Central business district low-carbon intelligent traffic management and control system and method based on Internet of Things

By deploying monitoring equipment on the road network of the central business district, combining reinforcement learning algorithms to predict changes in carbon footprints, low-carbon travel suggestions and dynamic traffic control instructions are generated, the problem of insufficient comprehensive analysis of multi-source data in traditional traffic control systems is solved, and the efficient and low-carbon operation of the traffic system is achieved.

CN120452222APending Publication Date: 2025-08-08CHONGQING JIANGBEIZUI PROPERTY SERVICE CO LTD +1
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Patent Information

Application Number
CN202510515621.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-23
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

Traditional traffic control systems lack comprehensive analysis and dynamic regulation capabilities for multi-source data such as traffic flow, vehicle emissions and ambient air quality, and it is difficult to effectively respond to complex and changeable urban transportation needs.

Method used

By deploying monitoring equipment on the road network of the central business district, multi-source data is collected in real time, combined with reinforcement learning algorithms to predict the change trend of carbon footprints, low-carbon travel suggestions are generated, and traffic light timing is dynamically adjusted and public transportation scheduling is optimized.

Benefits of technology

Accurate low-carbon travel suggestions and traffic control have been achieved, effectively alleviating traffic congestion and improving travel efficiency, especially during peak hours and congested road sections.

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Abstract

The invention discloses a central business district low-carbon intelligent traffic management and control system and method based on the Internet of Things, and belongs to the field of intelligent traffic. The invention discloses a central business district low-carbon intelligent traffic management and control system and method based on the Internet of Things. The system comprises a data acquisition unit, a data analysis unit and a collaborative execution unit. The problem that complex and changeable urban traffic demands are difficult to effectively cope with in the prior art is solved, the carbon footprint change trend is predicted by monitoring and analyzing multi-source data in real time in combination with a reinforcement learning algorithm, low-carbon travel suggestions can be generated and pushed to users, and the user experience is improved. Therefore, the user can be guided to reasonably plan the travel mode and time, the travel efficiency is improved, the traffic control instruction is generated based on the prediction result, the traffic signal lamp time sequence is dynamically adjusted, the public traffic scheduling is optimized, and the traffic jam can be effectively relieved.
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