelling & financing analytics

  1.  Objectives: Forecast lifecycle costs, perform scenario analyses (climate, traffic growth), and support financing decisions (PPP, O&M).
  2. Topics: Whole‑life costing, sensitivity/uncertainty analysis, O&M contracting models, performance‑based contracts (SBO), economic appraisal (BCR, NPV) under uncertainty.
  3. Tools: Monte Carlo simulation, cost models, Excel/Python integration, project finance templates.
  4. Lab: Produce a lifecycle cost and risk analysis for a candidate corridor under traffic and climate scenarios and compare financing options.

Operationalisation, digital twins, MLOps & governance

  1. Objectives: Deploy production pipelines (digital twins), monitoring, drift detection, audit logs, and SOPs for enforcement/action/maintenance.
  2. Topics: Digital twins for road networks, model registry and CI/CD for analytics, monitoring for model/data drift, human‑in‑the‑loop maintenance approvals, integration with ERP/asset management systems.
  3. Tools: Docker/Kubernetes, MLflow, Grafana, PostGIS, BIM/CAD integration patterns.
  4. Lab: Deploy an end‑to‑end alerting pipeline: sensor ingestion → pothole detection → maintenance work order generation → audit log and dashboard.

Ethics, social & environmental safeguards, procurement & capstone

  1. Objectives: Cover ESIA/ESMP integration, community engagement, procurement transparency and present capstones.
  2. Topics: Environmental/social safeguards, FPIC where applicable, grievance redress mechanisms, procurement transparency (tender monitoring), contractor performance analytics, vendor risk and data‑use clauses.
  3. Capstone: Teams deliver a reproducible pipeline (e.g., predictive pavement maintenance + dynamic scheduling; UAV‑based construction progress verification + payment evidence; traffic/ safety hotspot analysis + intervention prioritisation) plus governance/engagement brief and demo.

Capstone structure

  1. Problem selection, data assembly & baseline KPIs
  2. Pipeline & prototype implementation (ingest → model → dashboard/alerts/work orders)
  3. Evaluation, procurement/safeguard statement, SOPs and presentation
  4. Deliverables: reproducible repo + Dockerfile, provenance logs, sample evidence packet (for verification or payment), evaluation report, model card and procurement/contract clause suggestions.

Operational KPIs & evaluation metrics

  1. Asset condition: % network above target PCI/IRI, rate of deterioration, area under maintenance backlog curve.
  2. Maintenance: % preventive vs reactive maintenance, average time from defect detection to repair, cost per km per year.
  3. Traffic & mobility: AADT accuracy, travel time reliability, reduction in congestion hotspots.
  4.  Safety: number of crashes/serious injuries at hotspots, predicted vs observed crash reductions from interventions.
  5. Construction: % milestones verified by remote sensing, variance in earthworks volumes, quality non‑conformance rate.
  6.  Operational: alerts per analyst/day, false alarm rates, time‑to‑verification, procurement compliance metrics.

Recommended tools, libraries & datasets

  1. Languages/infra: Python, R, Docker, Airflow/Prefect, PostGIS, QGIS
  2. Remote sensing & geospatial: Google Earth Engine, Sentinel/Landsat/Copernicus, Planet (commercial), SRTM/DEM, OpenDroneMap, PDAL, OpenStreetMap
  3.  CV & ML: OpenCV, TensorFlow/PyTorch (UNet/Mask R‑CNN), scikit‑learn, XGBoost/LightGBM
  4. Traffic & simulation: SUMO, MATSim, PTV Vissim (commercial), Probe data/APIs (TomTom, HERE), OpenTraffic
  5. Pavement & engineering: Pavement Management System patterns, FWD processing scripts, IRI/PCI calculation toolkits, HDM‑4/AASHTO (where licensed)
  6. ITS & IoT: MQTT, Kafka, TensorFlow Lite, edge frameworks, WIM and sensor analytics
  7. Hydrology & geotech: InSAR tools (SNAP), hydrological models, DEM processing libraries
  8.  MLOps & monitoring: MLflow, Evidently/WhyLabs, Grafana, Prometheus
  9. Data collection: ODK/KoBo for field inspections, GPS logging, mobile data capture
  10.  Public datasets: OpenStreetMap, Global Roads Open Access Data Set (gROADS), Copernicus DEM, SRTM, national traffic counts and crash databases where available
  11.  Synthetic/sample data: generators for mobile LiDAR/UAV/traffic probes to avoid exposing sensitive project specifics

Key risks, safeguards & mitigation

  1. Privacy & security: protect individual vehicle/driver identities in probe data and camera feeds; use aggregation/geomasking for public outputs; secure enclaves for sensitive assets.
  2. Safety & liability: ensure human sign‑off for automated work orders that affect public safety; robust QA before construction payments.
  3. Social & land risks: integrate FPIC and ESIA processes for works affecting communities; grievance redress mechanisms and transparent land‑acquisition records.
  4. Model risk & overreliance: monitor model drift, retain engineer oversight for design/structural decisions, maintain versioned models and audit logs.
  5. Procurement & vendor risk: require reproducibility, documented training data, source code escrow where appropriate, performance SLAs and data‑use restrictions in contracts.
  6. Data quality & coverage gaps: invest in representative surveys (traffic, pavement tests), document uncertainties, conservative decision thresholds.
  7. Dual‑use & political risk: restrict distribution of sensitive infrastructure vulnerabilities and coordinate with security agencies as appropriate.

Practical lab/project ideas

  1. Network‑level predictive maintenance: ingest mobile surveys, build deterioration model and produce optimal preventive maintenance plan.
  2. UAV construction monitoring: produce earthworks volume estimates and milestone verification evidence for payment release.
  3. Pothole/crack detection pipeline with automated work‑order generation and audit logging.
  4. Corridor traffic/OD estimation and safety counterfactual analysis for proposed upgrades.
  5. Flood/ drainage risk mapping integrated with road network to prioritise climate adaptation interventions.