JP2022051432A - Estimation device, estimation method, and estimation program - Google Patents

Abstract

To enable efficient acquisition of cross-sectional traffic volume data.SOLUTION: An estimation device disclosed herein comprises an acquisition unit configured to acquire location information from a terminal device mounted on a vehicle, and an estimation unit configured to estimate modes of travel of vehicles traveling through a given point during a given period based on the acquired location information, estimate the number of vehicles that traveled on a given road for each vehicle type, and efficiently acquire cross-sectional traffic volume data.SELECTED DRAWING: Figure 1

Description

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

Conventionally, when measuring cross-sectional traffic volume, vehicle detectors (for example, traffic counters) are installed on various roads to measure the traffic volume. The cross-sectional traffic volume data showing the measurement result of such cross-sectional traffic volume is utilized in various fields such as traffic control, provision of traffic congestion information, traffic safety measures, road maintenance, and road management. In order to acquire such cross-sectional traffic volume data, a system has been proposed in which the travel history information (probe information) of the vehicle mounted on the in-vehicle device is collected via a roadside detector or an optical beacon and processed at a traffic control center or the like. Has been done. In addition, when a company considers a new store, a traffic volume survey is requested, and a traffic surveyor may measure the traffic volume.

Japanese Unexamined Patent Publication No. 2002-31286 JP-A-2015-212863

However, the above-mentioned conventional technique has a problem that the cross-sectional traffic volume data cannot be efficiently acquired. For example, in the method of installing a roadside detector or the like on a road, there is a problem that the measurement point is fixed and the measurement point cannot be flexibly changed to collect data.

In addition, if a traffic volume inspector is hired and traffic volume data is collected by a dedicated counter, it takes time and effort.

In view of the above, the present application aims to efficiently acquire cross-sectional traffic volume data.

The estimation device according to the present application is an estimation unit that acquires position information from a terminal device mounted on a vehicle and estimates the number of vehicles that have traveled in a predetermined position within a predetermined period based on the acquired position information. It is characterized by having a part.

According to one aspect of the embodiment, there is an effect that the cross-sectional traffic volume data can be efficiently acquired.

FIG. 1 is a diagram showing an example of an estimation process for estimating a traffic flow rate. FIG. 2 is a diagram showing an example of data for estimating the cross-sectional traffic volume. FIG. 3 is a diagram showing an example of a method of setting a range in which the user terminal device acquires driving information. FIG. 4 is a diagram showing an example of traveling information detected by the user terminal device. FIG. 5 is a diagram showing a configuration example of the estimation device according to the embodiment. FIG. 6 is a flowchart showing a processing procedure for estimating a traffic flow rate, which is executed by the estimation device according to the embodiment. FIG. 7 is a diagram showing an example of a hardware configuration.

Hereinafter, a mode for carrying out the estimation device, the estimation method, and the estimation program according to the present application (hereinafter referred to as “embodiment”) will be described in detail with reference to the drawings. It should be noted that this embodiment does not limit the estimation device, estimation method and estimation program according to the present application. Details of one or more embodiments are described in the following description and drawings. Further, each of the one or a plurality of embodiments can be appropriately combined as long as the processing contents do not contradict each other. Further, in the following one or a plurality of embodiments, the same parts are designated by the same reference numerals, and duplicate description will be omitted.

[1. Estimate processing]
First, an example of the estimation process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of an estimation process for estimating the number of vehicles traveling at a predetermined position on a road within a predetermined period. In the example shown in FIG. 1, an example in which a process of estimating the cross-sectional traffic volume at a predetermined intersection is executed by the estimation system 1 according to the embodiment is described.

As shown in FIG. 1, the estimation system 1 includes user terminal devices 10 ₁ to 10 _n , an estimation device 100, and an information center server 200 (n is an arbitrary natural number). In the specification, when it is not necessary to distinguish between the user terminal devices 10 ₁ to 10 _n , the user terminal devices 10 ₁ to 10 _n are collectively referred to as "user terminal device 10". Although not shown in FIG. 1, the estimation system 1 may include a plurality of estimation devices 100 and a plurality of information center servers 200.

The user terminal device 10 is an information processing device used by the user. The user terminal device 10 may be any type of information processing device including a smartphone, a desktop PC (Personal Computer), a notebook PC, and a tablet PC.

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 smartphone on which a car navigation application (called a car navigation application) is installed. The smartphone is installed, for example, in the user's vehicle. Such a smartphone can be attached to an in-vehicle holder. The term "user terminal device 10 installed in the user's vehicle" may include "user terminal device 10 mounted in the user's vehicle". That is, the term "installation of the user terminal device 10" may include "permanent installation of the user terminal device 10 (eg, mounting the user terminal device 10)". Further, the user terminal device 10 may acquire the probe information detected by the vehicle information acquisition device mounted on the vehicle, for example, the probe device by wire or wirelessly, or the user terminal device 10 itself may detect the traveling information. Can be included.

The information center server 200 is realized by a server device or a cloud system used by a traffic information provider or the like. For example, the information center server 200 is used by a private organization, a private company, or the like that promotes the utilization of driving information from a vehicle in society as a whole.

The estimation device 100 is an information processing device that estimates the traffic flow rate. The estimation device 100 may be any type of information processing device including a server. Although not shown in FIG. 1, the estimation device 100 communicates with the user terminal device 10 and the information center server 200 via a network network (for example, an Internet network) by wire or wirelessly.

In the example of FIG. 1, the estimation device 100 estimates the traffic flow rate and estimates the cross-sectional traffic volume. For example, the estimation device 100 estimates the cross-sectional traffic volume for two-wheeled vehicles, ordinary vehicles, and large-sized vehicles, for example, for various moving objects such as two-wheeled vehicles and automobiles within a predetermined range near an intersection.

In the example of FIG. 1, the two-wheeled vehicle is, for example, a motorcycle or a motorized bicycle. The ordinary car is, for example, a light car, a passenger car, a light freight car, a small freight car, and a passenger car. Large vehicles are, for example, buses, ordinary freight vehicles, and special vehicles.

Here, the traffic cross-sectional amount will be described. The traffic cross-section survey is a mode in which a moving body flows in and out of a predetermined position or area (for example, a predetermined position of an intersection or an arterial road; hereinafter collectively referred to as a “predetermined position”). This is information indicating that. Here, the inflowing mode is a mode in which what kind of moving body has entered a predetermined position from which direction and how. Further, the outflow mode is information such as what kind of moving body has gone out from a predetermined position in which direction.

In order to estimate such a traffic cross-sectional amount, the estimation device 100 determines, for example, the inflow and the outflow from the flow of the vehicle when viewed from the center of the intersection. For example, the estimation device 100 estimates that when a vehicle enters an intersection from each road, the vehicle has flowed out to a predetermined position. To give a more specific example, inflow means traveling from road (1) to road (4) to any road in FIG. 1, and outflow means road (1) in FIG. ) -A vehicle that has flowed into an intersection from a road (4) has traveled to one of the roads (1)-(4).

Then, the estimation device 100 shows the traffic volume of each road at the intersection as the cross-sectional traffic volume for each cross section, and the number of inflows of vehicles traveling from the intersection to each road and the number of outflows of vehicles traveling from each road to the intersection. Estimate the total. For example, in FIG. 1, the estimation device 100 is a total of vehicles that have flowed in from the road (1) to the intersection and vehicles that have flowed in from the roads (2) to (4) and have flowed out to the road (1). , The total of inflow and outflow vehicles on the road (1). Then, the estimation device 100 obtains the total of the inflow and outflow vehicles on each of the roads (1) to (4) intersecting at the intersection in the example of FIG. 1, and obtains the cross-sectional traffic volume in the vicinity of the intersection shown in FIG. You can get them all.

Here, an example of a traffic cross-sectional amount survey will be described with reference to FIG. FIG. 2 is a diagram showing an example of a traffic cross-sectional amount survey according to an embodiment. For example, if the intersection is a four-way intersection, vehicles will flow in from a certain road toward the intersection to make a right turn, a straight line, or a left turn. In such a case, the estimation device 100 is used for each direction of travel (that is, for each turn right, straight, and left), the total number of motorcycles (total of motorcycles by direction), the total number of ordinary vehicles (total of ordinary vehicles by direction), and the total of large vehicles. The total (large vehicle total by direction) will be estimated. Further, the estimation device 100 estimates the total of ordinary vehicles and large vehicles (total of vehicles by direction) from the two-wheeled vehicle meter by direction, the ordinary vehicle meter by direction, and the large vehicle meter by direction, and uses the estimated vehicle total by direction. On the other hand, the inflow route meter is estimated by adding the total of two-wheeled vehicles.

On the other hand, all vehicles traveling from the intersection to the road will be leaked. Therefore, the estimation device 100 estimates the number of motorcycles that have flowed out from the intersection for each of the two-wheeled vehicle, the ordinary vehicle, and the large-sized vehicle, and is used as an outflow road two-wheeled vehicle meter, an outflow road ordinary vehicle meter, and an outflow road large-sized vehicle meter, respectively. Then, the estimation device 100 calculates the outflow road meter by adding the outflow road motorcycle meter, the outflow road ordinary vehicle meter, and the outflow road large vehicle meter. Further, the estimation device 100 is a total of two-wheeled vehicles that have flowed into the intersection, a total of two-wheeled vehicles that have flowed out of the intersection, a total of two-wheeled vehicle cross sections, and a total of vehicles that have flowed into the intersection and a total of vehicles that have flowed out of the intersection. Estimate the total.

Returning to FIG. 1, the explanation will be continued. Conventionally, in order to obtain cross-sectional traffic volume, the police chief collects information on cross-sectional traffic volume collected by prefectural police nationwide with measuring devices such as vehicle detectors as cross-sectional traffic volume measurement, and (public interest incorporated foundation) Japan Road. Provided by the Traffic Information Center. For example, a system that collects travel history information (probe information) of a vehicle mounted on an in-vehicle device via a roadside detector or an optical beacon and processes it at a traffic control center or the like has also been put into practical use. In some cases, a traffic surveyor uses a dedicated counter.

However, in the former case, it takes time and cost because the position information of the measurement point must be updated and the vehicle detector must be installed as necessary. In the latter case, the measurement location is limited, and it is necessary to secure human resources and labor costs.

Therefore, in the exemplary embodiment of FIG. 1, the estimation device 100 executes the estimation process described below in order to estimate the cross-sectional traffic volume without spending a lot of time and money.

First, the user terminal device 10 carried by the user riding in each vehicle, that is, the user terminal device 10 mounted on each vehicle acquires various sensing data as traveling information at predetermined time intervals. For example, the user terminal device 10 acquires the own vehicle position (for example, latitude, longitude), time, etc. acquired by GPS (Global Positioning System). The user terminal device 10 may acquire vibration information, a traveling direction, a point speed, an acceleration, etc. detected by a self-accelerometer sensor, a gyro sensor, or the like as traveling information. Further, the user terminal device 10 may acquire, for example, steering wheel operation, point speed, turn signal information, and the like as travel information from various control devices mounted on the vehicle.

Next, the estimation device 100 acquires the travel information acquired by the user terminal device 10 (step S11). For example, in the estimation determination device 100, every time the user terminal device 10 acquires the driving information, the time interval is longer than the time interval for acquiring the driving information (for example, every day), or the user terminal device 10 is connected to WiFi. At that time, the history of the traveling information acquired by the user terminal device 10 is acquired.

In such a case, the estimation device 100 estimates the vehicle type, the movement mode, and the traffic flow rate based on the travel information (step S12). For example, the estimation device 100 estimates the vehicle type of the vehicle traveling on the road R1 based on the information such as vibration information, speed information, acceleration information, etc. included in the travel information. For example, it is considered that the vibration measured by the user terminal device 10 differs depending on the vehicle type. Therefore, the estimation device 100 estimates whether it belongs to a two-wheeled vehicle, an ordinary vehicle, or a large vehicle based on the characteristics of the vibration shape acquired from the vibration information. It should be noted that such processing is performed, for example, by a machine learning model (for example, a neural network or the like) that learns the relationship between the vehicle type and the characteristics of the vibration information acquired by the user terminal device 10 mounted on the vehicle of the vehicle type. Can be expressed using. The estimation device 100 may acquire information on the vehicle type registered by the user from each user terminal device 10, and may specify the vehicle type of each vehicle based on the acquired information.

Next, the estimation device 100 estimates the movement mode of each vehicle based on the acquired travel information. For example, the estimation device 100 estimates how each vehicle has moved on the road based on the history of location information. Then, the estimation device 100 estimates how each vehicle flows in and out at the intersection to be investigated based on the estimation result.

Further, for example, the estimation device 100 estimates the movement mode of each vehicle based on the turn signal information in the travel information. More specifically, if a vehicle has a turn signal indicating a right turn at an intersection, the estimation device 100 estimates that the vehicle has turned right at the intersection. Further, when the turn signal information cannot be acquired, the estimation device 100 may estimate, for example, by the amount of change in the numerical value in the traveling direction included in the traveling information.

Further, the estimation device 100 may estimate that the change amount of the numerical value in the traveling direction indicated by various sensor information exceeds a predetermined threshold value, turn left or right, and if the change amount does not exceed the threshold value, estimate that the vehicle goes straight. .. For example, the estimation device 100 may estimate a left turn, a right turn, or a straight line of the vehicle based on the amount of change of the acceleration sensor. Further, the estimation device 100 refers to the map information acquired by the user terminal device 10 and calculates the degree of approximation between the route acquired by using the position information related to the position of the own vehicle and the road on the map, and turns left. It may be estimated to go straight or turn right. Further, the estimation device 100 may reflect the traffic rules such as one-way traffic and no entry, and the reel time information such as traffic closure due to the cause such as during construction in the estimation process by using the map information. It should be noted that these processes may be realized by using various known techniques for estimating the traveling position of the vehicle.

Next, the estimation device 100 estimates the movement mode of turning left, going straight, and turning right according to the vehicle type for the vehicle in which the user terminal device 10 is installed, and estimates the traffic flow rate (step S12). For example, the estimation device 100 identifies vehicles that have flowed in or out of the intersection that is the target of the traffic cross-sectional amount survey, based on the movement mode. Subsequently, the estimation device 100 specifies each vehicle type of the specified vehicle. Then, the estimation device 100 generates the result of the traffic cross-sectional amount survey for each vehicle type. After that, the estimation device 100 provides the result of the traffic cross-section amount survey to the information center server 200 as needed (step S13).

[2. About estimation based on actual measurement values]
In the above description, the estimation device 100 estimates the traffic cross-sectional amount based on the travel information measured by each user terminal device 10. However, if the user terminal terminal 10 is not mounted on all vehicles, the estimation device 100 may not be able to estimate an appropriate traffic cross-section amount.

Therefore, the estimation device 100 estimates the total cross-section of the two-wheeled vehicle and the total cross-section of the automobiles within a predetermined time using the user terminal device 10 at the measurement target point, and then the ratio with the correct answer data (actual measurement value) and the user terminal device. The final estimated value is calculated in consideration of the utilization rate of 10. That is, the estimation device 100 may estimate the traffic cross-sectional amount at a predetermined position by using the travel information acquired by the user terminal device 10 as the travel information of the vehicle as a probe.

For example, the estimation device 100 acquires the result of the traffic cross-section amount survey manually acquired at the intersection to be the survey target of the traffic cross-section amount. Further, the estimation device 100 estimates the traffic cross-sectional amount using the travel information acquired by the user terminal device 10. Then, the estimation device 100 generates a correction model for converting the traffic cross-section amount estimated by using the travel information acquired by the user terminal device 10 into the traffic cross-section amount manually acquired. It should be noted that such a correction model can be realized by various machine learning techniques such as various regression analysis, SVM (Support vector machine) and neural network. The estimation device 100 may generate such a correction model for each position to be estimated, for each area including the position for estimation, for each day of the week or time zone, for each vehicle type, and the like. It may be generated for each combination of attributes.

Subsequently, the estimation device 100 estimates the traffic cross-sectional amount at the position to be estimated, which is the traffic cross-sectional amount in the primary to be estimated, by using the travel information acquired by the user terminal device 10. Then, the estimation device 100 may obtain the final estimation result by correcting the estimated traffic cross-sectional amount by the generated correction model.

[3. About geo-fence settings]
Further, in the above description, the user terminal device 100 is determined to acquire traveling information at predetermined time intervals, but the embodiment is not limited to this. For example, if the time interval for acquiring the travel information is long, there is a possibility that the travel information indicating how the user terminal device 100 has moved at the intersection to be investigated cannot be acquired. Therefore, the estimation device 100 may acquire a geo-fence in advance at the intersection to be investigated. When such a geo-fence is set, the user terminal device 100 collects travel information inside the geo-fence in more detail than outside the geo-fence.

For example, FIG. 3 is a diagram showing an example of a method of setting a range in which a user terminal device acquires traveling information. For example, as shown in FIG. 3, the estimation device 100 surrounds a predetermined range with a virtual fence around a predetermined intersection to be measured (for example, geo-fencing). Geo-fencing is a function that uses location information to set a virtual geographical boundary on a map, automatically record entry and exit within that boundary, and use it in real time. It is a function that can notify predetermined information. By installing a geo-fence, vehicles entering and exiting the fence can be targeted for acquisition of driving information.

For example, the user terminal device 10 accepts the distribution of information indicating a pre-geofensed area. Then, when the user terminal device 10 invades the geo-fencing area, the user terminal device 10 acquires the traveling information at a shorter time interval than the outside of the geo-fencing area. For example, the user terminal device 10 acquires travel information every 30 minutes or every 2 kilometers outside the geo-fence, and acquires travel information every 30 seconds or every 10 m inside the geo-fence. You may.

Further, the user terminal device 10 may change the timing of transmitting the traveling information between the outside and the inside of the geo-fence. For example, the user terminal device 100 may transmit travel information every 30 minutes outside the geo-fence, and may transmit travel information every time the travel information is acquired inside the geo-fence.

[4. Configuration of estimation device]
Next, a configuration example of the estimation device 100 according to the embodiment will be described with reference to FIG. FIG. 5 is a diagram showing a configuration example of the estimation device 100 according to the embodiment. As shown in FIG. 5, the estimation device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The estimation device 100 has an input unit (for example, a keyboard, a mouse, etc.) that receives various operations from an administrator or the like who uses the estimation device 100, and a display unit (liquid crystal display, etc.) for displaying various information. You may.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. The communication unit 110 is connected to the network network by wire or wirelessly, and transmits / receives information between the user apparatus 10 and the information center server 200 via the network network.

(Memory unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 5, the storage unit 120 includes a traveling information storage unit 121, a vehicle estimation model storage unit 122, a movement mode estimation model storage unit 123, and a training data storage unit 124.

(Traveling information storage unit 121)
The travel information storage unit 121 stores travel information.
FIG. 4 is a diagram showing an example of data stored in the traveling information storage unit 121 according to the embodiment. As described above, as for the traveling information, the user terminal device 10 itself may detect that the probe information detected by the probe device mounted on the vehicle is acquired by the user terminal device 10 by wire or wirelessly. Can also be included.

The travel information storage unit 121 stores, for example, travel information received by the reception unit 132. In the example of FIG. 4, "travel information" is stored in the travel information storage unit 121 for each "user identifier". By way of example, the "travel information" includes the item "sensor information", the item "time" and the item "own vehicle position".

The "user identifier" indicates an identifier for identifying a user who uses the user terminal device 10 installed in the vehicle. The "sensor information" is information acquired by the user terminal device 10 or information detected by the user terminal device 10 itself, which is the information detected by the probe device installed in the vehicle. The "sensor information" includes the item "vibration information", the item "traveling direction information", the item "point velocity information", the item "acceleration information" and the item "winker information". Hereinafter, a case where the user terminal device 10 itself detects driving information will be described.

"Vibration information" indicates information on body chassis vibration and engine vibration during traveling detected by the user terminal device 10. "Traveling direction information" indicates information regarding the traveling direction detected by the user terminal device 10. The "point speed information" indicates information regarding the speed for each position of the own vehicle detected by the user terminal device 10. The "acceleration information" indicates information regarding the acceleration detected by the user terminal device 10. "Turn signal information" indicates information regarding the usage status of the turn signal detected by the user terminal device 10. The “time” indicates information regarding the time acquired by the user terminal device 10. The “own vehicle position” indicates information regarding the latitude and longitude acquired by the user terminal device 10.

For example, FIG. 4 shows that the "vibration information VI 11" was collected by the user terminal device 10 used by the user identified by the user identifier "U1". The vibration information VI 11 corresponds to, for example, parameters such as vibration displacement, vibration velocity, and vibration acceleration.

Further, for example, FIG. 4 shows that the “travel direction information TD11” was collected by the user terminal device 10 used by the user identified by the user identifier “U1”. For example, it corresponds to parameters such as traveling direction and vehicle body orientation.

Further, for example, FIG. 4 shows that the "point speed information VE11" was collected by the user terminal device 10 used by the user identified by the user identifier "U1". For example, it corresponds to a parameter such as speed for each position of the own vehicle.

Further, for example, FIG. 4 shows that the “acceleration information AC11” was collected by the user terminal device 10 used by the user identified by the user identifier “U1”. For example, it corresponds to parameters such as front-back acceleration, left-right acceleration, and yaw acceleration.

Further, for example, FIG. 4 shows that the "turn signal information WIN 11" is collected by the user terminal device 10 used by the user identified by the user identifier "U1". For example, it corresponds to the parameter of the usage status of the turn signal.

Further, for example, FIG. 4 shows that the "time TM11" was acquired by the user terminal device 10 used by the user identified by the user identifier "U1".

Further, for example, FIG. 4 shows that the "own vehicle position SP11" was acquired by the user terminal device 10 used by the user identified by the user identifier "U1". The own vehicle position SP11 corresponds to parameters such as latitude and longitude, for example.

(Vehicle estimation model storage unit 122)
The vehicle estimation model storage unit 122 stores a vehicle estimation model (that is, machine learning model data) which is a machine learning model for estimating the vehicle type of the vehicle. The vehicle estimation model storage unit 122 stores, for example, the vehicle estimation model received by the reception unit 132 described below. The vehicle estimation model storage unit 122 may store the vehicle estimation model generated by the vehicle estimation unit 133 described below. The vehicle estimation unit 133 can generate a vehicle estimation model from predetermined training data as described later.

(Movement mode estimation model storage unit 123)
The movement mode estimation model storage unit 123 stores a movement mode estimation model (that is, data of the machine learning model) which is a machine learning model for estimating the movement mode. The movement mode estimation model storage unit 123 stores, for example, the movement mode estimation model received by the reception unit 132 described below. The movement mode estimation model storage unit 123 may store the movement mode estimation model generated by the movement mode estimation unit 134 described below. As will be described later, the movement mode estimation unit 134 can generate a movement mode estimation model from predetermined training data.

(Training data storage unit 124)
The training data storage unit 124 stores a data set of training data (that is, a training data set). The training data storage unit 124 stores, for example, the training data set received by the reception unit 132 described below. The stored training dataset contains instances (also called feature vectors). For example, the stored training dataset contains instances with known labels. More specifically, the stored training dataset contains an instance that is a vector of features values and a label for this instance (ie, a label associated with this instance).

For example, the training data storage unit 124 stores a training data set used for generating the vehicle estimation model described above. In this case, the instance corresponds to the above-mentioned traveling information, which is information detected from a user terminal device (for example, the user terminal device 10) of a vehicle traveling on a predetermined road, for example. On the other hand, the label of this instance corresponds to, for example, the type of vehicle (for example, vehicle type) in which the user terminal device 10 is installed.

Further, for example, the training data storage unit 124 stores a training data set used for generating the above-mentioned movement mode estimation model. In this case, the instance corresponds to the above-mentioned traveling information, which is information detected from a user terminal device (for example, the user terminal device 10) of a vehicle traveling on a predetermined road, for example. On the other hand, the label of this instance corresponds to, for example, a movement mode (for example, right turn, straight ahead, left turn) when traveling on this predetermined road. The movement mode is not limited to the above movement mode depending on the road shape.

(Estimated traffic volume storage unit 125)
The estimated traffic volume storage unit 125 stores the estimated traffic flow rate. Specifically, the data shown in FIG. 2 is stored for each road related to the set measurement point. The final cross-section traffic volume is the total cross-section of motorcycles and the total cross-section of automobiles.

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

As shown in FIG. 5, the control unit 130 includes an acquisition unit 131, a reception unit 132, a vehicle estimation unit 133, a movement mode estimation unit 134, and a map information providing unit 135, and the information described below is described. Realize or execute the function or action of processing. The internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 5, and may be any other configuration as long as it is configured to perform information processing described later.

(Acquisition unit 131)
The acquisition unit 131 acquires travel information detected by the user terminal device of some of the vehicles traveling on a predetermined road. For example, the user terminal device of some of the vehicles traveling on a predetermined road is a terminal device that moves with the vehicle, a terminal device installed in the vehicle, a terminal device mounted on the vehicle, or a user coupled to the vehicle. A user terminal device (for example, a user terminal device 10) existing in a vehicle such as a terminal device, a terminal device owned by a user who is driving the vehicle, a terminal device inside the vehicle, and a terminal device attached to the outside of the vehicle. ..

For example, the acquisition unit 131 acquires vibration information indicating vibration detected by the user terminal device. Further, for example, the acquisition unit 131 acquires the traveling direction information indicating the traveling direction detected by the user terminal device. Further, for example, the acquisition unit 131 acquires the point speed information indicating the point speed detected by the user terminal device. Further, for example, the acquisition unit 131 acquires acceleration information indicating the acceleration detected by the user terminal device. Further, for example, the acquisition unit 131 acquires the turn signal information indicating the usage status of the turn signal detected by the user terminal device. Further, for example, the acquisition unit 131 acquires time information indicating the time acquired by the user terminal device. Further, for example, the acquisition unit 131 acquires the own vehicle position information indicating the own vehicle position acquired by the user terminal device.

The acquisition unit 131 may acquire the travel information received by the reception unit 132 described below. For example, the acquisition unit 131 may acquire vibration information, traveling direction information, point speed information, acceleration information, turn signal information, time information, or own vehicle position information received by the receiving unit 132. Further, the acquisition unit 131 may acquire travel information from a predetermined storage device. For example, the acquisition unit 131 may acquire travel information from the above-mentioned travel information storage unit 121. The acquisition unit 131 may store the acquired travel information in the above-mentioned travel information storage unit 121.

In one example, the acquisition unit 131 acquires travel information (for example, first travel information) and other travel information (for example, second travel information).

The acquisition unit 131 may acquire the above-mentioned vehicle estimation model (that is, data of the machine learning model). The acquisition unit 131 may acquire, for example, the vehicle estimation model received by the reception unit 132 described below. The acquisition unit 131 may acquire the vehicle estimation model from a predetermined storage device. For example, the acquisition unit 131 may acquire a vehicle estimation model from the vehicle estimation model storage unit 122 described above. The acquisition unit 131 may store the acquired vehicle estimation model in the vehicle estimation model storage unit 122 described above.

The acquisition unit 131 may acquire the above-mentioned movement mode estimation model (that is, data of the machine learning model). The acquisition unit 131 may acquire, for example, a movement mode estimation model received by the reception unit 132 described below. The acquisition unit 131 may acquire a movement mode estimation model from a predetermined storage device. For example, the acquisition unit 131 may acquire the movement mode estimation model from the above-mentioned movement mode estimation model storage unit 123. The acquisition unit 131 may store the acquired movement mode estimation model in the movement mode estimation model storage unit 123 described above for movement.

(Receiver 132)
The receiving unit 132 receives the traveling information detected by the user terminal device of some of the vehicles traveling on a predetermined road. For example, the receiving unit 132 receives travel information from a predetermined information processing device. For example, the receiving unit 132 receives traveling information from the device of an entity related to the estimation device 100 (for example, a user of a car navigation application, a predetermined company, an organization that controls business related to road traffic). The receiving unit 132 may store the received travel information in the travel information storage unit 121.

For example, the receiving unit 132 receives vibration information indicating vibration detected by the user terminal device. Further, for example, the receiving unit 132 receives the traveling direction information indicating the traveling direction detected by the user terminal device. Further, for example, the receiving unit 132 receives the point speed information indicating the point speed detected by the user terminal device 10. Further, for example, the receiving unit 132 receives acceleration information indicating the acceleration detected by the user terminal device 10. Further, for example, the receiving unit 132 receives the blinker information indicating the usage status of the blinker detected by the user terminal device 10. Further, for example, the receiving unit 132 receives time information indicating the time acquired by the user terminal device 10. Further, for example, the receiving unit 132 may further receive the own vehicle position information indicating the position acquired by the user terminal device 10.

In one example, the receiving unit 132 receives, for example, the first traveling information from the first user terminal device 10 installed in the vehicle traveling on the first road. For example, the receiving unit 132 receives the traveling information from the first user terminal device 10 (for example, the user terminal device 10 ₁ ) via the car navigation application installed in the first user terminal device 10. Further, the receiving unit 132 receives other traveling information (for example, the first) from the second user terminal device 10 (for example, the user terminal device 10 ₂ ) installed in another vehicle traveling on the first road. 2 driving information) is received.

The receiving unit 132 may receive the above-mentioned vehicle estimation model (that is, data of the machine learning model). For example, the receiving unit 132 may receive the vehicle estimation model from a predetermined information processing device. For example, the receiving unit 132 may receive the vehicle estimation model from the device of the entity (for example, a predetermined company, a predetermined university, a predetermined research institute) related to the estimation device 100. The receiving unit 132 may store the received vehicle estimation model in the vehicle estimation model storage unit 122.

The receiving unit 132 may receive the above-mentioned movement mode estimation model (that is, data of the machine learning model). For example, the receiving unit 132 may receive the movement mode estimation model from a predetermined information processing device. For example, the receiving unit 132 may receive the aspect estimation model from the device of the entity (for example, a predetermined company, a predetermined university, a predetermined research institute) related to the estimation device 100. The receiving unit 132 may store the received mode estimation model in the moving mode estimation model storage unit 123.

The receiving unit 132 may receive the above-mentioned data set of training data. For example, the receiving unit 132 may receive a training data set such as a training data set used for generating the above-mentioned vehicle estimation model and a training data set used for generating the above-mentioned movement mode estimation model. The receiving unit 132 may receive the training data set from a predetermined information processing device. For example, the receiving unit 132 may receive a training data set from a device of an entity related to the estimation device 100 (for example, a user of a car navigation application, a predetermined company, or an organization that controls operations related to road traffic). The receiving unit 132 may store the received training data set in the training data storage unit 126.

(Vehicle estimation unit 133)
The vehicle estimation unit 133 estimates the types of some of the vehicles traveling on a predetermined road. For example, a user terminal device (eg, user terminal device 10) is present in some of these vehicles. The vehicle estimation unit 133 estimates the type of vehicle in which the user terminal device is installed based on the travel information. For example, the vehicle estimation unit 133 estimates the type of vehicle in which the user terminal device 10 is installed, based on the travel information acquired by the acquisition unit 131.

For example, the vehicle estimation unit 133 estimates the type of vehicle based on the characteristics of acceleration indicated by the acceleration information. For example, the vehicle estimation unit 133 estimates the type of vehicle based on the acceleration characteristics indicated by the acceleration information acquired by the acquisition unit 131.

Further, for example, the vehicle estimation unit 133 estimates the type of vehicle based on the characteristics of vibration indicated by the vibration information. For example, the vehicle estimation unit 133 estimates the type of vehicle based on the vibration characteristics indicated by the vibration information acquired by the acquisition unit 131.

Further, for example, the vehicle estimation unit 133 estimates the type of vehicle based on the characteristics of the speed indicated by the speed information. For example, the vehicle estimation unit 133 estimates the type of vehicle based on the speed characteristics indicated by the speed information acquired by the acquisition unit 131.

The vehicle estimation unit 133 may estimate the drive type of the vehicle and estimate the type of the vehicle.

In one example, the vehicle estimation unit 133 estimates the vehicle type of each vehicle traveling on the first road based on the travel information acquired by the acquisition unit 131. For example, the travel information acquired by the acquisition unit 131 includes sensor information. The sensor information includes, for example, information such as vibration information and acceleration information. The vehicle estimation unit 133 can estimate the vehicle type of each vehicle traveling on the first road and the movement mode when traveling on the first road, based on information such as vibration information and acceleration information.

For example, the travel information received from the first user device 10 (for example, the user device 10 ₁ ) includes the first sensor information. For illustration purposes, the vibration information included in the first sensor information states that "the magnitude of the pitching of the vehicle in which the first user device 10 is installed satisfies a predetermined condition (for example, the first threshold value). Suppose that it indicates "nothing". In this example, the vehicle estimation unit 133 estimates that the vehicle type of the vehicle in which the first user device 10 is installed is a "normal vehicle". In other words, the vehicle estimation unit 133 estimates the vehicle type of the vehicle in which the first user device 10 is installed, based on how the vehicle in which the first user device 10 is installed sways.

Further, for example, the traveling information received from the second user device 10 (for example, the user device 10 ₂ ) includes the second sensor information. For illustration purposes, the vibration information included in the second sensor information states that "the magnitude of the pitching of the vehicle in which the second user device 10 is installed satisfies a predetermined condition (for example, the first threshold value). It is assumed to indicate "that". In this example, the vehicle estimation unit 133 estimates that the vehicle type of the vehicle in which the second user device 10 is installed is a "large vehicle".

In another example, the vehicle estimation unit 133 may estimate the vehicle type of the vehicle in which the user device 10 is installed by using the vehicle estimation model described above. Further, the vehicle estimation unit 133 may generate a vehicle estimation model from predetermined training data (for example, a training data set stored in the training data storage unit 124). For example, the vehicle estimation unit 133 uses a vehicle estimation model (for example, a decision tree, SVM (Support)) for an instance corresponding to the travel information acquired by the acquisition unit 131 (for example, a vector of a feature amount value corresponding to the travel information). The vehicle type may be output by inputting to Vector Machine), Naive Bayes classifier). The vehicle estimation unit 133 may estimate the vehicle type of the vehicle in which the first user device 10 is installed as the "output vehicle type". Further, for example, the vehicle estimation unit 133 inputs an instance corresponding to the travel information acquired by the acquisition unit 131 into the vehicle estimation model (for example, a neural network) to "provide a vehicle in which the user device 10 is installed. The probability that the vehicle type is a predetermined vehicle type may be output. The vehicle estimation unit 133 may estimate the vehicle type of the vehicle in which the user device 10 is installed based on the output probability.

In some embodiments, the vehicle estimation unit 133 may receive information from the user device 10 indicating the type (for example, vehicle type) of the vehicle driven by the user of the user terminal device 10. As a result, the vehicle estimation unit 133 displays the label of the instance included in the training data set used for generating the vehicle estimation model based on the received information indicating the type of the vehicle driven by the user of the user apparatus 10. Can be decided.

(Movement mode estimation unit 134)
Based on the travel information, the movement mode estimation unit 134 estimates the movement mode when some of the vehicles traveling on the predetermined road travel on the predetermined road. For example, the user terminal device 10 exists in this part of the vehicle. The movement mode estimation unit 134 estimates the movement mode when the vehicle on which the user terminal device 10 is installed travels on a predetermined road based on the travel information. For example, based on the travel information acquired by the acquisition unit 131, the movement mode when the vehicle on which the user terminal device 10 is installed travels on a predetermined road is estimated.

For example, the movement mode estimation unit 134 estimates the movement mode of the vehicle when traveling on a predetermined road based on the characteristics of acceleration indicated by the acceleration information. For example, the movement mode estimation unit 134 estimates the movement mode of the vehicle when traveling on a predetermined road based on the acceleration characteristics indicated by the acceleration information acquired by the acquisition unit 131.

Further, for example, the movement mode estimation unit 134 estimates at least one of a right turn, a straight line, and a left turn movement mode when the vehicle travels on a predetermined road as the vehicle movement mode.

The movement mode estimation unit 134 may estimate the movement mode of the vehicle when traveling on a predetermined road based on the characteristics of the acceleration detected by the user terminal device 10 when the position information indicates a predetermined road. good. For example, when the position information acquired by the acquisition unit 131 indicates a predetermined road, the movement mode estimation unit 134 travels on a predetermined road based on the acceleration characteristics indicated by the acceleration information acquired by the acquisition unit 131. The movement mode of the vehicle may be estimated.

In one example, the movement mode estimation unit 134 estimates the movement mode when traveling on the first road based on the travel information acquired by the acquisition unit 131. For example, the travel information acquired by the acquisition unit 131 includes sensor information. The sensor information includes, for example, information such as vibration information and acceleration information. The movement mode estimation unit 134 can estimate the movement mode when traveling on the first road based on information such as vibration information and acceleration information.

In another example, the movement mode estimation unit 134 may estimate the movement mode when traveling on the first road by using the above-mentioned movement mode estimation model. Further, the movement mode estimation unit 134 may generate a movement mode estimation model from predetermined training data (for example, a training data set stored in the training data storage unit 126). For example, the movement mode estimation unit 134 uses an instance corresponding to the travel information acquired by the acquisition unit 131 (for example, a vector of a feature amount value corresponding to the travel information) to a movement mode estimation model (for example, a decision tree, SVM). , Simple Bayes classifier) may be used to output the movement mode. The movement mode estimation unit 134 may estimate the movement mode when traveling on the first road as the “output movement mode”. Further, for example, the movement mode estimation unit 134 "travels on the first road" by inputting an instance corresponding to the travel information acquired by the acquisition unit 131 into the movement mode estimation model (for example, a neural network). The probability that the movement mode is a predetermined movement mode may be output. The movement mode estimation unit 134 may estimate the movement mode when traveling on the first road based on the output probability.

Regarding the estimation of the movement mode of the vehicle, for example, the acquisition unit 131 is a plurality of vehicles on which the user device 10 in which the car navigation application is installed is installed based on the position information included in the acquired travel information. The number of vehicles traveling on the first road in a predetermined period (for example, "1 day") is determined. Further, the movement mode estimation unit 134 acquires the utilization rate of the car navigation application installed in the user device 10 from a predetermined storage device. Based on the determined number of a plurality of vehicles and the acquired utilization rate of the car navigation application, the movement mode estimation unit 134 "corresponds to a predetermined vehicle type and a predetermined movement mode, and is the first in a predetermined period. The number of multiple vehicles traveling on the road can be estimated. For example, a plurality of vehicles on which a user device 10 in which a car navigation application is installed are installed, correspond to a vehicle type "ordinary vehicle" and a movement mode "right turn", and are on the first road in "1 day". It is assumed that the number of a plurality of vehicles traveling on the vehicle is "5". Further, it is assumed that the usage rate of the car navigation application installed on the user device 10 is "10%". In this example, the movement mode estimation unit 134 corresponds to the vehicle type "ordinary vehicle" and the movement mode "right turn", and the number of a plurality of vehicles traveling on the first road in "a day" is set to a plurality of vehicles. It is estimated that the product of "5 units" and "10 (that is, 1 / (10% = 0.1))" is "50 units".

(Map Information Provider 135)
The map information providing unit 135 may provide not only the basic map information but also the map information to which the situation such as traffic congestion information, traffic regulation, construction information, etc. is added.

[5. Estimating processing flow]
Next, the procedure of the estimation process by the estimation device 100 according to the embodiment will be described.

FIG. 6 is a flowchart showing a processing procedure for estimating a traffic flow rate, which is executed by the estimation device 100 according to the embodiment.

As shown in FIG. 6, in one embodiment, the estimation device first sets the measurement point (step S101).

Next, the estimation device sets the geofence (step S102).

Next, the acquisition unit 131 acquires the travel information detected by the user terminal device of some of the vehicles traveling on the predetermined road (step S103). For example, the acquisition unit 131 acquires vibration information indicating vibration of the vehicle body detected by the user terminal device. Further, for example, the acquisition unit 131 acquires the traveling direction information indicating the traveling direction of the vehicle detected by the user terminal device. Further, for example, the acquisition unit 131 acquires the point speed information indicating the point speed of the vehicle detected by the user terminal device. Further, for example, the acquisition unit 131 acquires acceleration information indicating the acceleration of the vehicle detected by the user terminal device. Further, for example, the acquisition unit 131 acquires the turn signal information indicating the usage status of the turn signal of the vehicle detected by the user terminal device. Further, for example, the acquisition unit 131 acquires the time acquired by the user terminal device. Further, for example, the acquisition unit 1313 acquires the own vehicle position acquired by the user terminal device.

Next, the vehicle estimation unit 133 estimates the vehicle type of the vehicle (step S104).

Next, the movement mode estimation unit 134 estimates the movement mode of the vehicle based on the travel information detected by the user terminal device (step S105).

Next, the movement mode estimation unit 134 estimates the traffic flow rate and estimates the cross-sectional traffic volume based on the movement mode estimated in step S104 (step S106).

[6. others〕
Further, among the processes described in the above-described embodiment, a part of the processes described as being automatically performed can also be performed manually. Alternatively, all or part of the process described as being performed manually can be automatically performed by a known method. 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 figure is not limited to the information shown in the figure.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of them may be functionally or physically distributed / physically in any unit according to various loads and usage conditions. Can be integrated and configured.

For example, a part or all of the storage unit 120 shown in FIG. 4 may not be held by the estimation device 100, but may be held by a storage server or the like. In this case, the estimation device 100 acquires various information such as traveling information by accessing the storage server.

[7. Hardware configuration]
Further, the estimation device 100 according to the above-described embodiment is realized by, for example, a computer 1000 having a configuration as shown in FIG. 7. FIG. 7 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to the output device 1010 and the input device 1020, and the arithmetic unit 1030, the primary storage device 1040, the secondary storage device 1050, the output IF (Interface) 1060, the input IF 1070, and the network IF 1080 are connected by the bus 1090. Have.

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

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

The input device 1020 is, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), or a tape. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like. Further, the input device 1020 may be an external storage medium such as a USB memory.

The network IF 1080 receives data from another device via the network N and sends it to the arithmetic unit 1030, and also transmits the data generated by the arithmetic unit 1030 to the other device via the network N.

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

For example, when the computer 1000 functions as the estimation device 100, the arithmetic unit 1030 of the computer 1000 realizes the function of the control unit 130 by executing the program loaded on the primary storage device 1040.

[8. effect〕
As described above, the estimation device 100 according to the embodiment includes an acquisition unit 131, a vehicle estimation unit 133, and a movement mode estimation unit 134.

In the estimation device 100 according to the embodiment, the acquisition unit 131 acquires the travel information detected by the user terminal device 10 of some of the vehicles traveling on a predetermined road. Further, in the estimation device 100 according to the embodiment, the vehicle estimation unit 133 estimates the type of a part of the vehicles traveling on a predetermined road based on the travel information. Further, in the estimation device 100 according to the embodiment, the movement mode estimation unit 134 estimates the movement mode of the vehicle based on the acquired traveling information.

Further, in the estimation device 100 according to the embodiment, the acquisition unit 131 acquires acceleration information indicating the acceleration detected by the user terminal device 10. Further, in the estimation device 100 according to the embodiment, the vehicle estimation unit 133 estimates the type of vehicle based on the characteristics of acceleration indicated by the acceleration information.

Further, in the estimation device 100 according to the embodiment, the acquisition unit 131 acquires vibration information indicating the vibration detected by the user terminal device 10. Further, in the estimation device 100 according to the embodiment, the vehicle estimation unit 133 estimates the type of the vehicle based on the characteristics of the vibration indicated by the vibration information.

Further, in the estimation device 100 according to the embodiment, the acquisition unit 131 acquires speed information indicating the speed detected by the user terminal device 10. Further, in the estimation device 100 according to the embodiment, the vehicle estimation unit 133 estimates the type of the vehicle based on the characteristics of the speed indicated by the speed information.

Further, the estimation device 100 according to the embodiment includes a movement mode estimation unit 134 that estimates the movement mode when some of the vehicles traveling on the predetermined road travel on the predetermined road based on the travel information. Have.

Further, in the estimation device 100 according to the embodiment, the acquisition unit 131 acquires the travel direction information indicating the travel direction detected by the user terminal device 10. Further, in the estimation device 100 according to the embodiment, the movement mode estimation unit 134 estimates the movement mode of the vehicle when traveling on a predetermined road based on the characteristics of the traveling direction indicated by the traveling direction information.

Further, in the estimation device 100 according to the embodiment, the acquisition unit 131 acquires point speed information indicating the point speed detected by the user terminal device 10. Further, in the estimation device 100 according to the embodiment, the movement mode estimation unit 134 estimates the movement mode of the vehicle when traveling on a predetermined road based on the characteristics of the point speed indicated by the point speed information.

Further, in the estimation device 100 according to the embodiment, the acquisition unit 131 acquires acceleration information indicating the acceleration detected by the user terminal device 10. Further, in the estimation device 100 according to the embodiment, the movement mode estimation unit 134 estimates the movement mode of the vehicle when traveling on a predetermined road based on the characteristics of acceleration indicated by the acceleration information.

Further, in the estimation device 100 according to the embodiment, the acquisition unit 131 acquires the turn signal information indicating the turn signal usage status detected by the user terminal device 10. Further, in the estimation device 100 according to the embodiment, the movement mode estimation unit 134 estimates the movement mode of the vehicle when traveling on a predetermined road based on the characteristics of the turn signal usage state indicated by the turn signal information.

Further, in the estimation device 100 according to the embodiment, the movement mode estimation unit 134 determines at least one of a right turn, a straight-ahead or a left-turn movement mode when the vehicle travels on a predetermined road as the vehicle movement mode. presume.

Further, in the estimation device 100 according to the embodiment, the acquisition unit 131 further acquires the position information indicating the position of the user terminal device 10 detected by the user terminal device 10. Further, in the estimation device 100 according to the embodiment, the movement mode estimation unit 134 travels on a predetermined road based on the characteristics of acceleration detected by the user terminal device 10 when the position information indicates a predetermined road. Estimate the movement mode of the vehicle at the time.

Further, the estimation device 100 according to the embodiment may have a user terminal device 10 and may be a terminal device installed in a vehicle.

Further, in the estimation device 100 according to the embodiment, the acquisition unit 131 acquires the traveling information detected by the terminal device of the vehicle using the user terminal device 10 possessed by the terminal device of the vehicle via a predetermined network.

Further, in the estimation device 100 according to the embodiment, when it is estimated that a plurality of users are in the same vehicle and the plurality of user identifiers correspond to the same driving information, a plurality of user terminal devices in the same vehicle. Is estimated to be installed, and the number of vehicles that have traveled in a predetermined position within a predetermined period is estimated.

Further, the estimation device 100 according to the embodiment estimates the number of vehicles that have traveled in a plurality of predetermined positions within a predetermined period by setting a plurality of geo-fences.

Further, the estimation device 100 according to the embodiment is a vehicle in which a user who obtains a user's attribute from a user terminal device installed in the vehicle and satisfies a predetermined condition is on board, and holds a predetermined position for a predetermined period. Estimate the number of vehicles that have traveled inside. For example, the number of vehicles that have traveled in a predetermined position within a predetermined period is estimated for each region, gender, age, etc., which is the user's living area.

By each of the above-mentioned processes, the estimation device 100 can efficiently acquire the cross-sectional traffic volume data.

Although some of the embodiments of the present application have been described in detail with reference to the drawings, these are examples, and various modifications are made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure column of the invention. It is possible to carry out the present invention in other modified forms.

Further, the estimation device 100 described above may be realized by a plurality of server computers, and depending on the function, an external platform or the like may be called by API (Application Programming Interface), network computing, or the like to realize the configuration. It can be changed flexibly.

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

The vehicle estimation unit 133 and the movement mode estimation unit 134 can be collectively read as an estimation unit.

1 Estimating system 10 User terminal device 100 Estimating device 200 Information center server 110 Communication unit 120 Storage unit 121 Driving information storage unit 122 Vehicle estimation model storage unit 123 Movement mode estimation model storage unit 124 Training data storage unit 125 Estimated traffic volume storage unit 130 Control unit 131 Acquisition unit 132 Reception unit 133 Vehicle estimation unit 134 Movement mode estimation unit 135 Map information provision unit

Claims (11)

An acquisition unit that acquires location information from a terminal device mounted on a vehicle,
An estimation device including an estimation unit that estimates the number of vehicles that have traveled in a predetermined position within a predetermined period based on the acquired position information. The acquisition unit acquires vehicle type information indicating the vehicle type of the vehicle from the terminal device, and obtains vehicle type information.
The first aspect of claim 1, wherein the estimation unit estimates the number of vehicles that have traveled in the predetermined position within a predetermined period based on the vehicle type information and the position information for each vehicle type. Estimator. The estimation unit generates a model for estimating the movement mode of the vehicle corresponding to the position information based on the position information acquired from the acquisition unit, and uses the model to determine a predetermined position. The estimation device according to claim 1 or 2, wherein the movement mode of the vehicle traveled within the period is estimated. One of claims 1 to 3, wherein the acquisition unit acquires position information of the vehicle transmitted from the terminal device when entering a predetermined range from the predetermined position. The estimation device described in. The estimation unit is based on the number of terminal devices that have traveled in the predetermined position within a predetermined period based on the ratio of the vehicle equipped with the terminal device capable of acquiring the position information to the other vehicle. The estimation device according to any one of claims 1 to 4, wherein the number of vehicles traveling in a predetermined position within a predetermined period is estimated. The acquisition unit acquires the attributes of the user from the terminal device of the user who is in the vehicle.
The estimation unit is the number of vehicles that have traveled in a predetermined position within a predetermined period based on the position information acquired from a terminal device whose user's attribute satisfies a predetermined condition among the acquired position information. The estimation device according to any one of claims 1 to 5, wherein the number of vehicles boarded by a user who satisfies the predetermined condition is estimated. The acquisition unit acquires the map information corresponding to the location information, and obtains the map information.
The estimation device according to any one of claims 1 to 6, wherein the estimation unit estimates the movement mode of the vehicle from the map information based on the map information acquired by the acquisition unit. The estimation unit estimates a plurality of terminal devices mounted on the same vehicle based on the history of the position information, and based on the estimation result, determines the number of vehicles that have traveled in a predetermined position within a predetermined period. The estimation device according to any one of claims 1 to 7, wherein the estimation is performed. The estimation device according to any one of claims 1 to 8, wherein the estimation unit estimates the number of vehicles that have traveled in a plurality of predetermined positions within a predetermined period. An estimation method performed by a computer
The acquisition process to acquire location information from the terminal device mounted on the vehicle,
An estimation method including an estimation step of estimating the number of vehicles that have traveled in a predetermined position within a predetermined period based on the acquired position information. An acquisition means for acquiring location information from a terminal device mounted on a vehicle,
An estimation program characterized by having a computer execute an estimation means for estimating the number of vehicles that have traveled in a predetermined position within a predetermined period based on the acquired position information.

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