Passenger Distribution Prediction With CO2, Humidity, And Temperature Sensing
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Solution Overview
Problem
Existing passenger density detection methods in subway cars face challenges such as inaccurate detection in crowded conditions, invasive reconstruction requirements, and ineffective guidance systems like voice prompts and display prompts.
Innovation Solution
A system using temperature, humidity, and CO2 sensors to create a spatial coordinate system, convert passenger distribution into binary images, and utilize LSTM neural networks to predict and guide passenger flow by adjusting lighting based on density changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If image detection method is used for passenger density detection, then detection coverage is improved, but detection precision deteriorates in cases of crowd flow and obscuration
Solution Approach 1:
The patent introduces environmental parameters (temperature, humidity, CO2 concentration) as intermediary indicators to indirectly measure passenger density. Instead of directly detecting passengers with cameras or sensors, the system uses environmental changes caused by passenger presence as a mediator, which remains effective even when passengers are moving or obscured.
Solution Approach 2:
The patent replaces the mechanical/optical detection system (image detection) with an environmental sensing system. By substituting direct visual detection with environmental parameter measurement, the system maintains detection capability in conditions where visual methods fail due to crowd flow and obscuration.
2Measurement precision
If piezoelectric sensors are installed on the floor for passenger density detection, then detection precision is improved, but device complexity and installation cost increase due to invasive reconstruction
Solution Approach 1:
The patent extracts the detection function from the floor structure itself, eliminating the need for invasive sensor installation. Instead of embedding sensors in the floor, the system uses standalone environmental sensors that measure temperature, humidity, and CO2 concentration, thereby removing the complexity of floor reconstruction while maintaining detection capability.
Solution Approach 2:
The patent uses environmental parameters as intermediary indicators to replace direct physical contact detection. By measuring environmental changes rather than applying sensors to the floor, the system achieves detection precision without requiring invasive installation or complex device reconstruction.
3Loss of information
If voice prompts are used for passenger guidance, then information delivery is improved, but guidance effectiveness deteriorates in noisy travel conditions
Solution Approach 1:
The patent replaces acoustic guidance (voice prompts) with optical guidance (lighting system). By substituting sound-based information delivery with light-based signaling, the system overcomes the limitation of noisy environments where voice prompts cannot be heard, thereby improving guidance effectiveness while maintaining information delivery.
4Illumination intensity
If display prompts are used for passenger guidance, then information visibility is improved, but guidance effectiveness deteriorates due to difficulty in getting passenger attention
Solution Approach 1:
The patent applies local quality by using spatially differentiated lighting to provide guidance information. Instead of uniform display prompts that compete for attention, the system creates localized light signals in specific areas (such as brighter lighting in less crowded zones) that naturally attract passenger attention and provide directional guidance through environmental cues.
Data Source
AI summary
The present invention discloses a method and system for training a passenger distribution prediction model, and a method and system for guiding passengers. In an embodiment, the passenger distribution is intelligently sensed by means of the characteristics of temperature, humidity and CO2 concentration distribution changes in cars caused by passenger density changes, which avoids the problems of crowd flow and obscuration faced by passenger distribution detection conducted with images, and avoids the difficulty in floor intrusive transformation faced by passenger distribution detection conducted with pressure sensors; the passenger flow is guided by adjusting the brightness of lighting tubes in the cars, for example, the lighting tubes in areas with high passenger density are dimmed, and the lighting tubes in areas with low passenger density are brightened, to guide ordered flow of passengers toward areas with low passenger density. Further details are disclosed herein.