Parking demand recommendation method and system based on big data

Abstract

The invention relates to the field of parking demand analysis, and discloses a parking demand recommendation method and system based on big data, and the method comprises the steps: collecting the vehicle flow data of each parking lot and the basic information of the parking lot; classifying the parking lots according to the basic information of the parking lots, and establishing a parking lot classification system; vehicle position information and parking demands are collected, and a parking optimization scheme is made for vehicle owners with the parking demands according to the parking lot classification system; a parking lot parking space reservation service is bound in the parking optimization scheme, so that a vehicle owner can perform remote parking service reservation, and the parking demand is realized; the system comprises an acquisition module, a classification statistics module, an integral sorting module, a parking scheme generation module, a fee calculation module and a parking reservation module. According to the invention, the vehicle owner can reserve the parking space in advance, the parking lot screening is more detailed, multiple factors are considered to improve the parking experience of the vehicle owner, the parking lot system is more sound, and the parking service is more professional.

Description

Technical Field

[0001] This invention relates to the field of parking demand analysis, and in particular to a parking demand recommendation method and system based on big data. Background Technology

[0002] As people's material lives improve and their living standards and quality of life increase, private cars are becoming more and more common. People are starting to drive their private cars frequently for business trips and travel, which in turn leads to an increase in parking lots that are designed to accommodate private cars.

[0003] Currently, due to the abundance of parking lots, drivers often struggle to choose a parking space when traveling or on business trips in unfamiliar areas. They typically rely on navigation platforms, but these platforms often prioritize the nearest available parking lot without other filtering options. This results in parking lots not being available for the driver's overdue period, leading to a poor parking experience. Furthermore, the lack of reservation functions means that upon arrival, there are often no available spaces, requiring drivers to search for parking again, wasting time and further complicating the parking experience. Summary of the Invention

[0004] The present invention aims to provide a parking demand recommendation method and system based on big data, in order to solve the problems of enabling car owners to reserve parking spaces in advance, filtering parking lots in more detail, considering multiple factors to improve the parking experience for car owners, making the parking system more complete, and making parking services more professional.

[0005] To achieve the above objectives, the present invention provides the following method:

[0006] This invention provides a parking demand recommendation method based on big data:

[0007] S1: Collect vehicle flow data and basic information about each parking lot;

[0008] S2: Classify the parking lots according to the basic information of the parking lots and establish a parking lot classification system;

[0009] S3: Collect the vehicle's geographical location information and the owner's parking needs, and formulate the preferred parking solution for the owner with parking needs based on the parking lot classification system.

[0010] S4: In the parking optimization scheme, a parking space reservation service is bound to the parking space reservation service, allowing car owners to make remote parking service reservations and meet their parking needs.

[0011] Preferably, the vehicle flow data for each parking lot includes: daily vehicle flow data, monthly vehicle flow data, and flow data for different types of vehicles; the basic information of the parking lot includes: the geographical location of each parking lot, the routes around the parking lot, the size of the parking lot, and the type of parking lot.

[0012] Preferably, the types of parking lots include: parking duration type, parking location type, and parking distribution type; the parking duration type includes: temporary parking lots, long-term parking lots, and emergency parking lots; the parking location type includes: tourist attraction parking lots, shopping mall parking lots, residential area parking lots, and open-air parking lots; the parking distribution type includes: densely distributed parking areas and sparsely distributed parking areas.

[0013] Preferably, the step of classifying parking lots according to their basic information and establishing a parking lot classification system includes: classifying all parking lots according to their type; binding parking lots to three types of parking lot classification tags based on their parking duration type, location type, and distribution type; evaluating parking lot recommendation scores based on traffic congestion around each parking lot and vehicle saturation within each parking lot; creating parking lot classification lists based on their classification tags; and sorting multiple parking lot classification lists from highest to lowest recommendation scores to obtain the parking lot classification system.

[0014] Preferably, the step of evaluating parking lot recommendation scores based on traffic congestion around each parking lot and vehicle saturation in each parking lot includes: ranking parking lots by convenience based on the number of roads surrounding each parking lot, vehicle saturation on the roads around the parking lot, and duration of vehicle saturation on the roads around the parking lot; ranking parking lots by parking fees from low to high; and evaluating parking lot recommendation scores based on the convenience ranking and the parking fee ranking, where a higher convenience level and a lower parking fee result in a higher recommendation score.

[0015] Preferably, the more roads surrounding each parking lot, the more convenient the parking lot will be;

[0016] The lower the vehicle saturation of the parking lot roads, the higher the convenience of the parking lot; the shorter the duration of vehicle saturation on the parking lot roads, the higher the convenience of the parking lot.

[0017] Preferably, the step of collecting vehicle location information and car owner parking needs, and formulating a preferred parking solution for car owners with parking needs based on the parking lot classification system, includes: collecting vehicle location information and car owner requirements for parking lot location and price in real time; filtering parking lots in the parking lot classification system based on the car owner's requirements for parking lot location and price to obtain multiple preliminary recommended parking lots; further filtering and sorting the multiple preliminary recommended parking lots according to different parking lot classification tags to obtain a secondary recommended parking lot list; and further filtering the secondary recommended parking lot list by considering additional parking lot services and parking lot environment to select one primary recommended parking lot and multiple secondary recommended parking lots to obtain the preferred parking solution.

[0018] Preferably, the step of binding a reserved parking space service to the parking optimization scheme, allowing car owners to remotely reserve parking services and fulfill their parking needs, includes: binding the reserved parking space service to the parking lot in the parking optimization scheme; calculating the parking time and cost for the car owner based on the reserved parking lot information, obtaining a parking cost-duration curve; recommending the optimal parking time and displaying the parking cost based on the parking cost-duration curve; and reminding the car owner when the parking time is reached based on the estimated parking time selected by the car owner, thus fulfilling the car owner's parking needs; the reserved parking space service includes: planning the optimal route to the parking lot for the car owner based on the geographical location information of the car owner's vehicle and the reserved parking lot; planning a parking space navigation route for the car owner based on the location of the reserved parking space; and recommending the optimal parking time and optimal parking duration for the car owner based on the reserved parking lot.

[0019] Preferably, when a vehicle arrives at the entrance / exit, it initiates a parking space request. Based on the location of the remaining vacant parking spaces in the parking area and road information, the optimal parking location and route are planned for the vehicle, and this moment is recorded as the start parking time. The planned optimal parking location and route are pushed to the vehicle, and the optimal parking location is marked as non-vacant. One of the remaining vacant parking spaces is selected as the target parking space, and the parking space number is designated as the vacant parking space. Taking the entrance / exit where the vehicle entered as the starting point, the target parking space as the ending point, and all other parking spaces as obstacles, path planning is performed based on the obstacles from the starting point to the ending point to obtain the optimal path and the total path distance. The proportion of paths with a road width less than a width threshold in the optimal path is obtained. The parking space recommendation index is obtained based on the total path distance and the proportion of paths with a road width less than the width threshold. The above operations are repeated to obtain the optimal path and the parking space recommendation index for all remaining vacant parking spaces as target parking spaces.

[0020] This invention provides a parking demand recommendation system based on big data, characterized in that the system comprises:

[0021] Data collection module: Collects information on each parking lot, roads near the parking lot, the location information of the parking lot and the car owner, and the car owner's parking needs;

[0022] The classification and statistics module categorizes and statistically analyzes parking lots according to their different types.

[0023] Points-based ranking module: Evaluates and ranks parking lots based on traffic congestion around the parking lots and vehicle saturation in each parking lot;

[0024] Parking plan generation module: Develops optimal parking plans for car owners with parking needs based on the parking lot classification system;

[0025] Fee calculation module: Calculates parking fees based on the duration of parking.

[0026] Parking reservation module: Car owners can reserve parking spaces online.

[0027] The beneficial effects of this invention are reflected in the following aspects: This invention collects information on various parking lots, nearby roads, the location of parking lots and car owners, and the parking needs of car owners to classify parking lots and establish a parking lot classification system. Parking lots are divided into three major systems and bound with corresponding tags, enabling parking lots to better meet the needs of car owners with different requirements. The convenience of parking lots is ranked based on the number of roads surrounding the parking lot, the vehicle saturation of the roads around the parking lot, and the duration of road vehicle saturation. Parking fees are ranked from low to high and scored, thus filtering out parking lots with higher value and improving the parking experience for car owners. Furthermore, the invention incorporates considerations of additional parking services, making the comparison of parking lots more refined and improving the parking service experience for car owners. Attached Figure Description

[0028] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0029] Figure 1 This is a flowchart illustrating a parking demand recommendation method based on big data, provided in an embodiment of the present invention.

[0030] Figure 2 This is a flowchart illustrating the process of formulating a parking optimization scheme according to an embodiment of the present invention.

[0031] Figure 3 This is a flowchart illustrating a parking demand recommendation system based on big data, provided in an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0034] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0035] Currently, due to the abundance of parking lots, drivers often struggle to choose a parking space when traveling or on business trips in unfamiliar areas. They typically rely on navigation platforms, but these platforms often prioritize the nearest available parking lot without other filtering options. This results in parking lots not being available for the driver's overdue period, leading to a poor parking experience. Furthermore, the lack of reservation functions means that upon arrival, there are often no available spaces, requiring drivers to search for parking again, wasting time and further complicating the parking experience.

[0036] The present invention aims to provide a parking demand recommendation method and system based on big data, in order to solve the problems of enabling car owners to reserve parking spaces in advance, filtering parking lots in more detail, considering multiple factors to improve the parking experience for car owners, making the parking system more complete, and making parking services more professional.

[0037] This invention provides a parking demand recommendation method based on big data, as described in the following embodiments. Figure 1 As shown, it includes the following steps:

[0038] S1: Collect vehicle flow data and basic information about each parking lot.

[0039] In this embodiment of the invention, the vehicle flow data for each parking lot includes: daily vehicle flow data, monthly vehicle flow data, and flow data for different types of vehicles; the basic information of the parking lot includes: the geographical location of each parking lot, the routes around the parking lot, the size of the parking lot, and the type of parking lot; the type of parking lot includes: parking duration type, parking location type, and parking distribution type; the parking duration type includes: temporary parking lot, long-term parking lot, and emergency parking lot; the parking location type includes: tourist attraction parking lot, shopping mall parking lot, residential area parking lot, and open-air parking lot; the parking distribution type includes: densely distributed parking areas and sparsely distributed parking areas.

[0040] S2: Classify each parking lot according to its basic information and establish a parking lot classification system.

[0041] In this embodiment of the invention, all parking lots are classified according to their type. Parking lots are then tagged with three categories: parking duration type, parking location type, and parking distribution type. A parking lot recommendation score is assigned based on the traffic congestion around each parking lot and the vehicle saturation level within each parking lot. Parking lots are ranked for convenience based on the number of roads surrounding them, the vehicle saturation level of those roads, and the duration of that road saturation. Parking fees are ranked from lowest to highest. A parking lot recommendation score is then assigned based on both convenience and price rankings: higher convenience and lower fees result in a higher recommendation score; more roads surrounding a parking lot indicate greater convenience; lower road saturation and shorter road saturation times also indicate greater convenience. Parking lots are then categorized into parking lot ranking lists based on their classification tags. These lists are then ranked from highest to lowest based on the parking recommendation score, resulting in a parking lot classification system.

[0042] S3: Collect vehicle location information and owner parking needs, and develop optimal parking solutions for owners with parking needs based on a parking lot classification system, such as... Figure 2 As shown.

[0043] In this embodiment of the invention, the geographical location information of vehicles and the location and price requirements of car owners for parking lots are collected in real time. Based on the location and price requirements of car owners for parking lots, parking lots are screened in the parking lot classification system to obtain multiple preliminary recommended parking lots. The multiple preliminary recommended parking lots are screened and sorted according to different parking lot classification tags to obtain a secondary recommended parking lot list. The secondary recommended parking lot list is further screened by considering the additional services and environment of the parking lot to select one primary recommended parking lot and multiple secondary recommended parking lots to obtain the optimal parking solution.

[0044] S4: Integrate the parking reservation service into the parking optimization plan, allowing car owners to make remote parking reservations and fulfill their parking needs.

[0045] In this embodiment of the invention, the parking optimization scheme binds a parking space reservation service to the parking lot; based on the car owner's reserved parking lot information, the parking time and cost are calculated for the car owner, resulting in a parking cost-duration curve; based on the parking cost-duration curve, the optimal parking duration is recommended to the car owner, and the parking cost is displayed; based on the car owner's selected estimated parking duration, a parking time reminder is given to the car owner to meet the parking needs; the reserved parking space service includes: planning the optimal route to the parking lot for the car owner based on the vehicle's geographical location information and the reserved parking lot; planning a parking space navigation route for the car owner based on the location of the reserved parking space; recommending the optimal parking duration and optimal parking time for the car owner based on the reserved parking lot; and initiating a parking space acquisition request when the vehicle arrives at the entrance / exit, based on the remaining vacant parking spaces in the parking area. Location and road information are used to plan the optimal parking location and route for the vehicle, and this time is recorded as the start parking time. The planned optimal parking location and route are pushed to the vehicle, and the optimal parking location is marked as non-empty. One remaining empty parking space is selected as the target parking space, and the parking space number is marked as an empty parking space. The entrance / exit where the vehicle enters is used as the starting point, the target parking space is used as the ending point, and all other parking spaces are used as obstacles. Path planning is performed between the starting point and the ending point based on the obstacles to obtain the optimal path and the total path distance. The proportion of paths with a road width less than a width threshold in the optimal path is obtained. The parking space recommendation index is obtained based on the total path distance and the proportion of paths with a road width less than the width threshold. The above operations are repeated to obtain the optimal path and the parking space recommendation index for all remaining empty parking spaces as target parking spaces.

[0046] This invention provides a parking demand recommendation system based on big data, the method of which is as follows: Figure 3 As shown, it includes the following sections:

[0047] Data collection module: Collects information on each parking lot, roads near the parking lot, the location information of the parking lot and the car owner, and the car owner's parking needs;

[0048] The classification and statistics module categorizes and statistically analyzes parking lots according to their different types.

[0049] Points-based ranking module: Evaluates and ranks parking lots based on traffic congestion around the parking lots and vehicle saturation in each parking lot;

[0050] Parking plan generation module: Develops optimal parking plans for car owners with parking needs based on the parking lot classification system;

[0051] Fee calculation module: Calculates parking fees based on the duration of parking.

[0052] Parking reservation module: Car owners can reserve parking spaces online.

[0053] The beneficial effects of this invention are reflected in the following aspects: This invention collects information on various parking lots, nearby roads, the location of parking lots and car owners, and the parking needs of car owners to classify parking lots and establish a parking lot classification system. Parking lots are divided into three major systems and bound with corresponding tags, enabling parking lots to better meet the needs of car owners with different requirements. The convenience of parking lots is ranked based on the number of roads surrounding the parking lot, the vehicle saturation of the roads around the parking lot, and the duration of road vehicle saturation. Parking fees are ranked from low to high and scored, thus filtering out parking lots with higher value and improving the parking experience for car owners. Furthermore, the invention incorporates considerations of additional parking services, making the comparison of parking lots more refined and improving the parking service experience for car owners.

[0054] The above descriptions are merely embodiments of the present invention, and common knowledge regarding specific technical solutions or characteristics is not elaborated upon here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the scope of the present invention, and these should also be considered within the protection scope of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A big data-based parking demand recommendation method, characterized by, The method comprises: S1: collecting vehicle flow data of each parking lot and basic information of the parking lot; S2: classifying each parking lot according to the basic information of the parking lot, and establishing a parking lot classification system; S3: collecting geographical location information of the vehicle and parking demand of the vehicle owner, and formulating a parking optimization scheme for the vehicle owner with parking demand according to the parking lot classification system; S4: binding a predetermined parking lot service in the parking optimization scheme, allowing the vehicle owner to make a remote parking service reservation, and realizing the parking demand.

2. The parking demand recommendation method based on big data according to claim 1, wherein: the vehicle flow data of each parking lot comprises daily vehicle flow data, monthly vehicle flow data, and flow data of different types of vehicles of each parking lot; and the basic information of the parking lot comprises geographical location of each parking lot, routes around the parking lot, scale of the parking lot, and type of the parking lot.

3. The parking demand recommendation method based on big data according to claim 2, wherein: the type of the parking lot comprises parking duration type, location type, and distribution type; the parking duration type comprises temporary parking lot, long-term parking lot, and emergency parking lot; the location type comprises tourist attraction type, shopping mall type, community type, and open-air type; and the distribution type comprises densely distributed parking lot area and sparsely distributed parking lot area. The step of classifying each parking lot according to the basic information of the parking lot and establishing a parking lot classification system comprises: According to the type of the parking lot, all parking lots are classified, and the parking lots are bound with three types of parking lot classification labels according to the parking duration type, the location type, and the distribution type; The parking lots are evaluated for parking lot recommendation degree points according to the traffic congestion of the roads around each parking lot and the vehicle saturation in each parking lot; The parking lots are classified according to the different parking lot classification labels to obtain a parking lot classification list; The parking lot classification lists are sorted from high to low according to the parking lot recommendation degree points to obtain a parking lot classification system. The step of evaluating the parking lots for parking lot recommendation degree points according to the traffic congestion of the roads around each parking lot and the vehicle saturation in each parking lot comprises:

4. The method of claim 3, wherein, According to the number of roads around each parking lot, the parking lot road vehicle saturation, and the parking lot road vehicle saturation duration, the parking lots are sorted for convenience degree; According to the parking fee of each parking lot, the parking lots are sorted from low to high for charging price to obtain a parking lot charging price sorting; The parking lot recommendation degree points are evaluated according to the parking lot convenience degree sorting and the parking lot charging price sorting, the higher the parking lot convenience degree, the lower the parking lot charging price, and the higher the parking lot recommendation degree points.

6. The parking demand recommendation method based on big data according to claim 5, wherein: ​ 5. The method of claim 4, wherein, ​ ​ ​ ​ ​ The more the number of roads around the parking lot, the higher the convenience of the parking lot; The lower the road vehicle saturation of the parking lot, the higher the convenience of the parking lot; The shorter the road vehicle saturation time of the parking lot, the higher the convenience of the parking lot.

7. The method of claim 4, wherein, The steps of collecting the geographical position information of the vehicle and the parking demand of the vehicle owner, and formulating a parking optimization scheme for the vehicle owner with parking demand according to the parking lot classification system, include: Collecting the geographical position information of the vehicle and the location and price requirements of the vehicle owner for the parking lot in real time; Filtering the parking lot in the parking lot classification system according to the location and price requirements of the vehicle owner for the parking lot, to obtain a plurality of preliminary recommended parking lots; Secondary filtering and sorting the plurality of preliminary recommended parking lots according to different parking lot classification labels of the parking lot, to obtain a secondary recommended parking lot list; Filtering in the secondary recommended parking lot list by considering the additional services of the parking lot and the environment of the parking lot, to filter out a main recommended parking lot and a plurality of secondary recommended parking lots, and to obtain a parking optimization scheme.

8. The big data based parking demand recommendation method of claim 7, wherein, The steps of binding a reserved parking lot service in the parking optimization scheme, and allowing the vehicle owner to make a remote parking service reservation, to realize the parking demand, include: Binding a reserved parking lot service in the parking optimization scheme; According to the reserved parking lot information of the vehicle owner, calculating the parking time cost for the vehicle owner, to obtain a parking cost-time curve; According to the parking cost-time curve, recommending the best parking time and displaying the parking cost for the vehicle owner; According to the selected expected parking time of the vehicle owner, reminding the vehicle owner of the arrival of the parking time, to realize the parking demand of the vehicle owner; The reserved parking lot service includes: planning an optimal arrival parking lot route for the vehicle owner according to the geographical position information of the vehicle and the reserved parking lot of the vehicle owner, planning a parking lot space navigation route for the vehicle owner according to the reserved parking lot space position of the vehicle owner, and recommending the best parking time and the best parking time for the vehicle owner according to the reserved parking lot of the vehicle owner.

9. The big data-based parking demand recommendation method of claim 8, wherein: a parking space acquisition application is initiated when the vehicle arrives at an entrance, a best parking position and route are planned for the vehicle based on the position of the remaining idle parking spaces and road information in the parking area, and the time is recorded as the start parking time; the planned best parking position and route are pushed to the vehicle, and the best parking position is marked as non-idle; one of the remaining idle parking spaces is taken as a target parking space, and the number of the parking space is an idle parking space; an entrance through which the vehicle enters is taken as a starting point, the target parking space is taken as an ending point, and all other parking spaces are taken as obstacles; a path between the starting point and the ending point is planned based on the obstacles, to obtain a best path and a total path distance; the proportion of the path in which the road width is less than a width threshold is obtained in the best path; a parking space recommendation index of the parking space is obtained based on the total path distance and the proportion of the path in which the road width is less than the width threshold; the best path and the parking space recommendation index of the target parking space are obtained for all the remaining idle parking spaces according to the above operations. 10.A big data based parking demand recommendation system, characterized in that, The system comprises: An acquisition module: acquiring information of each parking lot, road information near the parking lot, location information of the parking lot and the vehicle owner, and parking demand of the vehicle owner; A classification and statistics module: classifying and counting the parking lots according to different types; An integral ranking module: evaluating the parking lots according to the traffic congestion of the roads around the parking lots and the vehicle saturation in each parking lot, and ranking the parking lots according to the parking recommendation integral; A parking scheme generation module: formulating a parking optimization scheme for the vehicle owner with parking demand according to the parking classification system; A fee calculation module: calculating the parking fee according to the parking duration of the vehicle owner; A parking reservation module: reserving the parking space of the parking lot online by the vehicle owner.

Images