Freight trucks are essential to regional economies, but they are also strongly affected by stop-and-go operations at signalized intersections. Compared with passenger vehicles, heavy trucks require more time and energy to decelerate, stop, and accelerate again. Repeated stops increase travel delay, fuel use, emissions, and uncertainty for freight operators. On strategic freight corridors, these delays can also make preferred truck routes less attractive, pushing heavy vehicles toward less desirable routes through local communities.

Our design is a roadside sensing-based freight signal priority system that helps traffic signals respond more intelligently to approaching trucks. Instead of requiring every truck to be equipped with a special transmitter, GPS unit, or connected-vehicle device, the system uses infrastructure-mounted sensors to observe traffic directly. Roadside LiDAR and camera sensors detect, classify, and track freight vehicles in real time. The system estimates each truck’s speed, direction, distance to the intersection, and expected arrival time, then uses this information to support smarter signal-priority decisions, such as green extension or priority request generation.

The key innovation is the complete sensing-to-signal pipeline. The system is not only a detector and not only a signal-control concept; it connects roadside perception, truck trajectory tracking, estimated arrival-time calculation, priority request generation, and traffic-controller interaction into a deployable field prototype. This infrastructure-based approach can detect eligible freight vehicles regardless of fleet participation or connected-vehicle penetration, making it practical for mixed traffic environments where many trucks are not connected.

A working prototype was developed, tested, and deployed in a real-world freight corridor research project. The project included laboratory testing, field installation, sensor calibration, LiDAR-based detection algorithm development, FSP control unit development, triggering strategy design, and field data collection. Two deployment configurations were tested: an intersection-only configuration for locations with good sensor visibility, and an intersection-plus-midblock configuration for locations where roadway curvature or occlusion requires supplemental detection. The prototype used roadside LiDAR, camera sensing, edge computing, wireless communication when needed, and traffic-controller relay integration.

Field evaluation data included roadside sensor data, onboard video, GPS probe-truck data, traffic-controller information, and traffic counting data. In the field deployment, 66 probe truck trips were recorded under before-FSP and after-FSP scenarios, covering peak and off-peak periods. Preliminary corridor-level analysis estimated travel-time savings of approximately 2 minutes 43.5 seconds for northbound trucks and 2 minutes 7.8 seconds for southbound trucks under the evaluated scenario. No traffic complaints or noticeable negative impacts were observed during the field deployment period.

Potential users include state and local transportation agencies, freight corridors, port-access routes, logistics corridors, and smart-city programs. Because the system uses commercially available roadside sensors, edge computers, communication devices, and traffic-signal infrastructure, it can be deployed incrementally at high-value intersections and scaled to corridor-level freight operations. By helping trucks move more smoothly through signalized corridors, the system supports more efficient, sustainable, and community-conscious freight movement.