CN109300312B - Road condition analysis method and system based on vehicle big data - Google Patents
Road condition analysis method and system based on vehicle big data Download PDFInfo
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- CN109300312B CN109300312B CN201811486581.9A CN201811486581A CN109300312B CN 109300312 B CN109300312 B CN 109300312B CN 201811486581 A CN201811486581 A CN 201811486581A CN 109300312 B CN109300312 B CN 109300312B
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- 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
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- 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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
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Abstract
The invention provides a road condition analysis method and a road condition analysis system based on vehicle big data, wherein the road condition analysis method comprises the following steps: step S1, acquiring longitude and latitude data set of the vehicle by collecting the position information; step S2, calibrating the acquired longitude and latitude data set; step S3, road section division is carried out on the calibrated data, vehicles in road section intervals after the road section division are sampled, and the positions of the vehicles in the road section intervals are counted; step S4, calculating an average speed in the section according to the change of the vehicle position, counting the number of vehicles in the section, calculating a congestion coefficient of the section, and comparing the congestion coefficient with a preset congestion threshold value to determine the specific road condition in the section. The method corrects the error caused by inaccurate positioning of the vehicle-mounted terminal, and calculates the road congestion coefficient through the branch section interval and the designed special algorithm, so that the accuracy and the effectiveness of road congestion analysis can be effectively improved.
Description
Technical Field
The present invention relates to a traffic analysis method, and more particularly, to a traffic analysis method based on vehicle big data, and a traffic analysis system using the traffic analysis method based on vehicle big data.
Background
At present, traffic road condition analysis mainly comprises specific discrimination analysis aiming at a certain road section, vehicle position calculation speed is determined according to the longitude and latitude of GPS (global positioning system) or Beidou positioning uploaded by a vehicle, the technology mainly relies on the longitude and latitude of the GPS or the Beidou positioning for analysis, and the accuracy of vehicle motion trail is improved mainly by means of the positioning precision of a vehicle-mounted terminal under the condition that errors caused by calibration are ignored. The accuracy of GPS positioning is affected by many environmental factors, which results in positioning deviation and errors in road condition analysis.
In the prior art, the accuracy of the vehicle position depends on the positioning accuracy of the vehicle-mounted terminal, the accuracy of the track depends on the accuracy of the map calibration data, the GPS drift affects a plurality of positioning drifts, and the speed calculation may have errors due to the GPS accuracy.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a road condition analysis method based on vehicle big data, which can improve accuracy, and further provide a road condition analysis system using the road condition analysis method based on vehicle big data.
Therefore, the invention provides a road condition analysis method based on vehicle big data, which comprises the following steps:
step S1, acquiring longitude and latitude data set of the vehicle by collecting the position information;
step S2, calibrating the acquired longitude and latitude data set;
step S3, road section division is carried out on the calibrated data, vehicles in road section intervals after the road section division are sampled, and the positions of the vehicles in the road section intervals are counted;
step S4, calculating an average speed in the section according to the change of the vehicle position, counting the number of vehicles in the section, calculating a congestion coefficient of the section, and comparing the congestion coefficient with a preset congestion threshold value to determine the specific road condition in the section.
In a further improvement of the present invention, in the step S3, the process of dividing the calibrated data into segments is as follows: comparing the acquired longitude and latitude data with longitude and latitude data of an actual road, and dividing n road section intervals according to the longitude and latitude data of the actual road according to average distribution, wherein n is a natural number; and then, the positioning data of the vehicle is put into the corresponding road section intervals, and the vehicle position and the vehicle speed in each road section interval are counted.
A further refinement of the invention is that said step S4 comprises the following sub-steps:
step S401, calculating the average speed of the same vehicle in a preset time range according to the section length and the time difference of the same vehicle in the section;
step S402, counting the number of vehicles in the section of the road section, and calculating a road section congestion coefficient;
and step S403, judging the specific road condition of the section of the road section according to the road congestion coefficient.
In a further improvement of the present invention, in the step S401, an average speed of the vehicle is calculated by a formula v ═ L2-L1)/(t2-t1, where t1 is a start time of the preset time range, t2 is an end time of the preset time range, L1 is a start anchor point of the preset time range, and L2 is an end anchor point of the preset time range.
In a further improvement of the present invention, in the step S402, the link congestion coefficient M is calculated by a formula M ═ ((Vmax-V) ÷ (V-Vmin) ÷ V) × × × μ, where Vmax is a maximum speed of the vehicle, V is an average speed of the vehicle, Vmin is a minimum speed of the vehicle, and μ is a fixed coefficient, and a ratio of the number of vehicles in the link section to the total number of vehicles.
In a further improvement of the present invention, in the step S403, the congestion coefficient M of the road section is compared with a preset congestion threshold value, and if the congestion coefficient M of the road section is less than the preset congestion threshold value, the specific road condition in the section of the road section is determined to be congestion.
The invention is further improved in that the preset congestion critical value is 0.15-0.3.
A further improvement of the present invention is that the step S2 is to calibrate the acquired longitude and latitude data set by: uploading the original longitude and latitude data in the longitude and latitude data set of the vehicle obtained in the step S1 through calling an application programming interface of the hundred-degree map, calibrating the original longitude and latitude data into the longitude and latitude data of the hundred-degree map, and then returning to obtain calibrated data.
In a further improvement of the present invention, in the step S4, the average speed in the section is automatically calculated again every preset fixed time period, and the number of vehicles in the section is counted to calculate the corresponding congestion coefficient of the road section.
The invention also provides a road condition analysis system based on the vehicle big data, which adopts the road condition analysis method based on the vehicle big data.
Compared with the prior art, the invention has the beneficial effects that: through the step S2, the error of inaccurate positioning of the vehicle-mounted terminal GPS under various reasons is corrected, and the road congestion coefficient is calculated through the branch section interval and the designed special algorithm, so that the accuracy and the effectiveness of road congestion analysis can be effectively improved, and the error of road condition analysis is reduced; on the basis, the problem of poor real-time performance of road section analysis is further corrected.
Drawings
FIG. 1 is a schematic workflow diagram of one embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, the present embodiment provides a road condition analysis method based on vehicle big data, including the following steps:
step S1, acquiring longitude and latitude data set of the vehicle by collecting the position information;
step S2, calibrating the acquired longitude and latitude data set;
step S3, road section division is carried out on the calibrated data, vehicles in road section intervals after the road section division are sampled, and the positions of the vehicles in the road section intervals are counted;
step S4, calculating an average speed in the section according to the change of the vehicle position, counting the number of vehicles in the section, calculating a congestion coefficient of the section, and comparing the congestion coefficient with a preset congestion threshold value to determine the specific road condition in the section.
The method comprises the steps of obtaining the current movement speed of a vehicle in a certain time range of a certain road section by using navigation data provided by a third party, obtaining the movement speed of the vehicle in a large range, calculating the average speed of the vehicle, calculating a road section congestion coefficient by a self-designed algorithm, and comparing the road section congestion coefficient with a preset congestion critical value to obtain an accurate specific road condition.
In this example, step S1 obtains the original trajectory data uploaded by the terminal on the platform in the positioning mode, and step S2 calibrates the data according to the map type specified by the request, and requests the third party for the path planning data of the map corresponding to the third party.
Step S3 in this example realizes speed interval segmentation, and obtains positioning information uploaded by the vehicle; in step S3, if the uploading frequency of the positions is greatly different, which may be caused by the difference in signal problem transmission, the vehicle data should be discarded, and the total number of the vehicles should be counted. In the step S4, the average speed of the specific vehicle speed uploaded within a certain time period is calculated, and then the maximum speed and the minimum speed are compared with the average speed for analysis, and a specific value of the road congestion coefficient is obtained through an autonomously designed algorithm.
More specifically, in step S1 in this example, the position information may be collected through an existing vehicle-mounted terminal to obtain longitude and latitude data of the vehicle, so as to form a longitude and latitude data set in a time period. The step S2 is to calibrate the acquired longitude and latitude data set by: uploading the original longitude and latitude data in the longitude and latitude data set of the vehicle obtained in the step S1 through calling an application programming interface of the hundred-degree map, calibrating the original longitude and latitude data into the longitude and latitude data of the hundred-degree map, and then returning to obtain calibrated data.
In step S3 in this example, the process of segment division for the calibrated data is as follows: comparing the collected longitude and latitude data with the longitude and latitude data of an actual road, and dividing n road section intervals according to the longitude and latitude data of the actual road, wherein n is a natural number (the value of n can be adjusted and set according to actual needs); and then, the positioning data of the vehicle is put into the corresponding road section intervals, and the vehicle position and the vehicle speed in each road section interval are counted.
Step S4 in this example includes the following substeps:
step S401, calculating the average speed of the same vehicle in a preset time range according to the section length and the time difference of the same vehicle in the section;
step S402, counting the number of vehicles in the section of the road section, and calculating a road section congestion coefficient;
and step S403, judging the specific road condition of the section of the road section according to the road congestion coefficient.
In step S401, the average speed of the vehicle is calculated according to the formula v ═ L2-L1)/(t2-t1, where t1 is the start time of the preset time range, t2 is the end time of the preset time range, L1 is the start anchor point of the preset time range, and L2 is the end anchor point of the preset time range. The preset time range can be customized and adjusted according to actual needs.
In step S402, the link congestion coefficient M is calculated by the formula M ═ ((Vmax-V) ÷ (V-Vmin) ÷ V) × × μ, where Vmax is the maximum speed of the vehicle, V is the average speed of the vehicle, Vmin is the minimum speed of the vehicle, and μ is a fixed coefficient, and the ratio of the number of vehicles in the link section to the total number of vehicles. The calculated congestion condition calculated by calculating the road section congestion coefficient M is high in accuracy, wherein the calculated congestion condition is the ratio of the number of vehicles in the road section to the total number of vehicles, namely the ratio of the number of vehicles in the road section to the total number of vehicles on the actual road subjected to road section division, namely the algorithm related to the embodiment not only considers the condition in the road section but also considers the condition of the actual road subjected to road section division; mu is a fixed coefficient, preferably 0.35-0.75, and in practical application, the fixed coefficient mu can be set and adjusted in a user-defined manner according to actual needs.
In step S403, comparing the congestion coefficient M of the road segment with a preset congestion critical value, and if the congestion coefficient M of the road segment is smaller than the preset congestion critical value, determining that the specific road condition in the section of the road segment is congested; otherwise, the specific road condition in the section of the road section is judged to be smooth.
In this example, the preset congestion critical value is 0.15 to 0.3, and the optimal congestion critical value is 0.175, but of course, the selection of this value may also be adjusted and set according to actual needs, and may also be divided into a plurality of different congestion levels by setting a plurality of different congestion threshold values.
In step S4, the average speed in the section is automatically calculated again every preset fixed time period, and the number of vehicles in the section is counted to calculate the corresponding congestion coefficient of the section, thereby correcting the problem of poor real-time performance of section analysis.
The embodiment also provides a road condition analysis system based on the vehicle big data, and the road condition analysis method based on the vehicle big data is adopted.
Compared with the prior art, the invention has the beneficial effects that: through the step S2, the error of inaccurate positioning of the vehicle-mounted terminal GPS under various reasons is corrected, and the road congestion coefficient is calculated through the branch section interval and the designed special algorithm, so that the accuracy and the effectiveness of road congestion analysis can be effectively improved, and the error of road condition analysis is reduced; on the basis, the problem of poor real-time performance of road section analysis is further corrected.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (7)
1. A road condition analysis method based on vehicle big data is characterized by comprising the following steps:
step S1, acquiring longitude and latitude data set of the vehicle by collecting the position information;
step S2, calibrating the acquired longitude and latitude data set;
step S3, road section division is carried out on the calibrated data, vehicles in road section intervals after the road section division are sampled, and the positions of the vehicles in the road section intervals are counted;
step S4, calculating the average speed in the section of the road section according to the change of the position of the vehicle, meanwhile, counting the number of the vehicles in the section of the road section, calculating the congestion coefficient of the road section, and comparing the congestion coefficient of the road section with a preset congestion critical value to judge the specific road condition in the section of the road section;
the step S4 includes the following sub-steps:
step S401, calculating the average speed of the same vehicle in a preset time range according to the section length and the time difference of the same vehicle in the section;
step S402, counting the number of vehicles in the section of the road section, and calculating a road section congestion coefficient;
step S403, judging the specific road condition of the section of the road section according to the road congestion coefficient;
in the step S402, a road congestion coefficient M is calculated by a formula M ═ ((Vmax-V) ÷ V + (V-Vmin) ÷ V) × × μ, where Vmax is a maximum speed of the vehicle, V is an average speed of the vehicle, Vmin is a minimum speed of the vehicle, and is a ratio of the number of vehicles in a road section to the total number of vehicles, that is, a ratio of the number of vehicles in the road section to the total number of vehicles on an actual road on which the road section is divided, μ is a fixed coefficient, the preset congestion critical value is 0.15 to 0.3, and the fixed coefficient μ is 0.35 to 0.75.
2. A traffic analysis method according to claim 1, wherein in step S3, the process of dividing the calibrated data into segments is as follows: comparing the acquired longitude and latitude data with longitude and latitude data of an actual road, and dividing n road section intervals according to the longitude and latitude data of the actual road according to average distribution, wherein n is a natural number; and then, the positioning data of the vehicle is put into the corresponding road section intervals, and the vehicle position and the vehicle speed in each road section interval are counted.
3. A road condition analysis method based on vehicle big data as claimed in claim 1 or 2, characterized in that in step S401, the average speed of the vehicle is calculated by formula v ═ (L2-L1)/(t2-t1), where t1 is the start time of the preset time range, t2 is the end time of the preset time range, L1 is the start anchor point of the preset time range, and L2 is the end anchor point of the preset time range.
4. A traffic analysis method according to claim 1 or 2, wherein in step S403, a congestion coefficient M of a road segment is compared with a preset congestion threshold value, and if the congestion coefficient M of the road segment is less than the preset congestion threshold value, the specific traffic in the section of the road segment is determined to be congested.
5. A road condition analysis method based on vehicle big data as claimed in claim 1 or 2, characterized in that the step S2 is to calibrate the obtained longitude and latitude data set by: uploading the original longitude and latitude data in the longitude and latitude data set of the vehicle obtained in the step S1 through calling an application programming interface of the hundred-degree map, calibrating the original longitude and latitude data into the longitude and latitude data of the hundred-degree map, and then returning to obtain calibrated data.
6. A traffic analysis method according to claim 1 or 2, wherein in step S4, the average speed in the section is automatically calculated again every preset fixed time period, and the number of vehicles in the section is counted to calculate the congestion coefficient of the corresponding section.
7. A road condition analysis system based on vehicle big data is characterized in that the road condition analysis method based on the vehicle big data as claimed in any one of claims 1 to 6 is adopted.
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| CN110047276B (en) * | 2019-03-11 | 2020-11-27 | 广州文远知行科技有限公司 | Method and device for determining congestion state of obstacle vehicle and related product |
| CN111833593B (en) * | 2019-04-18 | 2022-12-27 | 北京京东乾石科技有限公司 | Method and device for controlling vehicle, control method executed by vehicle and vehicle |
| CN111739283B (en) * | 2019-10-30 | 2022-05-20 | 腾讯科技（深圳）有限公司 | Road condition calculation method, device, equipment and medium based on clustering |
| CN112071088B (en) * | 2020-08-28 | 2023-05-30 | 深圳市城铭科技有限公司 | Anti-blocking traffic light control system and control method |
| CN112652170B (en) * | 2020-12-24 | 2022-04-08 | 航天科工智能运筹与信息安全研究院(武汉)有限公司 | Method and device for positioning traffic jam hot spot |
| CN112463911A (en) * | 2021-02-02 | 2021-03-09 | 智道网联科技(北京)有限公司 | Road activity determination method and device and storage medium |
| CN112990678B (en) * | 2021-03-04 | 2022-06-24 | 国网安徽省电力有限公司电力科学研究院 | Icing early warning judgment method based on multi-source data fusion |
| CN113808411A (en) * | 2021-09-13 | 2021-12-17 | 天津津航计算技术研究所 | Automobile safety speed limiting device based on cooperative work of GPS, Internet of vehicles and CANFD bus |
| CN114267188A (en) * | 2021-12-17 | 2022-04-01 | 航天科技控股集团股份有限公司 | Method and system for reducing road vehicle congestion |
| CN115547030A (en) * | 2022-07-26 | 2022-12-30 | 北京万集科技股份有限公司 | Traffic state early warning method and system, storage medium and electronic device |
| CN116128224B (en) * | 2022-12-23 | 2024-01-26 | 山东省人工智能研究院 | Intelligent driving decision method integrating road network equalization and accident risk assessment |
| CN116206478B (en) * | 2023-01-17 | 2023-08-29 | 青岛交通科技信息有限公司 | Traffic transportation cloud data sharing management system and method based on big data |
| CN116778719A (en) * | 2023-08-24 | 2023-09-19 | 中汽科技(北京)有限公司 | Real-time road condition prediction method and system based on GPS data in PEMS |
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| CN103065472A (en) * | 2012-12-24 | 2013-04-24 | 中国科学院深圳先进技术研究院 | Real-time traffic status analysis method and real-time traffic status analysis system |
| CN105509753B (en) * | 2015-12-28 | 2019-01-25 | 北京握奇智能科技有限公司 | A kind of map-matching method and system based on Floating Car satellite location data |
| CN105489008B (en) * | 2015-12-28 | 2018-10-19 | 北京握奇智能科技有限公司 | Urban road congestion computational methods and system based on Floating Car satellite location data |
| CN106169243B (en) * | 2016-08-25 | 2019-03-15 | 武汉理工大学 | A kind of real-time road estimating system and method based on car networking |
| CN108280995B (en) * | 2017-12-29 | 2020-10-23 | 广州斯沃德科技有限公司 | Road condition data processing method and device of Internet of vehicles and computer equipment |
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