CN112819226A - Parking lot recommendation method and device, storage medium and terminal - Google Patents

Parking lot recommendation method and device, storage medium and terminal Download PDF

Info

Publication number
CN112819226A
CN112819226A CN202110142024.0A CN202110142024A CN112819226A CN 112819226 A CN112819226 A CN 112819226A CN 202110142024 A CN202110142024 A CN 202110142024A CN 112819226 A CN112819226 A CN 112819226A
Authority
CN
China
Prior art keywords
recommended
parking lot
recommendation
parking
scheme
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.)
Granted
Application number
CN202110142024.0A
Other languages
Chinese (zh)
Other versions
CN112819226B (en
Inventor
张宁
夏曙东
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Qianfang Jietong Technology Co ltd
Beijing China Transinfo Stock Co ltd
Original Assignee
Qianfang Jietong Technology Co ltd
Beijing China Transinfo Stock Co ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Qianfang Jietong Technology Co ltd, Beijing China Transinfo Stock Co ltd filed Critical Qianfang Jietong Technology Co ltd
Priority to CN202110142024.0A priority Critical patent/CN112819226B/en
Publication of CN112819226A publication Critical patent/CN112819226A/en
Application granted granted Critical
Publication of CN112819226B publication Critical patent/CN112819226B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • 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

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Development Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a parking lot recommendation method, a device, a storage medium and a terminal, wherein the method comprises the following steps: inputting at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of a target user for going to and returning to the parking lot to be recommended into a recommendation model, and outputting at least one scheme to be recommended; according to the method and the device, a recommendation scheme is screened from at least one scheme to be recommended according to input keywords in an input instruction of a target user, and the recommendation scheme is pushed to terminal equipment of the target user, so that by adopting the embodiment of the application, the current parking lot saturation data of the parking lot to be recommended is input into a recommendation model in real time, and the finally obtained recommendation scheme has real-time performance and is more consistent with the actual situation; because the input keywords related to the user are introduced, the finally screened recommendation scheme better conforms to the preference degree of the user, better interaction with the user is realized, and the user experience degree is improved.

Description

Parking lot recommendation method and device, storage medium and terminal
Technical Field
The invention relates to the technical field of traffic, in particular to a parking lot recommendation method and device, a storage medium and a terminal.
Background
The traditional parking resource dynamic allocation is only based on parking indexes and does not contain road condition dynamic data. The user can only consult in advance whether the selected parking lot is saturated before arriving at the selected parking lot, but in this way, when the user arrives at the parking lot, the user still can be ensured to have free parking spaces in the arrival time, so that not only is precious time wasted, but also the parking experience of the user is influenced.
Therefore, how to provide a real-time and accurate parking lot recommendation method is a technical problem to be solved.
Disclosure of Invention
The embodiment of the application provides a parking lot recommendation method and device, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a parking lot recommendation method, where the method includes:
acquiring a recommendation model for recommending a parking lot to a target user;
inputting at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of the target user to come to and return to the parking lot to be recommended into the recommendation model, and outputting at least one scheme to be recommended, wherein the scheme to be recommended comprises first address information of the parking lot to be recommended and first arrival path information of the parking lot to be recommended;
and screening a recommended scheme from at least one scheme to be recommended according to an input keyword in the input instruction of the target user, and pushing the recommended scheme to the terminal equipment of the target user, wherein the recommended scheme comprises second address information of a recommended parking lot, second arrival path information of the recommended parking lot and recommendation degree sorting information of the recommended parking lot.
In one embodiment, the recommendation model includes a parking saturation model, a traffic congestion model, and a resource allocation model, and the method further includes:
acquiring a traffic jam index value determined based on the traffic jam model;
acquiring a parking lot saturation numerical value determined based on the parking saturation model;
inputting the traffic jam index numerical value and the parking lot saturation numerical value into the resource distribution model to obtain a plurality of distribution parameters for dynamically distributing the parking lot resources;
and determining at least one key distribution parameter from the plurality of distribution parameters, and dynamically distributing the parking lot resources according to the at least one key distribution parameter.
In one embodiment, the key allocation parameters include a parking lot recommended ranking corresponding to each parking lot to be recommended, the determining at least one key allocation parameter from the plurality of allocation parameters, and the dynamically allocating parking lot resources according to the at least one key allocation parameter includes:
and determining key distribution parameters from the plurality of distribution parameters as recommended sequencing of the parking lots corresponding to the parking lots to be recommended, and taking the plurality of first parking lots to be recommended as first recommended parking lots, wherein the recommended sequencing of the parking lots is within a preset sequencing threshold.
In one embodiment, the key allocation parameter includes the estimated average time data, the determining at least one key allocation parameter from the plurality of allocation parameters, and the dynamically allocating parking lot resources according to the at least one key allocation parameter includes:
and determining a key distribution parameter as the estimated average time consumption data from the plurality of distribution parameters, and taking a plurality of second parking lots to be recommended, of which the time consumption durations in the plurality of estimated average time consumption data are within a preset time duration threshold value, as second recommended parking lots.
In one embodiment, the key allocation parameters include saturation values of parking lots to be recommended, the determining at least one key allocation parameter from the plurality of allocation parameters, and the dynamically allocating parking lot resources according to the at least one key allocation parameter includes:
and determining key distribution parameters as saturation values of the parking lots to be recommended from the distribution parameters, and taking a plurality of third parking lots to be recommended, which are all within preset parking lot saturation threshold values, as third recommended parking lots.
In one embodiment, the traffic congestion index value determined by the traffic congestion model and the parking lot saturation value determined by the parking lot saturation model are in an inverse correlation relationship.
In one embodiment, the recommended scheme further includes parking lot real-time saturation information and real-time idle parking space information corresponding to the recommended parking lot.
In a second aspect, an embodiment of the present application provides a parking lot recommendation device, where the device includes:
the system comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring a recommendation model for recommending a parking lot to a target user;
the processing unit is used for inputting at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of the target user for going to and fro the parking lot to be recommended into the recommendation model obtained by the obtaining unit and outputting at least one scheme to be recommended, wherein the scheme to be recommended comprises first address information of the parking lot to be recommended and first arrival path information of the parking lot to be recommended;
the screening unit is used for screening a recommended scheme from at least one to-be-recommended scheme obtained by processing of the processing unit according to an input keyword in the input instruction of the target user, wherein the recommended scheme comprises second address information of a recommended parking lot, second arrival path information of the recommended parking lot and recommendation degree ranking information of the recommended parking lot;
and the pushing unit is used for pushing the recommendation scheme screened by the screening unit to the terminal equipment of the target user.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of a target user for going to and returning to the parking lot to be recommended are input into a recommendation model, and at least one scheme to be recommended is output; according to the method and the device, a recommendation scheme is screened from at least one scheme to be recommended according to input keywords in an input instruction of a target user, and the recommendation scheme is pushed to terminal equipment of the target user, so that by adopting the embodiment of the application, the current parking lot saturation data of the parking lot to be recommended is input into a recommendation model in real time, and the finally obtained recommendation scheme has real-time performance and is more consistent with the actual situation; in addition, because the input keywords related to the user are introduced, the finally screened recommendation scheme better conforms to the preference degree of the user, better interaction with the user can be realized, and the user experience degree is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flowchart of a parking lot recommendation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a parking lot recommendation device according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The existing parking lot recommendation method has time delay, and a real-time and accurate parking length recommendation scheme cannot be provided. Therefore, the application provides a parking lot recommendation method, a parking lot recommendation device, a storage medium and a terminal, so as to solve the problems in the related art. According to the technical scheme, at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of a target user for going to and returning to the parking lot to be recommended are input into a recommendation model, and at least one scheme to be recommended is output; according to the method and the device, a recommendation scheme is screened from at least one scheme to be recommended according to input keywords in an input instruction of a target user, and the recommendation scheme is pushed to terminal equipment of the target user, so that by adopting the embodiment of the application, the current parking lot saturation data of the parking lot to be recommended is input into a recommendation model in real time, and the finally obtained recommendation scheme has real-time performance and is more consistent with the actual situation; in addition, because the input keywords related to the user are introduced, the finally screened recommendation scheme better conforms to the preference of the user, better interaction with the user can be realized, and the user experience is improved.
The parking lot recommendation method provided by the embodiment of the present application will be described in detail below with reference to fig. 1. The parking lot recommendation method can be realized by relying on a computer program and can be operated on a parking lot recommendation device. The computer program may be integrated into the application or may run as a separate tool-like application. The user terminal in the embodiment of the present application includes, but is not limited to: personal computers, tablet computers, handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and the like. The user terminals may be called different names in different networks, for example: user equipment, access terminal, subscriber unit, subscriber station, mobile station, remote terminal, mobile device, user terminal, wireless communication device, user agent or user equipment, cellular telephone, cordless telephone, Personal Digital Assistant (PDA), terminal equipment in a 5G network or future evolution network, and the like.
As shown in fig. 1, the flowchart of the parking lot recommendation method provided in the embodiment of the present application is schematically illustrated, and the parking lot recommendation method is applied to a server side; as shown in fig. 1, the parking lot recommendation method according to the embodiment of the present application may include the following steps:
s101, acquiring a recommendation model for recommending a parking lot to a target user;
in the embodiment of the application, recommendation algorithms of corresponding recommendation models can be configured according to different application scenarios, and in a specific application scenario, the recommendation algorithms corresponding to the adopted recommendation models are specifically as follows:
p ═ (T1+ T2)/F; wherein, P is a numerical value corresponding to the recommended parking lot degree ranking information, T1 is an estimated time duration for the target user to travel to the recommended parking lot, T2 is an estimated time duration for the target user to walk from the recommended parking lot to the destination, and F is the current parking lot saturation of the recommended parking lot.
The recommendation algorithm in one application scenario is just listed, and can be adjusted according to the requirements of different application scenarios, which is not described herein again.
In the embodiment of the present application, the method for constructing the recommendation model according to the recommendation algorithm is a conventional method, and details are not repeated herein.
S102, inputting at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of a target user to come to and return to the parking lot to be recommended into a recommendation model, and outputting at least one scheme to be recommended, wherein the scheme to be recommended comprises first address information of the parking lot to be recommended and first arrival path information of the parking lot to be recommended;
in the embodiment of the application, the estimated time-use data at ordinary times is also obtained based on the congestion index.
In the embodiment of the present application, the traffic congestion index may include the following indexes, which are specifically described as follows:
the shape of the intersection: nailed intersections, crossroads, bends, etc.;
timing of the signal lamp: red light duration, yellow light duration, green light duration;
road indexes are as follows: single lane, double lane, multiple lane, etc.;
the traffic flow index (vehicle/hour) ═ traffic flow rate × number of lanes;
the driving speed (km/h) is the driving time.
The above lists only common traffic jam indexes, and other traffic jam indexes can be introduced according to the requirements of different application scenarios, which is not described herein again.
In one possible implementation, the recommendation model includes a parking saturation model, a traffic congestion model, and a resource allocation model, and the method further includes the steps of:
acquiring a traffic jam index value determined based on a traffic jam model;
acquiring a parking lot saturation numerical value determined based on a parking saturation model;
inputting the traffic jam index numerical value and the parking lot saturation numerical value into a resource distribution model to obtain a plurality of distribution parameters for dynamically distributing the parking lot resources;
determining at least one key distribution parameter from the plurality of distribution parameters, and dynamically distributing the parking lot resources according to the at least one key distribution parameter; therefore, the parking lot resources can be dynamically allocated according to at least one key allocation parameter, and the diversity of dynamic allocation schemes is realized.
In the embodiment of the present application, the following description is made for the parking saturation model:
according to a parking saturation formula of a single parking lot, taking a cycle as one week, combining periodic changes of parking amount in different time intervals (counted by hours) every day, refining a parking saturation change rule (including change forms of different time intervals such as early and late peaks of a working day, weekends and the like) every day to form saturation indexes in different forms;
according to the parking space amount in the region plot, counting parking saturation indexes of different time points (according to hours) every day in the period, and displaying corresponding saturation changes according to a saturation range standard to form a parking saturation model, wherein the parking saturation model is a region parking saturation model.
Meanwhile, the parking saturation model provided by the embodiment of the application can be used for machine learning according to the continuous change of the parking space dynamic allocation, and updating and prejudging the parking space saturation at different time points.
In the embodiment of the present application, the model saturation range standard adopted by the parking saturation model is specifically as follows:
the parking saturation data of the parking lot per hour is less than 60 percent, so that the parking lot is sufficient;
the parking saturation data per hour is 60-80% of the parking tension;
the parking saturation data of every hour is between 80 and 99 percent, which indicates that the parking space is seriously tensed;
hourly parking saturation data equal to 100% is that the berth is full.
In the embodiment of the application, according to the parking saturation model, the saturation change of each parking lot at any time in any day in the future in the area can be predicted by combining the time influence factors.
In the embodiment of the application, the key distribution parameters comprise parking lot recommended sequences corresponding to parking lots to be recommended, at least one key distribution parameter is determined from the distribution parameters, and the dynamic distribution of the parking lot resources according to the at least one key distribution parameter comprises the following steps:
and determining key distribution parameters from the plurality of distribution parameters as recommended sequencing of the parking lots corresponding to the parking lots to be recommended, and taking the plurality of first parking lots to be recommended as first recommended parking lots, wherein the recommended sequencing of the parking lots is within a preset sequencing threshold.
In the embodiment of the application, the key distribution parameters comprise pre-estimated average time data, at least one key distribution parameter is determined from a plurality of distribution parameters, and the step of dynamically distributing the parking lot resources according to the at least one key distribution parameter comprises the following steps:
and determining a key distribution parameter as estimated average time-use data from the plurality of distribution parameters, and taking a plurality of second parking lots to be recommended, of which the time-use durations in the plurality of estimated average time-use data are within a preset time-use threshold value, as second recommended parking lots.
In the embodiment of the application, the key distribution parameters comprise saturation values of all parking lots to be recommended, at least one key distribution parameter is determined from the distribution parameters, and the dynamic distribution of the parking lot resources according to the at least one key distribution parameter comprises the following steps:
and determining key distribution parameters as saturation values of the parking lots to be recommended from the distribution parameters, and taking a plurality of third parking lots to be recommended, which are all within preset parking lot saturation threshold values, as third recommended parking lots.
Although three main key allocation parameters are mentioned above, in different application scenarios, other key allocation parameters may be introduced, and are not described herein again.
In addition, the three distribution parameters can be combined in pairs and used as key distribution parameters, or the three distribution parameters are used as key distribution parameters and configured with different weight values respectively, and finally, a plurality of recommended parking lots are optimized from a plurality of parking lots to be recommended, and parking lot resources are dynamically distributed according to the recommended parking lots. Reference is made herein to the description of the same or similar parts as previously described and will not be repeated here.
In the embodiment of the present application, the weighted values of the three or even more key distribution parameters are not specifically limited, and may be configured according to different application scenarios, which is not described herein again.
In the embodiment of the application, the traffic jam index value determined by the traffic jam model and the parking lot saturation value determined by the parking lot saturation model are in an inverse correlation relationship.
Specifically, if the traffic congestion index value determined by the traffic congestion model is larger, fewer vehicles can enter the corresponding parking lot, and the parking lot saturation value determined by the parking lot saturation model is smaller; and vice versa, and will not be described in detail herein.
In the embodiment of the present application, the traffic congestion model has a characteristic day attribute, which is specifically described as follows:
according to the road condition congestion data analysis of the past half year or more in the area, the real-time road condition congestion change is analyzed within 7 days from Monday to Sunday by taking week as a period, and the change classification can be carried out according to a certain standard, wherein the standard is specifically as follows:
the traffic flow data per minute is less than 60 percent, namely smooth;
the traffic flow data per minute is slow between 60% and 80%;
the traffic data per minute is congested between 80% and 10%;
traffic data greater than 100% per minute is heavily congested.
In an actual application scenario, the indexes affecting the traffic congestion model are specifically as follows:
the shape of the intersection: nailed intersections, crossroads, bends, etc.;
timing of the signal lamp: red light duration, yellow light duration, green light duration;
road indexes are as follows: single lane, double lane, multiple lane, etc.;
the traffic flow index (vehicle/hour) ═ traffic flow rate × number of lanes;
the driving speed (km/h) is the driving time.
In an actual application scene, according to the traffic jam model, the traffic jam model is a dynamic traffic jam model, and the traffic jam condition of any time point in any day in the future in the area can be predicted by combining time data and traffic flow data.
S103, according to input keywords in an input instruction of a target user, a recommended scheme is screened from at least one scheme to be recommended, and the recommended scheme is pushed to terminal equipment of the target user, wherein the recommended scheme comprises second address information of a recommended parking lot, second arrival path information of the recommended parking lot and recommendation degree ranking information of the recommended parking lot.
In the embodiment of the application, the recommendation scheme further comprises parking lot real-time saturation information and real-time idle parking space information corresponding to the recommended parking lot; therefore, the target user can reasonably arrange the time of arriving at the recommended parking lot according to the real-time saturation information of the parking lot of the recommended parking lot and the real-time idle parking space information.
In the embodiment of the application, the step of screening the recommendation scheme from at least one scheme to be recommended according to the input keyword in the input instruction of the target user comprises the following steps:
determining at least one key recommendation parameter of a recommendation model matched with an input keyword according to the input keyword in the input instruction of the target user;
in a possible implementation manner, determining at least one key recommendation parameter of a recommendation model matched with an input keyword according to the input keyword in an input instruction of a target user comprises the following steps:
and if the input keyword in the input instruction of the target user is the shortest time between the input keyword and the recommended parking lot, determining that at least one key recommendation parameter of the recommendation model matched with the input keyword is the shortest time between the input keyword and the recommended parking lot.
In a possible implementation manner, determining at least one key recommendation parameter of a recommendation model matched with an input keyword according to the input keyword in an input instruction of a target user comprises the following steps:
and if the input keyword in the input instruction of the target user is that the current parking lot saturation of the recommended parking lot is the lowest, determining that at least one key recommendation parameter of the recommendation model matched with the input keyword is that the current parking lot saturation of the recommended parking lot is the lowest.
In a possible implementation manner, determining at least one key recommendation parameter of a recommendation model matched with an input keyword according to the input keyword in an input instruction of a target user comprises the following steps:
and if the input keywords in the input instruction of the target user are the time of use parameter of the recommended parking lot and the current parking lot saturation parameter of the recommended parking lot, determining that at least one key recommendation parameter of the recommendation model matched with the input keywords is the time of use parameter of the recommended parking lot and the current parking lot saturation parameter of the recommended parking lot.
And screening a recommendation scheme from at least one scheme to be recommended according to at least one key recommendation parameter.
In an embodiment of the present application, the parking lot recommendation method provided in the embodiment of the present application further includes the following steps:
if the number of the recommended parking lots is two or more, randomly determining any one parking lot as the recommended parking lot; generating a corresponding recommendation scheme according to the randomly determined recommended parking lot; thus, the limited parking spaces of each parking lot can be utilized conveniently.
In an embodiment of the present application, the parking lot recommendation method provided in the embodiment of the present application further includes the following steps:
if the number of the recommended parking lots is two or more, the parking lot information of the two or more recommended parking lots is pushed to the terminal equipment of the target user, so that the target user can select a better parking lot from the two or more recommended parking lots according to the preference of the target user, multiple possibilities can be provided for the target user, and the experience of the target user is improved.
In the embodiment of the present application, if the recommendation algorithm corresponding to the adopted recommendation model is:
p ═ (T1+ T2)/F; wherein P is a numerical value corresponding to the recommended parking lot degree ranking information, T1 is an estimated average time duration of the target user when the vehicle runs to the recommended parking lot, T2 is an estimated average time duration of the target user walking back to the destination from the recommended parking lot, and F is the current parking lot saturation of the recommended parking lot; then, a recommended scheme can be screened from at least one scheme to be recommended according to input keywords in an input instruction of a target user, and the recommended scheme is pushed to terminal equipment of the target user, wherein the recommended scheme comprises second address information of recommended parking lots, second arrival path information of the recommended parking lots and recommendation degree ranking information of the recommended parking lots.
In an application scenario, if an input keyword in an input instruction of a target user is the shortest time between the input keyword and a recommended parking lot, determining that at least one key recommendation parameter of a recommendation model matched with the input keyword is the shortest time between the input keyword and the recommended parking lot, screening a recommendation scheme from at least one scheme to be recommended according to the key recommendation parameter, and pushing the recommendation scheme to terminal equipment of the target user.
For example, in an application scenario where the shortest elapsed time between the past return and the recommended parking lot is used as a key recommendation parameter, there are three schemes to be recommended, which are specifically described as follows:
the first scheme to be recommended is as follows: f is 90%, T1 is 3, T2 is 5, P is 8.9;
and B, a scheme II to be recommended: f is 30%, T1 is 5, T2 is 10, P is 50;
and a third scheme to be recommended: f65%, T1 4, T2 7 and P16.9.
The key recommendation parameters for recommending the target user are as follows: the time spent on the round trip and the recommended parking lot is shortest, so the recommended scheme selected from the three schemes to be recommended is as follows: the first solution to be recommended is described above.
In another application scenario, if an input keyword in an input instruction of a target user is the lowest saturation of the current parking lot of the recommended parking lot, determining that at least one key recommendation parameter of a recommendation model matched with the input keyword is the lowest saturation of the current parking lot of the recommended parking lot, screening a recommendation scheme from at least one scheme to be recommended according to the key recommendation parameter, and pushing the recommendation scheme to the terminal device of the target user.
For example, in an application scenario in which the current parking lot saturation of a recommended parking lot is the lowest as a key recommendation parameter, there are three schemes to be recommended, which are specifically described as follows:
the first scheme to be recommended is as follows: f is 90%, T1 is 3, T2 is 5, P is 8.9;
and B, a scheme II to be recommended: f is 30%, T1 is 5, T2 is 10, P is 50;
and a third scheme to be recommended: f65%, T1 4, T2 7 and P16.9.
The key recommendation parameters for recommending the target user are as follows: the current parking lot saturation of the recommended parking lot is the lowest, so the recommended scheme screened from the three schemes to be recommended is as follows: and the second scheme to be recommended.
In another application scenario, if the input keywords in the input instruction of the target user are the time of use parameter of the recommended parking lot and the current parking lot saturation parameter of the recommended parking lot, determining that at least one key recommendation parameter of the recommendation model matched with the input keywords is the time of use parameter of the recommended parking lot and the current parking lot saturation parameter of the recommended parking lot, screening a recommendation scheme from at least one scheme to be recommended according to the key recommendation parameters, and pushing the recommendation scheme to the terminal device of the target user.
In the embodiment of the application, the parking lot saturation parameter has a characteristic day attribute. The feature day attribute of the parking lot saturation parameter is specifically explained as follows:
according to a parking saturation formula of a single parking lot, taking a cycle as one week, combining periodic changes of parking amount in different time intervals (counted by hours) every day, refining a parking saturation change rule (including change forms of different time intervals such as early and late peaks of a working day, weekends and the like) every day to form saturation indexes in different forms;
according to the parking space amount in the region plot, counting parking saturation indexes of different time points (according to hours) every day in the period, and displaying corresponding saturation changes according to a saturation range standard to form a parking saturation model, wherein the parking saturation model is a region parking saturation model.
For example, in an application scenario where a time-of-use time parameter of a recommended parking lot and a current parking lot saturation parameter of the recommended parking lot are used as key recommendation parameters, there are three schemes to be recommended, which are specifically as follows:
the first scheme to be recommended is as follows: f is 90%, T1 is 3, T2 is 5, P is 8.9;
and B, a scheme II to be recommended: f is 30%, T1 is 5, T2 is 10, P is 50;
and a third scheme to be recommended: f65%, T1 4, T2 7 and P16.9.
The key recommendation parameters for recommending the target user are as follows: the time parameter of using time to come to and go from the recommended parking lot and the current parking lot saturation parameter of the recommended parking lot are calculated, and therefore the recommended scheme selected from the three schemes to be recommended is as follows: and the third scheme to be recommended is described.
In the embodiment of the present application, the traffic congestion index data is input into the traffic congestion model.
In an actual application scenario, the indexes affecting the traffic congestion model are specifically as follows:
the shape of the intersection: nailed intersections, crossroads, bends, etc.;
timing of the signal lamp: red light duration, yellow light duration, green light duration;
road indexes are as follows: single lane, double lane, multiple lane, etc.;
the traffic flow index (vehicle/hour) ═ traffic flow rate × number of lanes;
the driving speed (km/h) is the driving time.
In the embodiment of the application, at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of a target user for going to and returning to the parking lot to be recommended are input into a recommendation model, and at least one scheme to be recommended is output; according to the method and the device, a recommendation scheme is screened from at least one scheme to be recommended according to input keywords in an input instruction of a target user, and the recommendation scheme is pushed to terminal equipment of the target user, so that by adopting the embodiment of the application, the current parking lot saturation data of the parking lot to be recommended is input into a recommendation model in real time, and the finally obtained recommendation scheme has real-time performance and is more consistent with the actual situation; in addition, because the input keywords related to the user are introduced, the finally screened recommendation scheme better conforms to the preference degree of the user, better interaction with the user can be realized, and the user experience degree is improved.
The following is an embodiment of the parking lot recommendation apparatus of the present invention, which can be used to execute the embodiment of the parking lot recommendation method of the present invention. For details that are not disclosed in the embodiment of the parking lot recommendation device of the present invention, refer to the embodiment of the parking lot recommendation method of the present invention.
Referring to fig. 2, a schematic structural diagram of a parking lot recommendation device according to an exemplary embodiment of the present invention is shown. The parking lot recommendation device can be realized by software, hardware or a combination of the two to form all or part of the terminal. The parking lot recommendation device includes an acquisition unit 10, a processing unit 20, a screening unit 30, and a pushing unit 40.
Specifically, the acquiring unit 10 is configured to acquire a recommendation model for recommending a parking lot to a target user;
the processing unit 20 is configured to input at least one item of traffic congestion index data, current parking lot saturation data of a parking lot to be recommended, and estimated average time data of a target user going to and from the parking lot to be recommended into the recommendation model acquired by the acquisition unit 10, and output at least one scheme to be recommended, where the scheme to be recommended includes first address information of the parking lot to be recommended and first arrival path information of the parking lot to be recommended;
the screening unit 30 is configured to screen a recommended scheme from at least one to-be-recommended scheme obtained through processing by the processing unit 20 according to an input keyword in an input instruction of a target user, where the recommended scheme includes second address information of a recommended parking lot, second arrival path information of the recommended parking lot, and recommendation degree ranking information of the recommended parking lot;
and the pushing unit 40 is configured to push the recommended scheme screened by the screening unit 30 to the terminal device of the target user.
Optionally, the recommendation model includes a parking saturation model, a traffic congestion model, and a resource allocation model, and the obtaining unit 10 is further configured to:
acquiring a traffic jam index value determined based on a traffic jam model; acquiring a parking lot saturation numerical value determined based on the parking saturation model;
the processing unit 20 is further configured to:
inputting the traffic congestion index numerical value and the parking lot saturation numerical value acquired by the acquisition unit 10 into a resource allocation model to obtain a plurality of allocation parameters for dynamically allocating parking lot resources; and determining at least one key allocation parameter from the plurality of allocation parameters.
Optionally, the key distribution parameters include a recommended parking lot ranking corresponding to each parking lot to be recommended, and the processing unit 20 is specifically configured to:
and determining key distribution parameters from the plurality of distribution parameters as recommended sequencing of the parking lots corresponding to the parking lots to be recommended, and taking the plurality of first parking lots to be recommended as first recommended parking lots, wherein the recommended sequencing of the parking lots is within a preset sequencing threshold.
Optionally, the key distribution parameters include estimated average time-use data, and the processing unit 20 is specifically configured to:
and determining a key distribution parameter as estimated average time-use data from the plurality of distribution parameters, taking a plurality of second parking lots to be recommended, of which the time-use time lengths in the plurality of estimated average time-use data are all within a preset time length threshold value, as second recommended parking lots, and dynamically distributing parking lot resources according to the plurality of second recommended parking lots.
Optionally, the key distribution parameter includes a saturation value of each parking lot to be recommended, and the processing unit 20 is specifically configured to:
and determining key distribution parameters as saturation values of the parking lots to be recommended from the distribution parameters, and taking a plurality of third parking lots to be recommended, which are all within preset parking lot saturation threshold values, as third recommended parking lots.
Optionally, the traffic congestion index value determined by the traffic congestion model and the parking lot saturation value determined by the parking lot saturation model are in an inverse correlation relationship.
Optionally, the recommended scheme further includes real-time saturation information of a parking lot corresponding to the recommended parking lot and information of a real-time idle parking space.
It should be noted that, when the parking lot recommendation apparatus provided in the foregoing embodiment executes the parking lot recommendation method, only the division of the functional modules is illustrated, and in practical applications, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the parking lot recommendation device and the parking lot recommendation method provided by the embodiment belong to the same concept, and the implementation process is detailed in the embodiment of the parking lot recommendation method, which is not described herein again.
In the embodiment of the application, the processing unit is used for inputting at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of a target user for going to and returning to the parking lot to be recommended into the recommendation model and outputting at least one scheme to be recommended; the screening unit is used for screening a recommendation scheme from at least one to-be-recommended scheme obtained by processing of the processing unit according to an input keyword in an input instruction of a target user, and the pushing unit is used for pushing the recommendation scheme screened by the screening unit to the terminal device of the target user, so that by adopting the embodiment of the application, because the current parking lot saturation data of the parking lot to be recommended is input into the recommendation model in real time, the finally obtained recommendation scheme has real-time performance and is more consistent with the actual situation; in addition, because the input keywords related to the user are introduced, the finally screened recommendation scheme better conforms to the preference degree of the user, better interaction with the user can be realized, and the user experience degree is improved.
The present invention also provides a computer readable medium, on which program instructions are stored, which when executed by a processor implement the parking lot recommendation method provided by the above-mentioned method embodiments.
The present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the parking lot recommendation method described in the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.

Claims (10)

1. A parking lot recommendation method, the method comprising:
acquiring a recommendation model for recommending a parking lot to a target user;
inputting at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of the target user to come to and return to the parking lot to be recommended into the recommendation model, and outputting at least one scheme to be recommended, wherein the scheme to be recommended comprises first address information of the parking lot to be recommended and first arrival path information of the parking lot to be recommended;
and screening a recommended scheme from at least one scheme to be recommended according to an input keyword in the input instruction of the target user, and pushing the recommended scheme to the terminal equipment of the target user, wherein the recommended scheme comprises second address information of a recommended parking lot, second arrival path information of the recommended parking lot and recommendation degree sorting information of the recommended parking lot.
2. The method of claim 1, wherein the recommendation model comprises a parking saturation model, a traffic congestion model, and a resource allocation model, the method further comprising:
acquiring a traffic jam index value determined based on the traffic jam model;
acquiring a parking lot saturation numerical value determined based on the parking saturation model;
inputting the traffic jam index numerical value and the parking lot saturation numerical value into the resource distribution model to obtain a plurality of distribution parameters for dynamically distributing the parking lot resources;
and determining at least one key distribution parameter from the plurality of distribution parameters, and dynamically distributing the parking lot resources according to the at least one key distribution parameter.
3. The method according to claim 2, wherein the key allocation parameters include parking lot recommended ranks corresponding to parking lots to be recommended, the determining at least one key allocation parameter from the plurality of allocation parameters, and the dynamically allocating parking lot resources according to the at least one key allocation parameter includes:
and determining key distribution parameters from the plurality of distribution parameters as recommended sequencing of the parking lots corresponding to the parking lots to be recommended, and taking the plurality of first parking lots to be recommended as first recommended parking lots, wherein the recommended sequencing of the parking lots is within a preset sequencing threshold.
4. The method of claim 2, wherein the key allocation parameters include the estimated average time of use data, wherein determining at least one key allocation parameter from the plurality of allocation parameters, and wherein dynamically allocating parking lot resources based on the at least one key allocation parameter comprises:
and determining a key distribution parameter as the estimated average time consumption data from the plurality of distribution parameters, and taking a plurality of second parking lots to be recommended, of which the time consumption durations in the plurality of estimated average time consumption data are within a preset time duration threshold value, as second recommended parking lots.
5. The method of claim 2, wherein the key allocation parameters include saturation values of parking lots to be recommended, wherein the determining at least one key allocation parameter from the plurality of allocation parameters and the dynamically allocating parking lot resources according to the at least one key allocation parameter includes:
and determining key distribution parameters as saturation values of the parking lots to be recommended from the distribution parameters, and taking a plurality of third parking lots to be recommended, which are all within preset parking lot saturation threshold values, as third recommended parking lots.
6. The method of claim 2,
the traffic jam index value determined by the traffic jam model and the parking lot saturation value determined by the parking lot saturation model are in an inverse correlation relationship.
7. The method of claim 1,
the recommendation scheme further comprises parking lot real-time saturation information corresponding to the recommended parking lot and real-time idle parking space information.
8. A parking lot recommendation device, the device comprising:
the system comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring a recommendation model for recommending a parking lot to a target user;
the processing unit is used for inputting at least one item of traffic jam index data, current parking lot saturation data of a parking lot to be recommended and estimated average time data of the target user for going to and fro the parking lot to be recommended into the recommendation model obtained by the obtaining unit and outputting at least one scheme to be recommended, wherein the scheme to be recommended comprises first address information of the parking lot to be recommended and first arrival path information of the parking lot to be recommended;
the screening unit is used for screening a recommended scheme from at least one to-be-recommended scheme obtained by processing of the processing unit according to an input keyword in the input instruction of the target user, wherein the recommended scheme comprises second address information of a recommended parking lot, second arrival path information of the recommended parking lot and recommendation degree ranking information of the recommended parking lot;
and the pushing unit is used for pushing the recommendation scheme screened by the screening unit to the terminal equipment of the target user.
9. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to carry out the method steps according to any one of claims 1 to 7.
10. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1 to 7.
CN202110142024.0A 2021-02-02 2021-02-02 Parking lot recommendation method and device, storage medium and terminal Active CN112819226B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110142024.0A CN112819226B (en) 2021-02-02 2021-02-02 Parking lot recommendation method and device, storage medium and terminal

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110142024.0A CN112819226B (en) 2021-02-02 2021-02-02 Parking lot recommendation method and device, storage medium and terminal

Publications (2)

Publication Number Publication Date
CN112819226A true CN112819226A (en) 2021-05-18
CN112819226B CN112819226B (en) 2024-05-28

Family

ID=75860503

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110142024.0A Active CN112819226B (en) 2021-02-02 2021-02-02 Parking lot recommendation method and device, storage medium and terminal

Country Status (1)

Country Link
CN (1) CN112819226B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113838303A (en) * 2021-09-26 2021-12-24 千方捷通科技股份有限公司 Parking lot recommendation method and device, electronic equipment and storage medium
US11727806B2 (en) 2021-09-16 2023-08-15 Toyota Motor Engineering & Manufacturing North America, Inc. Identifying a parking spot based on congestion-dependent parking navigation preferences
CN117392874A (en) * 2023-12-13 2024-01-12 湖北省长投智慧停车有限公司 Method and system for dynamically reserving and distributing shared parking spaces of multiple yards

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009211253A (en) * 2008-03-03 2009-09-17 Toyota Motor Corp Parking lot information providing system, server and information terminal
CN101604480A (en) * 2008-06-11 2009-12-16 爱信艾达株式会社 Parking lot guide device, parking lot guide method and program
CN105096636A (en) * 2015-06-23 2015-11-25 中国联合网络通信集团有限公司 Parking lot dynamic selection method and system
US20160189545A1 (en) * 2014-12-31 2016-06-30 Venuenext, Inc. Modifying directions to a parking lot associated with a venue based on traffic conditions proximate to the parking lot
CN106971604A (en) * 2017-04-12 2017-07-21 青岛海信网络科技股份有限公司 A kind of parking stall resource allocation method and device
CN107527520A (en) * 2017-08-30 2017-12-29 重庆电子工程职业学院 Wisdom parking cloud platform based on big data
CN110517528A (en) * 2019-08-26 2019-11-29 温州易发现创想环保科技有限公司 A kind of City-level vehicle parking management system and its operation method
KR20200045636A (en) * 2018-10-23 2020-05-06 김형철 Car sharing service method and system, and parking control method
CN111125515A (en) * 2019-11-27 2020-05-08 大众问问(北京)信息科技有限公司 Parking place recommendation method, device and equipment
US20200191587A1 (en) * 2017-09-01 2020-06-18 Gil Emanuel Fuchs Multimodal Vehicle Routing System and Method with Vehicle Parking
CN111862664A (en) * 2019-04-09 2020-10-30 阿里巴巴集团控股有限公司 Parking scheduling method and device
KR20200132067A (en) * 2019-05-15 2020-11-25 (주)이지정보기술 Intelligent parking guidance service system and method by deep learning based parking lot usage analysis
CN112115372A (en) * 2020-11-18 2020-12-22 中电科新型智慧城市研究院有限公司 Parking lot recommendation method and device

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009211253A (en) * 2008-03-03 2009-09-17 Toyota Motor Corp Parking lot information providing system, server and information terminal
CN101604480A (en) * 2008-06-11 2009-12-16 爱信艾达株式会社 Parking lot guide device, parking lot guide method and program
US20160189545A1 (en) * 2014-12-31 2016-06-30 Venuenext, Inc. Modifying directions to a parking lot associated with a venue based on traffic conditions proximate to the parking lot
CN105096636A (en) * 2015-06-23 2015-11-25 中国联合网络通信集团有限公司 Parking lot dynamic selection method and system
CN106971604A (en) * 2017-04-12 2017-07-21 青岛海信网络科技股份有限公司 A kind of parking stall resource allocation method and device
CN107527520A (en) * 2017-08-30 2017-12-29 重庆电子工程职业学院 Wisdom parking cloud platform based on big data
US20200191587A1 (en) * 2017-09-01 2020-06-18 Gil Emanuel Fuchs Multimodal Vehicle Routing System and Method with Vehicle Parking
KR20200045636A (en) * 2018-10-23 2020-05-06 김형철 Car sharing service method and system, and parking control method
CN111862664A (en) * 2019-04-09 2020-10-30 阿里巴巴集团控股有限公司 Parking scheduling method and device
KR20200132067A (en) * 2019-05-15 2020-11-25 (주)이지정보기술 Intelligent parking guidance service system and method by deep learning based parking lot usage analysis
CN110517528A (en) * 2019-08-26 2019-11-29 温州易发现创想环保科技有限公司 A kind of City-level vehicle parking management system and its operation method
CN111125515A (en) * 2019-11-27 2020-05-08 大众问问(北京)信息科技有限公司 Parking place recommendation method, device and equipment
CN112115372A (en) * 2020-11-18 2020-12-22 中电科新型智慧城市研究院有限公司 Parking lot recommendation method and device

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
GAO, HONGYAN，等: "Smartphone-based parking guidance algorithm and implementation", 《JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS》, vol. 25, no. 4, pages 412 - 422 *
付加滨: "面向泊车用户的信息推荐模型研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》, no. 6, pages 034 - 193 *
齐保良，等: "一种具有语音交互功能的停车诱导系统设计", 《计算机测量与控制》, vol. 27, no. 6, pages 167 - 171 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11727806B2 (en) 2021-09-16 2023-08-15 Toyota Motor Engineering & Manufacturing North America, Inc. Identifying a parking spot based on congestion-dependent parking navigation preferences
CN113838303A (en) * 2021-09-26 2021-12-24 千方捷通科技股份有限公司 Parking lot recommendation method and device, electronic equipment and storage medium
CN117392874A (en) * 2023-12-13 2024-01-12 湖北省长投智慧停车有限公司 Method and system for dynamically reserving and distributing shared parking spaces of multiple yards
CN117392874B (en) * 2023-12-13 2024-03-12 湖北省长投智慧停车有限公司 Method and system for dynamically reserving and distributing shared parking spaces of multiple yards

Also Published As

Publication number Publication date
CN112819226B (en) 2024-05-28

Similar Documents

Publication Publication Date Title
CN112819226A (en) Parking lot recommendation method and device, storage medium and terminal
Xiao et al. Online task assignment for crowdsensing in predictable mobile social networks
Zhou et al. Optimizing taxi driver profit efficiency: A spatial network-based markov decision process approach
CN106919996A (en) A kind of destination Forecasting Methodology and device
KR20210052499A (en) e-hailing service
CN104240496B (en) A kind of determination method and apparatus of trip route
CN105531746A (en) Management of data collected for traffic analysis
CN103971170A (en) Method and device for forecasting changes of feature information
CN103546583B (en) Group intellectual perception system and group intellectual perception method
CN106293309A (en) A kind of application icon aligning method and device
CN109768869A (en) A kind of traffic forecast method, system and computer storage medium
CN110147514B (en) Resource display method, device and equipment thereof
CN113987105B (en) Label perception graphics stream sketch construction method and application based on sliding window
CN116362431B (en) Scheduling method and device for shared vehicle, computer equipment and storage medium
CN110300123A (en) Abnormal flow recognition methods, device, electronic equipment and storage medium
CN117561517A (en) Computer-implemented apparatus and method for predicting traffic conditions in a route planning application
CN111311193B (en) Method and device for configuring public service resources
Hui et al. Trajectory waveNet: A trajectory-based model for traffic forecasting
CN110986992A (en) Navigation method and device for unmanned vending vehicle, electronic equipment and storage medium
CN105357637A (en) Location and behavior information prediction system and method
CN110516017A (en) Location information processing method, device, electronic equipment and storage medium based on terminal device
CN113191029A (en) Traffic simulation method, program, and medium based on cluster computing
CN111627210A (en) Traffic flow prediction method, device, equipment and medium
Moreira-Matias et al. An online recommendation system for the taxi stand choice problem (Poster)
CN110619748A (en) Traffic condition analysis and prediction method, device and system based on traffic 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
GR01 Patent grant
GR01 Patent grant