Created a digital twin that learns city-scale traffic dynamics from sensors, map topology, and events. The system predicts congestion 5 to 60 minutes ahead and estimates intervention impact before rollout, enabling planners to test policies with lower operational risk.
The twin fuses stream data, learns spatiotemporal patterns, predicts pressure points, and evaluates policy candidates before deployment.
Ingests loop detectors and camera counters to estimate flow, occupancy, and queue pressure in real time.
Represents intersections and corridors as a graph with lane-level connectivity and turn constraints.
Adds dynamic context from weather, incidents, and event schedules to anticipate non-stationary demand spikes.
Encodes day-of-week and special-day priors to stabilize long-range forecasts and reduce shift sensitivity.
Graph model predicts near-term congestion propagation across connected corridors and bottleneck nodes.
Simulates timing plans and routing policies to estimate delay, throughput, and spillback impact before rollout.
Ranks candidate interventions by projected uplift with uncertainty-aware confidence scoring.
Map visual is a dashboard-style abstraction used for rapid policy preview, not a geospatial rendering.
Predicted corridor load over 60 minutes
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Delay reduction and throughput gain
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Bubble size = queue spillback probability
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Traffic disturbance source mix
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Model resilience in event-heavy windows
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Deployment tradeoff across model sizes
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City operations teams lack reliable forecasting and simulation tools to test interventions before field rollout.
Digital twin architecture integrating traffic streams, graph forecasting, and intervention scenario simulation.
Lower congestion peaks, faster planning cycles, and better evidence for policy decisions.
I can support urban mobility teams building predictive simulation and decision-support platforms.
Forecasting baseline and scenario simulator for one priority corridor or district.
Data reliability checks, model drift controls, and stakeholder-facing decision dashboards.
Roadmap and implementation support for city-scale mobility intelligence programs.
Observability and guardrails for tool-using agent systems.
Condition-aware autonomous perception with adaptive fusion.
Evidence-grounded QA over enterprise documentation.
Audio-visual detection and motion awareness at the edge.