KR102501090B1 - Navigation method and system using density prediction - Google Patents

Abstract

Disclosed are a route guidance method and a route guidance system using density prediction. The route guidance method performed in a server device according to an aspect of the present invention includes the steps of: setting a starting point and a destination by communicating with a user terminal; searching for a plurality of routes from the starting point to the destination; estimating density of predicted times for target spaces among stopovers for each searched route; and selecting and guiding an optimal route to a user terminal based on information on predicted densities among the searched routes. Accordingly, the route guidance method and the system using density prediction can predict and guide the density of a route in advance to prevent accidents.

Description

Route guidance method and system using density prediction {Navigation method and system using density prediction}

The present invention relates to a route guidance method and system using density prediction.

Information about the amount of traffic or flow is conveyed to the public through broadcasting media such as television or radio, but this information is usually only to the extent that the actual situation is seen through closed-circuit cameras installed in major areas and delivered in real time.

On the other hand, from the point of view of an individual, information on a specific place at a specific time is often required. For example, even when a meeting place is set, there is a case in which a person wants to know the amount of traffic and density of the population at the meeting place. Safety accidents may occur due to the concentration of many people, and moreover, if many people are crowded in a small space at high speed, a big problem occurs that risks life.

In addition, various route guidance services for public transportation, bicycles, and walking, as well as automobiles, have recently been provided. However, in the case of route guidance for public transportation and walking, only services such as the shortest time and shortest distance are provided, so there is still a risk of safety accident when passing through a crowded place. It is necessary to guide the main route.

Korean Patent Publication No. 10-2006-0090109 Apparatus for providing density service and method for providing the same

Accordingly, the present invention has been made to solve the above-mentioned problems, and is to provide a route guidance method and system using density prediction that predicts and guides the density of a route in advance to prevent safety accidents.

Other objects of the present invention will become clearer through preferred embodiments described below.

According to one aspect of the present invention, in a route guidance method performed in a server device, the steps of setting a starting point and a destination by communicating with a user terminal; Searching for a plurality of routes from the starting point to the destination; estimating density of predicted times for target spaces among stopovers for each searched path; and selecting an optimal route based on information on predicted densities among the searched routes and guiding the user terminal to a route guidance method using density prediction and a computer program executing the method. do.

Here, the destination density of the estimated time of arrival for the destination is predicted, and if the destination density is greater than or equal to a threshold value, a recommended destination corresponding to the destination may be searched for and provided to the user terminal.

In addition, when schedule information is input from the user terminal, one or more recommended places corresponding to the schedule information may be selected, and a destination may be set by predicting the density of each recommended place at a certain time.

In addition, by acquiring and analyzing wireless signals from terminal devices possessed by people located in the target space, the number of densely populated people in the target space is estimated, and based on the learning contents of the density history for the target space, the dense population The density at the predicted time corresponding to the number of people can be calculated.

In addition, a photographed image of the target space may be acquired and analyzed to predict the density.

In addition, the average moving speed and average separation distance of people in the target space may be further analyzed using the radio signal and the captured image, and used to predict the density.

In addition, a movement rate, which is a ratio of people moving to the target space, may be further analyzed to calculate the density by acquiring and analyzing a captured image of the surroundings of the target space.

According to another aspect of the present invention, the communication unit for communicating with the user terminal to set the starting point and destination; a route search unit for searching a plurality of routes from the starting point to the destination; a density predicting unit that predicts the density of predicted times for target spaces among waypoints for each searched path; and an information provision unit for selecting an optimal route based on information on predicted densities among the searched routes and guiding the user terminal to the route guidance system using density prediction.

Here, the density prediction unit predicts the density of the destination at the expected arrival time for the destination, and the information providing unit searches for a recommended destination corresponding to the destination when the destination density is greater than or equal to a threshold value, and provides it to the user terminal.

In addition, when schedule information is input from the user terminal, a recommendation unit for selecting one or more recommended places corresponding to the schedule information may be further included, and a destination may be set by estimating the density of each recommended place at a certain time. .

In addition, the density prediction unit estimates the number of dense people, that is, the number of people in the target space, by acquiring and analyzing wireless signals from terminal devices possessed by people located in the target space, and learning information about the density history for the target space. Based on this, it is possible to calculate the density at the predicted time corresponding to the number of densely populated people.

Other aspects, features and advantages other than those described above will become apparent from the following drawings, claims and detailed description of the invention.

According to the present invention, it is possible to prevent safety accidents caused by crowding of many people by predicting the density of each space on the path and providing a path that is not crowded.

1 is a configuration diagram schematically showing an entire system for density prediction for a target space according to an embodiment of the present invention;
2 is a functional block diagram showing the configuration of a service device according to an embodiment of the present invention.
3 is a flowchart illustrating a process of predicting density in a service device according to an embodiment of the present invention;
4 is a flowchart illustrating a process of predicting density by further utilizing CCTV images according to an embodiment of the present invention.
5 is a flowchart illustrating a process of calculating density using spatial information of a target space by image analysis according to an embodiment of the present invention;
6 is an exemplary diagram illustrating a user interface screen of a user terminal displaying density information according to an embodiment of the present invention.
7 is a flowchart illustrating a route guidance process using density prediction according to an embodiment of the present invention;
8 is an exemplary diagram illustrating each path using density prediction information according to an embodiment of the present invention;
9 is a flowchart illustrating a route guidance process considering the density of destinations according to an embodiment of the present invention.
10 is an exemplary diagram illustrating a user interface screen of a user terminal displaying a recommended destination according to an embodiment of the present invention.
11 is a flowchart illustrating a route guidance process to a destination using a user schedule according to an embodiment of the present invention.

Since the present invention can make various changes and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, or substitutes included in the spirit and technical scope of the present invention.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, terms such as a first threshold value and a second threshold value, which will be described later, may be substantially different from each other or partially identical to each other. Since there is room, terms such as first and second are written together for convenience of classification.

Terms used in this specification are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "include" or "have" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

In addition, the components of the embodiments described with reference to each drawing are not limitedly applied only to the corresponding embodiment, and may be implemented to be included in other embodiments within the scope of maintaining the technical spirit of the present invention, and also separate Even if the description is omitted, it is natural that a plurality of embodiments may be re-implemented as an integrated embodiment.

In addition, in the description with reference to the accompanying drawings, the same or related reference numerals are given to the same components regardless of reference numerals, and overlapping descriptions thereof will be omitted. In describing the present invention, if it is determined that a detailed description of related known technologies may unnecessarily obscure the subject matter of the present invention, the detailed description will be omitted.

First, a method of predicting the density of a specific space according to an example of the present invention will be described with reference to FIGS. 1 to 6, and a method of providing a route guidance service using the density prediction method will be described in FIGS. 7 to 11 will be explained with reference to.

1 is a configuration diagram schematically showing an entire system for density prediction for a target space according to an embodiment of the present invention.

Referring to FIG. 1, the entire system according to this embodiment includes one or more user terminals 100, a wireless receiving device 10, a CCTV camera 20 and a service device 30.

The wireless receiving device 10 receives wireless signals (bluetooth signals, wi-fi signals, etc.) from terminal devices owned by people located in the target space (the target space for measuring the density). do. In general, users use a terminal device such as a smart phone while driving a Bluetooth function for a Bluetooth device such as a wireless earphone and a Wi-Fi function for Wi-Fi communication. Accordingly, wireless communication signals transmitted from user terminals 100 possessed by people located in the target space are received. The wireless signal includes unique identification information such as a MAC address for identifying the corresponding terminal device.

The service device 30 may be implemented in the form of a server, and it is possible to estimate the number of densely populated people, i.e., the number of people in the target space, by analyzing the wireless signal of the user terminal 100. For example, if there are 10 beer places identified according to wireless signals, it can be determined that 10 people are located in the target space, or according to statistics based on learning, only 90% of people drive the wireless communication function. If it is determined that 10 beers are recognized, it may be determined that 11 people are actually located.

And, according to an example, the predicting device may acquire information about the user terminal 100 by various base stations from the mobile communication system and use it to estimate the number of people located in the target space. For example, if the number of mobile communication terminals connected to the base station A located at Exit 1 of Gangnam Station is 100, the corresponding information can be used to estimate the number of people concentrated in Gangnam Station Exit 1, which is a target space. In addition, if there are member terminals, location information by GPS received from the member terminals may be further utilized.

The service device 30 calculates the density at the predicted time corresponding to the number of densely populated people based on the learning contents of the density history of the target space. That is, the density of the predicted time according to the number of dense people at the current time is predicted by using the artificial intelligence learned based on the learning data for the density history for each time period in the target space.

Here, the service device 30 can predict the density in the target space by additionally analyzing the video by the CCTV camera 20 photographing the target space, as shown in the drawing. That is, the service device 30 obtains and analyzes a photographed image of the target space and uses it to estimate the number of people in the crowd.

In addition, the service device 30 further analyzes the average moving speed and average separation distance of people in the target space using the wireless signal and the captured image, and uses them to predict the degree of density together with the estimated number of densely populated people. For example, it is possible to obtain an average value by measuring the separation distance between people in the screen by CCTV images, and to obtain the average value by measuring the speed at which people move. Alternatively, the movement speed may be estimated using a change in reception strength of a radio signal of an arbitrary terminal device.

In addition, although not shown in the drawing, since the CCTV camera 20 may be provided not only in the target space but also in its surroundings, the service device 30 may further use images around the target space.

According to another example, the service device 30 may further utilize wireless signal information and images by the wireless receiving device 10 or the CCTV camera 20 provided in a related space of another place corresponding to the target space. For example, if the target space is exit 1 of Gangnam Station, exits 2 to 16 of Gangnam Station may be set as related spaces.

2 is a functional block diagram showing the configuration of a service device according to an embodiment of the present invention, and FIG. 3 is a flowchart illustrating a density prediction process in a service device according to an embodiment of the present invention.

First, referring to FIG. 2, the service device 30 includes a communication unit 210, a storage unit 220, and a control unit 230, but the control unit 230 includes a number estimation unit 231 and a density estimation unit 232 And it may include an information providing unit (233).

The communication unit 210 is a communication means for communicating with the wireless receiving device 10, the CCTV system, and the user terminal 100 requesting density information through a communication network, which will be obvious to those skilled in the art, so a detailed description thereof will be omitted.

The storage unit 220 stores necessary information for the controller 230 to function. According to an example, artificial intelligence learning data for density prediction, density history information for each target space, and the like are stored in the storage unit 220.

The control unit 230 analyzes the radio signal acquired through the wireless receiving device 10 to estimate the number of dense people, which is the number of people in the target space, and corresponds to the dense number of people based on the learning contents of the density history for the target space. Calculate the density at the predicted time.

To describe the function of each functional unit of the control unit 230 together with FIG. 3, when the service device 30 acquires the radio signals of the terminal devices located in the target space (S310), the radio signal is obtained by the person estimation unit 231. is analyzed to estimate the number of densely populated people in the target space (S320). Since the method for estimating the number of people using a radio signal has been described above, duplicate descriptions will be omitted.

The density prediction unit 232 of the control unit 230 calculates the density at a required predicted time corresponding to the number of people in a density based on the learning content of the density history (S330).

The information providing unit 233 of the control unit 230 guides information on the calculated density (S340), and transmits, for example, a message according to the density information to the requested user terminal (see FIG. 6).

Here, according to an example, the information providing unit 233 may output a warning message including risk factor guidance information based on analysis of a captured image when the predicted density is greater than or equal to a threshold value. For example, if objects that are determined to be risk factors such as obstacles, construction site machines, and cargo-loaded trucks exist in the image by object analysis of the captured image, risk factor guide information is generated and guided along with density information.

Also, as will be described later, the information providing unit 233 provides a route guidance service based on the information about the predicted density. That is, when a user inputs a destination from a starting point, a plurality of routes from the starting point to the destination are searched, and the density in a specific space is calculated on each route, so that a route passing through a space with a low density is given priority. The route guidance service process will be described later in detail with reference to related drawings ( FIGS. 7 to 11 ).

4 is a flowchart illustrating a process of predicting density by further utilizing CCTV images according to an embodiment of the present invention.

Referring to FIG. 4 , the service device 30 acquires a photographed image of not only the target space but also the surroundings and related places of the target space from the CCTV system (S410). For convenience of understanding, for example, if the target space is Gangnam Station Exit 1, the Gangnam Station platform video and Gangnam Station Exit 1 and the closest surrounding CCTV camera images are acquired as surrounding images, and images taken in related places As , the photographed images of the remaining exits of Gangnam Station are acquired.

The average moving speed and average separation distance of people in the target space are analyzed by analyzing the acquired images, and the movement rate, which is the ratio of people moving to the target space, is analyzed by analyzing the surrounding captured images (S420).

Analysis information derived by image analysis is used for density prediction (S430). In other words, by artificial intelligence learning, the ratio of people moving to the target space, the number of people, the number of people around the target space, etc. It is further utilized to predict the density of the target space.

In addition, the service device 30 may determine the priority (or different weights) used for density prediction according to the predicted time when there are a plurality of related places. For example, if A and B exist as related places, related place A is preferentially used from 6:00 pm to 12:00 pm and related place B is used for the rest of the time.

Here, although not shown in the drawings, according to an example, the service device 30 obtains route providing service information for the target space as a destination from a navigation system providing a route guidance service, thereby determining how many people are in the target space at the predicted time. It can be used to predict the density by further utilizing whether it arrives as a destination.

5 is a flowchart illustrating a process of calculating density using spatial information of a target space by image analysis according to an embodiment of the present invention.

Referring to FIG. 5 , the service device 30 performs spatial analysis on a photographed image of a target space (S510). For example, area estimation for how wide the space is, obstacle estimation for whether there are random obstacles, etc. can be performed, or the area of the space changes as a random object occupies the space compared to before. can be analyzed, etc.

As a result of this analysis, it is determined whether there is a change in comparison with the previous one (S520), and if there is no change, the density is calculated using previously stored spatial information (S530).

Unlike this, if it is determined that the spatial information has changed, the spatial information is updated and stored, and the updated spatial information is used to calculate the density (S540). For example, even if the number of people is the same, if the space is judged to be narrow, the density can increase.

Information on the predicted density may be provided to the user's terminal device.

6 is an exemplary diagram illustrating a user interface screen of a user terminal displaying density information according to an embodiment of the present invention.

Referring to FIG. 6, as a user interface screen displayed on a user terminal, information 610 on a target space and density information 620 at a prediction time may be displayed. Alternatively, recommendation information 630 for a relatively low density among related spaces may be displayed. For example, the recommended space is selected mainly from a place that is closest to the target space and has the lowest density.

Hereinafter, a method for providing a route guidance service in a service device will be described in detail.

Referring back to FIG. 2 , the control unit 230 of the service device 30 may further include a path search unit 234 and a recommendation unit 235 .

The route search unit 234 searches for a plurality of routes from a starting point to a destination. And, by predicting the density of predicted times for target spaces among waypoints for each path searched by the density predictor, the information providing unit 233 based on the information on the predicted densities among the searched paths. It selects the optimal route and guides it to the user terminal.

When schedule information is input from the user terminal, the recommendation unit 235 selects one or more recommended places corresponding to the schedule information. Then, the destination may be set by predicting the density of each recommended place at a certain time. This will be described in detail with reference to FIG. 11 .

7 is a flowchart illustrating a route guidance process using density prediction according to an embodiment of the present invention, and FIG. 8 is an exemplary diagram illustrating each route using density prediction information according to an embodiment of the present invention.

Referring to Figure 7, the service device 30 communicates with the user terminal to set the starting point and destination (S710). For example, the service device 30 provides a user interface in the form of a web page or a view page to a user terminal accessed using a web or application program, so that the user can set the starting point and destination. According to another example, the current location of the user terminal (using GPS-based location measurement, etc.) may be automatically set as a starting point, and the destination may be automatically set using schedule information obtained from a schedule program installed in the user terminal.

The service device 30 searches for a plurality of routes from the set departure point to the destination (S720). Referring to FIG. 8 , for example, two routes are searched from a starting point to a destination.

Then, the densities of predicted times for target spaces among stopovers are predicted for each searched path (S730). The service device 30 manages management information for high-density spaces (a space where people are often crowded, hereinafter referred to as a management space) by learning data, and each prediction for the management space among waypoints on the route It is to predict all the density of time (expected time of arrival at the stop). For convenience of understanding, for example, if the first stopover is scheduled to arrive at 1:10 pm and the second stopover is scheduled to arrive at 1:25 pm, the density at 1:10 pm for the first stopover is predicted. and the second waypoint is to predict the density at 1:25 pm. Referring to FIG. 8, both the first route and the second route each have two management spaces as waypoints, so that the density of each is predicted.

Among the searched routes, an optimal route is selected based on the information on predicted densities and guided to the user terminal (S740). That is, it is possible to recommend or select a route mainly focused on places with low density (for example, an average value or a maximum value of density can be compared) among stopovers with priority. According to FIG. 8, the first route with relatively low density of waypoints is preferentially selected.

9 is a flowchart illustrating a route guidance process considering the density of destinations according to an embodiment of the present invention, and FIG. 10 illustrates a user interface screen of a user terminal displaying recommended destinations according to an embodiment of the present invention. is an example

Referring to FIG. 9 , the service device 30 predicts the destination density at the expected arrival time for the set destination (S910). That is, not only stopovers on the route according to FIG. 7 are predicted, but also destination density with the final destination as the target space.

It is determined whether the destination density is greater than or equal to a threshold value (S920), and if it is less than the threshold value, route guidance to the destination is performed through the same process as in FIG. 7 (S930).

In contrast, if the destination density is greater than or equal to the threshold value, recommended destinations in the vicinity with a lower density are searched for and guided (S940).

Referring to FIG. 10 showing a user interface displayed on a user terminal by the processing process according to the present embodiment, destination information 1010 and information 1020 on destination density are displayed, and when the destination density is high, the destination Information 1030 on a recommended destination to replace is also displayed. As recommended destination information, prediction information on the density of the corresponding space and information on the distance from the original destination can be provided together.

Accordingly, the user who checks the screen according to the present embodiment can move to a place that is close to the original destination and not crowded with people as a destination.

11 is a flowchart illustrating a route guidance process to a destination using a user schedule according to an embodiment of the present invention.

Referring to FIG. 11 , when the service device 30 acquires schedule information from the user terminal (S1110), it selects one or more recommended places corresponding to the schedule information (S1120). For example, check a schedule (eg, Gangnam Station 6:00 PM friend appointment) from Google Calendar, and recommend a place suitable for the schedule (eg, Gangnam Station Exit 1, a cafe near Gangnam Station, a restaurant near Gangnam Station, etc.) choose

The density of each selected recommended place is predicted at a certain time (for example, 6 o'clock), and a target destination is selected according to the predicted density (S1130). For example, among recommended places, only places with a density below the threshold are selected as the target destination. Using the above example, if the density of Gangnam Station Exit 1 is above the threshold, only cafes and restaurants near Gangnam Station are selected as the target destination. choose with

The selected target destination may be finally recommended to the user. And according to another example, as shown in the drawing, each route moving from the current location of the user terminal to the target destinations is searched, and the destination having the optimal route mainly based on low density of waypoints, low expected travel time, and short travel distance is recommended. and guides the route upon user selection (S1140).

According to the present embodiment, a place suitable for a certain time and place is automatically recommended by checking a schedule, and a destination with relatively low density and convenient movement can be recommended with priority.

A computer program stored in a computer-readable medium may be provided to perform the path guidance method using density prediction according to the present invention described above.

In addition, the above-described path guidance method using density prediction can be implemented as computer readable code on a computer readable recording medium. Computer-readable recording media includes all types of recording media in which data that can be decoded by a computer system is stored. For example, there may be read only memory (ROM), random access memory (RAM), magnetic tape, magnetic disk, flash memory, optical data storage device, and the like. In addition, the computer-readable recording medium may be distributed to computer systems connected through a computer communication network, and stored and executed as readable codes in a distributed manner.

In addition, although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art can make the present invention within the scope not departing from the spirit and scope of the present invention described in the claims below. It will be appreciated that various modifications and variations may be made.

10: wireless receiving device 20: CCTV camera
30: service device 100: user terminal

Claims (12)

In the route guidance method performed in the server device,
Setting a starting place and a destination by communicating with a user terminal;
Searching for a plurality of routes from the starting point to the destination;
estimating density of predicted times for target spaces among stopovers for each searched path; and
Selecting an optimal route based on information on predicted densities among the searched routes and guiding it to the user terminal,
By acquiring and analyzing wireless signals from terminal devices possessed by people located in the target space, the number of people in the target space is estimated, and based on the learning contents of the density history for the target space, the number of dense people is calculated. A route guidance method using density prediction, which calculates density at a corresponding predicted time.
The method of claim 1,
Predicting the destination density of the expected arrival time for the destination, and searching for a recommended destination corresponding to the destination when the destination density is greater than or equal to a threshold value, and providing the recommended destination to the user terminal. A route guidance method using density prediction.
The method of claim 1,
When schedule information is input from the user terminal, one or more recommended places corresponding to the schedule information are selected, and a destination is set by predicting the density of each recommended place at a certain time.
delete The method of claim 1,
A route guidance method using density prediction, wherein a photographed image of the target space is acquired and analyzed and used to predict the density.
The method of claim 5,
A route guidance method using density prediction, wherein the average moving speed and average separation distance of people in the target space are further analyzed using the radio signal and the captured image and used to predict the density.
The method of claim 1,
A method of guiding a route using density prediction, in which a movement rate, which is a ratio of people moving to the target space, is further analyzed by acquiring and analyzing a surrounding photographed image taken around the target space and used to calculate the density.
A computer program stored in a computer-readable medium for performing a path guidance method using density prediction, the computer program causing a computer to perform the following steps, the steps comprising:
Setting a starting place and a destination by communicating with a user terminal;
Searching for a plurality of routes from the starting point to the destination;
estimating density of predicted times for target spaces among stopovers for each searched path; and
Selecting an optimal route based on information on predicted densities among the searched routes and guiding it to the user terminal,
By acquiring and analyzing wireless signals from terminal devices possessed by people located in the target space, the number of people in the target space is estimated, and based on the learning contents of the density history for the target space, the number of dense people is calculated. A computer program stored on a computer-readable medium that calculates a density at a corresponding predicted time.
Communication unit for communicating with the user terminal to set the starting point and destination;
a route search unit for searching a plurality of routes from the starting point to the destination;
a density predicting unit that predicts the density of predicted times for target spaces among waypoints for each searched path; and
Including an information providing unit for selecting an optimal route based on information on predicted densities among the searched routes and guiding it to the user terminal,
The density prediction unit estimates the number of dense people, which is the number of people in the target space, by acquiring and analyzing wireless signals from terminal devices possessed by people located in the target space, and based on the learning contents of the density history for the target space A route guidance system using density prediction, which calculates the density at the predicted time corresponding to the number of people in the density.
The method of claim 9,
The density prediction unit predicts the destination density of the expected arrival time for the destination, and the information providing unit searches for a recommended destination corresponding to the destination when the destination density is greater than or equal to a threshold value and provides it to the user terminal. A route guidance system using density prediction.
The method of claim 9,
When schedule information is input from the user terminal, a recommendation unit for selecting one or more recommended places corresponding to the schedule information is further included;
A route guidance system using density prediction in which a destination is set by predicting the density of each recommended place at a certain time.
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