Recently, the scientific and technological achievement "Traffic Signal Cloud Control Platform Based on the Integration of Artificial Intelligence and Micro-Simulation," developed by Duolun Technology, stood out among numerous outstanding projects and was awarded the Second Prize of the Jiangsu Provincial Comprehensive Transportation Science and Technology Award, in recognition of its exceptional technological innovation and remarkable application performance.

Addressing Urban Congestion Challenges, Defining a New Paradigm for Signal Control

As urbanization accelerates, the limitations of traditional traffic signal control methods have become increasingly evident. In response to the complex challenge of traffic systems dynamically influenced by multiple factors including vehicles, road conditions, and the environment, Duolun Technology independently developed the Traffic Signal Cloud Control Platform based on the integration of artificial intelligence and micro-simulation. This platform employs a multi-agent control system using deep reinforcement learning algorithms, achieving model-free, data-driven online closed-loop control through continuous interaction between AI agents and the traffic environment.

Core Technological Breakthroughs: Six Major Innovations

The platform encompasses six core technological innovations that establish a robust technical moat:

1. Deep Integration of Multi-Source Data: Solves the challenges of collecting and fusing multi-source traffic data from radar, video, license plate recognition, and other sources, establishing a precise full-day control period classification model.

2. High-Precision Micro-Simulation: Builds an intersection signal control simulation platform based on SUMO, achieving high-precision reproduction and evaluation of traffic operations across various scenarios.

3. Single-Intersection Intelligent Optimization: Abstracts traffic flow parameters into reinforcement learning state sets, training globally optimal single-intersection signal control strategies to effectively alleviate intersection congestion.

4. Arterial Coordinated Green Wave: Builds upon single-intersection optimization by incorporating binary variables for green wave control, maximizing overall arterial corridor traffic efficiency.

5. Scenario-Based Solution Implementation: Generates time-series timing plans that align with actual traffic characteristics based on optimal strategies within aggregated time periods, solving the "last mile" challenge of AI strategy deployment.

6. Visualized Tool System: Develops comprehensive visual management tools covering the entire process from simulation parameter input to strategy generation and status monitoring.

Validating Innovation Through Real-World Results

The true value of technology lies in its application. The Traffic Signal Cloud Control Platform based on the integration of artificial intelligence and micro-simulation has already been deployed in multiple cities and regions, delivering significant economic and social benefits:

• Congestion Relief and Efficiency Improvement: Practical application feedback indicates that the system can dynamically adjust signal timing, significantly reducing peak-hour congestion rates and effectively improving road network throughput.

• Lifesaving Support: The platform integrates an "Emergency Vehicle Cloud-Vehicle-Road Coordination System," which has already been implemented to plan green-light routes for special vehicles, substantially reducing emergency response times and demonstrating how technology safeguards lives.

• Emission Reduction and Carbon Savings: By reducing frequent vehicle stops, starts, and idling, the system effectively lowers fuel consumption and exhaust emissions, contributing technological support to urban carbon neutrality goals.

Continued Deepening to Empower a New Vision for Smart Transportation

Looking ahead, Duolun Technology will take this achievement as a new starting point, continuing to increase R&D investment in the "AI + Transportation" field. The company will further iterate its algorithmic models, deepen application scenarios, and provide urban administrators with more intelligent, efficient, and green comprehensive traffic signal optimization solutions.