LiDAR-enabled AI traffic management system for dynamic signal optimization and multimodal safety enhancement

Background

Urban transportation networks are becoming increasingly complex as cities face rising volumes of vehicles, pedestrians and cyclists competing for limited road space. Departments of Transportation need comprehensive, real‐time insights to manage congestion, reduce delays and improve safety for all users. Traditional traffic monitoring relies on point-based sensors and static signal timing plans that cannot adapt to shifting demand patterns, low‐visibility conditions or multi‐modal flows. With urban populations growing and mobility modes diversifying, there is a critical need for technologies that deliver accurate, continuous data streams without extensive infrastructure replacement or compromising individual privacy.

Existing traffic control systems suffer from narrow detection capabilities and rigid operational logic. Inductive loops register only metal objects and overlook cyclists and pedestrians, while camera systems struggle in low light or adverse weather and raise privacy concerns through image capture. Static timing schedules fail to reflect real‐time fluctuations, leading to unnecessary idling and safety blind spots. Moreover, agencies lack scalable platforms to aggregate data across multiple intersections for coordinated corridor management, near‐miss analysis and long‐term performance tuning, forcing reliance on fragmented, labor‐intensive processes.

Technology

LiTSMO uses LiDAR sensors deployed at intersections, corridors, and transit hubs to continuously scan roadways, generating three-dimensional point cloud data that on‐site AI modules immediately analyze to detect and classify vehicles, pedestrians, and cyclists, identify near-miss events, and preserve privacy by forgoing image capture. This processed data dynamically drives adaptive signal controllers to adjust phase timings based on traffic volume and composition, coordinate signals along corridors, activate pedestrian beacons under low-light or work-zone conditions, and grant transit priority. A cloud-based platform aggregates data from multiple sites, offering dashboards, analytics, and parameter tuning that enable agencies to optimize signal plans, plan safety interventions, and manage multimodal operations without replacing existing infrastructure.

Unlike camera-based systems, LiTSMO delivers reliable detection in all lighting and weather conditions, maintains complete privacy by avoiding facial recognition, and uses edge AI to reduce latency and bandwidth demands. Its seamless integration with existing traffic infrastructure makes deployment cost-effective and scalable, enabling rapid multi-site expansion. The combination of real-time 3D monitoring, adaptive signal control, corridor coordination, and multimodal prioritization distinguishes this solution, offering transportation agencies unprecedented accuracy, flexibility, and actionable insights for safer, more efficient urban mobility.

Benefits

• Adaptive traffic signal control reduces congestion and travel times in real time
• Enhanced safety through near-miss detection and prioritized protection for pedestrians and cyclists
• Privacy-preserving LiDAR sensing avoids image capture and facial recognition
• Seamless integration with existing infrastructure minimizes deployment and maintenance costs
• Multi-modal coordination improves transit priority and corridor traffic flow
• Cloud-based analytics enable data-driven decision-making and long-term planning

Applications

• Adaptive signal timing control
• Pedestrian-cyclist safety monitoring
• Corridor traffic flow management
• Work-zone safety monitoring
• Transit priority signaling

Additional info

LiTSMO uses a network of LiDAR sensors at intersections, corridors and transit hubs to generate real-time 3D point-clouds. Edge AI classifies vehicles, pedestrians and cyclists, detects near-misses and preserves privacy. Outputs drive adaptive signal controllers for dynamic phase timing, corridor coordination, pedestrian beacon activation and transit priority. A cloud platform aggregates data for dashboards, analytics and parameter tuning. Modular design spans signal optimization, corridor management, work-zone safety, transit hub coordination and vulnerable-user protection.