Dynamic Traffic Management

What’s inside

• Network‑wide congestion prediction (5–30 min horizon)
• Adaptive signal timing & corridor coordination
• Incident detection and diversion recommendations
• What‑if simulation to test interventions safely

Traffic analysis using computer vision

Traffic analysis uses visual data to monitor and manage vehicular movement. It plays a central role in road safety and in optimizing traffic flow.

Real‑time Monitoring

• Live video feeds from roadside or intersection cameras are analyzed to monitor vehicle movement.
• Systems detect congestion, accidents and other incidents in real time using computer‑vision models.

Vehicle Detection & Classification

• Algorithms identify vehicles (cars, trucks, buses, motorcycles) and track their trajectories across lanes.
• This classification reveals directional patterns, lane usage and demand surges for smarter signal timing.

Traffic Flow Analysis

• We compute speed and density to infer Level‑of‑Service and queue lengths.
• Insights feed adaptive signal control to shrink delay and reduce emissions.

Incident Detection

• Automatic detection of crashes, stalled vehicles, wrong‑way driving or debris triggers alerts and workflows.
• Earlier detection shortens response times for emergency services and reduces secondary collisions.

Data for Urban Planning

• Historic patterns inform corridor redesigns and transit planning.
• Aggregated, privacy‑preserving analytics support policy without exposing PII.

Smart‑City Integration

Traffic analysis is foundational to intelligent transportation systems, enabling citywide orchestration that improves mobility and lowers environmental impact.

Vehicle Detection & Classification

Identify vehicles and categorize by attributes to enhance traffic management, safety and automation.

Detection Techniques

• Video‑based: camera streams + image processing for real‑time detection.
• Radar & LiDAR: complementary sensing for speed and presence.
• Inductive loops: roadway sensors detecting metal mass for durable counts.

Classification Methods

• Size/shape categories: cars, trucks, buses, motorcycles.
• Behavioral features: speed and maneuver patterns.
• Machine learning: models improve with continual learning.

Applications

• Traffic Management: optimize signals and reduce congestion.
• Urban Planning: support infrastructure and safety programs.
• Tolling: classify for pricing and revenue integrity.

Traffic Flow Monitoring

Continuous observation and analysis of flow keeps networks efficient and safe.

Monitoring Techniques

• CCTV for real‑time analysis and response.
• Radar/IR/acoustic sensors for counts and speeds.
• Mobile data for crowdsourced context where appropriate.

Key Metrics

• Volume: vehicles per unit time.
• Speed: average/percentile travel speeds.
• Density: vehicles per unit length.

Benefits

• Pinpoint bottlenecks, reduce delay and improve safety.
• Lower emissions via smoother flow and fewer idles.

Incident Detection

Identify unexpected events that disrupt normal operations and trigger response workflows.

Methods

• Automated analytics detect anomalies in speed/trajectory.
• Human monitoring in TOCs adds verification.
• Data analytics predicts risk windows from history.

Types

• Accidents and secondary crashes.
• Road hazards like debris or weather impacts.
• Violations e.g., red‑light running, speeding.

Response

• Real‑time alerts to responders and operators.
• Detours and rerouting to minimize disruption.
• Public messaging on VMS and mobile apps.