Passenger comprehensive service method and system based on comprehensive transportation hub

By collecting and analyzing passenger behavior data in real time, combining convolutional neural networks and multimodal real-time resource scheduling models, the resource configuration of the comprehensive transportation hub is automatically adjusted, and the existing system's insufficient response speed and accuracy are solved, achieving more efficient resource utilization and better passenger experience.

CN119721571AInactive Publication Date: 2025-03-28南京忠设智能科技有限公司
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Patent Information

Application Number
CN202411761932.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-03
Publication Date
2025-03-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

When facing large-scale passenger flow fluctuations and resource scheduling complexity, the existing integrated transportation hub service system lacks response speed and accuracy, unable to meet the growing demand, and lacks real-time perception and response mechanisms, resulting in unreasonable resource allocation and affecting passenger travel experience.

Method used

By collecting passenger behavior data in real time, preprocessing and standardizing, and using convolutional neural network models to analyze historical and current data, predict future fuzzy needs. Then, the passenger seat allocation and safety channel opening situation are automatically adjusted through the multimodal real-time resource scheduling model, and the resource configuration is monitored and optimized in real time.

Benefits of technology

Dynamic resource scheduling and intelligent adjustment of the service area of ​​the comprehensive transportation hub has been achieved, operational efficiency and passenger service quality have been improved, waiting time and idle resources have been reduced, and intelligent management capabilities have been enhanced.

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Abstract

The invention provides a passenger comprehensive service method and system based on a comprehensive transportation hub. According to the system, passenger behavior data including passenger flow, seat utilization rate and service use condition are collected in real time, and the passenger behavior data are preprocessed and standardized. Then, historical data and current data are analyzed by using a convolutional neural network model, and future fuzzy demands are predicted. Based on this, the system automatically adjusts the passenger seat distribution of each service area and the opening condition of the safety channel through a multi-mode real-time resource scheduling model. The resource use condition of the current service area is monitored in real time, further optimization is performed in combination with passenger flow, smooth passage of passengers is ensured, and resources are fully utilized. According to the method, the operation efficiency of the transportation hub and the passenger service experience are remarkably improved.
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