JP2021047184A - Method, apparatus, device and computer readable storage medium to be used in navigation - Google Patents

Abstract

To provide a method, apparatus, device and computer readable storage medium for selecting an appropriate destination address in advance, and taking effective parking measures.SOLUTION: The method includes: receiving a first user input related to a navigation destination from a user; determining at least one destination address related to the navigation destination and parking difficulty information around the destination address; and providing the user with the at least one destination address and the parking difficulty information around the destination address at the same time.EFFECT: The method supports the user so that the user can select an appropriate destination address in advance and take effective parking measures by providing the user with the destination address related to the navigation destination, as well as, providing the parking difficulty information around the destination address.SELECTED DRAWING: Figure 2

Description

Embodiments of the present disclosure relate to the field of navigation, and more specifically to the methods, devices, devices, and computer-readable storage media used for navigation.

Traditional navigation apps typically only provide destination address information about the navigation destination, not information about whether the area around the destination address is easy to park. There are parking lots that can provide real-time parking information in some way and present the real-time parking status of the parking lot (eg, total number of parking spaces, number of parking spaces in use and / or number of empty parking spaces, etc.), but navigation Apps typically do not provide this real-time parking information. Such real-time parking information often corresponds to only some parking lots, and there are many parking lots that cannot provide real-time parking information. Further, such real-time parking information only indicates the current parking state of the parking lot, and cannot predict or present the parking state at a future time point (for example, when the user arrives) of the parking lot.

Further, when actually using the navigation application, the user often does not set a parking lot as a navigation destination, but sets an interest point such as a shopping center or a restaurant as a destination. Conventional solutions cannot provide parking difficulty information around such points of interest. Therefore, the user cannot determine whether the area around the destination to be parked is easy to park before departure.

To the above problems, exemplary embodiments of the present disclosure provide solutions used for navigation. The solution provides the user with a destination address associated with the navigation destination, and also provides the user with parking difficulty information around the destination address, so that the user can use the appropriate destination in advance. We will support you in choosing an address and developing effective parking measures.

A first aspect of the present disclosure provides a method used for navigation. The method involves receiving a first user input regarding a navigation destination from a user, determining at least one destination address related to the navigation destination, and determining parking difficulty information in the vicinity thereof, and at least one of the above methods. It includes providing the user with the parking difficulty information of one destination address and its surroundings at the same time.

In some embodiments, determining the parking difficulty information is determining the time when the user arrives at at least one destination address and around the at least one destination address at that time. Includes predicting the parking difficulty information of the above.

In some embodiments, providing the user with the at least one destination address and the parking difficulty information in the vicinity thereof at the same time means that the parking difficulty in the at least one destination address and its surroundings is provided to the user at the same time. It includes displaying the degree information to the user at the same time.

In some embodiments, the parking difficulty information includes at least one of an index indicating the parking difficulty and the number of vacant parking spaces.

In some embodiments, the method further selects a destination from the user in response to the user selecting one of the at least one destination address based on the parking difficulty information. It includes accepting a second user input regarding an address, determining a navigation route from the user's current position to a selected destination address, and providing the user with the navigation route.

In some embodiments, predicting the parking difficulty information around the at least one destination address at the time corresponds to the at least one destination address, said at least one destination address. Based on the determination of the parking area including one or more parking lots around the parking lot and the historical parking information of at least one parking lot in the historical time corresponding to the time, the parking demand of the parking area at the time is determined. It includes predicting and predicting parking difficulty information around each destination address of the at least one destination address at the time based on the parking demand.

In some embodiments, predicting the parking demand of the parking area at the time determines the historical parking demand of at least one parking lot at the historical time based on the historical parking information. The parking demand of the parking area at the time based on the determination of the mapping relationship between the historical parking demand and the spatial feature of the at least one parking lot, and the spatial feature of the parking area and the mapping relationship. Includes predicting.

In some embodiments, the spatial feature comprises at least one of a distance to at least one point of interest and a number of points of interest in the vicinity.

In some embodiments, the method further comprises predicting parking difficulty information for each of the one or more parking lots at the time, based on the parking demand. Here, the one or more parking lots include parking lots that cannot provide historical parking information.

A second aspect of the present disclosure provides a device used for navigation. The device receives a first reception module arranged to receive a first user input regarding a navigation destination from a user, at least one destination address related to the navigation destination, and parking difficulty information in the vicinity thereof. It includes a first confirmation module arranged to be confirmed and a first provision module arranged to simultaneously provide the user with the at least one destination address and the parking difficulty information in the vicinity thereof. ..

In some embodiments, the first confirmation module comprises a confirmation unit arranged to determine the time when the user arrives at the at least one destination address and the at least one destination address at the time. Includes a prediction unit arranged to predict the parking difficulty information around the.

In some embodiments, the first providing module includes a display unit arranged to simultaneously display the at least one destination address and the parking difficulty information around it to the user.

In some embodiments, the parking difficulty information includes at least one of an index indicating the parking difficulty and the number of vacant parking spaces.

In some embodiments, the device further selects a destination from the user in response to the user selecting one of the at least one destination address based on the parking difficulty information. A second reception module arranged to accept a second user input regarding an address, a second confirmation module arranged to determine a navigation route from the user's current position to a selected destination address, and the above. It includes a second providing module arranged to provide the navigation route to the user.

In some embodiments, the predictive unit further determines a parking area that includes one or more parking lots around the at least one destination address that corresponds to the at least one destination address. Based on the historical parking information of at least one parking lot in the history time corresponding to the time, the parking demand of the parking area at the time is predicted, and based on the parking demand, of the at least one destination address at the time. Arranged to predict parking difficulty information around each of our destination addresses.

In some embodiments, the prediction unit further determines the historical parking demand of the at least one parking lot at the historical time based on the historical parking information, and the historical parking demand and the at least one parking lot. Is arranged so as to determine the mapping relationship with the spatial feature of the parking area and predict the parking demand of the parking area at the time based on the spatial feature of the parking area and the mapping relationship.

In some embodiments, the spatial feature comprises at least one of a distance to at least one point of interest and a number of points of interest in the vicinity.

In some embodiments, the prediction unit is further arranged to predict parking difficulty information for each of the one or more parking lots at the time based on the parking demand. Here, the one or more parking lots include parking lots that cannot provide historical parking information.

A third aspect of the present disclosure provides a device used for navigation. The device comprises at least one processing unit and at least one memory coupled to the at least one processing unit and storing instructions executed by the at least one processing unit, wherein the instruction is at least one. When performed by one processing unit, the device is made to perform the method described in the first aspect of the present disclosure.

A fourth aspect of the present disclosure provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium, and the computer program causes the device to perform the method described in the first aspect of the present disclosure when executed by the device. ..

As can be understood from the above description, the solution used for navigation in the embodiments of the present disclosure provides the user with a destination address associated with the navigation destination and the user with the destination address. Provide information on parking difficulty in the surrounding area. Further, in the solution, the user selects an appropriate destination address in advance and is effective by predicting the parking difficulty information around the destination address when the user arrives at the destination address. We can support you so that you can take parking measures.

It is understood that the content described in the context of the invention is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Should be. Other features of the disclosure should be readily apparent with the following description.

Hereinafter, detailed description will be given with reference to the drawings, and the above-mentioned features and other features, advantages and embodiments in each embodiment of the present disclosure will be further clarified. In the drawings, the same or similar drawing reference numerals indicate the same or similar elements.

It is a block diagram of an exemplary environment according to the embodiment of the present disclosure.

It is a flowchart of an exemplary method used for navigation by an embodiment of the present disclosure.

FIG. 3 is a block diagram of an exemplary system for determining parking difficulty information around at least one destination address at a particular time according to an embodiment of the present disclosure.

FIG. 5 is a flowchart of an exemplary method for determining parking difficulty information around at least one destination address at a particular time according to an embodiment of the present disclosure.

It is a schematic diagram which divides the parking area by embodiment of this disclosure.

FIG. 5 is a flowchart of an exemplary method for predicting parking demand in a target parking area at a specific time according to an embodiment of the present disclosure.

It is a schematic diagram which generates the time series model by embodiment of this disclosure.

FIG. 3 is a block diagram of an exemplary device used for navigation according to an embodiment of the present disclosure.

It is a block diagram of the computer device which can carry out embodiment of this disclosure.

Hereinafter, embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the figures show some embodiments of the present disclosure, the present disclosure can be implemented in a variety of forms and should not be construed as being limited to the embodiments described herein. , These embodiments are provided for a more thorough and complete understanding of the present disclosure. This point must be understood. It should also be understood that the drawings and embodiments of the present disclosure are merely exemplary and are not intended to limit the scope of protection of the present disclosure.

In the description of embodiments of the present disclosure, the terms "including" and similar terms should be understood to be open, i.e., "including, but not limited to,". The term "based on ..." should be understood as "at least partially based." The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second" and the like can indicate different or identical objects. Further explicit and implicit definitions may be included in the text below.

FIG. 1 shows a block diagram of an exemplary environment 100 according to an embodiment of the present disclosure. As shown in FIG. 1, the exemplary environment 100 as a whole can include a user 110 and a navigation device 120. The navigation device 120 can include, but is not limited to, a mobile phone, a computer, a personal digital assistant, a game machine, a wearable terminal, an in-vehicle communication device, a virtual reality device, an augmented reality device, and the like. The navigation device 120 can provide an input device operated by the user 110 such as a touch panel and a keyboard. Further, the navigation device 120 can provide an output device such as a display and a speaker that provides information to the user 110. The navigation device 120 can also be equipped with a position locating device for determining the location. A navigation application that provides a navigation service to the user 110 can be installed on the navigation device 120.

FIG. 2 shows a flowchart of the method 200 used for navigation according to the embodiment of the present disclosure. For example, the method 200 can be performed by the navigation device 120 shown in FIG. It should be understood that the method 200 can further include additional blocks (not shown) and / or blocks that can be omitted. For example, in some embodiments, blocks 240-250 are optional. The scope of this disclosure is not limited in this regard.

At block 210, the navigation device 120 receives user input regarding the navigation destination (also referred to as "first user input" in the present specification) from the user 110.

In some embodiments, the navigation destination entered by the user 110 can indicate the name of any point of interest the user is heading for. The "points of interest" described here can include restaurants, shopping centers, schools, parking lots, and the like. For example, the navigation destination input by the user 110 may be "Wangfujing Bookstore", "Beijing City Department Store", "Wangfujing Department Store Parking Lot", or the like. Optionally, in some other embodiments, the navigation destination entered by the user 110 does not specifically indicate a specific point of interest in any area the user is heading to, for example, "Ofui". , "Zhongguancun" may indicate the name of the area.

At block 220, the navigation device 120 determines at least one destination address associated with the navigation destination and parking difficulty information around it.

In some embodiments, if the navigation destination entered by the user 110 indicates the name of an interest point, the navigation device 120 determines the address of the interest point as the destination address (eg, by map search). be able to. The navigation device 120 can further determine the parking difficulty information around the point of interest. For example, when the navigation destination entered by the user 110 is "Beijing City Department Store", the navigation device 120 determines that the address of the Beijing City Department Store is Wangfujing Main Street 255 by searching the map, and further Beijing. Parking difficulty information around the city department store can be confirmed.

Optionally, in some other embodiments, if the navigation destination entered by the user 110 indicates the name of an area, the navigation device 120 is the corresponding address of one or more points of interest within that area. May be confirmed as the destination address, and the parking difficulty information around each interest point may be confirmed. For example, when the navigation destination entered by the user 110 is "Wangfujing", the navigation device 120 searches the map to search for one or more points of interest in the area (for example, "Wangfujing Bookstore", etc. It is possible to determine the corresponding addresses of "Wangfujing Bookstore", "Wangfujing Department Store Parking Lot", etc.), and further to determine the parking difficulty information around these destination addresses.

In some embodiments, the parking difficulty information around the destination address can be expressed as an index of parking difficulty around the destination address. For example, a high index indicates that parking is difficult. On the contrary, if the index is low, it means that the difficulty of parking is low. Furthermore, the exponent can also be represented as a tag. For example, if the index is higher than a particular threshold, the tag can indicate "parking difficulty". If the index is below a certain threshold, the tag can be indicated as "easy to park". Additionally or optionally, the parking difficulty information around the destination address may indicate the number of remaining empty parking spaces in one or more parking lots around the destination address.

In some embodiments, the navigation device 120 determines parking difficulty information around at least one destination address at the current time (eg, the time or time zone in which the user 110 enters the navigation destination). be able to. Optionally or additionally, in some embodiments, the navigation device 120 determines the time (eg, time or time zone) at which the user arrives at at least one destination address from his current location. The parking difficulty information around at least one destination address may be predicted when the user arrives at at least one destination address. The determination of the parking difficulty level information will be described in detail below with reference to FIGS. 3 to 7.

At block 230, the navigation device 120 simultaneously provides the user 110 with at least one confirmed destination address and parking difficulty information in the vicinity thereof.

In some embodiments, the navigation device 120 simultaneously displays at least one destination address and parking difficulty information around it to the user 110 in a visible manner (eg, graphic or textual). Can be done. In some embodiments, the parking difficulty information can be displayed in association with at least one destination address in an information link manner. In some other embodiments, the navigation device 120 further provides the user 110 with at least one confirmed destination address and parking around it in an audible way (eg, broadcasting information) or otherwise. Difficulty information can be provided at the same time. The scope of this disclosure is not limited in this regard.

In some embodiments, the user 110 can select the destination address he / she is heading for based on parking difficulty information around at least one destination address. For example, the user 110 can select a destination address that is easy to park from a plurality of destination addresses and set it as the destination that he / she is heading for.

Further, in the block 240, the navigation device 120 can accept a user input (referred to as "second user input" in the present specification) regarding the selected destination address from the user 110 according to the selection of the user 110. In block 250, the navigation device 120 determines the navigation route from the current position of the user 110 to the selected destination address. At block 260, the navigation device 120 provides the user 110 with the navigation route.

FIG. 3 shows a block diagram of an exemplary system 300 for determining parking difficulty information around at least one destination address at a particular time according to an embodiment of the present disclosure. The system 300 can be implemented, for example, in the navigation device 120 shown in FIG. 1 and in the block 220 shown in FIG. 2 at the current time or at least one destination when the user arrives at at least one destination address. It is used to determine parking difficulty information around the address. As shown in FIG. 3, the exemplary device 300 as a whole can include a time series analysis unit 310, a mapping relationship determination unit 320, a parking demand forecasting unit 330, and a spatial interpolation unit 340.

In some embodiments, the time series analysis unit 310 can acquire the historical parking information 301 of at least one parking lot 350 in the historical time. The history time can correspond to a specific time and may be a history time or a history time zone. For example, if the parking difficulty information around at least one destination address within the time of 10: 00-10: 30 am on Monday is determined, the historical time is 10: 00-10: 10 am on the previous Monday. It can be 30. For example, when determining the parking difficulty information around at least one destination address as of 10:00 am on Monday, the history time can be 10:00 am on the previous Monday. The history parking information 301 corresponds to each history time within the history time of the parking lot 350 (for example, when the history time is in the history time zone, the history time can be selected every 5 minutes, every 10 minutes, etc.). Parking data can be included. For example, the parking data for each historical time includes the number of parking spaces in use, the number of available parking spaces, the parking space in use (eg, area) and / or the available parking space in the parking lot 350 at that historical time. Etc. can be included.

In some embodiments, the time-series analysis unit 310 can perform time-series analysis on the acquired historical parking information 301 to obtain a time-series model corresponding to at least one historical time within the historical time. The time series model can explain the mapping relationship between parking demand and a group of factors that change parking demand over time. The time-series analysis unit 310 can determine the historical parking demand 302 of the parking lot 350 at the historical time using the obtained set of time-series models. The historical parking demand 302 may be an index indicating the parking difficulty of the parking lot 350 in the historical time (for example, if the index is high, it indicates that the parking difficulty is high, and conversely, the index is low. For example, the difficulty of parking is low). The historical parking demand 302 can be provided to the mapping relationship determination unit 320.

In some embodiments, the mapping relationship determination unit 320 can acquire a group of spatial features 303 of the parking lot 350 and a historical parking demand 302 determined by the time series analysis unit 310. The spatial feature 303 is a distance from the parking lot 350 to one or more points of interest (eg, a transportation terminal, a shopping center, etc.) and / or various points of interest (eg, within a threshold distance) around the parking lot 350 (eg, within a threshold distance). For example, it can include a corresponding number of schools, offices, tourist attractions, etc.). In some embodiments, the mapping relationship determination unit 320 may further determine the mapping relationship 304 between the parking demand of the parking lot and the spatial feature of the parking lot, based on the spatial feature 303 and the historical parking demand 302. it can. Since the mapping relationship 304 can be used to predict the parking demand of the parking area at a specific time, the mapping relationship 304 is also referred to as a prediction model 304 in the following sentences. The forecast model 304 can be provided to the parking demand forecasting unit 330.

In some embodiments, the parking demand forecasting unit 330 can determine the parking area 360 corresponding to the at least one destination address. The parking area 360 is also referred to as a "target parking area 360" in the following text. For example, the target parking area 360 can be determined based on the navigation destination entered by the user 110 and may include one or more parking lots around at least one destination address associated with the navigation destination. it can. Further, for example, as described below with reference to FIG. 5, the target parking area 360 can also be determined based on the division of a wider range of geographical areas. In some embodiments, the parking demand forecasting unit 330 can further determine a group of spatial features 305 of the target parking area 360. The spatial feature 305 can correspond to the spatial feature 303. For example, spatial feature 305 is the distance from the target parking area 360 to one or more points of interest (eg, transportation terminals, shopping centers, etc.) and / or various points of interest within the target parking area 360 (eg, schools, etc.). Can include the corresponding number of offices, tourist attractions, etc.). The parking demand forecasting unit 330 utilizes a forecasting model 304 to arrive at a specific time (eg, the current time the user 110 has entered a navigation destination, or the user arrives at at least one destination address, based on the spatial feature 305. The parking demand 306 of the target parking area 360 in time) can be predicted. Corresponding to the historical parking demand 302, the parking demand 306 may be an index indicating the parking difficulty level of the target parking area 360 at the specific time (for example, the higher the index, the higher the parking difficulty level). On the contrary, if the index is low, it means that the difficulty of parking is low). The parking demand 306 can be provided to the spatial interpolation unit 340.

In some embodiments, the spatial interpolation unit 340 predicts the parking demand 307 of each sub-parking area within the target parking area 360 at a particular time using spatial interpolation technology based on the parking demand 306 of the target parking area 360. can do. In some embodiments, each sub-parking area can be associated with one destination address. That is, the spatial interpolation unit 340 can predict the parking demand 307 around each destination address of at least one destination address at the specific time based on the parking demand 306 of the target parking area 360. For example, the parking demand 306 can be regarded as a parking difficulty index in the center of the target parking area 360. The parking demand 307 around each destination address at the specific time can be determined based on the parking demand 306 and the distance from the destination address to the center of the target parking area 360. Further, based on the parking demand around each destination address, parking difficulty information around the destination address can be generated. In some embodiments, each sub-parking area is one parking lot within the target parking area 360 (eg, a parking lot that can provide historical parking information, or a parking lot that cannot provide historical parking information, which is It may be different from the parking lot 350). That is, the space interpolation unit 340 can predict the parking demand of each parking lot in the target parking area 360 at the specific time based on the parking demand 306 of the target parking area 360. For example, the parking demand of each parking lot at the specific time can be determined based on the parking demand 306 and the distance from the parking lot to the center of the target parking area 360.

FIG. 4 shows a flowchart of an exemplary method 400 for determining parking difficulty information around at least one destination address at a particular time according to an embodiment of the present disclosure. For example, method 400 can be considered as an exemplary implementation of block 220 shown in FIG. Hereinafter, the method 400 will be described in detail with reference to FIG.

At block 410, the target parking area 360 corresponding to at least one destination address is determined. In some embodiments, the target parking area 360 can be determined based on the navigation destination entered by the user 110, eg, one around at least one destination address associated with the navigation destination or Can include multiple parking lots. Optionally, in some other embodiments, the geographic area of all parking lots that the user 110 can reach (eg, the city in which the user 110 is located) is divided into a group of parking areas, a group of parking areas. Of these, the parking area associated with at least one destination address may be selected as the target parking area 360.

FIG. 5 shows a schematic diagram for determining a target parking area according to the embodiment of the present disclosure. For example, FIG. 5 shows a geographical area 500 that includes a plurality of parking lots (circular icons in FIG. 5). In some embodiments, parking lots of a predetermined ratio (eg, 20%) from the geographical area 500, eg, parking lots 520-1, 520-2 ... 520-6 (generally or) of FIG. Alone (referred to as "parking lot 520") is randomly selected. In some embodiments, based on the selected parking lot 520, the geographical area 500 may be divided into a group of parking areas, each of which parking lots 520 may be included in one corresponding parking area. it can. Further, the number of parking lots in each parking area 510 is lower than a predetermined threshold value. Specifically, in some embodiments, a circular area is set up with the position of each selected parking lot 520 as the center of the circle, until the two circular areas meet, or the number of parking lots included in the circular area. Each circular area can be gradually expanded to include more parking lots until is reached a predetermined threshold. In this way, the geographical area 500 can be divided into a group of parking areas 510-1, 510-2 ... 510-6 (collectively referred to as "parking area 510" or "circular area 510"). Here, parking lots in the same parking area have spatial similarity. As the target parking area 560, for example, a parking area associated with at least one destination address among the parking areas 510-1, 510-2 ... 510-6 can be selected.

Additional or optionally, in some embodiments, one or more spatial division points (in addition to blank areas not included in circular area 510, to cover the entire range of geographic area 500). The square icon in FIG. 5) may be installed. By connecting these spatial division points, the geographical area 500 can be divided into a plurality of irregularly shaped parking areas. For example, of these irregularly shaped parking areas, a parking area associated with at least one destination address can be selected as the target parking area 360.

Returning to FIG. 4, in the block 420, based on the historical parking information 301 of the parking lot 350 in the historical time, the parking demand 306 of the target parking area 360 in the specific time corresponding to the historical time is predicted.

FIG. 6 shows a flowchart of an exemplary method 600 for predicting parking demand in a target parking area at a particular time according to an embodiment of the present disclosure. For example, method 600 can be considered as an exemplary implementation of block 420 shown in FIG.

In block 610, the historical parking demand 302 of the parking lot 350 at the historical time is determined based on the historical parking information 301.

In some embodiments, the acquired historical parking information 301 can be time-series analyzed to obtain a time-series model corresponding to at least one historical time within the historical time. The time series model can explain the mapping relationship between parking demand and a group of factors that change parking demand over time. For the sake of illustration, here, at least one historical time included in the historical time is expressed as (ti _{| i} ∈ [1, M]), where M represents the number of historical times and M ≧ 1.

ここで、ＰＤ_ｉは履歴時刻ｔ_ｉにおける駐車場の駐車需要を表し、ＰＬＤ、ＰＬＳＲ_ｉ、ＰＬＣＲ_ｉ及びＰＬＣ_ｉは、駐車需要を時間変動させる一群の要因を表し、ｗ１_ｉ、ｗ２_ｉ、ｗ３_ｉ及びｗ４_ｉは、これらの要因のそれぞれの重みを表す。具体的に、ＰＬＤは、該駐車場の周囲の駐車場の密集度を表すことができ、例えば、以下のように算出可能である。

ここで、Ｃｎｔ_ｍは、該駐車場を中心とするより小さいエリア（例えば、円形、四角形又は任意の形状のエリア）内の駐車場数を示し、Ｃｎｔ_ｎは、該駐車場を中心とするより大きいエリア内の駐車場数を示す。ＰＬＳＲ_ｉは、履歴時刻ｔ_ｉにおける該駐車場の使用可能空間と、周囲の駐車場の総使用可能空間との比率（本明細書では「第１比率」とも称する）を表すことができ、例えば以下のように算出可能である。

ここで、Ｓ_ｉは、時刻ｔ_ｉにおける該駐車場の使用可能空間を表し、Ｓｒ_ｉ（ｒ∈［ｌ，ｎ］）は、時刻ｔ_ｉにおける該駐車場の周囲の第ｒ駐車場の使用可能空間を表し、ｎは、該駐車場の周囲の駐車場数を表すＰＬＣＲ_ｉは、履歴時刻ｔ_ｉにおける該駐車場の使用可能駐車スペースの数と、周囲の駐車場の使用可能駐車スペースの総数との比率（本明細書では「第２比率」とも称する）を表すことができ、例えば、以下のように算出可能である。

ここで、Ｃ_ｉは、時刻ｔ_ｉにおける該駐車場の使用可能駐車スペース数を表し、Ｃｒ_ｉ（ｒ∈［ｌ，ｎ］）は、時刻ｔ_ｉにおける該駐車場の周囲の第ｒ駐車場の使用可能駐車スペース数を表し、ｎは該駐車場の周囲の駐車場数を表す。ＰＬＣ_ｉは、履歴時刻ｔ_ｉにおける該駐車場の空間利用率を表すことができ、例えば、以下のように算出可能である。

理解すべき点として、式（１）で選択されたこれらの要因は例示的なものにすぎず、本開示の範囲を制限することを意図するものではない。他の実施形態において、上述したものに限ることなく、その他の要因に基づき時系列モデルを確立することもできる。 In some embodiments, for each historical time t _i, series model when established can be expressed as follows.

Here, PD _i represents the parking demand for parking in history time _{t i, PLD, PLSR i,} PLCR i and PLC _i represents a group of factors to vary the parking demand _time, w1 _i, w2 i, w3 _i and w4 _i represent the weights of each of these factors. Specifically, the PLD can represent the density of parking lots around the parking lot, and can be calculated as follows, for example.

Here _{, CNT m} indicates the number of parking lots in a smaller area centered on the parking lot (for example, an area of a circle, a quadrangle, or an arbitrary shape), _{and CNT n} is larger than that centered on the parking lot. Indicates the number of parking lots in the area. PLSR _i has a usable space of the parking at the history time t _i, (also referred to herein as "first ratio") ratio of the total available space around the parking lot may represent, for example, It can be calculated as follows.

Here, _{S i} represents the available space in the parking at time _{t i, Sr i (r∈ [} l, n]) , the use of the r parking surrounding the parking at time _{t i} represents a possible space, n is, PLCR _i representing the parking Bakazu around the parking is, the number of available parking spaces of the parking in the history time t _i, the total number of available parking spaces around the parking lot The ratio with and (also referred to as “second ratio” in the present specification) can be expressed, and can be calculated as follows, for example.

Here, _{C i} represents the available parking space number of the parking at time _{t i, Cr i (r∈ [} l, n]) is the r parking surrounding the parking at time _{t i} Represents the number of available parking spaces, and n represents the number of parking lots around the parking lot. PLC _i can represent the spatial utilization of the parking at the history time t _i, for example, can be calculated as follows.

It should be understood that these factors selected in equation (1) are merely exemplary and are not intended to limit the scope of this disclosure. In other embodiments, time series models can be established based on other factors, not limited to those described above.

In order to establish the time series model shown in the equation (1), it is necessary to determine _{the weights w1 i} , w2 _i , w3 _i and w4 _{i of each factor.} In some embodiments, in the initial situation, these weights are set as default values, for example, w1 _{i is set} to 0.1, w2 _{i is set} to 0.1, w3 _i is set to 0.1, and w4 _i is set to 0.4. can do. Each history time in history hours | in _{(t i i∈ [1,6])} , an initial curve based on the actually used parking space number Pd _i in the parking lot 350, for example, to draw the initial curve 710 in FIG. 7 Can be done. A smooth curve 720 can be obtained by adjusting the weights w1 _i , w2 _i , w3 _i and w4 _{i to smooth the curve 710.} In this way, the weights w1 _i , w2 _i , w3 _i and w4 _i of each factor can be determined. It should be understood that the method of determining the weight of each factor described above is only exemplary. In other embodiments, the weighting of each factor can also be determined using any known or future developed method (eg, Random Forest). In this way, the mapping relationship between the parking demand and the group of factors that change the parking demand over time can be determined.

After establishing the time-series model shown in FIG. 7 and determining the weights of each factor, the historical parking demand 302 of the parking lot 350 at the historical time can be determined by using the equation (1). For example, for each historical time _{t i,} a group of factors _PLD affecting the parking demand of parking 350, can determine the value of the PLSR i, PLCR _i and PLC _i. Then, using equation (1), for example, as shown in a smooth curve 720 of Figure 7, it is possible to determine the parking demand PD _i parking 350 in history time _{t i.} In some embodiments, the average value of parking demand at a plurality of historical times can be obtained to determine the historical parking demand 302 of the parking lot 350 in the historical time zone.

In block 620, the mapping relationship 304 between the historical parking demand 302 and the spatial feature 303 of the parking lot 350 is determined.

In some embodiments, the spatial feature 303 of the parking lot 350 is the distance from the parking lot 350 to one or more points of interest (eg, a transportation terminal, a shopping center, etc.) and / or the perimeter of the parking lot 350 (eg, a traffic terminal, a shopping center, etc.). For example, it can include a corresponding number of various points of interest (eg, schools, offices, tourist attractions, etc.) within a threshold distance. Preferably, in some embodiments, the acquired spatial feature 303 of the parking lot 350 is the distance from the parking lot 350 to the subway station, the number of schools around the parking lot 350, the tourism around the parking lot 350. It may include the number of attractions and the distance from parking lot 350 to the shopping center.

ここで、ＰＤは駐車需要を表し、Ｄｓｕｂは地下鉄の駅までの距離を表し、Ｄｓｃｈは学校までの距離を表し、Ｄｍはショッピングセンターまでの距離を表し、Ｎａは観光名所の数を表す。式（６）で選択されたこれらの空間的特徴は例示的なものにすぎず、本開示の範囲を制限することを意図するものではない。他の実施形態において、上述したものに限ることなく、その他の空間的特徴に基づき、予測モデル３０４を確立することもできる。 In some embodiments, the mapping relationship 304 between the historical parking demand 302 and the spatial feature 303 of the parking lot 350 (ie, the prediction model 304) can be expressed, for example, as follows.

Here, PD represents the parking demand, Dsub represents the distance to the subway station, Dsch represents the distance to the school, Dm represents the distance to the shopping center, and Na represents the number of tourist attractions. These spatial features selected in formula (6) are exemplary only and are not intended to limit the scope of the present disclosure. In other embodiments, the prediction model 304 can also be established based on other spatial features, not limited to those described above.

In order to establish the prediction model 304 shown in the equation (6), it is necessary to determine the weights w1, w2, w3 and w4 of each spatial feature. In some embodiments, linear regression can be used to determine the weights w1, w2, w3 and w4 for each spatial feature based on the historical parking demand 302 and the spatial feature 303. Optionally, in some embodiments, any known or future developed method (eg, random forest) may be used to determine the weights w1, w2, w3 and w4 for each spatial feature.

In this way, the mapping relationship between the parking demand and the spatial characteristics of the parking area can be determined. The mapping relationship can reflect the relationship between parking demand and spatial characteristics, and can also consider the relationship between parking demand and a group of factors that cause the parking demand to fluctuate over time.

In block 630, the parking demand 306 of the target parking area 360 at a specific time is predicted based on the spatial feature 305 of the target parking area 360 and the mapping relationship 304.

In some embodiments, the spatial feature 305 can correspond to the spatial feature 303. For example, spatial feature 305 is the distance from the target parking area 360 to one or more points of interest (eg, transportation terminals, shopping centers, etc.) and / or various points of interest within the target parking area 360 (eg, schools, etc.). Can include the corresponding number of offices, tourist attractions, etc.). Preferably, in some embodiments, the acquired spatial feature 305 of the target parking area 360 is the distance from the target parking area 360 to the subway station, the number of schools in the target parking area 360, and within the target parking area 360. The number of tourist attractions in the area and the distance from the target parking area 360 to the shopping center may be included. In some embodiments, the spatial feature 305 can be substituted into the mapping relationship 304 represented by equation (6) to obtain a parking demand 306 for the target parking area 360 at a particular time.

Returning to FIG. 4, in block 430, based on the parking demand 306 of the target parking area 360, using the spatial interpolation method, the parking difficulty around each destination address of at least one destination address at the specific time. Predict degree information.

In some embodiments, based on the parking demand 306 of the target parking area 360, the parking demand 307 around each destination address of at least one destination address at that particular time can be predicted. For example, the parking demand 306 can be regarded as a parking difficulty index in the center of the target parking area 360. The parking demand 307 around each destination address can be determined based on the parking demand 306 and the distance from the destination address to the center of the target parking area 360. Further, based on the parking demand around each destination address, parking difficulty information around the destination address can be generated. In some embodiments, based on the parking demand around each destination address, parking difficulty information associated with the destination address is generated to indicate the difficulty of parking around the destination address. be able to. Optionally, by establishing a correspondence between the parking demand and the number of parking spaces, the parking demand around each destination address may be converted into the number of available parking spaces around the destination address. That is, the parking difficulty information around each destination address can indicate an index of the parking difficulty around the destination address and / or the number of vacant parking spaces around the destination address.

Additional or optionally, in some embodiments, each parking lot within the target parking area 360 at the particular time (eg, a parking lot capable of providing historical parking information) based on the parking demand 306 of the target parking area 360. , Or a parking lot for which historical parking information cannot be provided) may be predicted. For example, the parking demand of each parking lot at the specific time can be determined based on the parking demand 306 and the distance from the parking lot to the center of the target parking area 360. Similarly, based on the parking demand of each parking lot at the specific time, the parking difficulty level information of the parking lot can be generated to indicate the parking difficulty level of the parking lot. The parking difficulty information of each parking lot can indicate an index of the parking difficulty of the parking lot and / or the number of empty parking spaces in the parking lot.

As can be understood from the above description, the solutions used for navigation in the embodiments of the present disclosure can solve various problems in the prior art. The solution provides the user with a destination address associated with the navigation destination, and also provides the user with parking difficulty information around the destination address, so that the user can use the appropriate destination in advance. We will support you so that you can select and develop effective parking measures.

FIG. 8 shows a block diagram of an exemplary device 800 used for navigation according to an embodiment of the present disclosure. The device 800 can realize, for example, the navigation device 120 shown in FIG. As shown in FIG. 8, the device 800 includes a first reception module 810 arranged to receive a first user input regarding a navigation destination from a user, at least one destination address associated with the navigation destination, and the like. The first confirmation module 820 arranged to determine the parking difficulty information in the surrounding area, the first destination address arranged to provide at least one destination address, and the parking difficulty information in the surrounding area to the user at the same time. The provided module 830 and can be included.

In some embodiments, the first confirmation module 820 is located around a confirmation unit arranged to determine the time when a user arrives at at least one destination address and around at least one destination address at that time. Includes a prediction unit arranged to predict parking difficulty information.

In some embodiments, the predictive unit further determines a parking area that includes one or more parking lots around at least one destination address that corresponds to at least one destination address and corresponds to that time. Predict the parking demand of the parking area at that time based on the historical parking information of at least one parking lot at the time, and based on the parking demand, each destination address of at least one destination address at that time. Arranged to predict parking difficulty information around.

In some embodiments, the predictive unit further determines the historical parking demand for at least one parking lot at the historical time, based on the historical parking information, with the historical parking demand and the spatial features of the at least one parking lot. The mapping relationship is determined and arranged to predict the parking demand of the parking area at that time based on the spatial characteristics of the parking area and the mapping relationship.

In some embodiments, the spatial feature comprises at least one of the distance to at least one point of interest and the number of points of interest in the vicinity.

In some embodiments, the prediction unit is further arranged to predict parking difficulty information for each parking lot of one or more parking lots in time based on parking demand. Here, one or more parking lots include parking lots that cannot provide historical parking information.

In some embodiments, the first provided module 830 includes a display unit arranged to simultaneously display at least one destination address and parking difficulty information around it to the user.

In some embodiments, the parking difficulty information includes at least one of an index indicating the parking difficulty and the number of empty parking spaces.

In some embodiments, the device 800 further relates to a second destination address selected by the user in response to the user selecting one of at least one destination address based on parking difficulty information. A second reception module arranged to accept user input, a second confirmation module arranged to determine a navigation route from the user's current position to a selected destination address, and a navigation route provided to the user. Includes a second provided module that is arranged to do so.

Modules and / or units included in device 800 can be implemented in a variety of ways, including software, hardware, firmware or any combination thereof. In some embodiments, one or more units are feasible by instructions that can be executed by software and / or firmware, eg, a device stored on a storage medium. In addition to or as an alternative to the instructions that the device can execute, some or all units of device 800 can be implemented by at least one or more hardware logic components. Typical types of hardware logic components that can be used include, for example, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific general-purpose products (ASSPs), and system-on-chip (system-on-chip). SOC), complex programmable logic devices (CPLDs), etc., but not limited to these.

These modules and / or units shown in FIG. 8 can be realized in part or in whole as hardware modules, software modules, firmware modules or any combination thereof. In particular, in some embodiments, the flows, methods or processes described above are feasible by hardware in a storage system, a host corresponding to the storage system, or other computer device independent of the storage system.

FIG. 9 shows a schematic block diagram of an exemplary device 900 that can implement the embodiments of the present disclosure. The device 900 can realize the navigation device 120 shown in FIG. As shown in FIG. 9, the device 900 includes a central processing unit (CPU) 901. The CPU 901 performs various appropriate operations and processes based on the instructions of the computer program stored in the read-only memory (ROM) 902 or the instructions of the computer program loaded from the storage unit 908 into the random access memory (RAM) 903. Can be executed. The RAM 903 can further store various programs and data necessary for operating the device 900. The CPU 901, ROM 902 and RAM 903 are connected to each other via the bus 904. Input / output (I / O) ports 905 are also connected to bus 904.

A plurality of members in the device 900 are connected to the I / O port 905. The plurality of members include an input unit 906 such as a keyboard and a mouse, an output unit 907 such as various types of displays and speakers, a storage unit 908 such as a magnetic disk and an optical disk, and a network interface card, a modem, and wireless communication transmission / reception. A communication unit 909 such as a machine is included. The communication unit 909 allows the device 900 to exchange information / data with other devices via a computer network such as the Internet and / or various telegraph networks.

The processing unit 901 performs the various methods and processes described above, such as methods 200, 400 and / or 600. For example, in some embodiments, methods 200, 400 and / or 600 are feasible as computer software programs and are visibly included in a device readable medium such as storage unit 908. There is. In some embodiments, some or all of the computer programs can be loaded and / or installed on device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into the RAM 903 and executed by the CPU 901, one or more steps of the methods 200, 400 and / or 600 described above can be performed. Optionally, in other embodiments, the CPU 901 may be arranged to perform methods 200, 400 and / or 600 by any other suitable method (eg, using firmware).

The above-mentioned functions herein can be performed, at least in part, by one or more hardware logic components. For example, typical types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific general-purpose products (ASSPs), and system-on-chip (SOCs). ), Complex programmable logic devices (CPLDs), etc., but not limited to these.

The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general purpose computer, dedicated computer, or other programmable data processing device, and when the program code is executed by the processor or controller, the flowchart and / or block diagram The specified function / operation is performed. All program code can be executed on the device, and some can be executed on the device. It can run partially on the device as a separate software package, partially on the remote device, or entirely on the remote device or server.

In the content of the present disclosure, the medium readable by the device may be a tangible medium and is combined with an instruction execution system, device or program provided for use in the device, or instruction execution system, device or device. Can include or store programs used in. The device-readable medium may be a device-readable signal medium or a device-readable storage medium. Device-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic and infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of device-readable storage media include electrical connections with one or more cables, portable computer disksets, hard disks, random access memory (RAM), read-only memory (ROM), and erase. Includes writable ROM (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the above contents.

It should be noted that each operation has been described in a specific order, but in order to obtain the desired result, these operations are performed in the specific order shown, performed in order, or all the operations shown in the figure. It should be understood that it is required to carry out. In certain environments, multiple jobs and concurrency may be advantageous. Similarly, the above description includes some specific implementation details, but these should not be construed as restrictions on the scope of the present disclosure. Some of the features described in the content of the individual embodiments may be combined and realized in one embodiment. Conversely, the various features described in the content of one embodiment may be implemented in a plurality of embodiments alone or in any suitable combination.

Although the subject matter of the present invention has been described in terms specific to structural features and / or methods, logic, and behavior, it is understood that the subject matter limited by the appended claims is not necessarily limited to the above-mentioned features or behavior. It should be. Rather, the features and operations described above are merely exemplary forms that realize the claims.

Claims (20)

Accepting the first user input regarding the navigation destination from the user,
To determine at least one destination address related to the navigation destination and parking difficulty information in the vicinity thereof.
A method used for navigation, comprising providing the user with at least one destination address and the parking difficulty information in the vicinity thereof at the same time. Determining the parking difficulty information is
Determining the time when the user arrives at at least one destination address,
The method of claim 1, comprising predicting the parking difficulty information around the at least one destination address at the time. Providing the user with the at least one destination address and the parking difficulty information in the vicinity thereof at the same time
The method according to claim 1 or 2, which comprises simultaneously displaying the parking difficulty level information of the at least one destination address and its surroundings to the user. The method according to claim 1 or 2, wherein the parking difficulty information includes at least one of an index indicating the parking difficulty and the number of vacant parking spaces. further,
In response to the user selecting one of the at least one destination address based on the parking difficulty information, the user receives a second user input regarding the selected destination address.
To determine the navigation route from the user's current position to the selected destination address,
The method of claim 1 or 2, comprising providing the user with the navigation route. Predicting the parking difficulty information around the at least one destination address at the time is
Determining a parking area that includes one or more parking lots around the at least one destination address that corresponds to the at least one destination address.
Forecasting the parking demand of the parking area at the time based on the historical parking information of at least one parking lot at the time corresponding to the time.
The method of claim 2, comprising predicting parking difficulty information around each destination address of the at least one destination address at the time based on the parking demand. Predicting the parking demand of the parking area at the time is
To determine the historical parking demand of the at least one parking lot at the historical time based on the historical parking information.
To determine the mapping relationship between the historical parking demand and the spatial feature of at least one parking lot.
The method of claim 6, comprising predicting the parking demand of the parking area at the time based on the spatial features of the parking area and the mapping relationship. The method of claim 7, wherein the spatial feature comprises at least one of a distance to at least one point of interest and a number of points of interest in the vicinity. further,
Includes predicting parking difficulty information for each of the one or more parking lots at the time based on the parking demand.
The method according to claim 6, wherein the one or more parking lots include a parking lot that cannot provide historical parking information. A first reception module arranged to receive the first user input regarding the navigation destination from the user,
A first confirmation module arranged to determine at least one destination address associated with the navigation destination and parking difficulty information around it.
A device used for navigation, including a first providing module arranged to simultaneously provide the user with the at least one destination address and the parking difficulty information in the vicinity thereof. The first confirmation module is
A confirmation unit arranged to determine the time when the user arrives at the at least one destination address.
10. The apparatus of claim 10, comprising a prediction unit arranged to predict the parking difficulty information around the at least one destination address at the time. The first providing module according to claim 10 or 11, further comprising a display unit arranged to simultaneously display the at least one destination address and the parking difficulty information in the vicinity thereof to the user. apparatus. The device according to claim 10 or 11, wherein the parking difficulty information includes at least one of an index indicating the parking difficulty and the number of vacant parking spaces. further,
Arranged so as to accept a second user input regarding the selected destination address from the user in response to the user selecting one of the at least one destination address based on the parking difficulty information. 2nd reception module and
A second confirmation module arranged to determine the navigation route from the user's current position to the selected destination address, and
The device according to claim 10 or 11, comprising a second providing module arranged to provide the navigation route to the user. The prediction unit further
A parking area containing one or more parking lots around the at least one destination address corresponding to the at least one destination address is determined.
Based on the historical parking information of at least one parking lot in the historical time corresponding to the time, the parking demand of the parking area at the time is predicted.
The device according to claim 11, which is arranged so as to predict parking difficulty information around each destination address of the at least one destination address at the time based on the parking demand. The prediction unit further
Based on the historical parking information, the historical parking demand of the at least one parking lot at the historical time is determined.
The mapping relationship between the historical parking demand and the spatial feature of at least one parking lot is determined.
15. The device of claim 15, which is arranged to predict the parking demand of the parking area at the time based on the spatial features of the parking area and the mapping relationship. 16. The apparatus of claim 16, wherein the spatial feature comprises at least one of a distance to at least one point of interest and a number of points of interest in the vicinity. The prediction unit is further arranged to predict the parking difficulty information of each of the one or more parking lots at the time based on the parking demand.
The device according to claim 15, wherein the one or more parking lots include parking lots that cannot provide historical parking information. With at least one processing unit
Containing with at least one memory coupled to said at least one processing unit and storing instructions executed by said at least one processing unit.
A device used for navigation that performs the method according to any one of claims 1-9 when the instruction is executed by the at least one processing unit. The computer program is stored
A computer-readable storage medium that, when executed by a device, causes the device to perform the method according to any one of claims 1-9.

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