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INTELLIGENT TRANSPORT SYSTEM
JINGTAI Highway QIJI Section
Shandong, China
The Qihe–Jinan section of the Jingtai Expressway is China’s first highway expansion project upgrading from 6 lanes to 12 lanes. With a total length of 78.1 km, the project was officially opened to traffic on December 28, 2025, completing the full upgrade of the Shandong section and strengthening the expressway as a major north–south transportation corridor radiating from Beijing.
Installed
144
Radar
Across
78.1km
of highway
December
2025
Highway opened
CHANLLENGE
This project faced multiple challenges in both construction and application.
First, the high traffic load imposed greater demands on safety perception and traffic management. The Qihe–Jinan section of the Jingtai Expressway has long operated under near-saturated traffic conditions. Following the expansion, traffic density and operational complexity increased significantly. The system is required to rapidly and accurately detect risk events such as abnormal parking and pedestrian intrusion under high traffic volumes, in order to improve traffic efficiency and intrinsic road safety.
Second, multi-source sensor fusion and digital twin applications place higher requirements on edge intelligence capabilities. The project integrates millimeter-wave radar, high-definition video, and other sensing devices, requiring real-time data fusion and analysis at the edge. It also supports the construction of a digital twin of road operations, enabling traffic visualization, event mapping, and decision support. This demands strong computing power, low latency, and high stability from edge AI computing units.
Finally, system deployment and operation must balance technological advancement with cost efficiency. With a wide distribution of roadside sites and limited maintenance resources, the solution must provide high reliability, remote operation and maintenance, and low maintenance costs. At the same time, it must adhere to the principles of efficient, advanced, green, and safe construction, ensuring long-term stable operation while remaining economically practical.
SOLUTION
The system adopts an integrated edge AI architecture that deeply integrates millimeter-wave radar, high-definition cameras, edge servers, AI-based traffic algorithms, digital twin technology, and a unified visualization platform to build a full-scenario intelligent perception and management system for smart highways. Multi-source sensing enables continuous and accurate monitoring of traffic conditions across the entire roadway, while real-time intelligent analysis and event response at the edge significantly reduce dependence on network stability and cloud computing resources.
Digital Twin–Based Traffic Perception
Leveraging millimeter-wave radar, the system continuously tracks vehicles and trajectories across the full road section. Combined with structured data such as license plate information captured by checkpoint cameras, it establishes foundational data for a digital twin of traffic operations. This enables visual mapping of traffic entities, operating states, and events, providing strong support for situational awareness and decision assistance.
Merging Safety Warning
For merging safety, the system uses radar to perceive vehicle positions, speeds, and traffic flow on main lanes and ramps in real time. Edge servers perform rapid data parsing and risk assessment, and dynamically link with variable message signs to issue real-time merging warnings, effectively reducing merging conflicts and improving road safety.
Traffic Event and Flow Detection
With full-section millimeter-wave radar coverage and video-assisted verification, the system automatically detects traffic congestion, frequent lane changes, abnormal parking, wrong-way driving, and pedestrian intrusions. Events are processed in real time at the edge and reported to the event management platform, enabling rapid response and closed-loop management.
Scalable and Efficient Architecture
The system features a modular design that supports flexible deployment and expansion of AI traffic algorithms, maintaining stable detection performance in complex conditions such as nighttime, low visibility, and severe weather. The centralized visualization platform provides real-time event display, alarm notifications, and system health monitoring. By combining local edge inference with structured data transmission, the solution significantly reduces bandwidth and cloud resource consumption, improving overall system performance and operational efficiency.
RESULT
By deploying a smart highway solution that integrates millimeter-wave radar, high-definition cameras, edge AI computing, and digital twin technology, the project successfully addressed key challenges including high traffic load, multi-scenario perception, and distributed operation and maintenance. The system operates stably under complex traffic conditions as well as at night, in low-visibility environments, and in adverse weather, enabling continuous, all-weather perception of traffic conditions and critical events across the entire roadway.
Leveraging radar–vision fusion and real-time intelligent analysis at the edge, the system achieves accurate detection and rapid warning of multiple traffic events, including abnormal parking, pedestrian intrusion, wrong-way driving, road obstacles, traffic congestion, and merging risks. Through the digital twin platform, traffic conditions and events are visualized and mapped to support situational awareness and decision-making.
The system has passed acceptance by relevant authorities and has been officially put into operation. It has delivered significant results in improving roadside safety management efficiency, reducing accident risks, and optimizing traffic operations, providing a replicable and scalable reference for the large-scale deployment and operation of smart highway systems.
Intelligent Algorithms Applications
√ Smart Traffic Algorithm Applications
√ Radar-Based Trajectory Tracking Algorithm
√ Traffic Flow Statistics Algorithm: lane-wise and vehicle-type statistics including traffic volume, average speed, time occupancy, average headway, and average vehicle spacing
√ Radar–Vision Fusion
√ Traffic Event Detection: parking events, abnormal speeds, illegal/continuous lane changes, wrong-way driving, emergency lane occupation, lane violations, rapid acceleration/deceleration, sharp turns, intrusion events, traffic congestion, vehicle clearance, and slow-moving vehicles
√ Merging Safety Warning System
System Solutions
Shenzhen Arctec Innovation Technology Co., Ltd
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