In Colorado Springs, the Adaptive Traffic Management System Using Radar, LiDAR, Video Analytics, and “Digital Twin” of Intersection Operations Estimated Reductions in Traffic Delay Up to 23.7 percent.
Stage 1 of the Strengthening Mobility and Revolutionizing Transportation (SMART) Grant Project focused on developing and field-testing these advanced technologies.
Colorado Springs, Colorado, United States
Perception-Based Adaptive Traffic Management and Data Sharing
Summary Information
The City of Colorado Springs faces significant transportation challenges as its population grows, and traffic patterns become more complex. The next-generation adaptive traffic management system project was designed to address these challenges such as need to protect non-motorized road users, respond effectively to real-time environment changes, and manage the interaction of varying traffic types, within City of Colorado Springs and El Paso County, including urban, suburban, and rural areas.
The City of Colorado Springs has leveraged U.S. Department of Transportation (USDOT) SMART Grant funding to launch Stage 1 of a next-generation adaptive traffic management system. Stage 1 advances the first-generation system which proved the viability and value of real-time, perception-based signal control, by integrating new perception dimensions enabled by state-of-the-art sensor hardware including radar, Light Detection and Ranging (LiDAR), and video analytics as well as a “digital twin” of intersection operations. Stage 1 focused on developing and field-testing these advanced technologies at two intersections representative of Colorado Springs’ diverse transportation environments, including both urban and suburban-to-rural contexts.
Stage 2 will expand deployment to 48 intersections along two key corridors, extending these benefits citywide and across El Paso County, where the City has now assumed direct management of traffic signals. By scaling up the digital twin infrastructure and continuing to refine sensor integration, the project aims to deliver a robust, replicable model for next-generation traffic management—one that can be adopted by other municipalities nationwide.
METHODOLOGY
Stage 1 of the SMART Grant project had extensive field testing conducted at both permanent sensorized intersections and through National Renewable Energy Laboratory’s (NREL) Infrastructure Perception and Control (IPC) Mobile Laboratory, enabling rapid deployment and iterative evaluation of different sensor configurations across multiple intersection types from April 2024 to January 2025. This real-world testing was critical to assessing how the latest radar, LiDAR, video analytics, and Vehicle-to-Everything (V2X) equipment performed in varied operational and environmental conditions. Technologies evaluated in Stage 1 were measured against performance benchmarks that included data refresh rates, object detection accuracy, system latency, resilience to weather and lighting, and ease of integration with intersection controllers.
FINDINGS
- Using the validation simulation, tests compared the proposed trajectory-based control methods against conventional coordinated signal control. Results showed that total delay was reduced 15.8 to 23.7 percent by the trajectory-based methods compared to coordinated control, notably reducing both mainline and side street delays.
- Based on 2023 crash frequencies observed along the Nevada Ave and Meridian Road corridors (two corridors proposed for Stage 2 implementation), the Iowa State University (ISU) researchers estimated that the Project will achieve, over ten years, a reduction of approximately 2.2 fatal crashes, 6.2 incapacitating injury crashes, 48.3 non-incapacitating injury crashes, 145.5 possible injury crashes, and 288 property damage only crashes.
- These reductions are projected to result directly from the deployment of the advance signal control and perception technologies proposed for these corridors.
- These reductions are expected to produce $28 million in safety benefits over a 10-year project life.
- Simulations conducted by ISU showed that trajectory-based control reduced peak-period vehicle delay across test intersections by an average of 15.4 vehicle-hours.
- When scaled to Stage 2’s full 48-intersection deployment, this equated to an expected daily reduction of 79.3 vehicle-hours and an annual peak period delay reduction of approximately 33,314 vehicle-hours.
- These improvements in corridor throughput and delay mitigation would result in over $10 million in travel time saved and reduced vehicle operating costs over a 10-year period.
