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High-speed service area crowd density estimation system based on Wi-Fi data

A technology of crowd density and service area, which is applied in the field of crowd density estimation system in high-speed service area, can solve the problems that the overall accuracy needs to be improved and the density estimation is not comprehensive enough

Active Publication Date: 2019-12-06
SHANDONG TRAFFIC PLANNING DESIGN INST +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above-mentioned three existing Wi-Fi related technologies can only estimate the active personnel, or can only detect Wi-Fi-enabled devices, resulting in insufficient density estimation and overall accuracy that needs to be improved.

Method used

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  • High-speed service area crowd density estimation system based on Wi-Fi data
  • High-speed service area crowd density estimation system based on Wi-Fi data
  • High-speed service area crowd density estimation system based on Wi-Fi data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] The Wi-Fi data of this embodiment:

[0048] The coverage of wireless networks is getting wider and wider. The main advantage of Wi-Fi is that it does not require wiring and is not limited by wiring conditions. WIFI is a wireless network composed of AP (Access Point) and wireless network card. AP is generally called a network bridge or access point. It is used as a bridge between a traditional wired LAN and a wireless LAN. In addition to the connection time, the Wi-Fi data field also records the connected AP. In the service area, multiple Wi-Fi access points (APs) are generally installed in multiple locations for use by nearby users, such as toilets, restaurants, and supermarkets. Usually, users will connect to different APs when they are in different functional areas of the service area, so that the Wi-Fi data will record the user's behavior track in the service area. Generally speaking, the Wi-Fi data in the service area has the following fields: user ID, service area...

Embodiment 2

[0133] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented:

[0134] Collect geographical location-related information and Wi-Fi data of each service area; the Wi-Fi data includes the number of people connected to Wi-Fi in each hour interval of each service area, the total upload traffic, total download traffic, and user traffic of each service area. The average time and number of APs connected to Wi-Fi each time;

[0135] Use the regression model to estimate the number of people in the service area; the independent variable of the regression model is the number of people connected to Wi-Fi in each hour interval of each service area, and the dependent variable is the number of people in the corresponding service area; the slope of the independent variable is the reciprocal of the willingness to connect, and the willingness to connect is determined...

Embodiment 3

[0140] This embodiment provides a computer terminal, which includes a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the program, the following steps are implemented:

[0141] Collect geographical location-related information and Wi-Fi data of each service area; the Wi-Fi data includes the number of people connected to Wi-Fi in each hour interval of each service area, the total upload traffic, total download traffic, and user traffic of each service area. The average time and number of APs connected to Wi-Fi each time;

[0142] Use the regression model to estimate the number of people in the service area; the independent variable of the regression model is the number of people connected to Wi-Fi in each hour interval of each service area, and the dependent variable is the number of people in the corresponding service area; the slope of the independent variable is the reciprocal of the willingness to co...

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Abstract

The invention provides a high-speed service area crowd density estimation system based on Wi-Fi data, and the system comprises: a data collection device which is configured to collect the geographic position related information of each service area and the Wi-Fi data; and a data processer which is used for estimating the number of people in the service area by using a regression model, wherein theindependent variable of the regression model is the number of people connected with Wi-Fi in each hour interval of each service area, the dependent variable is the number of people in the corresponding service area, the slope of the independent variable is the reciprocal of the connection willingness, and the connection willingness is formed by multiplying the environment characteristics, the function positioning characteristics, the day characteristics and the hour characteristics of each service area by corresponding learning parameters, the environment features and the function positioningfeatures are extracted from Wi-Fi data and geographic position related information of the corresponding service areas respectively, the day features and the hour features are preset piecewise functions; behavior preferences of the users are predicted according to the estimated number of people in the service area and the sequence of the connection APs of the users entering the service area, further the number of people in each functional area in the service area is estimated, and finally the crowd density of the service area is obtained.

Description

technical field [0001] The disclosure belongs to the field of population estimation in high-speed service areas, and in particular relates to a crowd density estimation system in high-speed service areas based on Wi-Fi data. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Urban computing is a process of continuously acquiring, integrating and analyzing a variety of heterogeneous big data in cities to solve the challenges faced by cities (such as environmental degradation, traffic congestion, increased energy consumption, backward planning, etc.), and is committed to improving people's Quality of life, protection of the environment and promotion of urban efficiency. Urban computing helps us understand the nature of various urban phenomena and perceive urban dynamics reasonably and effectively. In urban computing scenarios, real-time crow...

Claims

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Application Information

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IPC IPC(8): H04W24/08H04W24/06H04W4/021H04W4/029G06N20/00
CPCH04W24/08H04W24/06H04W4/021H04W4/029G06N20/00
Inventor 李勇常健新刘伟吴伟令徐润金德鹏苏厉王奕彤
Owner SHANDONG TRAFFIC PLANNING DESIGN INST
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