CN111127882A - Tourist attraction parking lot coming vehicle number prediction method based on big data - Google Patents
Tourist attraction parking lot coming vehicle number prediction method based on big data Download PDFInfo
- Publication number
- CN111127882A CN111127882A CN201911304445.8A CN201911304445A CN111127882A CN 111127882 A CN111127882 A CN 111127882A CN 201911304445 A CN201911304445 A CN 201911304445A CN 111127882 A CN111127882 A CN 111127882A
- Authority
- CN
- China
- Prior art keywords
- parking lot
- parking
- vehicles
- vehicle
- coming
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention discloses a method for predicting the number of coming vehicles in a parking lot in a tourist attraction based on big data, which is characterized in that a plurality of electric police access points are arranged on all road sections passing through the parking lot in the scenic attraction within the range of 5-10 kilometers of the peripheral radius of the parking lot in the scenic attraction and on road sections having intersection with the road sections passing through the parking lot in the scenic attraction, and the driving flow data information including vehicle information, the position of the electric police access point where the vehicle passes, the coming vehicle direction of the vehicle, the distance between the vehicle and the parking lot in the scenic attraction and the driving speed of the vehicle is collected and uploaded to a data analysis platform in real time; the data analysis platform predicts the number of coming vehicles arriving at each parking lot according to a parking number algorithm of the parking lot, and each parking lot performs comparison analysis according to the predicted number of coming vehicles and the actual remaining parking spaces of the parking lot and sends out parking early warning; the invention can predict the number of vehicles in the parking lot in the scenic spot in advance, is convenient for a parking lot manager to make parking space management and planning in the parking lot in advance, avoids traffic jam caused by insufficient parking space, and improves the standardized management level of the scenic spot.
Description
Technical Field
The invention relates to a method for predicting the number of coming vehicles of a parking lot in a tourist attraction, in particular to a method for predicting the number of coming vehicles of the parking lot in the tourist attraction based on big data, and belongs to the technical field of internet big data.
Background
In recent years, with the improvement of living standard of people, more and more people go out to travel, the tourism industry is rapidly developed, the tourist acceptance of scenic spots is increased year by year, the pressure on traffic around the scenic spots is increased rapidly, the method is particularly prominent in holidays and busy seasons of tourism, and the current situation cannot be met by means of management and construction aid decision-making means of the current scenic spots.
Parking in scenic spots is always a difficult problem, and because the parking quantity around the scenic spots is not accurately predicted at present, the parking problem of the parking lot around the scenic spots cannot be efficiently solved, the phenomena of road traffic congestion and parking everywhere occur, and great troubles and troubles are caused for the standardized management of the scenic spots.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for predicting the number of coming vehicles in a parking lot of a tourist attraction based on big data, the method can predict the number of the coming vehicles in the parking lot of the tourist attraction in advance, the parking space management and planning of the parking lot can be conveniently performed in advance in the scenic attraction, traffic jam caused by insufficient parking space is avoided, and the standardized management level of the scenic attraction is improved.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method for predicting the number of coming vehicles in a parking lot of a tourist attraction based on big data comprises the following steps:
(1) arranging a plurality of electric alarm bayonets on all road sections passing through the scenic spot parking lots within the range of 5-10 kilometers of the peripheral radius of the scenic spot parking lots and on road sections having intersection with the road sections passing through the scenic spot parking lots, and acquiring traffic data information including vehicle information, positions of the electric alarm bayonets through which vehicles pass, the vehicle coming direction, the distance from the vehicles to the scenic spot parking lots and the vehicle running speed through the electric alarm bayonets;
(2) uploading driving flow data information including vehicle information, positions of the electric police checkpoints through which vehicles pass, vehicle coming directions, distances of the vehicles from scenic spot parking lots and vehicle driving speeds, which are acquired by each electric police checkpoint, to a data analysis platform in real time;
(3) the data analysis platform predicts the number of coming vehicles arriving at each parking lot after a certain time in the future according to a parking lot parking number algorithm, and sends a prediction result to each parking lot display platform in a scenic spot;
(4) and displaying and issuing the received predicted number of coming vehicles and information in each parking lot in the scenic spot, comparing and analyzing the received predicted number of coming vehicles and the actual remaining parking spaces in the parking lot, and sending out parking early warning.
As another preferred scheme, in the step (1), for the urban scenic spot, the urban traffic data can be directly called, all road sections passing through the scenic spot parking lot within a range of 5-10 kilometers of the peripheral radius of the scenic spot parking lot and traffic bayonets on the road sections having intersection with the road sections passing through the scenic spot parking lot are selected, and the traffic data information including vehicle information, the position of an electric police bayonets through which the vehicle passes, the vehicle coming direction, the distance from the vehicle to the scenic spot parking lot, and the vehicle running speed is acquired.
In the foregoing method for predicting the number of coming vehicles in parking lots in tourist attractions based on big data, specifically, the parking lot parking number algorithm in step (3) predicts the number of coming vehicles in each parking lot by a probability prediction method, a coming direction prediction method, and a distance + driving speed prediction method.
The method for predicting the number of coming vehicles in the parking lot of the tourist attraction based on the big data specifically comprises the following steps: the number of passing vehicles at the peripheral electric police checkpoints of the scenic spot is counted, the number of vehicles in each parking lot is calculated every ten minutes, a predicted probability value is gradually improved after long-time calculation and prediction, the number of possible coming vehicles in each parking lot can be predicted in advance through the probability value, and the probability value is distinguished according to holidays and ordinary days.
The method for predicting the number of coming vehicles in the parking lots in the tourist attraction based on the big data specifically comprises the steps of marking the driving tracks of the vehicles according to the vehicle information collected by each electric police access so as to obtain the driving tracks, and judging according to the driving tracks and the coming vehicle directions of the vehicles so as to predict which parking lot the vehicles can park.
The distance + driving speed prediction method predicts the parking number of the parking lot according to the distance between the vehicle and the parking lot of the scenic spot and the driving speed of the vehicle, and comprises the following steps: and predicting the time of the vehicle reaching the parking lot according to the distance from the electric police access to each parking lot and the vehicle running speed and the current traffic jam condition.
According to the tourist attraction parking lot incoming vehicle quantity prediction method based on the big data, as a more preferable scheme, after the step (4), the actual incoming vehicle quantity and the actual parking space condition of each parking lot can be fed back to the data analysis platform, the data analysis platform performs comparative analysis according to the calculated parking quantity and the actual parking quantity, and the parking quantity algorithm of the parking lots is optimized, so that the prediction result is more and more accurate.
The invention has the beneficial effects that: compared with the prior art, the method can predict the number of vehicles in the parking lot of the scenic spot in advance, particularly under the condition of a travel peak, a parking lot manager can obtain the number of vehicles probably coming from the parking lot in the future in advance, and can make parking space management and planning of the parking lot in advance by accurately predicting the number of the parked vehicles in the parking lot, so that traffic jam caused by insufficient parking spaces is avoided, and the standardized management level of the scenic spot is improved. The invention comprehensively adopts a probability prediction method, an incoming vehicle direction prediction method and a distance and driving speed prediction method to predict the parking quantity of the parking lots, the prediction data is very accurate, vehicles which just pass by are rejected, and can not select to park in the parking lots, the actual incoming vehicle quantity and the actual parking space condition of each parking lot are fed back to the data analysis platform, the data analysis platform carries out comparison analysis according to the calculated parking quantity and the actual parking quantity, the parking quantity algorithm of the parking lots is continuously optimized, and finally the prediction result and the actual result are infinitely close to or even equal to each other.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
The invention is further described with reference to the following figures and detailed description.
Detailed Description
Example 1: as shown in fig. 1, a method for predicting the number of coming vehicles in a parking lot in a tourist attraction based on big data includes the following steps:
(1) a plurality of electric alarm bayonets are arranged on all road sections passing through the scenic spot parking lot within the range of 5 kilometers of the peripheral radius of the scenic spot parking lot and on road sections having intersection with the road sections passing through the scenic spot parking lot, the electric alarm bayonets are main equipment for road monitoring, and can quickly capture hit-and-run vehicles, violation vehicles and blacklist vehicles, and can continuously and automatically record the composition, flow distribution and violation conditions of road running vehicles all the year round, and the driving flow data information comprising vehicle information (including license plate number, vehicle type, vehicle body color and the like), the position of the electric alarm bayonets passed by the vehicles, the vehicle coming direction, the distance of the vehicles from the scenic spot parking lot and the vehicle driving speed is collected through the electric alarm bayonets. For the urban scenic spot with a perfect traffic monitoring system, the urban traffic data can be directly called, all road sections passing through the scenic spot parking lot within the range of 5-10 kilometers of the peripheral radius of the scenic spot parking lot and traffic bayonets on the road sections having intersection with the road sections passing through the scenic spot parking lot are selected, and the traffic data information including vehicle information, the positions of electric police bayonets passed by the vehicles, the vehicle coming direction, the distance between the vehicles and the scenic spot parking lot and the vehicle running speed is acquired.
(2) Uploading the traffic data information including the vehicle information, the position of the electric police access where the vehicle passes, the vehicle coming direction, the distance between the vehicle and the parking lot in the scenic spot and the vehicle running speed, which are acquired by each electric police access, to a data analysis platform in real time through optical fibers;
(3) the data analysis platform predicts the number of coming vehicles arriving at each parking lot after 10 minutes or 1 hour in the future according to the parking number algorithm of the parking lots; and sending the prediction result to each parking lot display platform in the scenic spot. The parking lot parking number algorithm predicts the number of coming vehicles of each parking lot through a probability prediction method, a coming vehicle direction prediction method and a distance and driving speed prediction method, wherein the probability prediction method comprises the following processes: the number of passing vehicles at the peripheral electric police checkpoints of the scenic spot is counted, the number of vehicles in each parking lot is calculated every ten minutes, a predicted probability value is gradually improved after long-time calculation and prediction, the number of possible coming vehicles in each parking lot can be predicted in advance through the probability value, and the probability value is distinguished according to holidays and ordinary days. The incoming direction prediction method comprises the steps of marking the driving track of a vehicle according to vehicle information collected by each electric police checkpoint so as to obtain the driving track, judging according to the driving track and the incoming direction of the vehicle, and predicting which parking lot the vehicle can park; the initial prediction results will not be very accurate, but the more times the system predicts, the more accurate the predicted results will be. The distance and driving speed predicting method predicts the parking number of the parking lot according to the distance between the vehicle and the parking lot in the scenic spot and the driving speed of the vehicle, and comprises the following steps: and predicting the time of the vehicle reaching the parking lot according to the distance from the electric police access to each parking lot and the vehicle running speed and the current traffic jam condition.
(4) And displaying the received predicted number of coming vehicles and issuing information in each parking lot in the scenic spot, comparing and analyzing the received predicted number of coming vehicles and the actual remaining parking spaces in the parking lot, and sending out parking early warning on a display platform of the parking lot.
(5) In order to enable the prediction result to be more and more accurate, the actual number of coming vehicles and the actual parking space condition of each parking lot can be fed back to the data analysis platform, the data analysis platform carries out comparison analysis according to the calculated parking number and the actual parking number, the parking number algorithm of the parking lot is continuously optimized, and finally the prediction result and the actual result are infinitely close to or even equal to each other.
The embodiments of the present invention are not limited to the above-described examples, and various changes made without departing from the spirit of the present invention are within the scope of the present invention.
Claims (7)
1. A tourist attraction parking lot coming vehicle number prediction method based on big data is characterized by comprising the following steps:
arranging a plurality of electric alarm bayonets on all road sections passing through the scenic spot parking lots within the range of 5-10 kilometers of the peripheral radius of the scenic spot parking lots and on road sections having intersection with the road sections passing through the scenic spot parking lots, and acquiring traffic data information including vehicle information, positions of the electric alarm bayonets through which vehicles pass, the vehicle coming direction, the distance from the vehicles to the scenic spot parking lots and the vehicle running speed through the electric alarm bayonets;
uploading driving flow data information including vehicle information, positions of the electric police checkpoints through which vehicles pass, vehicle coming directions, distances of the vehicles from scenic spot parking lots and vehicle driving speeds, which are acquired by each electric police checkpoint, to a data analysis platform in real time;
the data analysis platform predicts the number of coming vehicles arriving at each parking lot after a certain time in the future according to a parking lot parking number algorithm, and sends a prediction result to each parking lot display platform in a scenic spot;
and displaying and issuing the received predicted number of coming vehicles and information in each parking lot in the scenic spot, comparing and analyzing the received predicted number of coming vehicles and the actual remaining parking spaces in the parking lot, and sending out parking early warning.
2. The big data based tourist attraction parking lot coming number prediction method according to claim 1, characterized in that: in the step (1), for an urban scenic spot, the urban traffic data can be directly called, all traffic bayonets on road sections passing through the scenic spot parking lot and road sections intersecting with the road sections passing through the scenic spot parking lot within the range of 5-10 kilometers of the peripheral radius of the scenic spot parking lot are selected, and traffic data information including vehicle information, the positions of electric police bayonets through which vehicles pass, the direction of the vehicles coming, the distance from the vehicles to the scenic spot parking lot and the vehicle running speed is collected.
3. The big data based tourist attraction parking lot coming number prediction method according to claim 1, characterized in that: the number of the vehicles coming from the parking lot in the step (3) is predicted by a probability prediction method, a direction of the vehicles coming from the parking lot, and a distance and driving speed prediction method.
4. The big-data-based method for predicting the number of coming vehicles in a parking lot at a tourist attraction as claimed in claim 3, wherein: the probability prediction method comprises the following processes: the number of passing vehicles at the peripheral electric police checkpoints of the scenic spot is counted, the number of vehicles in each parking lot is calculated every ten minutes, a predicted probability value is gradually improved after long-time calculation and prediction, the number of possible coming vehicles in each parking lot can be predicted in advance through the probability value, and the probability value is distinguished according to holidays and ordinary days.
5. The big-data-based method for predicting the number of coming vehicles in a parking lot at a tourist attraction as claimed in claim 3, wherein: the incoming direction prediction method includes marking the driving track of the vehicle according to vehicle information collected by each electric police checkpoint so as to obtain the driving track, and judging which parking lot the vehicle can park according to the driving track and the incoming direction of the vehicle.
6. The big-data-based method for predicting the number of coming vehicles in a parking lot at a tourist attraction as claimed in claim 3, wherein: the distance and driving speed predicting method predicts the parking number of the parking lot according to the distance between the vehicle and the parking lot in the scenic spot and the driving speed of the vehicle, and comprises the following steps: and predicting the time of the vehicle reaching the parking lot according to the distance from the electric police access to each parking lot and the vehicle running speed and the current traffic jam condition.
7. The big data based tourist attraction parking lot coming number prediction method according to claim 1, characterized in that: and (4) feeding the actual number of coming vehicles and the actual parking space condition back to the data analysis platform by each parking lot after the step (4), and carrying out comparative analysis on the data analysis platform according to the calculated parking number and the actual parking number to optimize a parking number algorithm of the parking lot.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911304445.8A CN111127882A (en) | 2019-12-18 | 2019-12-18 | Tourist attraction parking lot coming vehicle number prediction method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911304445.8A CN111127882A (en) | 2019-12-18 | 2019-12-18 | Tourist attraction parking lot coming vehicle number prediction method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111127882A true CN111127882A (en) | 2020-05-08 |
Family
ID=70499392
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911304445.8A Pending CN111127882A (en) | 2019-12-18 | 2019-12-18 | Tourist attraction parking lot coming vehicle number prediction method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111127882A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112289041A (en) * | 2020-10-25 | 2021-01-29 | 储美红 | Intelligent parking lot management method based on hotspot positioning and information sharing and cloud platform |
| CN112802331A (en) * | 2020-12-30 | 2021-05-14 | 青岛中兴智能交通有限公司 | System and method for judging urban traffic travel rule according to parking data |
| CN114822070A (en) * | 2022-03-28 | 2022-07-29 | 阿里巴巴（中国）有限公司 | Parking lot state determination method and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104123833A (en) * | 2013-04-25 | 2014-10-29 | 北京搜狗信息服务有限公司 | Road condition planning method and device thereof |
| CN106128164A (en) * | 2016-08-26 | 2016-11-16 | 成都鑫原羿天科技有限责任公司 | The safety management system of tourist attraction |
| CN107613454A (en) * | 2017-09-21 | 2018-01-19 | 南京中高知识产权股份有限公司 | Parking stall Sharing Management platform |
| CN107730427A (en) * | 2017-10-09 | 2018-02-23 | 安徽畅通行交通信息服务有限公司 | A kind of scenic spot Traffic monitoring management system |
| CN110491157A (en) * | 2019-07-23 | 2019-11-22 | 中山大学 | A kind of vehicle correlating method based on parking data and bayonet data |
-
2019
- 2019-12-18 CN CN201911304445.8A patent/CN111127882A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104123833A (en) * | 2013-04-25 | 2014-10-29 | 北京搜狗信息服务有限公司 | Road condition planning method and device thereof |
| CN106128164A (en) * | 2016-08-26 | 2016-11-16 | 成都鑫原羿天科技有限责任公司 | The safety management system of tourist attraction |
| CN107613454A (en) * | 2017-09-21 | 2018-01-19 | 南京中高知识产权股份有限公司 | Parking stall Sharing Management platform |
| CN107730427A (en) * | 2017-10-09 | 2018-02-23 | 安徽畅通行交通信息服务有限公司 | A kind of scenic spot Traffic monitoring management system |
| CN110491157A (en) * | 2019-07-23 | 2019-11-22 | 中山大学 | A kind of vehicle correlating method based on parking data and bayonet data |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112289041A (en) * | 2020-10-25 | 2021-01-29 | 储美红 | Intelligent parking lot management method based on hotspot positioning and information sharing and cloud platform |
| CN112289041B (en) * | 2020-10-25 | 2021-12-03 | 上海智能交通有限公司 | Intelligent parking lot management method based on hotspot positioning and information sharing and cloud platform |
| CN112802331A (en) * | 2020-12-30 | 2021-05-14 | 青岛中兴智能交通有限公司 | System and method for judging urban traffic travel rule according to parking data |
| CN114822070A (en) * | 2022-03-28 | 2022-07-29 | 阿里巴巴（中国）有限公司 | Parking lot state determination method and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Yoon et al. | Surface street traffic estimation | |
| US11847908B2 (en) | Data processing for connected and autonomous vehicles | |
| CN111091720B (en) | Congestion road section identification method and device based on signaling data and floating car data | |
| CN110164130B (en) | Traffic incident detection method, device, equipment and storage medium | |
| CN109191876B (en) | Special vehicle traffic guidance system based on Internet of vehicles technology and control method thereof | |
| CN111127882A (en) | Tourist attraction parking lot coming vehicle number prediction method based on big data | |
| CN112598182A (en) | Intelligent scheduling method and system for rail transit | |
| CN107590999B (en) | Traffic state discrimination method based on checkpoint data | |
| CN109493606B (en) | Method and system for identifying illegal parking vehicles on expressway | |
| CN111915896A (en) | Intelligent traffic system and method based on Internet of things | |
| CN109979197B (en) | Method and system for constructing highway traffic time map based on fusion data | |
| CN113077084A (en) | Tourist attraction visitor flow early warning device | |
| CN110853358A (en) | Insurance processing method based on driving behaviors and Internet of things intelligent terminal | |
| CN110807929A (en) | Traffic police auxiliary command information analysis and research and judgment system | |
| CN109934161B (en) | Vehicle identification and detection method and system based on convolutional neural network | |
| CN111008747A (en) | Scenic spot passenger flow volume prediction method based on traffic data fusion | |
| CN114999181B (en) | Highway vehicle speed abnormality identification method based on ETC system data | |
| CN113284338B (en) | Method for calculating influence of motor vehicle emergency avoidance no-lamp control pedestrian crossing on traffic flow | |
| CN109377759B (en) | Method for acquiring train journey time in discontinuous traffic flow | |
| CN114038202A (en) | Parking guidance system and method based on intelligent park traffic flow | |
| CN114360264A (en) | Intelligent city traffic management method based on traffic flow regulation | |
| CN111861498B (en) | Monitoring method, device, equipment and storage medium for taxis | |
| CN114283615A (en) | Passenger flow early warning system and method based on traffic flows of multiple parking lots in scenic spot | |
| CN111626598A (en) | Road information management system based on cloud computing | |
| CN113128847A (en) | Entrance ramp real-time risk early warning system and method based on laser radar |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200508 |