Training method and system for passenger distribution prediction model, and passenger guiding method and system

Patent metadata

Field	Value
Publication number	US-2022391717-A1
Application number	US-202117776585-A
Country	US
Kind code	A1
Filing date	May 19, 2021
Priority date	Sep 27, 2020
Publication date	Dec 8, 2022
Grant date	—

Abstract

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.

First claim

1 . A training method for a distribution prediction model of passengers in a subway car, comprising:
establishing a spatial coordinate system of each car, installing a collection device constituted by temperature and humidity sensors and a CO 2 concentration sensor in each car, each collection device serving as a sample collection point, and obtaining installation coordinates of each sample collection point in the corresponding spatial coordinate system; obtaining a temperature time series, a humidity time series, and a CO 2 concentration time series at each sample collection point of each car during a sampling period, and obtaining an average temperature value and an average humidity value locally of the day, wherein the installation coordinates of each sample collection point, the temperature time series, the humidity time series, and the CO 2 concentration time series corresponding to the sample collection point, and the average temperature value and the average humidity value constitute an environmental data sample at each sample collection point of each car during the sampling period; recording passenger positions in each car at an end time of the sampling period, and obtaining coordinates of passengers in the corresponding spatial coordinate system, i.e. obtaining a passenger distribution for each car during the sampling period; converting the passenger distribution for each car during the sampling period into a binary image, and calculating a mean value, a variance, a maximum value and a minimum value of pixels in the binary image; calculating an amplitude and a gradient direction of each pixel in the binary image, and establishing a frequency distribution histogram with the gradient direction of all pixels as the abscissa axis and the pixel value corresponding to each pixel as the ordinate axis; extracting a frequency distribution feature of the frequency distribution histogram, the frequency distribution feature referring to a feature vector constituted by pixel values corresponding to each interval among R intervals equally divided from the abscissa axis of the frequency distribution histogram; combining the mean value, the maximum value, the minimum value, the variance and the frequency distribution feature of the pixels to form an original feature vector, and clustering the binary images with the original feature vector a input of a clustering algorithm to obtain image clustering results; coding the passenger distribution mode according to the image clustering results to obtain a passenger distribution code, each category of image clustering result corresponding to a passenger distribution mode, and each passenger distribution mode corresponding to a passenger distribution code; and establishing a distribution prediction model for each car, and training the distribution prediction model with training samples to obtain a trained distribution prediction model, the training sample taking the environmental data sample at each sample collection point of each car during the sampling period as input and the corresponding passenger distribution code as output.

2 . The training method for the distribution prediction model of passengers in the subway car according to claim 1 , wherein the spatial coordinate system is a two-dimensional rectangular coordinate system with a horizontal centerline of the roof of the car as the x-axis and a longitudinal centerline of the roof as the y-axis, or the spatial coordinate system is a two-dimensional rectangular coordinate system with a horizontal centerline of the ground of the car as the x-axis and a longitudinal centerline of the ground as the y-axis.

3 . The training method for the distribution prediction model of passengers in the subway car according to claim 1 , wherein 15 collection devices are installed on the roof of each car, and the 15 collection devices are arranged in a array.

4 . The training method for the distribution prediction model of passengers in the subway car according to claim 1 , wherein the specific process of converting the passenger distribution for each car during the sampling period into a binary image is as follows:
dividing each car into W equal parts along the x-axis of the spatial coordinate system of the car, and dividing each car into L equal parts along the y-axis of the spatial coordinate system of the car, to convert the space of each car into W×L independent units; setting each independent unit as a pixel, the number of passengers in each independent unit being a pixel value of the pixel; and obtaining pixel values of all pixels, and converting the passenger distribution into the binary image composed of W×L pixels.

5 . The training method for the distribution prediction model of passengers in the subway car according to claim 1 , wherein formulas for calculating the amplitude and the gradient direction of the pixel are respectively:

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Assignees

• Univ Central South

Inventors

• Liu Hui
• Duan Zhu
• Qin Jin
• Wu Haiping
• Yin Shi
• Chen Chao

Classifications

• G06N5/022 Primary

  Knowledge engineering; Knowledge acquisition · CPC title

• G06V20/59

  inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions · CPC title

• G06F16/5838

  using colour · CPC title

• G06F16/2474

  Sequence data queries, e.g. querying versioned data · CPC title

• G01D21/02

  Measuring two or more variables by means not covered by a single other subclass · CPC title