CN112862214A - Parking service recommendation method, device, medium and server based on big data - Google Patents
Parking service recommendation method, device, medium and server based on big data Download PDFInfo
- Publication number
- CN112862214A CN112862214A CN202110259468.2A CN202110259468A CN112862214A CN 112862214 A CN112862214 A CN 112862214A CN 202110259468 A CN202110259468 A CN 202110259468A CN 112862214 A CN112862214 A CN 112862214A
- Authority
- CN
- China
- Prior art keywords
- point
- target
- time
- parking
- congestion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000006243 chemical reaction Methods 0.000 claims abstract description 42
- 230000010365 information processing Effects 0.000 claims description 14
- 238000004891 communication Methods 0.000 description 6
- 230000009471 action Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000003111 delayed effect Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 235000012054 meals Nutrition 0.000 description 1
- 230000036651 mood Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Navigation (AREA)
- Traffic Control Systems (AREA)
Abstract
The application provides a parking service recommendation method, device, medium and server based on big data, wherein the method is applied to the server and comprises the following steps: acquiring the current position, the driving path, the current time point and the target time point of a user vehicle from a user terminal; acquiring a jammed road section and estimated passing time in a driving path, and determining whether a user vehicle reaches the jammed road section or not according to the current position; when the user vehicle does not reach the congested road section, determining a predicted time range based on the current time point and the predicted passing time length, and judging whether the predicted time range is consistent with a target time point or not; when the predicted time range does not accord with the target time point, all parking points in a search area with the current position as the center are obtained, the target parking points are determined from all parking points in the search area, conversion information containing the target parking points and a congestion-free commuting mode is generated, the conversion information is sent to a user terminal and displayed to a user, and the user can make a decision based on the conversion information.
Description
Technical Field
The application relates to the technical field of big data, in particular to a parking service recommendation method, device, medium and server based on big data.
Background
With the continuous development of social economy and the continuous improvement of the living standard of people, automobiles are owned by more and more families. The problem that comes with it is that the traffic jam problem is becoming more and more serious, and under various measures, the traffic jam problem is still very prominent.
For office workers, traffic jam is a normal state, and the office workers are always in a state of being blocked on the road and difficult to reach the office point in time one month, and the office workers are in daily use and have meals at home when the office workers are late. When the road is blocked, the mood of each driver can be seriously influenced, the risk of traffic accidents is increased, and the safe driving is not facilitated.
At present, for the situation that the road is blocked, workers usually have no function, and can only block the road and dry, so that effective measures are difficult to take to solve the problem. Especially when there is an important meeting or contact, the irrecoverable result is easy to be caused. For this reason, the driver cannot accurately predict the time of congestion, and usually cannot be applied because no feasible measures are available to change the existing conditions, so that energy consumption only causes late arrival on the road.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, an apparatus, a medium, and a server for recommending parking services based on big data, so that a user can arrive at a destination in time even when the user has driven a car and leaves the car and is about to face traffic congestion.
In order to achieve the above object, embodiments of the present application are implemented as follows:
in a first aspect, an embodiment of the present application provides a parking service recommendation method based on big data, which is applied to a server and includes: the method comprises the steps that the current position, a driving path, a current time point and a target time point of a user vehicle are obtained from a user terminal, wherein the driving path represents a passing path from a starting place to a destination, and the target time point represents the latest time point preset by a user and reaching the destination; acquiring a jammed road section and an estimated passing time length in the driving path, and determining whether the user vehicle reaches the jammed road section or not according to the current position; when the user vehicle does not reach the congested road section, determining a predicted time range based on the current time point and the predicted passing time length, and judging whether the predicted time range is consistent with the target time point; when the estimated time range does not accord with the target time point, all parking points in a search area with the current position as the center are obtained, the target parking points are determined from all parking points in the search area, conversion information including the target parking points and a congestion-free commuting mode is generated, the conversion information is sent to the user terminal and displayed to the user, and the user can make a decision based on the conversion information.
In the embodiment of the application, the current position, the driving path (the passing path from the departure place to the destination), the current time point and the target time point (the latest time point for reaching the destination preset by the user) of the user vehicle are obtained from the user terminal; and obtaining a jammed road section and estimated passing time in the driving path, and determining whether the user vehicle reaches the jammed road section according to the current position. When the user vehicle does not reach the congested road section, determining a predicted time range based on the current time point and the predicted passing time length, and judging whether the predicted time range is consistent with a target time point or not; when the predicted time range does not accord with the target time point, all parking points in a search area with the current position as the center are obtained, the target parking points are determined from all parking points in the search area, conversion information containing the target parking points and a congestion-free commuting mode is generated, the conversion information is sent to a user terminal and displayed to a user, and the user can make a decision based on the conversion information. In such a way, the latest time point (namely the target time point) of reaching the destination set by the user can be considered, and based on the target time point, when the user vehicle is estimated to be incapable of reaching the destination within the expected time range (namely the expected time range is not consistent with the target time point), a congestion-free commuting mode can be timely provided for the user so that the user can make a decision and does not need to worry about the problem of parking, and a reasonable congestion-free commuting mode suggestion can be provided for the user in a low-cost mode, so that the user can determine whether to switch the commuting mode or not to ensure that the destination is reached before the target time point, the delay on the road is avoided, and the user can also timely reach the destination in an effective way under the condition that the user already drives out and is confronted with traffic congestion.
With reference to the first aspect, in a first possible implementation manner of the first aspect, the determining a predicted time range based on the current time point and the estimated passage time includes: acquiring standard passing time when no congestion occurs in the driving path; calculating the projected time range according to the following formula:
T＝[Tmin,Tmax],
wherein T represents the predicted time range, TmaxRepresents the upper limit, T, of the predicted time rangeminRepresents the lower limit, T, of the predicted time rangenowRepresents said current point in time, TpreRepresenting said estimated passage time, TstaRepresenting the standard passage time, a and b are set constant values.
In the implementation mode, the standard passing time length when no congestion occurs in the driving path is obtained, the upper limit and the lower limit of the predicted time range are calculated by using a formula, the current time point, the predicted passing time length, the standard passing time length and other parameters are considered in the formula, and the set constants a and b can obtain more accurate and reasonable parameter values based on the calculation of a large number of data samples, so that the more accurate predicted time range is determined, and the judgment of the coincidence degree of the target time point and the predicted time range is facilitated.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the determining whether the predicted time range coincides with the target time point includes: determining a critical time point according to the upper limit of the predicted time range and the lower limit of the predicted time range, wherein the critical time point is determined in the following mode:
wherein, TcriRepresenting the critical time point, and the value range of c is [0.7, 1.0]]；
If the target time point is greater than or equal to the critical time point, the predicted time range conforms to the target time point; if the target time point is less than the critical time point, the predicted time range does not match the target time point.
In the implementation mode, a critical time point is determined through the upper limit of the predicted time range and the lower limit of the predicted time range, and if the target time point is greater than or equal to the critical time point, the predicted time range is consistent with the target time point; if the target time point is less than the threshold time point, the predicted time range does not match the target time point. The critical time point determination depends on the upper limit of the predicted time range and the lower limit of the predicted time range, and the parameter c with the value range of [0.7, 1.0] is combined, so that whether the predicted time range is consistent with the target time point or not can be accurately and effectively judged, whether the vehicle can continuously drive to the destination before the target time point or not is determined, and therefore a reasonable congestion-free commuting mode is favorably provided in advance for a user to select and implement, and the situation of delayed accidents is avoided. And the parameter c can determine a reasonable parameter value in the range by referring to different conditions (such as the number of lanes of the congested road section, the length of the congested road section, the reason of congestion and the like) so as to ensure the accuracy.
With reference to the first aspect, in a third possible implementation manner of the first aspect, the determining a target parking point from all parking points in the search area includes: determining a congestion-free commuting point in the search area, wherein the congestion-free commuting point is a station of a congestion-free commuting mode, the congestion-free commuting mode comprises subway commuting, rapid bus commuting and riding commuting, and correspondingly, the congestion-free commuting point comprises a subway station, a rapid bus station and a shared single-vehicle parking point; aiming at each parking point and each congestion-free commuting point, acquiring a distance between the parking point and the congestion-free commuting point; according to the distance between the parking point and the congestion-free commuting point, the commuting time required for reaching the destination from the current position through the parking point and the congestion-free commuting point is estimated, and the current time and the commuting time are added to obtain the arrival time corresponding to the parking point and the congestion-free commuting point; and determining target arrival time which is the earliest and is before the target time point from each arrival time, wherein the stop point corresponding to the target arrival time is the target stop point.
In the implementation mode, for each parking point (at least one of a roadside parking space, an indoor parking space and an outdoor parking space) and each congestion-free commuting point (a subway station, a fast bus station and a shared single-vehicle parking point) in a search area, acquiring a distance between the parking point and the congestion-free commuting point; according to the distance between the parking point and the congestion-free commuting point, the commuting time required for reaching the destination from the current position through the parking point and the congestion-free commuting point is estimated, and the current time and the commuting time are added to obtain the arrival time corresponding to the parking point and the congestion-free commuting point; and determining the target arrival time which is the earliest and is before the target time point from each arrival time, wherein the stop point corresponding to the target arrival time is the target stop point. The method can efficiently determine the commuting time required by the combined passing path between each stop point and each congestion-free commuting point, thereby determining the corresponding arrival time, determining the target arrival time which is the earliest and is before the target time point, so that a user can make a decision whether to arrive in time and select the congestion-free commuting mode to complete the rest distance to the destination or not, and arrive at the destination in time, and the problem that the user who drives at present can only block the road and delay the matters when facing congestion can be well solved.
With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner of the first aspect, the generating conversion information that includes the target parking point and a congestion free commute manner includes: and generating conversion information containing the target parking point according to the target parking point, the congestion-free commuting point corresponding to the target parking point, the target arrival time commonly corresponding to the target parking point and the congestion-free commuting point, and the target commuting path corresponding to the target parking point and the congestion-free commuting point.
In the implementation mode, conversion information including the target parking point is generated according to the target parking point, the congestion-free commuting point corresponding to the target parking point, the target arrival time corresponding to the target parking point and the congestion-free commuting point, the target parking point and the target commuting path corresponding to the congestion-free commuting point. Therefore, the user can conveniently check the conversion information on the user terminal, and the decision can be made in time.
In a second aspect, an embodiment of the present application provides a parking service recommendation device based on big data, which is applied to a server and includes: the information acquisition unit is used for acquiring the current position, a driving path, a current time point and a target time point of a user vehicle from a user terminal, wherein the driving path represents a passing path from a departure place to a destination, and the target time point represents the latest time point which is preset by a user and reaches the destination; the information processing unit is used for acquiring a jammed road section and estimated passing time in the driving path and determining whether the user vehicle reaches the jammed road section or not according to the current position; the information processing unit is further configured to determine a predicted time range based on the current time point and the predicted passing time length when the user vehicle does not reach the congested road section, and determine whether the predicted time range coincides with the target time point; the information processing unit is further configured to, when the predicted time range does not coincide with the target time point, acquire all parking points in a search area centered on the current position, determine a target parking point from all parking points in the search area, generate conversion information including the target parking point and a congestion-free commuting manner, and send the conversion information to the user terminal to be displayed to the user, so that the user makes a decision based on the conversion information.
In a third aspect, an embodiment of the present application provides a storage medium, where the storage medium includes a stored program, where, when the program runs, a device where the storage medium is located is controlled to execute the parking service recommendation method based on big data according to any one of the first aspect or possible implementation manners of the first aspect.
In a fourth aspect, an embodiment of the present application provides a server, including a memory and a processor, where the memory is configured to store information including program instructions, and the processor is configured to control execution of the program instructions, where the program instructions are loaded and executed by the processor to implement the big-data based parking service recommendation method according to the first aspect or any one of possible implementation manners of the first aspect.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a flowchart of a parking service recommendation method based on big data according to an embodiment of the present application.
Fig. 2 is a block diagram of a parking service recommendation device based on big data according to an embodiment of the present application.
Fig. 3 is a block diagram of a server according to an embodiment of the present disclosure.
Icon: 10-big data based parking service recommendation means; 11-an information acquisition unit; 12-an information processing unit; 20-a server; 21-a memory; 22-a communication module; 23-a bus; 24-a processor.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for recommending parking services based on big data according to an embodiment of the present disclosure.
The big data-based parking service recommendation method can be applied to a server and executed by the server. In the present embodiment, the big data-based parking service recommendation method may include step S10, step S20, step S30, and step S40.
In this embodiment, when a user drives to go out, parameters such as a destination and a target time point may be input on a user terminal (e.g., a smart phone, a tablet computer, a vehicle-mounted smart terminal, etc.), the user terminal may further obtain a departure place (and a current position) of the user vehicle through positioning (or user input), and the server may determine a corresponding driving path based on the destination and the departure place and return the driving path to the user terminal, so as to navigate the user.
In the driving process of a user, in order to provide reasonable and effective solutions for the user in time when congestion occurs, the server can execute a parking service recommendation method based on big data.
In the present embodiment, the server may perform step S10.
Step S10: the method comprises the steps of obtaining the current position, the driving path, the current time point and the target time point of a user vehicle from a user terminal, wherein the driving path represents a passing path from a starting place to a destination, and the target time point represents the latest time point preset by a user and reaching the destination.
In this embodiment, the server may obtain the current position, the driving path, the current time point, and the target time point of the user vehicle through the user terminal. The driving route here means a passing route from a departure point to a destination (i.e., a driving route for a user to navigate), and the target time point means a latest time point preset by the user to reach the destination.
For example, the user's departure point is a first place, the destination point is a second place, the current location is a second road segment between the first place and the second place (for example, six road segments are shared between the first place and the second place in the driving route, and the first road segment, the second road segment, the third road segment, the fourth road segment, the fifth road segment and the sixth road segment are respectively used), and the current time point is 10: 05, target time points 11: 00.
after acquiring the current location, the driving path, the current time point, and the target time point of the user vehicle, the server may perform step S20.
Step S20: and acquiring a jammed road section and estimated passing time in the driving path, and determining whether the user vehicle reaches the jammed road section according to the current position.
In this embodiment, the server may obtain a passing state of each road segment in the driving path, and if there is a road segment in a congestion state, the server may determine that the road segment in the congestion state is the congestion road segment, and the server may further estimate, based on the congestion road segment in the driving path, a time required for passing the remaining distance, so as to obtain the estimated passing time. Of course, the server may also obtain the congested road segment and the estimated transit time determined by other servers, for example, the server may cooperate with an existing service provider to obtain corresponding real-time driving data (for example, various driving parameters such as a driving path, the congested road segment, the estimated transit time, and the like) from the server of the service provider, which is not limited herein.
The server may then determine whether the user vehicle has reached the congested road segment based on the current location of the user vehicle.
When the user vehicle does not reach the congested road segment (not only the navigation driving route being used by the user, but also other driving routes that can reach the destination are congested), the server may perform step S30.
Step S30: and determining a predicted time range based on the current time point and the predicted passing time length, and judging whether the predicted time range is consistent with the target time point.
In this embodiment, the server may determine the expected time range based on the current time point and the estimated passage time.
For example, the server may obtain a standard transit time length when there is no congested road section in the traffic route (for example, the standard transit time length may be an average transit time length calculated by using a large amount of traffic data when there is no congested road section in the traffic route, or may be a reference transit time length obtained from a server of another service provider). In addition, the standard passing time length here may be a standard passing time length of the whole driving route, or may be a standard passing time length of the remaining route in the driving route. In this embodiment, the standard passing time of the remaining route in the driving route is taken as an example, but not limited thereto, and in other embodiments, the standard passing time of the whole driving route may also be obtained, and then the standard passing time of the remaining route in the driving route is determined based on the standard passing time of the whole driving route and the time that has been spent, which is not limited herein.
After the standard passing time length when no congestion road section exists in the driving path is obtained, the server can calculate the predicted time range according to the following formula:
T＝[Tmin,Tmax], (3)
wherein T represents the expected time range, TmaxDenotes the upper limit of the expected time range, TminRepresents the lower limit of the expected time range, TnowIndicates the current point in time, TpreIndicates the estimated passage time (estimated passage time of the remaining route), TstaIndicates a standard passage time (standard passage time of the remaining route), a and b are set constantAmount of the compound (A).
Here, the values of a and b may be the same or different, and in practical cases, the values are usually different, and the value of a is usually slightly larger than the value of b. For example, a has a value of 21 and b has a value of 17.
The method comprises the steps of obtaining standard passing time (standard passing time of the rest distance in the driving path) when no congestion occurs in the driving path, calculating the upper limit and the lower limit of a predicted time range by using a formula, taking parameters such as a current time point, predicted passing time, standard passing time and the like into consideration in the formula, and obtaining accurate and reasonable parameter values by the set constants a and b based on calculation of a large number of data samples, so that a precise predicted time range is determined, and the method is favorable for judging the coincidence degree of the target time point and the predicted time range. Moreover, the values of the constants a and b, namely the value of a is usually slightly larger than that of b, uncertainty caused by congestion (such as scratch and rear-end collision and the like, further aggravation of congestion) can be better considered, and a higher estimation space is provided for the upper limit of the estimated time range, so that the actual situation is better met.
After determining the projected time range, the server may further determine whether the projected time range coincides with the target time point.
For example, the server may determine the critical time point according to an upper limit of the predicted time range and a lower limit of the predicted time range. Here, the server determines the critical time point in the following manner:
wherein, TcriRepresenting the critical time point, the value range of c is [0.7, 1.0]]。
In this embodiment, if the target time point is greater than or equal to the critical time point, it indicates that the predicted time range matches the target time point. If the target time point is less than the threshold time point, it indicates that the predicted time range does not match the target time point. The value of c may be determined by referring to different routes and congestion conditions (for example, the number of lanes of a congested link, the length of the congested link, the cause of congestion, etc.), and is not limited herein.
Determining a critical time point through the upper limit of the predicted time range and the lower limit of the predicted time range, wherein if the target time point is more than or equal to the critical time point, the predicted time range is consistent with the target time point; if the target time point is less than the threshold time point, the predicted time range does not match the target time point. The critical time point determination depends on the upper limit of the predicted time range and the lower limit of the predicted time range, and the parameter c with the value range of [0.7, 1.0] is combined, so that whether the predicted time range is consistent with the target time point or not can be accurately and effectively judged, whether the vehicle can continuously drive to the destination before the target time point or not is determined, and therefore a reasonable congestion-free commuting mode is favorably provided in advance for a user to select and implement, and the situation of delayed accidents is avoided. And the parameter c can determine a reasonable parameter value in the range by referring to different conditions (such as the number of lanes of the congested road section, the length of the congested road section, the reason of congestion and the like) so as to ensure the accuracy.
It should be noted that, in this embodiment, for the step S30, a case when the user vehicle does not reach the congested road segment is described, and for the user vehicle already in the congested road segment, the server may also use the method of step S30 to perform, and of course, other methods may also be used to determine the suggestion of an effective congestion-free commuting method, but the user has higher difficulty in performing (because it is usually difficult to drive to the corresponding parking point in time when already in the congested road segment), but this should not be considered as a limitation of the present application.
When the predicted time range does not coincide with the target time point (i.e., it indicates that the user vehicle has a low possibility of reaching the destination before the target time point), the server may perform step S40.
Step S40: and acquiring all parking points in a search area with the current position as the center, determining a target parking point from all parking points in the search area, generating conversion information containing the target parking point and a congestion-free commuting mode, sending the conversion information to the user terminal, and displaying the conversion information to the user so that the user can make a decision based on the conversion information.
In this embodiment, the server may obtain all parking points in a search area (for example, within a radius of 1000 meters, or with a distance between the current location and a congested road segment as a radius, or when the distance between the current location and the congested road segment is greater than 1000 meters, the distance is taken as the radius, otherwise, 1000 meters is taken as the radius, so that when the distance is farther from the congested road segment, more commuting time may be saved by driving). The parking spot herein may include at least one of a roadside parking space, an indoor parking lot, and an outdoor parking lot.
The server may then determine a target parking point from all parking points within the search area.
Illustratively, the server can determine congestion-free commuting points in the search area, wherein the congestion-free commuting points are stations of a congestion-free commuting mode, the congestion-free commuting mode comprises subway commuting, rapid bus commuting and riding commuting, and correspondingly the congestion-free commuting points can comprise subway stations, rapid bus stations and shared bus parking points (shared buses, shared battery cars and the like).
The server can obtain the distance between the parking point and the congestion-free commuting point aiming at each parking point and each congestion-free commuting point, and then estimate the commuting time required for reaching the destination from the current position through the parking point and the congestion-free commuting point according to the distance between the parking point and the congestion-free commuting point. Because the congestion-free commuting mode is hardly influenced by the congestion road section, the estimated commuting time is accurate.
Then, the server can add the current time and the commute duration to obtain the arrival time corresponding to the parking point and the congestion-free commute point together. The server can further determine a target arrival time (one or more target arrival times can be provided herein, for example, an optional mode is provided for each different congestion-free commuting mode, where one target arrival time corresponds to a riding mode, one target arrival time corresponds to a subway mode, and one target arrival time corresponds to a bus rapid transit mode), where a stop point corresponding to the target arrival time is a target stop point.
Aiming at each parking point (at least one of a roadside parking space, an indoor parking lot and an outdoor parking lot) and each non-congestion commuting point (a subway station, a fast bus station and a shared bicycle parking point) in a search area, acquiring a distance between the parking point and the non-congestion commuting point; according to the distance between the parking point and the congestion-free commuting point, the commuting time required for reaching the destination from the current position through the parking point and the congestion-free commuting point is estimated, and the current time and the commuting time are added to obtain the arrival time corresponding to the parking point and the congestion-free commuting point; and determining the target arrival time which is the earliest and is before the target time point from each arrival time, wherein the stop point corresponding to the target arrival time is the target stop point. The method can efficiently determine the commuting time required by the combined passing path between each stop point and each congestion-free commuting point, thereby determining the corresponding arrival time, determining the target arrival time which is the earliest and is before the target time point, so that a user can make a decision whether to arrive in time and select the congestion-free commuting mode to complete the rest distance to the destination or not, and arrive at the destination in time, and the problem that the user who drives at present can only block the road and delay the matters when facing congestion can be well solved.
After the target parking point is determined, the server may generate conversion information including the target parking point and a congestion-free commuting manner. For example, the server may generate the conversion information including the target parking point according to the target parking point, the congestion-free commute point corresponding to the target parking point, the target arrival time corresponding to the target parking point and the congestion-free commute point, and the target parking point and the target commute path corresponding to the congestion-free commute point. The server can then send the conversion information to the user terminal for presentation to the user for the user to make decisions based on the conversion information. The mode is very convenient for the user to check the conversion information on the user terminal, thereby making a decision in time.
Certainly, in this embodiment, in order to facilitate the decision of the user, the server may further obtain the remaining parking space information, the parking service charging information, and the like of the target parking spot, and add the relevant information (the remaining parking space information, the parking service charging information, and the like) to the conversion information together and send the conversion information to the user terminal.
Referring to fig. 2, based on the same inventive concept, an embodiment of the present application further provides a parking service recommendation device 10 based on big data, applied to a server, including:
the information acquiring unit 11 is configured to acquire a current position of a user vehicle, a driving route, a current time point, and a target time point from a user terminal, where the driving route represents a passing route from a departure point to a destination, and the target time point represents a latest time point preset by a user to reach the destination.
And the information processing unit 12 is configured to acquire a congested road segment and an estimated passage time in the driving route, and determine whether the user vehicle reaches the congested road segment according to the current position.
The information processing unit 12 is further configured to determine a predicted time range based on the current time point and the predicted passing time length when the user vehicle does not reach the congested road section, and determine whether the predicted time range coincides with the target time point.
The information processing unit 12 is further configured to, when the predicted time range does not coincide with the target time point, acquire all parking spots in a search area centered on the current position, determine a target parking spot from all parking spots in the search area, generate conversion information including the target parking spot and a congestion-free commuting manner, and send the conversion information to the user terminal to be displayed to the user, so that the user makes a decision based on the conversion information.
In this embodiment, the information processing unit 12 is further configured to obtain a standard transit time length when there is no congested road segment in the driving route; calculating the projected time range according to the following formula:
T＝[Tmin,Tmaxl,
wherein T represents the predicted time range, TmaxRepresents the upper limit, T, of the predicted time rangeminRepresents the lower limit, T, of the predicted time rangenowRepresents said current point in time, TpreRepresenting said estimated passage time, TstaRepresenting the standard passage time, a and b are set constant values.
In this embodiment, the information processing unit 12 is further configured to determine a critical time point according to an upper limit of the predicted time range and a lower limit of the predicted time range, where the critical time point is determined as follows:
wherein, TcriRepresenting the critical time point, and the value range of c is [0.7, 1.0]](ii) a If the target time point is greater than or equal to the critical time point, the predicted time range conforms to the target time point; if the target time point is less than the critical time point, the predicted time range does not match the target time point.
In this embodiment, the parking point includes at least one of a roadside parking space, an indoor parking lot and an outdoor parking lot, and the information processing unit 12 is further configured to determine a congestion-free commuting point in the search area, where the congestion-free commuting point is a station of a congestion-free commuting mode, the congestion-free commuting mode includes subway commuting, fast bus commuting and riding commuting, and correspondingly, the congestion-free commuting point includes a subway station, a fast bus station and a shared single parking point; aiming at each parking point and each congestion-free commuting point, acquiring a distance between the parking point and the congestion-free commuting point; according to the distance between the parking point and the congestion-free commuting point, the commuting time required for reaching the destination from the current position through the parking point and the congestion-free commuting point is estimated, and the current time and the commuting time are added to obtain the arrival time corresponding to the parking point and the congestion-free commuting point; and determining target arrival time which is the earliest and is before the target time point from each arrival time, wherein the stop point corresponding to the target arrival time is the target stop point.
In this embodiment, the information processing unit 12 is further configured to generate conversion information including the target parking point according to the target parking point, the congestion-free commute point corresponding to the target parking point, the target arrival time commonly corresponding to the target parking point and the congestion-free commute point, and the target parking point and the target commute path corresponding to the congestion-free commute point.
Referring to fig. 3, fig. 3 is a block diagram of a server 20 according to an embodiment of the present disclosure.
In this embodiment, the server 20 may be a cloud server, a network server, a server cluster, and the like, which is not limited herein.
Illustratively, the server 20 may include: a communication module 22 connected to the outside world via a network, one or more processors 24 for executing program instructions, a bus 23, and a different form of memory 21, such as a disk, ROM, or RAM, or any combination thereof. The memory 21, the communication module 22, and the processor 24 may be connected by a bus 23.
Illustratively, the memory 21 has stored therein a program. Processor 24 may call and run these programs from memory 21 so that the big data based parking service recommendation method may be implemented by running the programs.
The embodiment of the application further provides a storage medium, which includes a stored program, and when the program runs, the device where the storage medium is located is controlled to execute the parking service recommendation method based on big data in the embodiment.
In summary, the embodiments of the present application provide a parking service recommendation method, apparatus, medium, and server based on big data, in which a current position, a driving path (a passing path from a departure location to a destination), a current time point, and a target time point (a latest time point for reaching the destination preset by a user) of a vehicle of the user are obtained from a user terminal; and obtaining a jammed road section and estimated passing time in the driving path, and determining whether the user vehicle reaches the jammed road section according to the current position. When the user vehicle does not reach the congested road section, determining a predicted time range based on the current time point and the predicted passing time length, and judging whether the predicted time range is consistent with a target time point or not; when the predicted time range does not accord with the target time point, all parking points in a search area with the current position as the center are obtained, the target parking points are determined from all parking points in the search area, conversion information containing the target parking points and a congestion-free commuting mode is generated, the conversion information is sent to a user terminal and displayed to a user, and the user can make a decision based on the conversion information. In such a way, the latest time point (namely the target time point) of reaching the destination set by the user can be considered, and based on the target time point, when the user vehicle is estimated to be incapable of reaching the destination within the expected time range (namely the expected time range is not consistent with the target time point), a congestion-free commuting mode can be timely provided for the user so that the user can make a decision and does not need to worry about the problem of parking, and a reasonable congestion-free commuting mode suggestion can be provided for the user in a low-cost mode, so that the user can determine whether to switch the commuting mode or not to ensure that the destination is reached before the target time point, the delay on the road is avoided, and the user can also timely reach the destination in an effective way under the condition that the user already drives out and is confronted with traffic congestion.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. A parking service recommendation method based on big data is applied to a server and comprises the following steps:
the method comprises the steps that the current position, a driving path, a current time point and a target time point of a user vehicle are obtained from a user terminal, wherein the driving path represents a passing path from a starting place to a destination, and the target time point represents the latest time point preset by a user and reaching the destination;
acquiring a jammed road section and an estimated passing time length in the driving path, and determining whether the user vehicle reaches the jammed road section or not according to the current position;
when the user vehicle does not reach the congested road section, determining a predicted time range based on the current time point and the predicted passing time length, and judging whether the predicted time range is consistent with the target time point;
when the estimated time range does not accord with the target time point, all parking points in a search area with the current position as the center are obtained, the target parking points are determined from all parking points in the search area, conversion information including the target parking points and a congestion-free commuting mode is generated, the conversion information is sent to the user terminal and displayed to the user, and the user can make a decision based on the conversion information.
2. The big data based parking service recommendation method according to claim 1, wherein the determining a predicted time range based on the current time point and the predicted passage time length comprises:
acquiring standard passing time when no congestion occurs in the driving path;
calculating the projected time range according to the following formula:
T＝[Tmin，Tmax]，
wherein T represents the predicted time range, TmaxRepresents the upper limit, T, of the predicted time rangeminRepresents the lower limit, T, of the predicted time rangenowRepresents said current point in time, TpreRepresenting said estimated passage time, TstaRepresenting the standard passage time, a and b are set constant values.
3. The big data-based parking service recommendation method according to claim 2, wherein the determining whether the predicted time range coincides with the target time point comprises:
determining a critical time point according to the upper limit of the predicted time range and the lower limit of the predicted time range, wherein the critical time point is determined in the following mode:
wherein, TcriRepresenting the critical time point, and the value range of c is [0.7, 1.0]]；
If the target time point is greater than or equal to the critical time point, the predicted time range conforms to the target time point; if the target time point is less than the critical time point, the predicted time range does not match the target time point.
4. The big data-based parking service recommendation method according to claim 1, wherein the parking spots comprise at least one of roadside parking spots, indoor parking spots and outdoor parking spots, and the determining the target parking spot from all parking spots in the search area comprises:
determining a congestion-free commuting point in the search area, wherein the congestion-free commuting point is a station of a congestion-free commuting mode, the congestion-free commuting mode comprises subway commuting, rapid bus commuting and riding commuting, and correspondingly, the congestion-free commuting point comprises a subway station, a rapid bus station and a shared single-vehicle parking point;
aiming at each parking point and each congestion-free commuting point, acquiring a distance between the parking point and the congestion-free commuting point;
according to the distance between the parking point and the congestion-free commuting point, the commuting time required for reaching the destination from the current position through the parking point and the congestion-free commuting point is estimated, and the current time and the commuting time are added to obtain the arrival time corresponding to the parking point and the congestion-free commuting point;
and determining target arrival time which is the earliest and is before the target time point from each arrival time, wherein the stop point corresponding to the target arrival time is the target stop point.
5. The big-data-based parking service recommendation method according to claim 4, wherein the generating of the conversion information containing the target parking spot and the congestion-free commute manner comprises:
and generating conversion information containing the target parking point according to the target parking point, the congestion-free commuting point corresponding to the target parking point, the target arrival time commonly corresponding to the target parking point and the congestion-free commuting point, and the target commuting path corresponding to the target parking point and the congestion-free commuting point.
6. The parking service recommendation device based on big data is applied to a server and comprises the following components:
the information acquisition unit is used for acquiring the current position, a driving path, a current time point and a target time point of a user vehicle from a user terminal, wherein the driving path represents a passing path from a departure place to a destination, and the target time point represents the latest time point which is preset by a user and reaches the destination;
the information processing unit is used for acquiring a jammed road section and estimated passing time in the driving path and determining whether the user vehicle reaches the jammed road section or not according to the current position;
the information processing unit is further configured to determine a predicted time range based on the current time point and the predicted passing time length when the user vehicle does not reach the congested road section, and determine whether the predicted time range coincides with the target time point;
the information processing unit is further configured to, when the predicted time range does not coincide with the target time point, acquire all parking points in a search area centered on the current position, determine a target parking point from all parking points in the search area, generate conversion information including the target parking point and a congestion-free commuting manner, and send the conversion information to the user terminal to be displayed to the user, so that the user makes a decision based on the conversion information.
7. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, the storage medium is controlled to execute the big data based parking service recommendation method according to any one of claims 1 to 5.
8. A server, comprising a memory for storing information including program instructions and a processor for controlling execution of the program instructions, the program instructions being loaded and executed by the processor to implement the big-data based parking service recommendation method of any of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110259468.2A CN112862214A (en) | 2021-03-10 | 2021-03-10 | Parking service recommendation method, device, medium and server based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110259468.2A CN112862214A (en) | 2021-03-10 | 2021-03-10 | Parking service recommendation method, device, medium and server based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112862214A true CN112862214A (en) | 2021-05-28 |
Family
ID=75993836
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110259468.2A Pending CN112862214A (en) | 2021-03-10 | 2021-03-10 | Parking service recommendation method, device, medium and server based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112862214A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114999015A (en) * | 2022-08-03 | 2022-09-02 | 济南澎湃信息技术有限公司 | Vehicle management system |
| CN115188211A (en) * | 2022-07-06 | 2022-10-14 | 王丽娟 | Intelligent automobile parking system |
| CN116011600A (en) * | 2023-01-17 | 2023-04-25 | 北京交通发展研究院 | Reservation method, device and system for congestion-free travel, electronic equipment and medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105788335A (en) * | 2016-04-05 | 2016-07-20 | 广东欧珀移动通信有限公司 | Navigation method and terminal |
| CN108133613A (en) * | 2017-12-19 | 2018-06-08 | 深圳先进技术研究院 | A kind of real-time release road-surface concrete Service Index method and system |
| CN110110981A (en) * | 2019-04-26 | 2019-08-09 | 重庆第二师范学院 | A kind of credit rating Default Probability estimates and method for prewarning risk |
| WO2019174167A1 (en) * | 2018-03-13 | 2019-09-19 | 山东科技大学 | Internet+ based intelligent stereo garage system and vehicle access method |
| CN110609962A (en) * | 2019-08-14 | 2019-12-24 | 中国平安财产保险股份有限公司 | Intelligent recommendation method, device and equipment for trip parking scheme and storage medium |
| US20200334583A1 (en) * | 2017-10-31 | 2020-10-22 | Grand Performance Online Pty Ltd | Autonomous and integrated system, method and computer program for dynamic optimisation and allocation of resources for defined spaces and time periods |
| CN112446590A (en) * | 2020-11-05 | 2021-03-05 | 重庆第二师范学院 | Comprehensive student management system, method, medium and terminal |
-
2021
- 2021-03-10 CN CN202110259468.2A patent/CN112862214A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105788335A (en) * | 2016-04-05 | 2016-07-20 | 广东欧珀移动通信有限公司 | Navigation method and terminal |
| US20200334583A1 (en) * | 2017-10-31 | 2020-10-22 | Grand Performance Online Pty Ltd | Autonomous and integrated system, method and computer program for dynamic optimisation and allocation of resources for defined spaces and time periods |
| CN108133613A (en) * | 2017-12-19 | 2018-06-08 | 深圳先进技术研究院 | A kind of real-time release road-surface concrete Service Index method and system |
| WO2019174167A1 (en) * | 2018-03-13 | 2019-09-19 | 山东科技大学 | Internet+ based intelligent stereo garage system and vehicle access method |
| CN110110981A (en) * | 2019-04-26 | 2019-08-09 | 重庆第二师范学院 | A kind of credit rating Default Probability estimates and method for prewarning risk |
| CN110609962A (en) * | 2019-08-14 | 2019-12-24 | 中国平安财产保险股份有限公司 | Intelligent recommendation method, device and equipment for trip parking scheme and storage medium |
| CN112446590A (en) * | 2020-11-05 | 2021-03-05 | 重庆第二师范学院 | Comprehensive student management system, method, medium and terminal |
Non-Patent Citations (2)
| Title |
|---|
| 云美萍等: "通勤出行中停车换乘选择行为分析与建模", 《同济大学学报（自然科学版）》, pages 1825 - 1830 * |
| 刘俐等: "上海典型停车换乘选择行为研究", 《交通信息与安全》, pages 11 - 16 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115188211A (en) * | 2022-07-06 | 2022-10-14 | 王丽娟 | Intelligent automobile parking system |
| CN114999015A (en) * | 2022-08-03 | 2022-09-02 | 济南澎湃信息技术有限公司 | Vehicle management system |
| CN116011600A (en) * | 2023-01-17 | 2023-04-25 | 北京交通发展研究院 | Reservation method, device and system for congestion-free travel, electronic equipment and medium |
| CN116011600B (en) * | 2023-01-17 | 2023-06-23 | 北京交通发展研究院 | Reservation method, device and system for congestion-free travel, electronic equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112862214A (en) | Parking service recommendation method, device, medium and server based on big data | |
| US20220212671A1 (en) | Method for vehicle lane changing control, device, storage medium, and program product | |
| US20200210905A1 (en) | Systems and Methods for Managing Networked Vehicle Resources | |
| CN110491147B (en) | Traffic information processing method, traffic information processing device and terminal equipment | |
| CN112149855A (en) | Order allocation method and device | |
| CN107845253B (en) | Reservation order early warning method and server | |
| CN107784412B (en) | Automatic order matching processing method and server | |
| CN113335292B (en) | Vehicle control method, device, equipment and computer storage medium | |
| CN111366166B (en) | Navigation path planning method and device | |
| CN113393137B (en) | Scheduling sharing system based on Internet of vehicles | |
| CN111189451A (en) | Community charging area route guiding method, system, computer device and storage medium | |
| CN112629524A (en) | Travel path recommendation management method and device and electronic equipment | |
| US20220341744A1 (en) | Order Management Method Applied to Electric Vehicle and Apparatus | |
| CN111341093A (en) | Control method, device, equipment and storage medium of motorcade | |
| CN107786600B (en) | Driver terminal recommendation processing method and server | |
| CN114256523A (en) | Charging control method and device for charging pile, electronic equipment and storage medium | |
| CN109916420B (en) | Vehicle navigation method and related device | |
| CN110910191A (en) | Car pooling order generation method and equipment | |
| JP2018139066A (en) | Task priority setting system, task priority setting method and program | |
| CN115218912A (en) | Navigation duration estimation method and device, vehicle and equipment | |
| CN113935565A (en) | Automatic distribution method and device of public transport means based on user requirements | |
| CN111524389A (en) | Vehicle driving method and device | |
| CN112783945A (en) | Bus stop judgment method and device | |
| US20240070581A1 (en) | Information processing method, information processing apparatus, information processing system, and non-transitory computer readable medium | |
| CN115798247B (en) | Intelligent public transportation cloud platform based on big data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210528 |
|
| RJ01 | Rejection of invention patent application after publication |