CN113870559B - Traffic flow calculation method based on big data Internet of vehicles - Google Patents

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

A traffic flow calculation method based on big data Internet of Vehicles

technical field

The invention belongs to the technical field of urban road traffic flow monitoring, and in particular relates to a traffic flow calculation method based on big data Internet of Vehicles.

Background technique

At this stage, with the sharp increase in the overall vehicle ownership in my country, traffic congestion occurs more frequently on urban roads, which will have a serious impact on daily travel and urban management. The implementation of traffic flow monitoring in some key road sections is of great significance for the management of traffic management departments and for guiding future urban road network planning. Existing traffic flow monitoring methods mainly rely on video acquisition equipment, which is achieved by photographing vehicles passing through corresponding road sections for a period of time and identifying and counting them. Due to the high data processing cost and the laying cost of video capture equipment, it is still impossible to achieve all-weather traffic statistics for the entire urban road network. Even for some important road sections, the long-term traffic flow is mostly estimated based on the measurement of a short period of time.

SUMMARY OF THE INVENTION

In view of this, the present invention aims to realize the global and all-weather traffic flow measurement of the urban road network through relatively small data processing and infrastructure construction costs based on the advantages of real vehicle big data and vehicle networking of new energy vehicles. The invention provides a traffic flow calculation method based on the big data Internet of Vehicles, which specifically includes the following steps:

Step 1: Use the public information on the network to extract all the bus station information set in the road, including: the station name, the line name, the station latitude and longitude coordinates, the bus station data of the stopped vehicles, etc. heavy and storage handling;

Step 2: Obtain the real vehicle data of the new energy buses running on the road, and calculate the new energy buses entering this area to the buses with two different station names in the area according to the circular area radiated by each bus station after processing. The travel vector of the station, determine whether the two stations belong to similar stations on the same main road according to the cross product of the two travel vectors, and merge the similar stations;

Step 3: traversing the stations of the new energy bus routes of each line, obtaining the traffic line topology table of each line, and fitting the actual road based on the topology table to obtain a digital traffic line map;

Step 4: Calculate the actual vehicle travel vector to determine the actual road where it is located; calculate the vehicle flow rate according to the speed of the vehicle passing through a certain road section;

Step 5: Determine the corresponding traffic congestion level according to the vehicle speed of different road sections, and provide corresponding information display on the digital traffic route map.

Further, in the step 1, the use of network public information to extract bus station data specifically includes:

Use crawler technology to crawl the map website data to obtain the site name, line name, site latitude and longitude coordinates, stopped vehicles and other data and save it as a string, and use the line name as the primary key to store and establish a bus driving data information table;

The sorting, deduplication and storage processing specifically include:

Sort each bus line according to the latitude and longitude coordinates to get all the station names included in the line, and store and create a form in the key value format and string data type of [line name, station name]; if the distance between adjacent stations in the sequence is greater than the predetermined value during sorting are determined to belong to different lines;

The form is deduplicated based on the latitude and longitude coordinates and the data of parked vehicles.

Further, step 2 specifically includes:

Calculate the travel vector of each new energy bus based on the difference of the latitude and longitude coordinates of the vehicles collected successively; if the cross product of the vehicles traveling to the bus stops with two different station names in the circular area is 0, then the two bus stops are determined. For similar stations on the same main road, the identified similar stations are merged.

Further, step 3 specifically includes:

After traversing the stations passed by the new energy buses of each line, add the adjacent station field in the [Line Name, Station Name] form to form the traffic line topology table of each line; based on the topology table and the new energy bus The digitized traffic route map is constructed by fitting the real vehicle coordinate collection points.

Further, step 4 specifically includes:

Determine the road where the road is located by including the latitude and longitude coordinates of Xinyuan buses and other private and commercial vehicles; according to the change in the longitude of the vehicle is greater than 0 or less than 0, the driving direction of the vehicle is obtained; based on the cross product of the driving vectors of different vehicles Traffic flow: Calculate the average speed of the same-direction traffic flow on the road section and use it as the vehicle speed at a certain time.

Further, step 4 further includes determining the road level corresponding to a certain interval based on the long-term vehicle flow rate, and using it to improve the digital traffic route map and subsequently determine the corresponding traffic congestion level.

Further, step 5 specifically includes:

For road grades corresponding to different road sections: expressways, arterial roads, secondary arterial roads and branch roads, respectively set the range of vehicle velocity thresholds corresponding to different congestion levels;

The traffic congestion index is calculated based on the following formula:

The congestion index is divided into 5 levels, the larger the value, the more serious the congestion; the congestion index is used to segment the traffic road and save the corresponding location and vehicle speed information; in the vehicle terminal according to the congestion index from high to low in the digital traffic route Dark red, red, orange, green, and dark green are displayed on the graph to provide an indication of segmented congestion.

The above method provided by the present invention builds a traffic route map of a specific area based on the urban new energy vehicle running data collected by the Internet of Vehicles platform and the road bus station information data, and can truly reflect the main roads and branch roads through relatively convenient means. and other complete routes of different levels; combined with the driving data of new energy vehicles collected by the Internet of Vehicles platform, after calculation and processing, a road flow feedback system based on big data is constructed. The result will have higher accuracy. Compared with some existing trunk road construction methods, it reduces manpower and economic costs, and has higher cost performance.

Detailed ways

The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The present invention provides a traffic flow calculation method based on the big data Internet of Vehicles, which mainly provides four major methods including traffic road traffic fitting, vehicle driving data and traffic road fitting, road traffic speed calculation and traffic speed indexing classification. functional module. The first major module, the traffic road fitting module, is realized by the following functions: basic bus station information capture function, error repair, bus station information data processing, inter-station road construction module, and new energy vehicle driving routes to fill the road function; The second module is vehicle driving data and traffic road fitting module, which consists of 4 functions: real-time capture of vehicle driving data, route determination function, and vehicle velocity calculation function; the third module, road traffic velocity calculation module, consists of two functions Composition: regional velocity vehicle selection function, and velocity calculation function; the last part of the module: traffic velocity indexing and grading module is composed of three functions: traffic velocity data index conversion function, index grading function, and visual display function.

The method specifically includes the following steps:

Step 1: Use the public information on the network to extract all the bus station information set in the road, including: the station name, the line name, the station latitude and longitude coordinates, the bus station data of the stopped vehicles, etc. heavy and storage handling;

Step 2: Obtain the real vehicle data of the new energy buses running on the road, and calculate the new energy buses entering this area to the buses with two different station names in the area according to the circular area radiated by each bus station after processing. The travel vector of the station, determine whether the two stations belong to similar stations on the same main road according to the cross product of the two travel vectors, and merge the similar stations;

Step 3: traversing the stations of the new energy bus routes of each line, obtaining the traffic line topology table of each line, and fitting the actual road based on the topology table to obtain a digital traffic line map;

Step 4: Calculate the actual vehicle travel vector to determine the actual road where it is located; calculate the vehicle flow rate according to the speed of the vehicle passing through a certain road section;

Step 5: Determine the corresponding traffic congestion level according to the vehicle speed of different road sections, and provide corresponding information display on the digital traffic route map.

Since detailed information on urban bus routes and stations is provided on many public service websites, it is relatively easy to obtain through big data means and does not need to rebuild a complex platform for data collection and processing and digital map integration. Therefore, in a preferred implementation of the present invention In the method, in the step 1, using the network public information to extract the bus stop data specifically includes:

Use crawler technology to crawl the map website data, obtain the site name, line name, site latitude and longitude coordinates, stopped vehicles and other data and save it as a string, use the line name as the primary key to store and establish a bus driving data information table ; Among them, the latitude and longitude coordinates can be based on the latitude and longitude fields of the AutoNavi map to retain 6 significant digits after the decimal point, and the data can also be processed on the crawled fields, and the bus stops with the same name can be selected, and the bus with the same name can be obtained by the following methods. The mean latitude and longitude of the station, defined as the geometric center point of the bus station:

为相同名称公交站点ID的几何中心点。Among them, lng is the longitude coordinate of the station, lat is the latitude coordinate of the station, and P _same is the longitude and latitude matrix of the bus stop ID of the same name,

is the geometric center point of the bus stop ID with the same name.

The data processing is to number the buses of different routes: the first 6 digits of the vehicle information are taken as the information number of the province and city (the coding standard is in accordance with the ID coding rules of each province and city), and the last 4 digits are composed of the actual route number of the bus and the placeholder symbol 0. , to convert the bus route information. The bus station name, longitude, latitude, and passing bus line name ID are saved in string type, and the bus line name ID is used as the primary key of the table to store as a bus travel data information table.

The sorting, deduplication and storage processing specifically include:

Sort each bus line according to the latitude and longitude coordinates to get all the station names included in the line, and store and create a form in the key value format and string data type of [line name, station name]; if the distance between adjacent stations in the sequence is greater than the predetermined value during sorting For example, when the distance is 3000 meters, it is determined that the two sites belong to different lines;

The form is deduplicated based on the latitude and longitude coordinates and the data of parked vehicles.

In a preferred embodiment of the present invention, in step 2, the vehicle latitude and longitude coordinate data can be converted from WGS-84 format to GCJ-02 format first. Merging similar bus stops can help the system reduce the amount of computation. According to the regulations of GB/T 51328-2018, bus stops shall not exceed 100 square meters/vehicle to 120 square meters/vehicle according to the connection area of link facilities of bus stations. The geometric center point of the bus station is the center of the circle, and the area radius is determined based on the following formula:

为圆形区域半径。S为公交车站区域面积，根据GB/T 51328-2018设S＝120㎡，可得半径r为6.18米圆形区域，计算公交车行驶向量：in,

is the radius of the circular area. S is the area of the bus station area. According to GB/T 51328-2018, set S=120㎡, the radius r can be obtained as a circular area of 6.18 meters, and the bus travel vector can be calculated:

V=P ₁ -P ₀

Among them, V is the vehicle driving vector, that is, [longitude change, latitude change], P ₀ is the longitude and latitude matrix of the vehicle location near the collection point on the vehicle during calculation, that is, [longitude, latitude], P ₁ is the collection during calculation. A matrix of longitude and latitude [longitude, latitude] of the vehicle's driving position.

Select the adjacent travel time data of the same vehicle, and subtract the latitude and longitude information with a larger time value from the latitude and longitude information with a smaller time value to obtain the vehicle travel vector; If the cross product of vehicles traveling is 0, then the two bus stops are determined to be similar stops on the same main road, and the determined similar stops are merged.

In a preferred embodiment of the present invention, step 3 specifically includes:

After traversing the stations passed by the new energy buses of each line, add the adjacent station fields in the [Line Name, Station Name] form to form the traffic line topology table of each line, so that the new energy buses on each bus line will be listed. The road nodes passed by the vehicle are collected completely. Since most of the bus lines in the urban traffic planning are consistent with the main roads, the topology table can also be used to reflect the situation of the main roads in the city. On this basis, based on the topology table and The digital traffic route map is constructed by fitting the real vehicle coordinate collection points of the new energy bus, and there is no need to re-develop the urban electronic map platform or to transform the existing platform and terminals.

After determining the traffic line topology and digital map using the bus system data, the traffic flow including new energy buses, private cars and many other commercial vehicles can be matched to the road where they are located. Therefore, in a preferred embodiment of the present invention, step 4 specifically includes:

Determine the road where the road is located by including the latitude and longitude coordinates of Xinyuan buses and other private and commercial vehicles; according to the change in the longitude of the vehicle is greater than 0 or less than 0, the driving direction of the vehicle is obtained; based on the cross product of the driving vectors of different vehicles Traffic flow: Calculate the average speed of the same-direction traffic flow on the road section and use it as the vehicle speed at a certain time.

For some areas with relatively large-scale public transport networks, it is not enough to distinguish road grades only based on public transport system information, but the road grades corresponding to a certain interval can be determined by long-term vehicle speed, and used to improve the digital traffic route map and The corresponding traffic congestion level is subsequently determined. According to "CJJ37-90 Urban Road Design Specifications", the main road is defined as "should be the main road connecting the urban sub-districts, mainly for traffic functions. When the traffic flow of bicycles is large, it is advisable to use non-motor vehicles to drive separately, such as Sanwei Road. Or four roads; the entrances and exits of public buildings that attract large traffic and people flow should not be set on both sides of the main road. The width is greater than 15 meters, and the width of the red line is greater than 3 meters. The vehicle speed is 60-80KM/H.” According to the above content, and Combined with the field names in the vehicle driving data of the big data Internet of Vehicles platform, a trunk road identification system is constructed. In order to calculate the vehicle velocity on the main road, it is necessary to identify the vehicles driving on the main road. Some existing research results show that the vehicle speed can be used as an important criterion for judging and identifying the main road. In order to judge the main road, it is necessary to define the vehicle speed judgment standard and analyze the reasons that affect the vehicle speed judgment. The vehicle speed is higher than the actual road requirement, which is speeding, and there are few daily average driving data, and because the vehicle speed is lower than the specified speed during peak hours, it is very common in cities with a large number of motor vehicles, so if there are 25% of the vehicles in the data table, if If there are 5 consecutive time collection points, the average speed is greater than 60 kilometers per hour and less than 80 kilometers per hour, and it can be determined that the vehicle is driving on the main road during this period. Store the result of the traffic velocity of the road where the vehicle is located, such as the traffic road velocity field in the vehicle driving data table, and keep the integer part of the result and save it in int type.

In order to perform more subdivided flow monitoring according to actual urban roads, in a preferred embodiment of the present invention, step 5 specifically includes:

For the road grades corresponding to different road sections: expressways, arterial roads, secondary arterial roads and branch roads, respectively set the range of vehicle velocity thresholds corresponding to different congestion levels:

Table 1

The calculation and research methods of traffic congestion indicators at home and abroad are mainly determined based on research objects such as road speed data, road traffic density, traffic volume, and road travel time. Although Beijing, Shanghai, Guangzhou and Shenzhen currently have different classification definitions and calculation methods for the degree of traffic congestion, they are all based on the proportion to reflect the congestion situation, and perform regular updates such as 15 minutes. Therefore, in a preferred embodiment of the present invention, the traffic congestion index is calculated based on the following formula:

The congestion index is divided into five levels (1, 2, 3, 4, 5). The larger the value, the more serious the congestion; the congestion index is used to segment the traffic road and save the corresponding location and vehicle speed information; The congestion index is displayed in dark red, red, orange, green, and dark green on a digitized traffic route map from high to low to provide a segmented display of congestion levels.

The digital traffic route map can be based on surveying and mapping, reward collection, local government planning and satellite aerial data to build urban road maps, and based on: clustering, rasterization, incremental fusion, node linking, computer graph theory and other methods to obtain vehicle driving The process of judging the road. Based on these methods, the road traffic map obtained by combining bus driving and station data can save manpower and material resources compared with traditional methods, and can be quickly updated according to the driving data of new energy vehicles connected to the Internet of Vehicles.

It should be understood that the size of the sequence numbers of the steps in the embodiments of the present invention does not imply the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention .

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, and substitutions can be made in these embodiments without departing from the principle and spirit of the invention and modifications, the scope of the present invention is defined by 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.