CN113870559B - Traffic flow calculation method based on big data Internet of vehicles - Google Patents
Traffic flow calculation method based on big data Internet of vehicles Download PDFInfo
- Publication number
- CN113870559B CN113870559B CN202111137546.8A CN202111137546A CN113870559B CN 113870559 B CN113870559 B CN 113870559B CN 202111137546 A CN202111137546 A CN 202111137546A CN 113870559 B CN113870559 B CN 113870559B
- Authority
- CN
- China
- Prior art keywords
- road
- bus
- traffic
- vehicle
- stop
- 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.)
- Active
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 15
- 239000013598 vector Substances 0.000 claims description 16
- 230000007774 longterm Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 abstract description 2
- 238000012544 monitoring process Methods 0.000 description 4
- 238000007726 management method Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000009193 crawling Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000009440 infrastructure construction Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
-
- 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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- 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]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a traffic flow calculation method based on a big data internet of vehicles, which is characterized in that a traffic route map of a specific area is constructed based on urban new energy automobile running data and road bus station information data collected by an internet of vehicles platform, and complete routes of different grades including trunk roads, branch roads and the like can be truly reflected by a convenient means; the road flow feedback system based on big data is constructed after calculation processing by combining new energy real vehicle running data collected by the Internet of vehicles platform, compared with the existing vehicle flow velocity calculation method, the road flow feedback system based on big data has more full data, and the result has higher accuracy. Compared with the existing trunk road construction methods, the method reduces the manpower and economic expenses and has higher cost performance.
Description
Technical Field
The invention belongs to the technical field of urban road traffic flow monitoring, and particularly relates to a traffic flow calculation method based on a big data internet of vehicles.
Background
At the present stage, with the rapid increase of the total vehicle retention in China, traffic jam occurs more frequently in urban roads, and serious influence is caused on daily traffic and urban management. The traffic flow monitoring is implemented on part of key road sections, and the method has very important significance for management of traffic management departments and guidance of future urban road network planning. The existing traffic flow monitoring means mainly rely on video acquisition equipment and are realized by shooting vehicles passing through corresponding road sections within a period of time and identifying and counting the vehicles. The method is limited by the requirements of higher data processing cost, higher laying cost of video acquisition equipment and the like, and all-weather traffic statistics cannot be realized on the whole urban road network. Even for some important road sections, the traffic flow in a long time range is estimated mostly based on metering in a short time period.
Disclosure of Invention
In view of this, the invention aims to realize global and all-weather traffic flow measurement of an urban road network through lower data processing and infrastructure construction cost based on the advantages of real vehicle big data and the Internet of vehicles of new energy vehicles. The invention provides a traffic flow calculation method based on big data internet of vehicles, which specifically comprises the following steps:
step one, extracting all bus stop information set in a road by using network public information, wherein the method comprises the following steps: carrying out coordinate sorting, duplicate removal and storage processing on the bus station data of the station name, the belonging line name, the station longitude and latitude coordinates, the passing vehicles and the like;
step two, acquiring real vehicle data of the new energy bus running on the road, calculating running vectors of the new energy bus entering the area to bus stops with two different stop names in the area aiming at the circular area range radiated by each bus stop after processing, determining whether the two stops belong to similar stops on the same main road according to the cross product of the two running vectors, and merging the similar stops;
step three; traversing the stops of the new energy bus routes of all the lines to obtain a traffic line topological table of each line, and fitting an actual road based on the topological table to obtain a digital traffic line map;
step four, calculating the running vector of the real vehicle to judge the actual road where the real vehicle is located; calculating the vehicle flow speed according to the speed of the vehicle passing through a certain road section;
and step five, determining corresponding traffic jam levels according to the vehicle flow rates of different road sections, and providing corresponding information for display on the digital traffic route map.
Further, the extracting the bus station data by using the network public information in the first step specifically includes:
crawling map website data by using a crawler technology to obtain the data such as the station name, the route name, the station longitude and latitude coordinates, the passing and stopping vehicles and the like, storing the data as character strings, and establishing a bus driving data information table by taking the affiliated route name as a main key for storage;
the sorting, deduplication and storage processing specifically includes:
all stop names contained in the lines are obtained according to the longitude and latitude coordinate sorting of all bus lines, and a table is stored and established according to a key value format of the [ line name, stop name ] and a character string data type; if the distance between the sites adjacent in sequence is greater than a preset value in the sequencing, determining that the sites belong to different lines;
and removing the weight of the form based on the longitude and latitude coordinates and the data of the vehicles stopped.
Further, the second step specifically comprises:
calculating the running vector of each new energy bus based on the difference of the longitude and latitude coordinates of the vehicles collected successively; and if the cross product of the vehicles running to the bus stops with the different stop names in the circular area is 0, determining that the two bus stops are similar stops on the same main road, and merging the determined similar stops.
Further, the third step specifically comprises:
after traversing the passing stops of the new energy buses of each line, adding adjacent stop fields into a [ line name, stop name ] form to form a traffic line topological table of each line; and constructing the digitized traffic route map based on the topological table and the real vehicle coordinate acquisition point connecting line fitting of the new energy bus.
Further, the fourth step specifically includes:
determining the road by longitude and latitude coordinates of a new source bus and other private and commercial operation vehicles; obtaining the driving direction of the vehicle according to the fact that the longitude variation of the vehicle is larger than 0 or smaller than 0; determining the traffic flow of the same-direction driving based on the cross products of the driving vectors of different vehicles; and calculating the average speed of the same-direction running traffic flow on the road section as the vehicle flow speed at a certain moment.
And further, the step four also comprises the step of determining the road grade corresponding to a certain section based on the long-term vehicle flow speed, and the road grade is used for perfecting the digitized traffic route map and subsequently determining the corresponding traffic jam grade.
Further, the fifth step specifically includes:
aiming at the road grades corresponding to different road sections: setting vehicle flow speed threshold ranges corresponding to different congestion degrees on the expressway, the main road, the secondary main road and the branch road respectively;
calculating a traffic congestion index based on the following formula:
the congestion index is divided into 5 levels, and the congestion is more serious when the numerical value is larger; segmenting a traffic road by using the congestion index and storing corresponding position and vehicle flow speed information; and displaying dark red, orange, green and dark green on the digitalized traffic route map according to the congestion indexes from high to low at the vehicle-mounted terminal so as to provide a display of the segmented congestion degree.
According to the method provided by the invention, the traffic route map of the specific area is constructed based on the running data of the urban new energy vehicles and the road bus station information data collected by the Internet of vehicles platform, and the complete routes including main roads, branches and the like at different levels can be truly reflected by a relatively convenient means; the road flow feedback system based on big data is constructed after calculation processing by combining new energy real vehicle running data collected by the Internet of vehicles platform, compared with the existing vehicle flow velocity calculation method, the road flow feedback system based on big data has more full data, and the result has higher accuracy. Compared with the existing trunk road construction methods, the method reduces the manpower and economic expenses and has higher cost performance.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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 invention provides a traffic flow calculation method based on a big data internet of vehicles, which mainly provides a four-function module comprising traffic road traffic fitting, vehicle driving data and traffic road fitting, road traffic flow rate calculation and traffic flow rate exponential classification. The first big module traffic road fitting module is realized by the following functions: the method comprises the following steps of performing a bus station basic information grabbing function, performing error repair, performing bus station information data processing, constructing a module for roads among stations, and filling the roads with new energy vehicles; the second big module is a vehicle driving data and traffic road fitting module and is composed of 4 parts of functions: the system comprises a vehicle running data real-time capturing function, a route judging function and a vehicle flow velocity calculating function; the third module road traffic flow velocity calculation module is composed of two functions: a regional flow rate vehicle selection function and a flow rate calculation function; and a last part of modules: the traffic flow rate indexing grading module consists of: the traffic flow rate data index conversion function, the index grading function and the visual display function 3.
The method specifically comprises the following steps:
step one, extracting all bus stop information set in a road by using network public information, wherein the method comprises the following steps: carrying out coordinate sorting, duplicate removal and storage processing on the bus station data in sequence by the bus station data such as the station name, the belonging line name, the station longitude and latitude coordinates, the stopped vehicles and the like;
step two, acquiring real vehicle data of the new energy bus running on the road, calculating running vectors of the new energy bus entering the area to bus stops with two different stop names in the area aiming at the circular area range radiated by each bus stop after processing, determining whether the two stops belong to similar stops on the same main road according to the cross product of the two running vectors, and merging the similar stops;
step three; traversing the stops of the new energy bus routes of all the lines to obtain a traffic line topological table of each line, and fitting an actual road based on the topological table to obtain a digital traffic line map;
step four, calculating the running vector of the real vehicle to judge the actual road where the real vehicle is located; calculating the vehicle flow speed according to the speed of the vehicle passing through a certain road section;
and step five, determining corresponding traffic jam levels according to the vehicle flow rates of different road sections, and providing corresponding information display on the digital traffic route map.
Because detailed urban bus route and stop information is provided in a plurality of public service websites, the urban bus route and stop information can be easily obtained by a big data means, and a complex platform for data collection processing and digital map synthesis does not need to be built again, in a preferred embodiment of the invention, the extracting of the bus stop data by using the network public information in the step one specifically comprises the following steps:
crawling map website data by using a crawler technology to obtain data such as the station name, the route name, the station longitude and latitude coordinates, the passing and stopping vehicles and the like, storing the data as character strings, and establishing a bus driving data information table by taking the affiliated route name as a main key for storage; the longitude and latitude coordinates can be based on a high-grade map, catch the longitude and latitude field and reserve 6 effective digits after decimal points, data processing can be carried out on the caught field, bus stops with the same name are selected, and the longitude and latitude mean value of the bus stops with the same name is obtained through the following method and defined as the geometric central point of the bus stop:
wherein, lng is station longitude coordinate, lat is station latitude coordinate, P same Is a longitude and latitude matrix of the ID of the bus stop with the same name,
the geometric center point of the ID of the bus stop with the same name.
The data processing is to number the buses on different lines: and taking the province and city information number as the first 6 bits of the vehicle information (the coding standard is according to the coding rule of each province and city identity card), and the last four bits of the vehicle information are formed by the actual bus line number and the placeholder 0, so as to convert the bus line information. And storing the name, longitude and latitude of the bus and the name ID of the bus route which is driven by the bus in a character string type, and storing the name, longitude and latitude of the bus as a bus route name ID as a main key of a table as a bus driving data information table.
The sorting, deduplication and storage processing specifically includes:
all stop names contained in the lines are obtained according to the longitude and latitude coordinate sorting of all bus lines, and a table is stored and established according to a key value format of the [ line name, stop name ] and a character string data type; if the distance between the stations adjacent in sequence is greater than a preset value, such as 3000 meters, the two stations are determined to belong to different lines;
and removing the weight of the form based on the longitude and latitude coordinates and the data of the vehicles stopped.
In a preferred embodiment of the present invention, the vehicle longitude and latitude coordinate data may be converted from the WGS-84 format to the GCJ-02 format in step two. Merging similar bus stops may help the system reduce the amount of computation. According to the regulations of GB/T51328-2018, the area radius is determined based on the following formula by taking the geometric center point of the bus stop as the center of a circle according to the condition that the connection area of the link facilities of the bus stop cannot exceed 100-120 square meters per bus:
wherein,
is the radius of the circular area. S is the area of a bus station area, a circular area with the radius r of 6.18 meters can be obtained by setting S as a square meter of 120 according to GB/T51328-containing 2018, and the driving vector of the bus is calculated as follows:
V＝P 1 -P 0
where V is a vehicle travel vector, i.e., [ longitude variation, latitude variation ]]，P 0 For calculating a longitude and latitude matrix of the vehicle position at a nearby acquisition point on the vehicle, i.e. [ longitude, latitude]，P 1 Then a longitude and latitude matrix [ longitude, latitude ] of the vehicle driving position is collected for calculation]。
Selecting adjacent running time data of the same vehicle, and subtracting the longitude and latitude information with smaller time from the longitude and latitude information with larger time value to obtain a vehicle running vector; and if the cross product of the vehicles running to the bus stops with the different stop names in the circular area is 0, determining that the two bus stops are similar stops on the same main road, and merging the determined similar stops.
In a preferred embodiment of the present invention, step three specifically includes:
after traversing the passing stops of the new energy buses of each line, adding adjacent stop fields into a [ line name, stop name ] form to form a traffic line topology table of each line, so that road nodes passed by the new energy buses on each bus line are completely collected, and most of the bus lines in urban traffic planning are consistent with main lines, so that the situation of the urban main lines can be reflected more truly by using the topology table; on the basis, the digitized traffic route map is constructed based on the topological table and real vehicle coordinate acquisition point connecting line fitting of the new energy bus, and an urban electronic map platform does not need to be redeveloped or the existing platform and the terminal do not need to be modified.
After the topological structure of the traffic lines and the digital map are determined by using the data of the public transport system, the matching of the roads of the traffic flows including new energy buses, private cars and other numerous commercial operation vehicles can be carried out. Therefore, in a preferred embodiment of the present invention, step four specifically comprises:
determining the road by longitude and latitude coordinates of a new source bus and other private and commercial operation vehicles; obtaining the vehicle running direction according to the fact that the longitude variation of the vehicle is larger than 0 or smaller than 0; determining the traffic flow of the same-direction driving based on the cross products of the driving vectors of different vehicles; and calculating the average speed of the equidirectional running traffic on the road section as the vehicle flow speed at a certain moment.
For some areas with large scale and developed public transportation networks, the road grade is not enough to be distinguished only according to the public transportation system information, but the road grade corresponding to a certain interval can be determined through the long-term vehicle flow rate, and the method is used for perfecting the digitized traffic line diagram and subsequently determining the corresponding traffic jam grade. According to CJJ37-90 City road design Specification, a main road is defined as a main road which is supposed to connect city partitions and mainly takes traffic functions. When the traffic flow of the bicycle is large, non-motor vehicles are preferably adopted for running separately, such as three roads or four roads; the two sides of the main road should not be provided with the entrances and exits of public buildings for absorbing large-flow traffic and people. The width is more than 15 meters, and the width of the red line is more than 3 meters. The speed per hour of the vehicle is 60-80 KM/H. The trunk road identification system is constructed according to the above contents and by combining the field names in the vehicle driving data of the big data Internet of vehicles platform. In order to calculate the flow rate of the vehicles on the main road, the vehicles running on the main road need to be identified. Some prior investigations have shown that the main road can be identified based on the vehicle running speed as an important criterion for judging and determining the main road, and that the vehicle running speed judgment criterion needs to be defined and the reason influencing the vehicle speed judgment needs to be analyzed for the main road judgment. If there are 25% of vehicles in the data table, if there are 5 consecutive time collection points with average speeds greater than 60 km/h and less than 80km/h, it can be determined that the vehicle is traveling in the trunk road during that time. And storing the traffic flow speed result of the road where the vehicle is located into a traffic flow speed field in a vehicle driving data table, and keeping an integral part of the result to be stored in an int type.
In order to perform more detailed flow monitoring according to the actual urban road, in a preferred embodiment of the present invention, step five specifically includes:
aiming at the road grades corresponding to different road sections: the expressway, the main road, the secondary main road and the branch road are respectively set with vehicle flow speed threshold ranges corresponding to different congestion degrees:
TABLE 1
The method for calculating and researching the traffic congestion indexes at home and abroad is mainly determined according to research objects such as road speed data, road traffic density, traffic volume, road travel time and the like. In the north, although the classification definitions of the congestion degrees of the traffic flows are different and the calculation methods are different, the congestion conditions are exponentially reflected on the basis of the proportion, and regular updating with a period of 15 minutes is performed. Thus, in a preferred embodiment of the invention, the traffic congestion index is calculated based on the following formula:
dividing the congestion index into (1, 2, 3, 4, 5)5 levels, wherein the higher the value is, the more serious the congestion is; segmenting the traffic road by using the congestion index and storing corresponding position and vehicle flow speed information; and displaying dark red, orange, green and dark green on the digitalized traffic route map according to the congestion indexes from high to low at the vehicle-mounted terminal so as to provide a display of the segmented congestion degree.
The digitized traffic route map can be constructed by combining satellite aerial photography data based on mapping, reward collection and local government planning, and is based on: and obtaining the judgment process of the road where the vehicle runs by using methods such as clustering, rasterizing, incremental fusion, node linking, computer theory and the like. Compared with the traditional method, the road traffic map obtained by combining the bus driving and stop data can save manpower and material resources, and can be quickly updated according to the driving data of the new energy automobile accessed into the internet of vehicles.
It should be understood that, the sequence numbers of the steps in the embodiment of the present invention do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A traffic flow calculation method based on big data Internet of vehicles is characterized in that: the method specifically comprises the following steps:
step one, extracting all bus stop information set in a road by using network public information, wherein the method comprises the following steps: the method comprises the following steps of storing data of a stop name, a name of a line to which the stop belongs, longitude and latitude coordinates of the stop, and bus stop data of a stopped bus as character strings, and establishing a bus driving data information table by using the name of the line to which the stop belongs as a main key; all stop names contained in the lines are obtained according to the longitude and latitude coordinate sorting of all bus lines, and a table is stored and established according to a key value format of the [ line name, stop name ] and a character string data type; if the distance between the sites adjacent in sequence is greater than a preset value in the sequencing, determining that the sites belong to different lines; removing the weight of the form based on the longitude and latitude coordinates and the data of the vehicles stopped;
step two, acquiring real bus data of the new energy bus running on the road, calculating running vectors of the new energy bus entering the area to bus stops with two different stop names in the area respectively according to the difference of longitude and latitude coordinates of the vehicle collected successively in a circular area range radiated by each bus stop after processing, determining whether the two stops belong to similar stops on the same main road according to the cross product of the two running vectors, and if the cross product of the vehicle running to the bus stops with the two different stop names in the circular area is 0, determining that the two bus stops are the similar stops on the same main road, and merging the similar stops;
step three; traversing the passing stops of the new energy buses of all lines to obtain a traffic line topological table of each line, and fitting an actual road based on the topological table to obtain a digital traffic line graph of a specific area, wherein the digital traffic line graph truly reflects complete lines of different grades, such as trunk lines, branch lines and the like;
step four, calculating the actual vehicle running vector by the longitude and latitude coordinates of the new energy bus and other private and commercial operation vehicles to judge the actual road where the new energy bus is located; obtaining the driving direction of the vehicle according to the fact that the longitude variation of the vehicle is larger than 0 or smaller than 0, and determining the traffic flow in the same direction based on the cross product of the driving vectors of different vehicles; calculating the vehicle flow speed according to the speed of the vehicle passing through a certain road section;
and step five, determining corresponding traffic jam levels according to the vehicle flow rates of different road sections, and providing corresponding information for display on the digital traffic route map.
2. The method of claim 1, wherein: the third step specifically comprises:
after traversing the passing stops of the new energy buses of each line, adding adjacent stop fields into a [ line name, stop name ] form to form a traffic line topological table of each line; and constructing the digitized traffic route map based on the topological table and the real vehicle coordinate acquisition point connecting line fitting of the new energy bus.
3. The method of claim 2, wherein: and step four, determining a road grade corresponding to a certain interval based on the long-term vehicle flow speed, and perfecting the digitized traffic route map and subsequently determining a corresponding traffic jam grade.
4. The method of claim 3, wherein: the fifth step specifically comprises:
aiming at the road grades corresponding to different road sections: setting vehicle flow speed threshold ranges corresponding to different congestion degrees for the expressway, the main road, the secondary main road and the branch road respectively;
calculating a traffic congestion index based on the following formula:
dividing the congestion index into 5 levels, wherein the higher the numerical value is, the more serious the congestion is; segmenting the traffic road by using the congestion index and storing corresponding position and vehicle flow speed information; and displaying dark red, orange, green and dark green on the digitalized traffic route map according to the congestion indexes from high to low at the vehicle-mounted terminal so as to provide a display of the segmented congestion degree.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111137546.8A CN113870559B (en) | 2021-09-27 | 2021-09-27 | Traffic flow calculation method based on big data Internet of vehicles |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111137546.8A CN113870559B (en) | 2021-09-27 | 2021-09-27 | Traffic flow calculation method based on big data Internet of vehicles |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113870559A CN113870559A (en) | 2021-12-31 |
| CN113870559B true CN113870559B (en) | 2022-08-16 |
Family
ID=78991382
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111137546.8A Active CN113870559B (en) | 2021-09-27 | 2021-09-27 | Traffic flow calculation method based on big data Internet of vehicles |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113870559B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116340305B (en) * | 2023-04-24 | 2023-10-20 | 上海叁零肆零科技有限公司 | Method and system for repairing uniqueness of meter line table in topology of gas pipe network |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103177561A (en) * | 2011-12-26 | 2013-06-26 | 北京掌城科技有限公司 | Method and system for generating bus real-time traffic status |
| CN106781472A (en) * | 2016-12-16 | 2017-05-31 | 北京世纪高通科技有限公司 | A kind of road conditions detection method and device based on public transport car data |
| CN106781506A (en) * | 2017-02-21 | 2017-05-31 | 济南全通信息科技有限公司 | The real time execution level evaluation method of urban public traffic network on a large scale based on bus GPS data |
| CN112699284A (en) * | 2021-01-11 | 2021-04-23 | 四川大学 | Bus stop optimization visualization method based on multi-source data |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009503638A (en) * | 2005-07-22 | 2009-01-29 | テラーゴ インコーポレイテッド | Method, apparatus and system for modeling a road network graph |
| CN106372619B (en) * | 2016-09-20 | 2019-08-09 | 北京工业大学 | A kind of detection of vehicle robust and divided lane reach summation curve estimation method |
| CN108806249B (en) * | 2018-06-07 | 2021-06-11 | 上海市城市建设设计研究总院（集团）有限公司 | Passenger trip optimization method based on bus APP software |
| GB2585243A (en) * | 2019-07-05 | 2021-01-06 | Valerann Ltd | Traffic event and road condition identification and classification |
| CN112183469B (en) * | 2020-10-27 | 2023-07-28 | 华侨大学 | Method for identifying congestion degree of public transportation and self-adaptive adjustment |
-
2021
- 2021-09-27 CN CN202111137546.8A patent/CN113870559B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103177561A (en) * | 2011-12-26 | 2013-06-26 | 北京掌城科技有限公司 | Method and system for generating bus real-time traffic status |
| CN106781472A (en) * | 2016-12-16 | 2017-05-31 | 北京世纪高通科技有限公司 | A kind of road conditions detection method and device based on public transport car data |
| CN106781506A (en) * | 2017-02-21 | 2017-05-31 | 济南全通信息科技有限公司 | The real time execution level evaluation method of urban public traffic network on a large scale based on bus GPS data |
| CN112699284A (en) * | 2021-01-11 | 2021-04-23 | 四川大学 | Bus stop optimization visualization method based on multi-source data |
Non-Patent Citations (1)
| Title |
|---|
| 基于爬虫及GIS 技术的路况数据获取及分析;阳利锋;《数字化与信息化》;20210430(第4期);第117-119页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113870559A (en) | 2021-12-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104050817B (en) | Speed limiting information base generation and speed limiting information detection method and system | |
| CN102521965B (en) | Effect evaluation method of traffic demand management measures based on identification data of license plates | |
| WO2019047905A1 (en) | Road traffic analysis system, method and apparatus | |
| CN110363985B (en) | Traffic data analysis method, device, storage medium and equipment | |
| CN105844362B (en) | Urban traffic comprehensive trip decision-making device | |
| CN104809112B (en) | A kind of city bus development level integrated evaluating method based on multi-source data | |
| CN104778834A (en) | Urban road traffic jam judging method based on vehicle GPS data | |
| CN114363316B (en) | Cross-regional road infrastructure intelligent networking monitoring and supervision system | |
| CN112036757B (en) | Mobile phone signaling and floating car data-based parking transfer parking lot site selection method | |
| CN110288198B (en) | Method for measuring bearing capacity of rental bicycle transportation facility based on traffic subareas | |
| CN110599765A (en) | Road passenger and cargo transportation volume index statistical method based on multi-source data fusion | |
| CN109147323A (en) | A kind of vehicle GPS data processing method for highway passenger and freight transportation indicator-specific statistics | |
| CN111340415A (en) | Vehicle spontaneous formation mode mining method based on freight track data | |
| CN108108859A (en) | A kind of traffic administration duties optimization method based on big data analysis | |
| CN106023592A (en) | Traffic jam detection method based on GPS data | |
| CN113868492A (en) | Visual OD (origin-destination) analysis method based on electric police and checkpoint data and application | |
| CN112017429A (en) | Overload control monitoring stationing method based on truck GPS data | |
| CN105046959A (en) | Method for extracting urban road travelling time based on bi-window sliding matching mechanism | |
| CN113870559B (en) | Traffic flow calculation method based on big data Internet of vehicles | |
| CN106373399A (en) | Identification system for transit bottleneck between bus stops | |
| CN117689304B (en) | Method and device for determining transportation route of large highway | |
| CN114078322B (en) | Bus running state evaluation method, device, equipment and storage medium | |
| CN112767686B (en) | Road network automobile emission estimation method based on multi-source data fusion | |
| CN111444286B (en) | Long-distance traffic node relevance mining method based on trajectory data | |
| CN1873721A (en) | Method and system in use for menstruating traffic movement on ground road network |
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 |