JP7297725B2 - Estimation device, estimation method and estimation program - Google Patents

Description

The present invention relates to an estimating device, an estimating method, and an estimating program.

Conventionally, in order to predict the traffic impact of customer-attracting facilities and events, detailed models are generated and simulations are performed for regional-level networks. Various approaches have been taken to model traffic flow to eliminate road congestion, and simulation methods have been developed for the purpose of dynamic traffic allocation, inflow restriction, and so on.

JP 2018-005264 A

However, the conventional technology described above is a highly accurate model that targets regional-level networks and uses a large number of parameters, so there is a problem that it is not general-purpose.

In addition, verification of the model using actual data is not sufficient.

In view of the above, an object of the present application is to efficiently estimate a traffic mode associated with the holding of a new event or the like.

An estimating device according to the present application includes an acquisition unit that acquires a traffic mode associated with the occurrence of a predetermined event within a predetermined period in a first area, and a second It is characterized by comprising an estimation unit for estimating a traffic mode associated with the occurrence of an event similar to the event within a predetermined period in the area.

According to one aspect of the embodiment, it is possible to efficiently estimate the traffic mode associated with the holding of a new event or the like.

FIG. 1 is a diagram showing an example of estimation processing for estimating traffic conditions. FIG. 2 is a diagram showing an example of cross-sectional traffic volume data. FIG. 3 is a diagram showing an example of obtaining an estimated degree of congestion from a cross-sectional traffic volume. FIG. 4 is a diagram showing an example of a method for setting a range in which a user terminal device receives designated traffic conditions. FIG. 5 is a diagram showing an example of traffic mode data. FIG. 6 is a diagram showing an example of event data. FIG. 7 is a diagram illustrating a configuration example of an estimation apparatus 200 according to an embodiment. FIG. 8 is a flowchart showing a processing procedure for estimating traffic conditions, which is executed by the estimating device 200 according to the embodiment. FIG. 9 is a diagram illustrating an example of a hardware configuration;

Embodiments for implementing an estimation device, an estimation method, and an estimation program (hereinafter referred to as "embodiments") according to the present application will be described in detail below with reference to the drawings. Note that the estimation device, estimation method, and estimation program according to the present application are not limited by this embodiment. The details of one or more embodiments are set forth in the following description and drawings. Moreover, each of one or more embodiments can be appropriately combined within a range that does not contradict the processing content. Also, in one or more embodiments below, the same parts are denoted by the same reference numerals, and overlapping descriptions are omitted.

[1. Estimation process]
First, an example of estimation processing according to an embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of estimation processing for estimating a traffic mode associated with the opening of a new event. In addition, in the example shown in FIG. 1, the example in which the estimation system 1 according to the embodiment executes the process of estimating the traffic mode in a predetermined area has been described.

As shown in FIG. 1, the estimation system 1 includes user terminal devices 10 ₁ to 10 _{n and} 20 ₁ to 20 _n , an estimating device 100 and an estimating device 200 (n is any natural number). In the specification, when it is not necessary to distinguish between the user terminals 10 ₁ to 10 _n , the user terminals 10 ₁ to 10 _n are referred to as “user terminals 10”, and the user terminals 20 ₁ to 20 _n need to be distinguished. , the user terminals 20 ₁ to 20 _n are collectively referred to as "user terminals 20". Although not shown in FIG. 1 , the estimation system 1 may include multiple estimation devices 100 and multiple estimation devices 200 .

The user terminal devices 10 and 20 are information processing devices used by users. The user terminal devices 10 and 20 may be any type of information processing device including smart phones, desktop PCs (Personal Computers), notebook PCs, and tablet PCs.

In the example of FIG. 1, the user terminal device 10 is a client device used by a user who drives a vehicle. In this example, the user terminal device 10 is a smart phone installed with a car navigation application (called a car navigation app). A smart phone is installed in a user's vehicle, for example. To give a more specific example, such a smartphone can be attached to an in-vehicle holder mounted on a user's vehicle. The term "user terminal device 10 installed in the user's vehicle" may include "user terminal device 10 installed in the user's vehicle". That is, the term "installation of the user terminal 10" can encompass "permanent installation of the user terminal 10 (e.g., mounting the user terminal 10)". In addition, the user terminal device 10 can acquire probe information detected by a vehicle information acquisition device mounted on a vehicle, for example, a probe device, by wire or wirelessly, or the user terminal device 10 itself can detect travel information. can be included. The same applies to the user terminal device 20 as well.

The estimation device 200 is an information processing device used by a traffic information provider or the like, and is realized by a server device or a cloud system. Although not shown in FIG. 1, the estimating device 200 communicates with the user terminal device 20 and the estimating device 100 by wire or wirelessly via a network (eg, Internet network). For example, the estimating device 200 is used by private organizations, private companies, etc. that predict the impact of traffic such as customer-attracting facilities and events. In the example of FIG. 1, the estimating device 200 predicts traffic conditions such as congestion around the event venue when a new event is held in B city, B prefecture, and provides the prediction results.

The estimating device 100 is an information processing device that generates cross-sectional traffic data. The estimating device 100 may be any type of information processing device, including a server. Although not shown in FIG. 1, the estimating device 100 communicates with the user terminal device 10 and the estimating device 200 by wire or wirelessly via a network (eg, Internet network).

In the example of FIG. 1, the estimation device 100 acquires travel information via the user terminal device 10 in area A, which is a predetermined range, when a predetermined event is held for a predetermined period in A City, A Prefecture, Generate cross-sectional traffic data. That is, the estimating device 100 generates cross-sectional traffic volume data for each segment on the road, which is included in area A, which is an area including the facility where the event occurred, within a predetermined period during which the event was held. .

In the example of FIG. 1, the estimating device 100 receives travel information (eg, time information) from a vehicle in which the user terminal device 10 (eg, a smartphone installed with a car navigation application (called a car navigation app)) is attached to an in-vehicle holder. , position information, movement mode, speed information, etc.) are acquired (step S11), and cross-sectional traffic volume data is generated for each segment (step S12). Here, the segment means a "route" determined by a road administrator, a so-called "street name", or the like.

In the example of FIG. 1, for example, the estimating device 100 estimates the traffic volume of various moving objects such as automobiles.

In order to estimate the cross-sectional traffic volume, the estimation device 100 determines, for example, inflow and outflow from the flow of vehicles when viewed from the center of the intersection. Here, the inflow is the flow of vehicles entering the intersection from each road, and the outflow is the flow of vehicles advancing from the intersection to each road.

The estimating device 100 estimates the number of inflowing vehicles proceeding from an intersection to each road and the outflowing number of vehicles proceeding from each road to the intersection. The estimating device 100 detects the vehicle position and time obtained by the user terminal device itself, vibration information detected by a self-acceleration sensor, gyro sensor, etc., traveling direction, speed at a point, acceleration, etc., based on traveling information including probe information. to estimate cross-sectional traffic volume.

FIG. 2 shows an example of cross-sectional traffic volume data generated by the estimation device 100 for major roads in area A around the event venue in A city. The estimating device 100 acquires travel information such as left turn, straight ahead, and right turn of each vehicle from the user terminal device 10, and calculates cross-sectional traffic volume for each segment. The cross-sectional traffic volume indicates the traffic volume of each road for each cross-section, and is information indicating, for example, the manner in which vehicles flow in and out of predetermined positions such as intersections and arterial roads. The number of inflows is the sum of the number of vehicles proceeding from the intersection to each road in a predetermined time, and the number of outflows is the sum of the number of vehicles proceeding from each road to the intersection in a predetermined period. In reality, the movement mode of the vehicle is not limited to left turn, straight line, and right turn, but in this embodiment, it is limited to right turn, straight movement, and left turn.

Next, the estimation processing performed by the estimation device 200 will be described using the example of FIG. For example, assume that an event scheduled to be held in B city, B prefecture is similar to an event already held in A city, A prefecture. Specifically, A city and B city are geographically and economically roughly the same size, have the same or similar organizers and performers, have similar audiences, The timing and the number of spectators that the event hall can accommodate are almost the same level of events.

The estimating device 200 acquires from the estimating device 100 cross-sectional traffic volume data in area A when a predetermined event is held for a predetermined period in A City, A Prefecture (step S13). Since the roads in Area A and Area B are geographically arranged differently, the estimation device 200 estimates the cross-sectional traffic volume of each segment in Area B based on the cross-sectional traffic volume data in Area A.

For example, the estimating device 200 first acquires from the estimating device 100 the cross-sectional traffic volume within a predetermined time in area A, which is a predetermined range centering on the event venue in A city, and obtains from the estimating device 100, each intersection heading to the event venue, Calculate the total cross-sectional traffic volume at each point on the road.

Next, the estimating device 200 sets a segment for a road (generally a roadway) for which traffic conditions are to be obtained. For a segment in area B centering on the event venue in B city, for each lane of a predetermined segment heading to the event venue, for example, the above total cross-sectional traffic volume is equally allocated. When allocating the total cross-sectional traffic volume, a model that takes into account road conditions, geographical conditions, etc. may be used to assign specific weights to each segment.

The estimating device 200 then calculates the traffic density for each segment in order to obtain the degree of traffic congestion. Traffic density is a unit representing the degree of traffic congestion, and is represented by the number of vehicles per kilometer. The estimating device 200 calculates the traffic density for each segment using the formula shown below.
Traffic volume (vehicles/h) = traffic density (vehicles/km) x average speed (km/h) (1)

The estimating device 200 uses, for example, the cross-sectional traffic volume of each segment as the traffic volume and the legal speed of each segment as the average speed in the equation (1). Estimation device 200 may use the average speed actually measured up to the time of estimation for each segment instead of the average speed.

Estimating device 200 then estimates the traffic mode in each segment. Specifically, the estimation device 200 estimates the degree of congestion for each segment according to a predetermined threshold based on the traffic density of the segment (step S14). FIG. 3 is a display example of the estimated congestion degree, and the thicker the arrow, the higher the congestion degree. The estimating device 200 provides the event operating company or the like with an estimation result of the traffic mode indicating the estimated degree of congestion (step S15).

[2. Estimation based on actual measurements]
The estimating device 100 estimates the total vehicle cross-section within a predetermined time using the user terminal device 10 at the measurement target point, and then considers the ratio of the correct data (actually measured value) and the utilization rate of the user terminal device 10. to calculate the final estimate. That is, the estimating device 100 may estimate the cross-sectional traffic volume at a predetermined position by using the travel information acquired by the user terminal device 10 as the travel information of the vehicle serving as the probe.

For example, the estimating device 100 acquires the result of a cross-sectional traffic volume survey obtained manually at an intersection targeted for cross-sectional traffic volume survey. Also, the estimation device 100 estimates the cross-sectional traffic volume using the travel information acquired by the user terminal device 10 . Then, the estimating device 100 generates a correction model for converting the cross-sectional traffic volume estimated using the travel information acquired by the user terminal device 10 into cross-sectional traffic volume manually acquired. Such a correction model can be realized by various machine learning techniques such as various regression analysis, SVM (Support Vector Machine), and neural network. Estimation apparatus 100 may generate such a correction model for each position to be estimated, for each region including the position to be estimated, for each day of the week or time period, for each vehicle type, and the like. It may be generated for each combination of attributes.

Subsequently, the estimating device 100 uses the travel information acquired by the user terminal device 10 to estimate the cross-sectional traffic volume at the position to be estimated, which is the cross-sectional traffic volume on the first order to be estimated. Then, the estimating device 100 may obtain a final estimation result by correcting the estimated cross-sectional traffic volume using the generated correction model.

[3. Geofence settings]
Here, the estimation device 100 may acquire travel information using a geofence technique. Geofencing is a location-based feature that allows you to set a virtual geographical boundary on a map, automatically record your entry and exit within that boundary, and use it to This is a function that can notify predetermined information. By constructing a geofence, it is possible to acquire traveling information from vehicles entering and exiting the fence, and to notify the traveling vehicle of predetermined information.

For example, the estimating devices 100 and 200 set geofences to set areas A and B, respectively. FIG. 4 is an example of setting a geofence for area B, and GF1 represents the boundary of the geofence. The estimating device 100 collects detailed travel information of the vehicle within the geofence. For example, on the day of the event, the estimating device 200 detects the estimated traffic mode, specifically the estimated Notify the congestion status of the road.

[4. Regarding the similarity of roads]
An intersection similar to the intersection in area B is identified from area A. For example, it is specified based on whether it is a 3-way intersection or a 4-way intersection, similarity of adjacent intersections (other intersections connected by roads), and the like. Then, when an event occurs in area B, the cross-sectional traffic volume of the intersection in area B is the cross-sectional traffic volume of a similar intersection and the cross-sectional traffic volume acquired when the event occurred in area A. can be estimated. Also, the estimation device 200 may estimate the traffic mode of the entire area B based on the cross-sectional traffic volume of each segment in the area B.

[5. Estimated traffic mode]
The estimating device 200 may provide information based on, for example, the difference between traffic at the time of the event and normal traffic. For example, if the number of vehicles turning right at an intersection in area B during an event is significantly higher than the number when the event does not occur, traffic jams occur at such intersections and roads on which cars travel to such intersections. can be presumed to occur. Also, the estimation device 200 may estimate various traffic modes (for example, behavior of each vehicle, etc.) at the time of occurrence of an event, using various known congestion generation techniques, etc., based on the estimated traffic volume.

[6. About data selection at the time of estimation]
The estimating apparatus 200 may select data to be used for estimation according to an area, an event, or the like to be estimated. Area A, which is similar to area B in terms of road structure, number of lanes, etc., is selected and the data of area A is used. Using data of area A when similar events occur Data of similar facilities where events occur are used. For example, if the facility where an event occurs in area B is a dome, let area A be an area around other domes where a similar event has occurred. Use data when similar events occur. For example, in the case of an idol, it is data related to the idol, and in the case of an enka singer, it is data related to the enka singer.

[7. About event]
The estimating device 200 targets arbitrary events. For example, concerts, various gatherings, new openings of facilities, fireworks festivals, and the like can be used.

[8. Configuration of estimation device]
Next, a configuration example of the estimation device 200 according to the embodiment will be described with reference to FIG. FIG. 6 is a diagram illustrating a configuration example of the estimation device 200 according to the embodiment. As shown in FIG. 6 , estimation device 200 includes communication section 210 , storage section 220 , and control section 230 . The estimating apparatus 200 has an input unit (for example, keyboard, mouse, etc.) that receives various operations from an administrator or the like who uses the estimating apparatus 200, and a display unit (liquid crystal display, etc.) for displaying various information. may

(Communication unit 210)
The communication unit 210 is implemented by, for example, a NIC (Network Interface Card) or the like. The communication unit 210 is connected to a network by wire or wirelessly, and transmits and receives information between the user device 20 and the information center server 200 via the network.

(storage unit 220)
The storage unit 220 is implemented by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 6 , the storage unit 220 has a cross-sectional traffic volume data storage unit 221 , a traffic condition data storage unit 222 , a traffic condition estimation model storage unit 223 and an event data storage unit 224 .

(Cross-sectional traffic volume data storage unit 221)
The cross-sectional traffic data storage unit 221 stores the cross-sectional traffic data acquired from the estimation device 100 .

The cross-sectional traffic volume data storage unit 221 stores, for example, the road ID, segment ID, time information, latitude, longitude, number of incoming vehicles, number of outgoing vehicles, etc. as shown in FIG. For example, a road ID is an ID number associated with a "route" determined by a road administrator, a so-called "street name", or the like. For example, the segment ID is an ID attached to a section having a predetermined distance along the road from the cross-sectional traffic volume measurement point. The time information is information such as the cross traffic volume acquisition start time and acquisition completion time. Latitude and longitude are self-vehicle position data acquired by the user terminal device using GPS. The number of incoming vehicles is the number of vehicles entering the intersection from the segment, and the number of outgoing vehicles is the number of vehicles entering the segment from the intersection.

(Traffic mode data storage unit 222)
The traffic mode data storage unit 222 stores, for example, road IDs, segment IDs, time information, latitudes, longitudes, incoming traffic densities, and outgoing traffic densities as shown in FIG. The road ID, segment ID, time information, latitude, and longitude are as described above. Incoming traffic density is the number of incoming vehicles per Km, and outgoing traffic density is the number of outgoing vehicles per Km.

(Traffic state estimation model storage unit 223)
The traffic state estimation model storage unit 223 stores a traffic state estimation model (that is, machine learning model data) which is a machine learning model for estimating a traffic state. The traffic condition estimation model storage unit 223 stores, for example, a traffic condition estimation model received by the reception unit 232 described below. The traffic condition estimation model storage unit 223 may store a traffic condition estimation model generated by the estimation unit 233 described below.

(Event data storage unit 224)
The event data storage unit 224 stores, for example, the event category, event name, date and time of occurrence, place, event area, etc. as shown in FIG.

(control unit 230)
The control unit 230 is a controller. For example, a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executes various programs (estimation programs) stored in a storage device inside the estimation device 100. (equivalent to one example) is implemented by executing the RAM or the like as a work area. Also, the control unit 130 is a controller, and may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 6, the control unit 230 includes an acquisition unit 231, a reception unit 232, and an estimation unit 233, and implements or executes information processing functions and actions described below. Note that the internal configuration of the control unit 230 is not limited to the configuration shown in FIG. 6, and may be another configuration as long as it performs information processing to be described later.

(Acquisition unit 231)
The acquisition unit 231 may acquire the above-described traffic mode estimation model (that is, machine learning model data). The obtaining unit 231 may obtain, for example, a traffic mode estimation model received by the receiving unit 232 described below. The acquisition unit 231 may acquire the traffic mode estimation model from a predetermined storage device. For example, the acquisition unit 231 may acquire the traffic mode estimation model from the traffic mode estimation model storage unit 123 described above. The acquisition unit 231 may store the acquired movement mode estimation model in the traffic mode estimation model storage unit 223 described above.

(Receiver 232)
The receiving unit 232 receives the event venue and event category from the user. The receiving unit 232 receives position information from vehicles that have entered the area B using the geofencing function. For example, this may cause the communication unit 210 to transmit estimated traffic mode data to a vehicle that has transmitted position information in area B. FIG.

(Estimation unit 233)
The estimating unit 233 estimates the traffic mode related to the second event in the second area based on the cross-sectional traffic volume data related to the first event in the first area. More specifically, the estimating unit 233 estimates the traffic mode related to the second event in the second region using the various methods described above.

For example, the estimating unit 233 may estimate the traffic mode in an area having road conditions similar to those in the first area as the second area. Moreover, the estimation unit 233 may estimate the traffic mode in an area including a facility similar to the facility where the first event occurred in the first area as the second area. Moreover, the estimation unit 233 may estimate the traffic mode in an area, as the second area, in which the second event occurs during a period similar to the period in which the first event occurs in the first area. For example, the estimating unit 233 identifies a day of the week and a time period in which the second event occurs in the second region, and determines when the first event occurs in the first region on a day of the week and a time period similar to the identified day of the week and time period. The traffic mode when the second event occurs in the second area may be estimated from the road conditions.

Further, the estimating unit 233 generates a model for estimating the traffic mode associated with the occurrence of the predetermined second event in the second area based on the traffic mode associated with the occurrence of the predetermined first event in the first area, The model may be used to estimate a traffic mode associated with the occurrence of the second event in the second area. For example, the estimating unit 233 uses the traffic mode associated with the occurrence of a predetermined first event in the first area as learning data and causes the model to learn the features of the learning data. As a more specific example, the estimating unit 233 obtains information about the roads in the first region (for example, the layout relationship of each intersection, the number of lanes on the road, whether the intersection is a three-way intersection or a four-way intersection, etc.) and , and information about the first event, the model is trained to output information indicating traffic conditions. For such learning, for example, various existing model learning methods such as back propagation can be adopted.

Then, the estimating unit 233 inputs the information about the road in the second area and the information about the second event to the learned model, thereby modeling the traffic mode when the second event occurs in the second area. can be estimated. The estimating unit 233 may further input data such as the day of the week and the time period when the event occurs, so that the estimating unit 233 can estimate the traffic mode when the predetermined event occurs in the predetermined area on the predetermined day of the week and the predetermined date and time. can be estimated.

[9. Estimation process flow]
Next, a procedure of estimation processing by the estimation device 200 according to the embodiment will be described.

FIG. 7 is a flowchart showing a processing procedure for estimating traffic conditions, which is executed by the estimating device 200 according to the embodiment.

First, the receiving unit 232 of the estimation device 200 receives an event venue and an event category from the user (step S101).

Next, the estimating unit 233 of the estimating device 200 sets, as a reference area, an area A similar to the area B where an event similar to the event scheduled to be held was held in the area B (step S102).

Next, the estimating unit 233 of the estimating device 200 identifies the date and time of the event designated in the area A (step S103).

Next, the acquiring unit 231 of the estimating device 200 acquires cross-sectional traffic data in area A (step S104).

Next, the estimating unit 233 of the estimating device 200 estimates the traffic mode related to the second event scheduled to be held in area B (step 105).

The communication unit 210 of the estimating device 200 then provides the estimated traffic mode (step 106).

[10. others〕
Also, among the processes described in the above embodiments, some of the processes described as being automatically performed can also be performed manually. Alternatively, all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

Also, each component of each device illustrated is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

For example, part or all of the storage unit 220 shown in FIG. 6 may be held in a storage server or the like instead of being held by the estimation device 200 . In this case, the estimation device 200 acquires various information such as travel information by accessing the storage server.

[11. Hardware configuration]
Also, the estimation device 100 according to the above-described embodiments is implemented by a computer 1000 configured as shown in FIG. 8, for example. FIG. 8 is a diagram illustrating an example of a hardware configuration; A computer 1000 is connected to an output device 1010 and an input device 1020, and an arithmetic device 1030, a primary storage device 1040, a secondary storage device 1050, an output IF (Interface) 1060, an input IF 1070, and a network IF 1080 are connected via a bus 1090. have

Arithmetic device 1030 operates based on programs stored in primary storage device 1040 and secondary storage device 1050, programs read from input device 1020, and the like, and executes various types of processing. The primary storage device 1040 is a memory device such as a RAM that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used for various calculations by the arithmetic device 1030 and various databases are registered, and is implemented by a ROM (Read Only Memory), HDD, flash memory, or the like.

The output IF 1060 is an interface for transmitting information to be output to the output device 1010 that outputs various types of information such as a monitor and a printer. It is realized by a connector conforming to a standard such as HDMI (registered trademark) (High Definition Multimedia Interface). Also, the input IF 1070 is an interface for receiving information from various input devices 1020 such as a mouse, keyboard, scanner, etc., and is realized by, for example, USB.

Note that the input device 1020 includes, for example, optical recording media such as CDs (Compact Discs), DVDs (Digital Versatile Discs), PDs (Phase change rewritable discs), magneto-optical recording media such as MOs (Magneto-Optical discs), and tapes. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like. Also, the input device 1020 may be an external storage medium such as a USB memory.

Network IF 1080 receives data from other devices via network N and sends the data to arithmetic device 1030, and also transmits data generated by arithmetic device 1030 via network N to other devices.

The arithmetic device 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 1070 . For example, arithmetic device 1030 loads a program from input device 1020 or secondary storage device 1050 onto primary storage device 1040 and executes the loaded program.

For example, when the computer 1000 functions as the estimation device 100 , the arithmetic device 1030 of the computer 1000 implements the functions of the control unit 230 by executing a program loaded on the primary storage device 1040 .

[12. effect〕
As described above, the estimation device 200 according to the embodiment has the acquisition unit 231 , the reception unit 232 and the estimation unit 233 .

The estimating apparatus 200 according to the embodiment acquires the traffic mode in the first area when a predetermined first event occurs in the first area, and based on the traffic mode in the first area, the first traffic mode in the second area. A traffic mode in the second area when a second event similar to the event occurs is estimated. As a result, the estimation device 200 can estimate traffic conditions such as traffic jams associated with the holding of the second event in the second area, and can cause event participants to prepare measures corresponding to this in advance.

The estimating device 200 according to the embodiment estimates the traffic mode in a second area in which road conditions are similar to those in the first area. In this case, the estimating device 200 can more accurately estimate traffic conditions such as traffic congestion associated with the holding of the second event in the second area.

The estimating device 200 according to the embodiment estimates a traffic mode in an area including facilities similar to the facility where the first event occurred in the first area as the second area. In this case, the estimating device 200 can more accurately estimate traffic conditions such as traffic congestion associated with the holding of the second event in the second region.

The estimating device 200 according to the embodiment estimates the traffic mode in the second area, in which the second event occurs during a period similar to the period in which the first event occurs in the first area. In this case, the estimating device 200 can estimate traffic conditions for a predetermined period of time, such as traffic congestion associated with the holding of the second event in the second area.

The estimating device 200 according to the embodiment acquires the traffic mode associated with the occurrence of a predetermined first event in the first area, and based on this, estimates the traffic mode associated with the occurrence of the predetermined second event in the second area. A model for is generated, and the model is used to estimate the traffic mode associated with the occurrence of the second event in the second region. As a result, the estimation device 200 can improve the accuracy of estimating the traffic mode while learning the model.

The estimating device 200 according to the embodiment acquires the position information of the vehicle transmitted from the terminal device when the vehicle in which the user carrying the terminal device rides enters the second area, and notifies the vehicle of the traffic mode. do. As a result, the estimation device 200 can allow the vehicle driver to avoid traffic congestion.

The estimating device 200 according to the embodiment estimates the traffic mode associated with the occurrence of the second event for each predetermined area in the second area based on the traffic mode related to the first event in the first area. As a result, estimation device 200 can pinpoint the traffic mode in the second area.

The estimating device 200 according to the embodiment acquires the traffic mode related to the first event in the first area for each predetermined user attribute, and estimates the traffic mode associated with the occurrence of the second event in the second area for each user attribute. do. Thereby, the estimation device 200 can estimate the traffic mode for each user attribute in the second area.

The estimating apparatus 200 according to the embodiment acquires the user's search query from the terminal device when the vehicle in which the user carrying the terminal device gets on enters the second region, and based on the search query, the second region Estimate the traffic mode associated with the occurrence of the second event in . Thereby, the estimation device 200 can estimate the traffic mode reflecting the information of the user's search query in the second area.

The estimating apparatus 200 according to the embodiment acquires the SNS information of the user from the terminal device when the vehicle boarded by the user who has the terminal device enters the second region, and based on the SNS information, determines the second region. Estimate the traffic mode associated with the occurrence of the second event in . Thereby, the estimation device 200 can estimate the traffic mode reflecting the user's SNS information in the second region.

Also, the estimation device 200 according to the embodiment may be a terminal device having the user terminal device 20 and installed in a vehicle.

In addition, by setting multiple geofences, the estimation device 200 according to the embodiment notifies the user terminal device 20 installed in a vehicle that has traveled a plurality of predetermined areas within a predetermined period of traffic conditions. may

Through the processes described above, the estimating device 200 can efficiently estimate the traffic mode associated with the holding of a new event or the like.

As described above, some of the embodiments of the present application have been described in detail based on the drawings. It is possible to carry out the invention in other forms with modifications.

In addition, the above-described estimating apparatus 200 may be implemented by a plurality of server computers, and depending on the function, may be implemented by calling an external platform or the like using an API (Application Programming Interface), network computing, or the like. Flexible to change.

Also, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

1 estimation system 10 user terminal device 20 user terminal device 100 estimation device 200 estimation device 210 communication unit 220 storage unit 221 cross-sectional traffic volume data storage unit 222 traffic condition data storage unit 223 traffic condition estimation model storage unit 224 event data storage unit 230 control Unit 231 Acquisition Unit 232 Reception Unit 233 Estimation Unit

Claims (12)

Based on the traveling information of the vehicle acquired by the user terminal device in the vehicle, a predetermined movement mode of the vehicle is acquired, and based on the acquired predetermined movement mode, the movement of each predetermined segment of the road in the predetermined area. an acquisition unit that acquires the traffic mode of the first region when a predetermined first event occurs in the first region by calculating a predetermined cross-sectional traffic volume;
an estimating unit for estimating, based on the traffic mode, the traffic mode in the second area when a second event similar to the first event occurs in the second area; An estimating device that notifies a vehicle traveling within a geographical range of the traffic mode of the second area at a predetermined timing related to the second event . The estimating device according to claim 1, wherein the estimating unit estimates, as the second area, a traffic mode in an area in which road conditions are similar to those in the first area. 3. The estimating unit according to claim 1, wherein the estimating unit estimates a traffic mode in an area including, as the second area, a facility similar to a facility in which the first event occurred in the first area. estimation device. 2. The estimating unit estimates, as the second area, traffic conditions in an area in which the second event occurs during a period similar to a period in which the first event occurs in the first area. 4. The estimating device according to any one of 1 to 3. The estimating unit is for estimating the traffic mode associated with the occurrence of the predetermined second event in the second area based on the traffic mode associated with the occurrence of the predetermined first event in the first area acquired by the acquiring unit. The estimating device according to any one of claims 1 to 4, wherein a model is generated, and the model is used to estimate a traffic mode associated with the occurrence of the second event in the second area. . The acquisition unit acquires the position information of the vehicle transmitted from the terminal device when the vehicle boarded by the user carrying the terminal device enters the second area,
The communication unit notifies the vehicle traveling within a predetermined geographical range related to the position information of the traffic mode of the second area at a predetermined timing. An estimating device according to any one of the preceding claims. The estimating unit estimates the traffic mode associated with the occurrence of the second event for each predetermined area in the second area based on the traffic mode related to the first event in the first area. 7. The estimating device according to any one of 6. The acquisition unit acquires a traffic mode related to a first event in the first region for each predetermined user attribute,
The estimating device according to any one of claims 1 to 7, wherein the estimating unit estimates, for each of the user attributes, the traffic mode associated with the occurrence of the second event in the second area. The acquisition unit acquires a user's search query from the terminal device when the vehicle in which the user who has the terminal device rides enters the second area,
The estimating device according to any one of claims 1 to 8, wherein the estimating unit estimates the traffic mode associated with the occurrence of the second event in the second area based on the search query. The acquisition unit acquires the SNS information of the user from the terminal device when the vehicle in which the user who has the terminal device gets on enters the second area,
The estimating device according to any one of claims 1 to 9, wherein the estimating unit estimates the traffic mode associated with the occurrence of the second event in the second area based on the SNS information. A computer implemented estimation method comprising:
Based on the traveling information of the vehicle acquired by the user terminal device in the vehicle, a predetermined movement mode of the vehicle is acquired, and based on the acquired predetermined movement mode, the movement of each predetermined segment of the road in the predetermined area. an obtaining step of obtaining a traffic mode of the first area when a predetermined first event occurs in the first area by calculating a predetermined cross-sectional traffic volume;
an estimating step of estimating the traffic mode of the second area when a second event similar to the first event occurs in the second area based on the traffic mode ;
An estimation method, comprising: notifying vehicles traveling within a predetermined geographical range in the second area of the traffic mode of the second area at a predetermined timing related to the second event. Based on the traveling information of the vehicle acquired by the user terminal device in the vehicle, a predetermined movement mode of the vehicle is acquired, and based on the acquired predetermined movement mode, the movement of each predetermined segment of the road in the predetermined area. an obtaining procedure for obtaining a traffic mode of a first area when a predetermined first event occurs in the first area by calculating a predetermined cross-sectional traffic volume;
causing a computer to execute an estimation procedure for estimating a traffic mode in a second area when a second event similar to the first event occurs in the second area based on the traffic mode ;
characterized by causing a computer to notify a vehicle traveling within a predetermined geographical range in the second area of the traffic mode of the second area at a predetermined timing related to the second event . estimating program.

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