JP2023055901A - Information provision system, information provision method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To present accurate information on the unoccupied state of a parking lot installed along a road.

SOLUTION: An information provision system 1 includes an arrival time prediction unit 120, a demand prediction unit 121, and a parking information creation unit 122. The arrival time prediction unit 120 predicts the arrival time at a parking lot, which is present in a traveling direction of an object vehicle on a road, and installed so as to be capable of being taken into and out of the road along the road, on the basis of a traffic state of each point on the road. The demand prediction unit 121 predicts a demand which is the number of vehicles which require the use of the parking lot at the predicted arrival time, on the basis of the traffic state of each point on the road. The parking information creation unit 122 determines an unoccupied state of the parking lot at the predicted arrival time on the basis of the predicted demand, creates parking information including the determination result on the unoccupied state, and outputs the information to the object vehicle.

SELECTED DRAWING: Figure 17

COPYRIGHT: (C)2023,JPO&INPIT

Description

TECHNICAL FIELD The present invention relates to an information providing system, an information providing method, and a program, and in particular, an information providing system, an information providing method, and a program for providing information on the availability of parking lots of facilities installed on roads. Regarding.

2. Description of the Related Art There is known a system that guides the congestion status of service facilities such as service areas and parking areas on motorways such as expressways and toll roads. For example, in Patent Document 1, based on the parking lot information of each service facility on the expressway, which is broadcast by FM (Frequency Modulation) multiplex broadcasting, the recent congestion of the parking lot of the service facility in front of the traveling direction of the vehicle is disclosed. A navigation device is disclosed that presents a situation.

As a related technology, Patent Literature 2 discloses a technology for changing a reservation when the estimated arrival time is changed after the parking lot is reserved. Patent Literature 3 discloses a technique for estimating the time of arrival at a destination based on deceleration factors located within each area. Patent Literature 4 discloses a technique for estimating the time required to reach a destination based on the driver's preferred driving speed. In addition, as other related technologies, Non-Patent Document 1 and Non-Patent Document 2 disclose a heterogeneous mixture learning technology that generates a prediction model for each group having the same data pattern and regularity. .

Japanese Patent No. 6054273 JP 2006-172061 A JP 2009-210513 A JP-A-2002-312885

Ryohei Fujimaki, Satoshi Morinaga, "Advanced data mining in the age of big data," NEC Technical Report, Vol.65, No.2, 2012, p.81-85 Riki Eto, et al., "Fully-Automatic Bayesian Piecewise Sparse Linear Models", Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014, p.238-246

The technology described in the above-mentioned Patent Literature 1 presents the recent congestion status of the parking lot of the service facility. However, the congestion status of service facilities changes from moment to moment depending on the congestion status of roads and other factors. Therefore, even if the parking lot of the service facility is not congested at the present time, it is likely that the vehicle will not be able to be parked due to congestion when the vehicle arrives at the service facility. In this case, even if the congestion status is presented, the user cannot effectively utilize the presented congestion status when planning the use of the service facility, such as taking a break or having a meal after several hours. .

An object of the present invention is to solve the above problems and provide an information providing system, an information providing method, and a program capable of presenting accurate information regarding the availability of parking lots along roads.

An information providing system according to one aspect of the present invention is installed along a road in the traveling direction of a target vehicle on the road based on the traffic conditions at each point on the road so as to be able to enter and exit the road. arrival time prediction means for predicting the arrival time of the target vehicle to the parking lot, and vehicles that need to use the parking lot at the predicted arrival time based on the traffic conditions at each point on the road. demand prediction means for predicting demand in terms of the number of vehicles; determining the availability of the parking lot at the predicted arrival time based on the predicted demand, and generating parking information including the determination result of the availability; and parking information generating means for outputting to the target vehicle.

An information provision method according to one aspect of the present invention is based on the traffic conditions at each point on the road, and is installed along the road in the traveling direction of the target vehicle on the road so that it can enter and exit from the road. Predicting the arrival time of the target vehicle to the parking lot, and predicting the demand, which is the number of vehicles that will need to use the parking lot at the predicted arrival time, based on the traffic conditions at each point on the road. Then, based on the predicted demand, the availability of the parking lot at the predicted arrival time is determined, and parking information including the determination result of the availability is generated and output to the target vehicle.

An information provision method according to one aspect of the present invention provides a computer with a traffic condition at each point on a road, and a vehicle that is located in the traveling direction of the target vehicle on the road and that can enter and exit the road along the road. Predict the arrival time of the target vehicle to the parking lot installed in the road, and based on the traffic conditions at each point on the road, the number of vehicles that need to use the parking lot at the predicted arrival time A process of predicting demand, determining the availability of the parking lot at the predicted arrival time based on the predicted demand, generating parking information including the determination result of the availability, and outputting the parking information to the target vehicle. to run.

An advantage of the present invention is that it is possible to present accurate information regarding the availability of parking lots installed along roads.

It is a figure which shows the example of the road in 1st Embodiment. 1 is a block diagram showing the configuration of a first embodiment; FIG. 4 is a diagram showing an example of traffic state information in the first embodiment; FIG. It is a figure which shows the example of a traffic prediction model in 1st Embodiment. It is a figure which shows the example of facility information in 1st Embodiment. It is a figure which shows the example of demand information in 1st Embodiment. It is a figure which shows the example of a traffic prediction model in 1st Embodiment. 1 is a block diagram showing the configuration of a parking information providing device 100 implemented by a computer in the first embodiment; FIG. 4 is a flowchart showing learning processing in the first embodiment; It is a flow chart which shows parking information offer processing in a 1st embodiment. It is a figure which shows the example of a parking information request|requirement in 1st Embodiment. FIG. 10 is a diagram showing an example of a prediction result of arrival times in the first embodiment; FIG. It is a figure which shows the example of the prediction result of demand in 1st Embodiment, and the determination result of the availability of a parking lot. It is a figure which shows the example of parking information in 1st Embodiment. It is a figure which shows the calculation example of waiting time in 1st Embodiment. It is a figure showing other examples of parking information in a 1st embodiment. 1 is a block diagram showing a characteristic configuration of a first embodiment; FIG. It is a block diagram which shows the structure of 2nd Embodiment. It is a figure which shows the example of reservation information in 2nd Embodiment. It is a figure which shows the example of the allocation information in 2nd Embodiment. It is a flowchart which shows parking information provision and reservation processing in 2nd Embodiment. It is a figure which shows the example of the prediction result of demand, the determination result of the availability of a parking lot, and the determination result of reservation availability of a parking lot in 2nd Embodiment. It is a figure which shows the example of parking information in 2nd Embodiment. It is a flow chart which shows reservation control processing in a 2nd embodiment.

DETAILED DESCRIPTION OF THE INVENTION A detailed description will be given of embodiments for carrying out the invention with reference to the drawings. In addition, in each drawing and each embodiment described in the specification, the same reference numerals are given to the same constituent elements, and the description thereof will be omitted as appropriate.

(First embodiment)
A first embodiment will be described.

First, roads and traffic conditions in the first embodiment will be described.

FIG. 1 is a diagram showing an example of roads in the first embodiment. In the first embodiment, the roads are roads for automobiles, such as highways and toll roads. Along the road, there are service facilities (hereinafter simply referred to as , facilities) are installed. Each service facility is provided with a parking lot for parking the vehicles of users who stop by the facility. The parking lot is provided with an entrance from the road and an exit to the road so that it is possible to enter and exit from the road.

In the example of FIG. 1, service facilities Si (i is an integer from 1 to M and M is the number of service facilities) are installed on the road.

Further, in the first embodiment, the values of indicators representing traffic conditions are measured at a plurality of points on the road. Flow rate, speed, and vehicle density are used as indices. Flow rate indicates, for example, the number of vehicles passing through a point per unit time (the number of passing vehicles). The speed indicates, for example, the average speed of multiple vehicles at a point. The vehicle density indicates, for example, the rate at which each vehicle spatially occupies a predetermined section including a point (spatial occupancy), or the rate at which each vehicle occupies a point temporally (temporal occupancy). . It is known that there is a correlation between flow rate, velocity and vehicle density, and from any of these measures the other can be deduced.

In the example of FIG. 1, the values of these indices are measured at each of the points Xj (j is an integer from 1 to N and N is the number of points).

In FIG. 1, for the sake of simplification, the road is assumed to have a single route.

First, the configuration of the first embodiment will be described. FIG. 2 is a block diagram showing the configuration of the first embodiment. Referring to FIG. 2, the information providing system 1 includes a parking information providing device 100 and a terminal device 200. FIG. The terminal device 200 is, for example, a navigation terminal mounted on a vehicle, or a smart phone or mobile phone of a user on board the vehicle. The parking information providing device 100 and the terminal device 200 are connected via a network or the like.

The parking information providing device 100 includes a road information storage unit 111, a traffic condition storage unit 112, a traffic prediction model learning unit 113, a traffic prediction model storage unit 114, a facility information storage unit 115, a demand information storage unit 116, and a demand prediction model learning unit. 117 and a demand forecast model storage unit 118 . The parking information providing device 100 further includes a request receiving unit 119 , an arrival time predicting unit 120 , a demand predicting unit 121 and a parking information generating unit 122 .

The road information storage unit 111 stores road information indicating the position of a road map, each point, and each service facility.

The traffic condition storage unit 112 stores traffic condition information. The traffic condition information indicates the history of traffic condition measurements at each point on the road. Traffic conditions are collected, for example, by a traffic condition collecting device (not shown). The traffic condition collecting device collects traffic conditions at predetermined collection intervals from sensors installed at various points on the road, for example.

FIG. 3 is a diagram showing an example of traffic condition information in the first embodiment. In the example of FIG. 3, the traffic conditions (flow rate, speed, and vehicle density) at each point Xj on the road in FIG. 1 are collected.

The traffic prediction model learning unit 113 generates (learns) a traffic prediction model based on the traffic state information stored in the traffic state storage unit 112 . A traffic prediction model is a model for predicting traffic conditions at a prediction target point based on the traffic conditions at each point. The traffic prediction model learning unit 113 uses, for example, machine learning technology to generate a traffic prediction model. In this case, the traffic prediction model may be generated using heterogeneous mixture learning techniques disclosed in Non-Patent Documents 1 and 2. Also, the traffic prediction model may be a general time-series model such as a linear regression model, an autoregressive model, an autoregressive moving average model, or the like. A traffic prediction model is generated for one or more of the indicators (flow rate, speed, vehicle density) representing the traffic conditions described above.

In the first embodiment, as a traffic prediction model, for example, the explanatory variable is the vehicle density at the current time at each point, and the objective variable is the vehicle density at the prediction target point at the prediction target time. Use a linear function.

Here, D _j (t) is the vehicle density at the point Xj at the current time t. D _{est_k} (t+T) is the predicted value of the vehicle density at time t+T at point Xk (k is an integer from 1 to N). α _jk is a coefficient that indicates the magnitude of the relationship between the vehicle density D _j (t) at the point Xj and the vehicle density D _k (t+T) at the point Xk.

FIG. 4 is a diagram showing an example of a traffic prediction model in the first embodiment. In the example of FIG. 4, for each point, the prediction target time is 0.5 hours later (T=0.5h (hour)), 1.0 hours later (T=1.0h), 1.5 hours later (T = 1.5h), 2.0 hours later (T = 2.0h), ... traffic prediction models are generated.

Note that the traffic prediction model is not limited to a linear function such as Equation 1, and any function in which traffic conditions at each point are set as explanatory variables may be used. Also, a plurality of traffic prediction models may be generated for the same prediction target point and prediction target time. In this case, these traffic prediction models can be used properly for each situation related to the prediction target time and the prediction target point, such as the weather at the prediction target point at the prediction target time, the presence or absence of an event in the vicinity, the day of the week and time zone at the prediction target time, etc. good. Also, variables representing these situations may be included in explanatory variables.

The traffic prediction model storage unit 114 stores the traffic prediction models generated by the traffic prediction model learning unit 113 .

The facility information storage unit 115 stores facility information. The facility information indicates the type of each service facility, the function related to the service provided by each service facility (hereinafter also referred to as service function), and the number of parking lots (total number of parking slots) of each service facility.

FIG. 5 is a diagram showing an example of facility information in the first embodiment. In the example of FIG. 5, presence/absence of toilets, shops, restaurants, and gas stations are set as service functions of each service facility. In addition, the presence or absence of other functions such as a dog run and accommodation facilities may be set as service functions provided by each service facility.

The demand information storage unit 116 stores demand information. The demand information indicates the history of measurements of demand for parking at each service facility. The demand for a parking lot is the number of vehicles that need to use the parking lot (hereinafter also referred to as the number of vehicles requested to use the parking lot). It is obtained from the sum of the number of vehicles waiting to be parked in the parking lot. Parking demand is collected, for example, by a demand collection device (not shown). The demand collection device collects measured values of the number of parked vehicles and the number of vehicles waiting to be parked at predetermined collection intervals from, for example, sensors installed in parking spaces and aisles in the parking lot. Calculate the demand for In addition, the demand collection device collects measured values of the number of vehicles that have passed through the entrance and the exit from sensors installed at the entrance and the exit of the parking lot, and calculates the demand for the parking lot from the difference between them. You may

FIG. 6 is a diagram showing an example of demand information in the first embodiment. In the example of FIG. 6, the demand for parking lots (the number of vehicles requested to use) at each service facility Si on the road in FIG. 1 is collected.

The demand forecast model learning unit 117 generates (learns) a demand forecast model based on the traffic condition information stored in the traffic condition storage unit 112 and the demand information stored in the demand information storage unit 116 . The demand forecast model is a model for forecasting demand for parking lots of service facilities based on traffic conditions at each location. The demand forecast model learning unit 117 generates a demand forecast model using machine learning technology, for example. In this case, the demand forecast model may be generated using the heterogeneous mixture learning technology disclosed in Non-Patent Documents 1 and 2. Also, the demand forecast model may be a general time-series model such as a linear regression model, an autoregressive model, or an autoregressive moving average model.

In the first embodiment, as a demand forecast model, for example, the explanatory variable is the vehicle density at each point at the current time, and the objective variable is the demand (the number of vehicles requested to be used) at the forecast target facility at the forecast target time. Use a linear function like Equation 2.

Here, _{Rest_i} (t+T) is the predicted value of demand at time t+T in the parking lot of service facility Si. β _ji is a coefficient indicating the magnitude of the relationship between the vehicle density D _j (t) at the point Xj and the parking lot demand R _i (t+T) at the service facility Si.

FIG. 7 is a diagram showing an example of a traffic prediction model in the first embodiment. In the example of FIG. 7, for each service facility, the prediction target time is 0.5 hours later (T = 0.5h (hour)), 1.0 hours later (T = 1.0h), 1.5 hours later ( T=1.5h), 2.0 hours later (T=2.0h), . . . demand forecast models are generated.

Note that the demand forecast model is not limited to a linear function such as Equation 2, and any function in which traffic conditions at each point are set as explanatory variables may be used. Also, a plurality of demand forecast models may be generated for the same forecast target facility and forecast target time. In this case, these demand forecast models may be used properly for each situation regarding the forecast target time and the forecast target facility, such as the weather at the forecast target facility at the forecast target time, the day of the week and the time slot at the forecast target time. Also, variables representing these situations may be included in explanatory variables. Further, the explanatory variables may include variables representing the presence/absence of various service functions of the facility to be predicted, and variables representing the number of vehicles accommodated in the parking lot of the facility.

The demand forecast model storage unit 118 stores the demand forecast model generated by the demand forecast model learning unit 117 .

The request receiving unit 119 receives a parking information request for requesting parking information from the terminal device 200 of the vehicle on the road. The parking information is information indicating availability of parking lots of service facilities.

The arrival time prediction unit 120 uses the traffic prediction model stored in the traffic prediction model storage unit 114 to predict whether the vehicle that has sent the parking information request (hereinafter also referred to as the target vehicle) to the parking lot of each service facility. Predict arrival times.

The demand prediction unit 121 uses the demand prediction model stored in the demand prediction model storage unit 118 to predict the parking lot at the predicted arrival time (hereinafter also referred to as the predicted arrival time) to the parking lot of each service facility. anticipate the demand for

The parking information generation unit 122 determines the availability of the parking lot at the predicted arrival time to the parking lot of each service facility based on the demand predicted by the demand prediction unit 121 (hereinafter also referred to as predicted demand). The parking information generation unit 122 generates parking information including the determination result of the parking space availability, and outputs (transmits) the parking information to the terminal device 200 of the target vehicle.

The parking information providing device 100 may be a computer that includes a CPU (Central Processing Unit) and a storage medium that stores a program, and that operates under control based on the program.

FIG. 8 is a block diagram showing the configuration of the parking information providing device 100 implemented by a computer in the first embodiment.

Referring to FIG. 8, the parking information providing device 100 includes a CPU 101, a storage device 102 (storage medium), an input/output device 103, and a communication device 104. FIG. The CPU 101 executes program instructions for realizing the traffic prediction model learning unit 113, the demand prediction model learning unit 117, the request reception unit 119, the arrival time prediction unit 120, the demand prediction unit 121, and the parking information generation unit 122. to run. The storage device 102 is, for example, a hard disk or memory, and stores a road information storage unit 111, a traffic condition storage unit 112, a traffic prediction model storage unit 114, a facility information storage unit 115, a demand information storage unit 116, and a demand prediction model. The data in the storage unit 118 are stored. The input/output device 103 is, for example, a keyboard, a display, etc., and receives a learning execution instruction from an administrator or the like. The communication device 104 receives the parking information request from the terminal device 200 and transmits the parking information to the terminal device 200 .

Also, some or all of the constituent elements of the parking information providing apparatus 100 may be implemented by general-purpose or dedicated circuitry, processors, or combinations thereof. These circuits and processors may be composed of a single chip, or may be composed of multiple chips connected via a bus. Also, part or all of the parking information providing device 100 may be implemented by a combination of the above-described circuits and the like and programs.

When some or all of the constituent elements of the parking information providing device 100 are realized by a plurality of information processing devices, circuits, etc., the plurality of information processing devices, circuits, etc. may be centrally arranged, They may be distributed. For example, the information processing device, circuits, and the like may be implemented as a client-and-server system, a cloud computing system, or the like, each of which is connected via a communication network.

Next, operation of the first embodiment will be described.

<Learning processing>
First, learning processing will be described.

FIG. 9 is a flowchart showing learning processing in the first embodiment.

1, the traffic condition information in FIG. 3, and the demand information in FIG. 6 are stored in the road information storage unit 111, traffic condition storage unit 112, and demand information storage unit 116, respectively. Assume there is

First, the traffic prediction model learning unit 113 of the parking information providing device 100 generates (learns) a traffic prediction model based on the history of traffic conditions at each point (step S101). The traffic prediction model learning unit 113 stores the generated traffic prediction model in the traffic prediction model storage unit 114 .

For example, the traffic prediction model learning unit 113 generates a traffic prediction model represented by Equation 1 as shown in FIG. 4 based on the traffic state information shown in FIG.

Next, the demand prediction model learning unit 117 generates (learns) a demand prediction model based on the history of traffic conditions at each point and the history of parking lot demand for each service facility (step S102). The demand forecast model learning unit 117 stores the generated demand forecast model in the demand forecast model storage unit 118 .

For example, the traffic prediction model learning unit 113 generates a demand prediction model represented by Equation 2 as shown in FIG. 7 based on the traffic state information in FIG. 3 and the demand information in FIG.

<Parking information provision processing>
Next, parking information providing processing will be described.

Here, the traffic prediction model in FIG. 4, the facility information in FIG. 5, and the demand prediction model in FIG. Assuming it is saved.

FIG. 10 is a flowchart showing parking information providing processing in the first embodiment.

First, the terminal device 200 of the vehicle (target vehicle) transmits a parking information request to the parking information providing device 100 (step S251). Here, the parking information request includes the identifier of the vehicle and the requirements of the service facility that should provide the parking information. As the requirements, for example, various service functions provided by the service facility and the required time from the current location to the parking lot are set.

FIG. 11 is a diagram showing an example of a parking information request in the first embodiment. For example, the terminal device 200 of the vehicle “CAR1” running at the point “X1” transmits a parking information request as shown in FIG. 11 at time “09:00”.

The request receiving unit 119 of the parking information providing device 100 receives the parking information request (step 201). Based on the road information and the facility information, the parking information generating unit 122 selects service facilities that satisfy the conditions of the service functions specified in the requirements from among the service facilities existing on the road in the direction in which the target vehicle travels. Extract (step S202).

For example, based on the facility information in FIG. 5, the parking information generation unit 122 satisfies the service function conditions in FIG. The service facilities "S2", "S4", and "S6" are extracted.

The parking information generation unit 122 instructs the arrival time prediction unit 120 to predict the arrival time at the parking lot of each service facility extracted in step S202. The arrival time prediction unit 120 predicts the arrival time to the parking lot of each service facility using a traffic prediction model (step S203).

Here, the arrival time prediction unit 120 applies the measured value of the vehicle density at the current time at each point to each traffic prediction model, predicts the vehicle density at each prediction target time at each point, and calculates the predicted vehicle density. Based on this, the speed at each prediction target time at each point is predicted. The arrival time prediction unit 120 divides the road from the current location of the target vehicle to each service facility at prediction target points of each traffic prediction model. The arrival time prediction unit 120 predicts the arrival time at the parking lot of each service facility by totaling the predicted travel time values calculated from the predicted speed values of the divided sections. Here, as the predicted value of the speed of each section, the predicted value of the speed at the starting point of the section at the time of arrival at the point is used.

In addition, if the arrival time to the parking lot of each service facility can be predicted, the arrival time prediction unit 120 can predict traffic conditions such as using a preset average speed or average required time between the current location of the target vehicle and each service facility. The arrival time may be predicted by a method other than the method using the prediction model.

FIG. 12 is a diagram illustrating an example of a prediction result of arrival times in the first embodiment. For example, the arrival time prediction unit 120 predicts the arrival times to the parking lots of service facilities "S2", "S4", and "S6" as shown in FIG. 12 using each traffic prediction model in FIG.

The parking information generation unit 122 extracts service facilities that satisfy the required time specified in the requirements from among the service facilities extracted in step S202 based on the predicted arrival time (step S204).

For example, the parking information generation unit 122 determines, based on the predicted arrival time of FIG. Extract "S4".

The parking information generation unit 122 instructs the demand prediction unit 121 to predict the demand for each service facility extracted in step S204. The demand prediction unit 121 uses the demand prediction model to predict the demand for the parking lot at the predicted arrival time to the parking lot of each service facility (step S205).

Here, the demand prediction unit 121 applies the measured value of the vehicle density at the current time at each point to the demand prediction model corresponding to the predicted arrival time, and predicts the demand at the predicted arrival time to the parking lot of each service facility. do.

FIG. 13 is a diagram showing an example of a demand prediction result and a determination result of parking space availability in the first embodiment. For example, the arrival time prediction unit 120 uses the demand prediction model corresponding to the predicted arrival times to the parking lots of the service facilities “S2” and “S4” among the demand prediction models in FIG. number of vehicles) is predicted as shown in FIG.

The parking information generation unit 122 determines the availability of the parking lot at the predicted arrival time based on the predicted demand for the parking lot of each service facility extracted in step S204 (step S206). Here, the parking information generation unit 122 determines that the vacancy status is "available" when the number of vehicles requested for use indicated by the predicted demand is less than the number of vehicles accommodated in the parking lot indicated by the facility information.

For example, the parking information generator 122 determines availability of parking lots of service facilities “S2” and “S4” as shown in FIG. 13 based on the number of parking lots in the facility information of FIG.

The parking information generation unit 122 generates parking information including the determination result of the parking space availability (step S207). The parking information generator 122 transmits the generated parking information to the terminal device 200 of the target vehicle (step S208).

FIG. 14 is a diagram showing an example of parking information in the first embodiment. In the parking information of FIG. 14, the determination results (predicted availability) of the parking lots of the service facilities "S2" and "S4" are "full" (no vacancy) and "vacant" (vacant), respectively. It is shown. For example, the parking information generator 122 generates parking information as shown in FIG. 14 and transmits it to the target vehicle "CAR1".

Upon receiving the parking information (step S252), the terminal device 200 of the target vehicle presents (displays) the parking information to the user (step S253).

For example, the terminal device 200 of the target vehicle “CAR1” presents the parking information of FIG. 14 to the user.

The parking information generation unit 122 may include in the parking information the waiting time from the predicted arrival time of the parking lot of the service facility for which the vacancy status was determined to be "no vacancy" in step S206. In this case, for example, the parking information generation unit 122 causes the demand prediction unit 121 to predict the parking lot demand at each time after the predicted arrival time using a demand prediction model. Then, the parking information generation unit 122 calculates the waiting time by determining the time when the number of requested vehicles indicated by the predicted demand becomes less than the number of vehicles accommodated in the parking lot as the time when parking becomes possible.

FIG. 15 is a diagram illustrating an example of waiting time calculation in the first embodiment. For example, the parking information generation unit 122 calculates the waiting time "1.0h" for the service facility "S2" using the number of requested vehicles after the predicted arrival time "10:00" as shown in FIG. .

The parking information generation unit 122 may calculate the waiting time by another method as long as the waiting time from the predicted arrival time of the parking lot can be calculated. For example, the parking information generation unit 122 predicts the number of vehicles waiting to park by subtracting the number of vehicles accommodated in the parking lot from the number of vehicles requested to use indicated by the predicted demand at the predicted arrival time, and based on the predicted number of vehicles waiting to park, A waiting time may be calculated. In this case, the waiting time is calculated, for example, by multiplying the predicted number of parking waiting vehicles by the average parking time.

FIG. 16 is a diagram showing another example of parking information in the first embodiment. In the parking information of FIG. 16, the waiting time is shown for the service facility "S2" whose vacancy status is "no vacancy". For example, the parking information generator 122 generates parking information including waiting time as shown in FIG. 16 and transmits it to the target vehicle "CAR1".

The above completes the operation of the first embodiment.

Next, a characteristic configuration of the first embodiment will be described.

FIG. 17 is a block diagram showing the characteristic configuration of the first embodiment. Referring to FIG. 17, the information providing system 1 includes an arrival time prediction section 120, a demand prediction section 121, and a parking information generation section 122. FIG. The arrival time prediction unit 120 predicts the arrival time of the target vehicle on the road at the parking lot of the facility on the road based on the traffic conditions at each point on the road. The demand prediction unit 121 predicts the demand, which is the number of vehicles that will need to use the parking lot of the facility at the predicted arrival time, based on the traffic conditions at each point on the road. The parking information generator 122 determines the availability of the parking lot of the facility at the predicted arrival time based on the predicted demand, generates parking information including the determination result of the availability, and outputs the parking information to the target vehicle.

Next, effects of the first embodiment will be described.

According to the first embodiment, it is possible to present accurate information regarding the availability of parking lots along roads. The reason for this is that the parking information generation unit 122 determines the availability of the parking lot at the predicted arrival time based on the predicted demand for the parking lot at the predicted arrival time of the target vehicle to the parking lot, and generates the parking information including the determination result. This is to generate As a result, the user can grasp the accurate vacancy status at the time of arrival at the parking lot of the service facility, etc. based on the presented parking information. It can be used effectively.

Further, according to the first embodiment, even if the parking lot availability is "no vacancy", whether the user should wait for the parking lot to use the service facility having the parking lot or not. can be judged. The reason is that the parking information generation unit 122 predicts the waiting time from the predicted arrival time of the parking lot when the determination result of the parking lot availability is "no vacancy", and the predicted waiting time is calculated. This is for generating parking information including:

In the first embodiment, the case where the parking lot whose availability is to be determined is a parking lot of a service facility such as a service area or a parking area installed along a road for automobiles has been described as an example. However, the parking lot for which the availability is determined is not limited to this, and any parking lot such as a commercial facility, a leisure facility, or a public facility parking lot may be used as long as it is installed along a road.

Further, in the first embodiment, the parking information generator 122 determined "no vacancy" or "vacant" as the vacancy status of the parking lot. Not limited to this, the parking information generation unit 122 may determine the degree of congestion as the availability of the parking lot. In this case, for example, when the predicted demand for the parking lot is less than the number of spaces accommodated in the parking lot, if the predicted demand is equal to or greater than a predetermined congestion determination threshold, the parking information generation unit 122 determines that "there is vacancy (congestion)" and the predetermined If it is less than the congestion determination threshold value, it may be determined that "there is vacancy (no congestion)".

(Second embodiment)
Next, a second embodiment will be described. The second embodiment differs from the first embodiment in that the parking lot is reserved based on the predicted demand for the parking lot of the service facility.

It is desirable for the user to be able to reserve a parking slot in the parking lot in order to park more reliably even if the parking lot is vacant at the predicted arrival time. On the other hand, for business operators that operate service facilities, if a parking space is reserved when the parking lot is crowded, vehicles other than the reserved vehicle will not be able to use the parking space during the reserved parking time. It cannot be used, and the operational efficiency of the parking lot decreases.

Therefore, in the second embodiment, the parking information providing device 100, if the parking lot is not crowded ( (predicted demand is less than a predetermined reservation determination threshold), a reservation for a parking slot is accepted from the terminal device 200 .

In the second embodiment, of all the parking spaces (parking spaces for the main number of vehicles) in the parking lot of the service facility, reservations are made for a number of parking spaces equal to or less than the maximum number of reservations. Parking slots for reservation are not fixed, and parking slots in which no vehicle is detected (not parked) at the reservation start time are assigned to the reserved vehicle. A vehicle that has not been reserved can be parked in a parking space that is not assigned to a reserved vehicle.

First, the configuration of the second embodiment will be described. FIG. 18 is a block diagram showing the configuration of the second embodiment. Referring to FIG. 18, the information providing system 1 in the second embodiment includes, in addition to the components of the information providing system 1 in the first embodiment (FIG. 2), a parking frame control device 300, a vehicle sensor 400, and a display device 500 . The parking frame control device 300 is arranged for each service facility. A vehicle sensor 400 and a display device 500 are arranged for each parking slot in the parking lot of each service facility. The parking information providing device 100 and the parking frame control device 300 are connected via a network or the like. The parking frame control device 300, the vehicle sensor 400, and the display device 500 are also connected via a network or the like.

Further, the parking information providing device 100 in the second embodiment further includes a reservation management unit 124 and a reservation information storage unit 123 in addition to the components of the parking information providing device 100 in the first embodiment (FIG. 2). including.

The parking information generation unit 122 determines whether the parking lot can be reserved in addition to determining the availability of the parking lot at the predicted arrival time. The parking information generation unit 122 generates parking information including the determination result of the availability of the parking lot and the determination result of whether or not a reservation is possible, and outputs the parking information to the terminal device 200 of the target vehicle.

The request receiving unit 119 receives, from the terminal device 200, a reservation request for a reservable parking lot in the parking information, in addition to the parking information request.

The reservation information storage unit 123 stores reservation information. The reservation information indicates the reservation status of the parking lot of each service facility.

FIG. 19 is a diagram showing an example of reservation information in the second embodiment. In the reservation information of FIG. 19, the identifier of the reserved vehicle (hereinafter also referred to as the reserved vehicle), the reservation start time, and the reservation end time are associated with the reservation status of the parking lot of each service facility. In addition, the reservation information further includes the number of reserved vehicles at each time.

The reservation management unit 124 reserves a parking lot (updates reservation information) in response to a reservation request from the terminal device 200 of the target vehicle. The reservation management unit 124 also transmits a parking frame control request to the parking frame control device 300 of the service facility based on the reservation information.

The parking frame control device 300 includes an allocation information storage section 311 and a control section 312 .

The allocation information storage unit 311 stores allocation information. The allocation information indicates the status of allocation of parking spaces to reserved vehicles.

FIG. 20 is a diagram showing an example of allocation information in the second embodiment. In the allocation information of FIG. 20, the identifier of the parking space, the identifier of the reserved vehicle to which the parking space is allocated, the reservation start time, and the reservation end time are associated.

The control unit 312, when receiving the parking space control request, based on the vehicle detection status obtained from the vehicle sensor 400 of each parking space and the allocation information stored in the allocation information storage unit 311, the parking space allocated to the reserved vehicle. determine the frame. The control unit 312 causes the display device 500 of the determined parking slot to display that the parking slot has been reserved (already assigned to the reserved vehicle).

Vehicle sensor 400 is, for example, an optical sensor, and detects whether the vehicle is parked in a parking frame.

The display device 500 is, for example, an electric light such as an LED (Light Emitting Diode) or an incandescent lamp, and displays that the parking slot has been reserved so that the user can identify it. Also, the display device 500 may be a display device. When the display device 500 is a lamp, one or more lamps may be installed along the parking frame, or may be installed on the road surface within the parking frame or in the upper space of the parking frame. Even when the display device 500 is a display device, the display device may be installed along the parking frame, or may be installed on the road surface within the parking frame or in the upper space of the parking frame.

Next, operation of the second embodiment will be described.

<Parking information provision and reservation processing>
First, parking information provision and reservation processing will be described.

Here, the traffic prediction model in FIG. 4, the facility information in FIG. 5, and the demand prediction model in FIG. Assuming it is saved. Also, it is assumed that the reservation information (before update) in FIG. 19 and the allocation information (before update) in FIG. 20 are stored in the reservation information storage unit 123 and the allocation information storage unit 311, respectively.

FIG. 21 is a flowchart showing parking information provision and reservation processing in the second embodiment.

Here, the processing (S351, steps S301 to S306) from the transmission of the parking information request to the parking lot availability determination is the same as the parking information provision processing (S251, steps S201 to S206) in the first embodiment. .

For example, the terminal device 200 of the vehicle “CAR1” running at the point “X1” transmits a parking information request as shown in FIG. 11 at time “09:00”. The arrival time prediction unit 120 predicts the arrival times to the parking lots of the service facilities “S2”, “S4”, and “S6” as shown in FIG. 12 using the traffic prediction models shown in FIG.

FIG. 22 is a diagram showing an example of a demand prediction result, a judgment result of a parking lot availability situation, and a judgment result of whether a parking lot can be reserved in the second embodiment.

The arrival time prediction unit 120 uses the demand prediction model corresponding to the predicted arrival times to the parking lots of the service facilities “S2” and “S4” among the demand prediction models in FIG. ) is predicted as shown in FIG. The parking information generation unit 122 determines availability of parking lots of the service facilities “S2” and “S4” as shown in FIG. 22 based on the number of parking lots in the facility information of FIG.

Next, the parking information generator 122 extracts service facilities for which the vacancy status determination result is "available" from among the service facilities extracted in step S304 (step S307).

The parking information generator 122 determines whether or not the parking lot of each service facility extracted in step S307 can be reserved at the predicted arrival time based on the predicted demand and reservation information (step S308).

Here, the parking information generation unit 122, for example, determines that the number of vehicles requested for use indicated by the predicted demand is less than a predetermined reservation determination threshold, and that the number of vehicles between the predicted arrival time and the parking time elapsed in the reservation information If the number of reservations at the time is less than the predetermined maximum number of reservations, it is determined that reservations can be made. For the reservation determination threshold value and the maximum number of reservations, a value that is equal to or less than the number of cars that can be accommodated in the parking lot (ratio to the number of cars that can be accommodated, etc.) is used. Also, for the parking time, for example, a predetermined reservation available time is used. As the parking time, an average parking time or a requested parking time acquired in advance from the target vehicle may be used.

For example, assume that the reservation determination threshold is "90%" of the number of parking lots, the maximum number of reservations is "10%" of the number of parking lots, and the available reservation time is "0.5 hours". In this case, the parking information generation unit 122 uses the number of parking spaces in the facility information in FIG. 5 and the reservation information (before update) in FIG. Whether or not the reservation can be made is determined as shown in FIG.

The parking information generation unit 122 generates parking information including the availability of parking lots and the determination result of whether or not reservations can be made for the parking lots (step S309). The parking information generator 122 transmits the generated parking information to the terminal device 200 of the target vehicle (step S310).

FIG. 23 is a diagram showing an example of parking information in the second embodiment. In the parking information of FIG. 22, the determination result (predicted availability) of the availability of the parking lots of the service facilities "S2" and "S4" is indicated by "empty" (available). Further, the determination results of whether or not reservations can be made for the parking lots of the service facilities "S2" and "S4" are indicated by "impossible" and "possible", respectively. Further, a button (reservation button) for instructing transmission of a reservation request is displayed for the service facility "S4" which can reserve a parking lot. For example, the parking information generator 122 generates parking information as shown in FIG. 23 and transmits it to the terminal device 200 of the target vehicle "CAR1".

Upon receiving the parking information (step S352), the terminal device 200 of the target vehicle presents (displays) the parking information to the user (step S353).

For example, the terminal device 200 of the target vehicle “CAR1” presents the parking information of FIG. 23 to the user.

When the user inputs an instruction to transmit a reservation request, the terminal device 200 transmits the reservation request to the parking information providing device 100 (step S354). Here, the reservation request includes the identifier of the target vehicle and the identifier of the service facility that reserves the parking lot.

For example, when the reservation button for the service facility "S4" is pressed in the parking information of FIG. A reservation request including the parking information is transmitted to the parking information providing device 100 .

The request receiving unit 119 of the parking information providing device 100 receives the reservation request from the terminal device 200 (step 311).

The reservation management unit 124 updates the reservation information according to the reservation request received from the terminal device 200 (step 312). Here, the reservation management unit 124 registers the identifier of the target vehicle, the reservation start time, and the reservation end time for the service facility specified by the reservation request. As the reservation start time and the reservation end time, the predicted arrival time and the time when the parking time has elapsed from the predicted arrival time are registered, respectively. In addition, the reservation management unit 124 adds 1 to the number of reserved vehicles from the reservation start time to the reservation end time.

For example, the reservation management unit 124 registers a reservation for the target vehicle "CAR1" in the parking lot of the service facility "S4" and updates the number of reservations, as in the reservation information (after update) in FIG.

The reservation management unit 124 transmits a reservation completion notification to the terminal device 200 (step 313). Here, the reservation completion notice includes the identifier of the service facility that reserved the parking lot.

For example, the reservation management unit 124 transmits a reservation completion notification including the service facility identifier "S4" to the terminal device 200 of the target vehicle "CAR1".

Upon receiving the reservation completion notice (step S355), the terminal device 200 presents (displays) the reservation completion to the user (step S356).

For example, the terminal device 200 of the target vehicle "CAR1" presents to the user that the reservation for the parking lot of the service facility "S4" has been completed.

When updating the reservation information, the reservation management unit 124 charges the user of the target vehicle for payment of the reservation fee via a charging system (not shown) provided by a credit card company or the like. processing may be performed.

<Reservation control processing>
Next, reservation control processing will be described.

Here, it is assumed that the reservation information (after update) in FIG. 19 and the allocation information (before update) in FIG. 20 are stored in the reservation information storage unit 123 and the allocation information storage unit 311, respectively.

FIG. 24 is a flowchart showing reservation control processing in the second embodiment. The reservation control process is executed at the reservation start time of each reservation registered in the reservation information.

The reservation management unit 124 of the parking information providing device 100 transmits a parking frame control request to the parking frame control device 300 of the service facility where the parking lot is reserved for the reservation that has reached the reservation start time (step S401). Here, the parking frame control request includes the identifier of the reserved vehicle, the reservation start time, and the reservation end time.

For example, at time "11:00", the reservation management unit 124 sends a parking frame control request including the reserved vehicle identifier "CAR1", the reservation start time "11:00", and the reservation end time "11:30". It is transmitted to the parking frame control device 300 of the facility "S4".

When the control unit 312 of the parking frame control device 300 receives the parking frame control request (step S451), the vehicle detection status obtained from the vehicle sensor 400 of each parking frame and the allocation information, based on the parking frame allocated to the reserved vehicle is determined (step S452). Here, the control unit 312 determines a parking slot to which no vehicle is assigned and in which no vehicle is parked as a parking slot to be assigned to the reserved vehicle.

For example, the control unit 312 allocates the parking slot “L3” to the vehicle “CAR1” based on the vehicle detection status and the reservation information (before update) in FIG. 20 .

Note that the control unit 312 may allocate to the reserved vehicle sequentially from the parking slots that are closest to a specific location, such as the entrance of the building of the service facility, among the parking slots that can be assigned to the reserved vehicle.

The control unit 312 updates the allocation information according to the parking frame allocation result (step S453). Here, the control unit 312 registers the identifier of the vehicle, the reservation start time, and the reservation end time for the allocated parking slot.

For example, the control unit 312 registers the allocation of the parking slot “L3” to the vehicle “CAR1” as in the reservation information (after update) in FIG. 20 .

The control unit 312 causes the display device 500 of the allocated parking space to display that the parking space has been reserved (step S454). Here, the control unit 312 may cause the display device 500 to display the identifier of the vehicle to which the parking frame is assigned.

For example, the control unit 312 causes the display device 500 of the parking slot "L3" to display that the reservation has been completed.

Thus, when the user of the reserved vehicle arrives at the parking lot of the service facility for which the parking lot has been reserved, the user can specify the parking slot allocated to the reserved vehicle and park the vehicle in the parking slot.

Further, the control unit 312 terminates the reserved display on the display device 500 of the parking space at the reservation end time of the allocated parking space, and deletes the allocation of the vehicle for the parking space from the allocation information. do.

If the reserved vehicle leaves the reserved parking lot before the reservation end time or passes through the parking lot without using the parking lot, the control unit 312 reserves the parking space allocated to the reserved vehicle. The display of completed parking may be terminated and the allocation of the parking frame concerned may be deleted from the allocation information. In this case, for example, the control unit 312 periodically acquires the position of the reserved vehicle after the reservation start time from the terminal device 200 of the reserved vehicle via the parking information providing device 100, and the position of the reserved vehicle from the parking lot of the reserved vehicle. Detect departures and passages.

The above completes the operation of the second embodiment.

Next, effects of the second embodiment will be described.

According to the second embodiment, a parking lot reservation service can be provided without lowering the operational efficiency of the parking lot installed along the road. The reason for this is that the parking information generating unit 122 determines whether the parking lot can be reserved based on the predicted demand at the predicted arrival time of the target vehicle at the parking lot, and if it is determined that the reservation is possible, the reservation management unit 124 However, this is because the parking lot is reserved according to the instruction from the target vehicle.

In the second embodiment, it is determined whether or not a reservation can be made when the predicted demand for the parking lot is less than the predetermined reservation determination threshold. You may judge whether a reservation is possible or not. In this case, the reservation determination threshold value may be greater than or equal to the number of parking spaces that can be accommodated in the parking lot, or may be less than the number of parking spaces.

Although the present invention has been described with reference to the embodiments, the present invention is not limited to the above embodiments. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

1 Information Providing System 100 Parking Information Providing Device 101 CPU
102 storage device 103 input/output device 104 communication device 111 road information storage unit 112 traffic condition storage unit 113 traffic prediction model learning unit 114 traffic prediction model storage unit 115 facility information storage unit 116 demand information storage unit 117 demand prediction model learning unit 118 demand Prediction model storage unit 119 request reception unit 120 arrival time prediction unit 121 demand prediction unit 122 parking information generation unit 123 reservation information storage unit 124 reservation management unit 200 terminal device 300 parking frame control device 311 allocation information storage unit 312 control unit 400 vehicle sensor 500 display device

Claims (10)

arrival time acquisition means for acquiring the arrival time of the target vehicle at the destination;
Congestion degree prediction means for predicting the degree of congestion of the parking lot at the acquired arrival time based on the operation record of the parking lot related to the destination;
output means for outputting prediction results regarding the congestion degree of the parking lot;
Information provision system with 2. The information providing system according to claim 1, wherein said parking lot is a parking lot installed along a road so that it can enter and exit from said road. The obtained arrival time is predicted based on traffic conditions at each point on the road,
3. The information providing system according to claim 1, wherein said traffic conditions include one or more of flow rate, speed and vehicle density. When the determination result of the congestion degree of the parking lot is congested, the output means predicts the waiting time from the predicted arrival time of the parking lot, and generates parking information including the predicted waiting time.
The information providing system according to any one of claims 1 to 3. The output means determines whether or not the parking lot can be reserved at the acquired arrival time based on the predicted congestion degree of the parking lot, and generates parking information including the determination result of whether or not the reservation can be made;
Further, reservation management means for making a reservation for the parking lot for the target vehicle in response to an instruction from the target vehicle when it is determined that the parking lot can be reserved.
The information providing system according to any one of claims 1 to 4. The output means determines that the parking lot can be reserved when the number of vehicles indicated by the predicted congestion degree of the parking lot is less than a predetermined threshold value equal to or less than the number of vehicles accommodated in the parking lot.
The information providing system according to claim 5. The reservation management means instructs a parking space control device that allocates the parking space of the parking lot to the reserved vehicle to allocate the parking space to the target vehicle from the acquired arrival time.
The information providing system according to claim 5 or 6. The parking lot is a parking lot of a facility arranged along a road,
The output means generates parking information including a determination result of the degree of congestion of the parking lot of the facility that satisfies the requirements specified by the target vehicle and exists in the traveling direction of the target vehicle.
The information providing system according to any one of claims 1 to 7. Get the arrival time of the target vehicle to the destination,
Predicting the degree of congestion of the parking lot at the acquired arrival time based on the operation performance of the parking lot related to the destination,
Outputting a prediction result regarding the degree of congestion of the parking lot;
How to provide information. to the computer,
Get the arrival time of the target vehicle to the destination,
Predicting the degree of congestion of the parking lot at the acquired arrival time based on the operation performance of the parking lot related to the destination,
Outputting a prediction result regarding the degree of congestion of the parking lot;
A program that causes an action to take place.

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