CN116822779B - Expressway motor vehicle carbon emission calculation method based on mobile phone signaling data - Google Patents

Expressway motor vehicle carbon emission calculation method based on mobile phone signaling data Download PDF

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CN116822779B
CN116822779B CN202310063938.7A CN202310063938A CN116822779B CN 116822779 B CN116822779 B CN 116822779B CN 202310063938 A CN202310063938 A CN 202310063938A CN 116822779 B CN116822779 B CN 116822779B
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expressway
base station
signaling data
user
data
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CN116822779A (en
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姚振兴
王雪梅
王亮
甘啸霖
姚谦
陈红
梁国华
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Changan University
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Abstract

The invention provides a method for calculating carbon emission of a motor vehicle on an expressway based on mobile phone signaling data, which relates to the technical field of mobile communication and comprises the steps of analyzing the travel characteristics of a user in the whole process of the expressway through algorithms such as judging that the user enters the expressway, matching a travel path, judging that the user leaves the expressway, and judging that the user takes the same time; and calculating real-time running speeds of vehicles in different sections of the expressway at different time intervals by means of an expressway network topology algorithm, and providing an expressway carbon emission calculation model based on the running speeds, the vehicle characteristics and the emission parameters so as to realize real-time monitoring of expressway carbon emission. According to the invention, the expressway carbon emission calculation model is established based on the running speed, the characteristics of the vehicle and the emission parameters, the carbon emission amount of the expressway van is calculated, the traffic carbon emission is reflected in real time, and the accurate emission reduction and effective treatment of the motor vehicle are realized.

Description

Expressway motor vehicle carbon emission calculation method based on mobile phone signaling data
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to a method for calculating carbon emission of a motor vehicle on an expressway based on mobile phone signaling data.
Background
The transportation industry is an important field for discharging greenhouse gases, and in all transportation modes, highway transportation is the transportation mode with the largest total carbon emission in China, and expressways are important components of a modern transportation system and are trunks of highway road network organizations. At present, motor vehicles on expressways are mainly divided into two parts, namely passenger cars and trucks, mainly consume traditional energy sources such as gasoline and diesel oil, and emit a large amount of greenhouse gases such as carbon dioxide in the driving process, so that the total carbon emission amount of the transportation industry is high to a certain extent, and the environmental problem is increasingly outstanding. Under the background, reducing the carbon dioxide emission generated by the motor vehicle on the expressway has become an effective way for promoting the transportation industry to achieve the 'double carbon' goal as early as possible and realizing green development; the construction of a scientific expressway motor vehicle carbon emission metering model has become a real requirement for implementing accurate emission reduction and source management in the traffic field.
Currently, methods for calculating carbon emissions in traffic can be broadly divided into two types: a top-down accounting method is used for measuring and calculating the total carbon emission of traffic according to the traffic energy consumption and the corresponding carbon emission coefficient, but is difficult to classify and measure the carbon emission generated by different traffic modes. The other is a bottom-up accounting method, which calculates the total energy consumption based on the driving mileage and unit mileage energy consumption of different vehicles and then calculates the carbon emission of the traffic by multiplying the carbon emission coefficient. However, the conventional "bottom-up" method also has some problems: 1) Lacking analysis based on high-precision spatial data, it is difficult to answer the distribution position of the high-value road section of the carbon emission of the traffic so as to realize accurate emission reduction. 2) The minimum unit of analysis is the carbon emission of all the motor vehicles on a certain expressway, and the carbon emission of a certain motor vehicle on a specific section of the expressway cannot be accurately obtained.
Disclosure of Invention
The coming of the big data age brings new opportunities for solving the problems, the individual mobile position data taking the mobile phone signaling data as the core is an important source of traffic big data, and the defects can be effectively avoided by utilizing the mobile phone signaling data to calculate the carbon emission of the motor vehicle on the expressway. The mobile phone signaling data has the advantages of large sample size, wide coverage range, continuous time and space, and the like, can continuously acquire the whole travel time-space track information of the traveler, is expected to identify the whole travel traffic position information with high precision through the excavation and analysis of the mobile phone signaling data, can accurately calculate the carbon emission of each road section in a specific period, and greatly improves the precision efficiency.
Therefore, the invention provides the method for calculating the carbon emission of the highway traffic by applying the mobile phone signaling data, effectively avoids the existing defects of the traditional model, has objective and efficient analysis process and wider technical application range, and has good industrial application prospect.
Aiming at the problems that the existing expressway traffic carbon emission calculation method in China is complex, cannot be obtained in real time, cannot be accurate to individuals and the like, the invention provides the following technical scheme: a method for calculating carbon emission of a motor vehicle on a highway based on mobile phone signaling data comprises the following steps:
acquiring mobile phone signaling data, spatial position data, base station data along a target expressway and base station data corresponding to each toll station in an expressway network, and establishing an expressway base station database;
Base station position matching is carried out on mobile phone signaling data to form signaling track data, and the signaling track data are sequenced according to a user numbering sequence and a time sequence to obtain a basic signaling database;
If the basic signaling database of the user has a certain interaction base station of signaling data belonging to the expressway base station database, the user is regarded as a suspected entering user;
Matching the position area number LAC of the suspected entering user with the cell number CELID and the expressway base station database to obtain a possible traveling calibration road section, searching a calibration road section common to all signaling data, and taking the common calibration road section with the largest occurrence number as a matching road section;
Calculating the average speed of the users in the base stations according to the key point positions and the time in each base station, and weighting according to the obtained average speed among each base station and the corresponding distance to obtain the average running speed of each matched road section of the expressway;
judging a co-riding user based on a dynamic time warping algorithm, calculating carbon emission of only one user ID for the co-riding user, and obtaining respective carbon emission parameters according to the self characteristics of different motor vehicles;
And calculating the carbon emission of each matched road section of the motor vehicle in the appointed time according to the average running speed, the self characteristics of the motor vehicle and the carbon emission parameters, and calculating the overall carbon emission of all the motor vehicles in the appointed time.
Preferably, the establishment of the expressway base station database includes the following steps:
Acquiring mobile phone signaling data of user travel by using space-time characteristic data obtained from communication companies such as Unicom, mobile and the like, wherein the mobile phone signaling data comprises 4G signaling data and communication base station data;
The 4G signaling data is used to represent the user ID, LAC, CELLID, date, time;
the communication base station data are used for representing longitude and latitude and service information of the base station;
Collecting, calibrating and arranging communication base station data along a target expressway through field test, and establishing an expressway base station database corresponding to an expressway road section and a base station, wherein the expressway base station database comprises road codes, road names, road section numbers, base stations LAC, base stations CELLID, base station longitude and latitude and advancing directions;
Through field test, the base station corresponding to each toll station in the expressway network is collected, calibrated and arranged, and a toll station base station database corresponding to the toll station and the base station is established, wherein the toll station base station database comprises toll station codes, toll station names, toll station longitudes and latitudes, base stations LAC, base stations CELLID and base station longitudes and latitudes.
Preferably, the base station position matching is performed on the mobile phone signaling data to form signaling track data, which comprises the following steps:
Performing base station position matching on mobile phone signaling data through key words LAC and CELLID;
and adding the field longitude and latitude into the mobile phone signaling data to obtain signaling track data.
Preferably, if the interactive base station with a certain signaling data belongs to the expressway base station database, the user is regarded as a suspected entering user, and the method specifically includes the following steps:
Judging the ith signaling data of the suspected entering user P and the 1 st data generated after 10 min;
if the two data are all in the expressway base station database and the distance between the two data is more than 5km, determining that the user P enters the expressway;
If not, making i=i+1, repeating the step until the ith signaling data does not belong to the expressway base station database;
for the i-th signaling data of the confirmed entering user P, the distances between the i-th signaling data and all toll stations are calculated, and the toll station with the smallest distance is taken as the entering point of the user P to generate an entering expressway user information table.
Preferably, the matching is performed between the location area number LAC of the suspected entering user and the cell number CELLID and the expressway base station database to obtain a calibration road section of possible traveling, and searching for a calibration road section common to all data, and regarding the common calibration road section with the largest occurrence number as a matching road section, which specifically includes the following steps:
Matching with the expressway base station database by using LAC and CELLID, and writing in a calibration road section where each piece of signaling data is likely to travel;
Starting from the ith signaling data, searching a calibration road section common to the i and the i+1 data;
if the public calibration section exists, continuing to judge the public calibration section of the (i+2) th signaling data and the (i+1) th signaling data;
gradually iterating until no public road section exists between the ith signaling data to the (i+n-1) th signaling data and the (i+n) th signaling data, and calculating the occurrence times of all calibration road sections in the ith signaling data to the (i+n-1) th signaling data;
the public calibration road section with the largest occurrence number is regarded as a matching road section of the ith to the (i+n-1) th signaling data;
Judging whether the user leaves the expressway, if the signaling data generated by the user does not belong to the expressway base station database, deleting the signaling data directly, and if not, continuing to match the road sections.
Preferably, the step of judging that the user leaves the expressway specifically includes the following steps:
judging whether the interactive base station of the ith signaling data of the user belongs to a highway base station database or not;
If the ith signaling data does not belong to the expressway base station database, and meanwhile, the 1 st signaling data generated after 5min does not belong to the expressway base station database, and all signaling data generated in the subsequent 5min of the ith signaling data do not belong to the expressway base station database, judging that the user drives away from the expressway;
If not, the i=i+1 is made, whether the signaling data is the signaling data of the station out of the expressway is judged, and if not, path matching is carried out;
And (3) after confirming the ith signaling data of the driving-away user P, calculating the distances between the ith-1 signaling data and all toll stations, and regarding the toll station closest to the ith signaling data as the expressway outbound station.
Preferably, the calculating the average speed of the user in the base station according to the key point position and time in each base station, and weighting according to the obtained average speed and the corresponding distance between each base station to obtain the average running speed of each matched road section of the expressway, specifically comprises the following steps:
Extracting the time corresponding to the 1 st signaling data of the user entering each base station service range and the last signaling data before leaving the base station service range, and taking the time as the origin-destination time of the user entering and leaving each base station service range;
calculating the position and time of a key point C n of a user in the service range of each base station;
Determining the travelling direction of the user according to the key point of the user at the base station and the position point of the first piece of data of the user entering the base station;
calculating the speed between key points according to the ratio of the distance between key points of the service ranges of two continuous base stations and the corresponding time to obtain the average speed between the base stations;
and calculating the running speed of each matched road section of the highway by weighting the distance between the end point of the matched road section and the adjacent base station and the average speed between the base stations.
Preferably, the method for determining the carbon emission of the co-located user based on the dynamic time warping algorithm only calculates the carbon emission of one user ID for the co-located user comprises the following steps:
extracting users with the same driving path and less than 5 minutes of time interval when the users enter and leave the expressway, and identifying whether the users take the same vehicle or not;
acquiring signaling data track speed sequence of user 1 Signaling track speed sequence/>, with user 2And calculates the track velocity/>The DTW distance value between the two is expressed as follows:
Track speed/> The DTW distance value between every two adjacent regular path points is added with the minimum value between the ith and the jth matching points;
Track speed/> A distance function between;
if the DTW distance value of the two-user track speed sequence is smaller than the threshold value 100, the two users can be judged as the same-time users.
Preferably, the calculation expression of the carbon emission of each matched road section of the motor vehicle in the appointed time according to the average running speed, the self-characteristics of the motor vehicle and the carbon emission parameters is as follows:
wherein, x=1, the fuel is gasoline, x=2, the fuel is diesel;
For the carbon emission quantity of the highway network passenger car within the time from t 1 to t 2,/> The carbon emission CL x of the highway network truck in the time from t 1 to t 2 is the energy consumption L/km per kilometer, ρ x is the energy density kg/L, q x is the energy equivalent value Tj/kg, e x is the energy consumption co 2 emission factor kg/Tj, v l,i is the running speed km/h of the first user in the i-th road section, tl, i is the running time (h) of the first user in the i-th road section, n is the total number of the first user running road sections in the time from t 1 to t 2, and m is the total motor vehicle quantity of the highway in the time from t 1 to t 2.
Preferably, the calculation of the total carbon emission of all the motor vehicles during this time comprises the following steps:
The total carbon emissions for all vehicles in the highway at time t 1 to t 2 were calculated as:
Compared with the prior art, the invention has the following beneficial effects:
According to the invention, the mobile phone signaling data with real-time dynamic property is provided by a communication company, the travel speed and the travel mileage of the motor vehicle are analyzed and calculated through the mobile phone signaling data, the carbon emission of the expressway passenger and freight car is calculated by adopting an expressway carbon emission calculation model based on the travel speed, the characteristics of the vehicle and the emission parameters, the traffic carbon emission is reflected in real time, and the accurate carbon emission of a certain motor vehicle on a specific section of the expressway and the distribution position of a high-value section of the traffic carbon emission are achieved, so that the accurate emission reduction and effective treatment of the motor vehicle are realized.
Drawings
FIG. 1 is a diagram of different scenarios of road segment matching according to the present invention;
FIG. 2 is a schematic diagram of the velocity extraction of the present invention;
FIG. 3 is a diagram of a section of the present invention including a plurality of base station drop foot diagrams;
FIG. 4 is a diagram of a section of road including a base station drop foot;
Fig. 5 is a diagram of a link of the present invention without a base station drop foot.
Detailed Description
The following describes the embodiments of the present invention further with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
According to the invention, through the mobile phone signaling data provided by a communication company and the communication signaling data around the expressway toll station obtained through field test, the travel characteristics of the user in the whole process of the expressway are analyzed through algorithms such as the judgment that the user enters the expressway, the travel path is matched, the judgment that the user leaves the expressway, the simultaneous judgment and the like; and calculating real-time running speeds of vehicles in different sections of the expressway at different time intervals by means of an expressway network topology algorithm, and providing an expressway carbon emission calculation model based on running characteristics, vehicle characteristics and emission characteristics to realize real-time monitoring of expressway carbon emission.
As shown in fig. 1-5, a method for calculating carbon emission of an expressway motor vehicle based on mobile phone signaling data comprises the following steps:
S1: and acquiring mobile phone signaling data, spatial position data, base station data along a target expressway and base station data corresponding to each toll station in an expressway network, and establishing an expressway base station database.
S2: and performing base station position matching on the mobile phone signaling data to form signaling track data, and sequencing the signaling track data according to the user numbering sequence and the time sequence to obtain a basic signaling database.
S3: if the basic signaling database of the user has a certain interaction base station of signaling data, which belongs to the expressway base station database, the user is regarded as a suspected entering user.
S4: and matching the position area number LAC of the suspected entering user with the cell number CELID and the expressway base station database to obtain a calibration road section of possible travel, searching a calibration road section common to all data, and taking the common calibration road section with the largest occurrence number as a matching road section.
S5: and judging that the user leaves the expressway to obtain a drive-off result.
S6: and calculating the average speed of the users in the base stations according to the positions and the time of the key points in the base stations, and weighting according to the obtained average speed among the base stations and the corresponding distance to obtain the average running speed of each matched road section of the expressway.
S7: and judging the co-riding users based on a dynamic time warping algorithm, calculating the carbon emission of only one user ID for the co-riding users, and obtaining respective carbon emission parameters according to the self characteristics of different motor vehicles.
S8: and calculating the carbon emission of each matched road section of the motor vehicle in the appointed time according to the average running speed, the self characteristics of the motor vehicle and the carbon emission parameters, and calculating the overall carbon emission of all the motor vehicles in the appointed time.
In S1, the signaling data of user travel is acquired by using space-time characteristic data obtained from communication companies such as Unicom and mobile, and specifically includes 4G signaling data and communication base station data, where the 4G signaling data is travel time and location information of the user, and includes user ID, LAC, CELLID, date and time, where LAC represents a location area LA number, CELLID represents a cell number, and the communication base station data is longitude and latitude and service information of the base station.
Table 1 signaling data primary field samples
For the construction of a subsequent algorithm model, each expressway is switched into a plurality of road sections connected end to end, coordinates of a starting point and an ending point of each road section are picked up through a hundred-degree map, straight line sections are split into a plurality of straight line sections according to line shapes, curve sections are approximately fitted through the plurality of straight line sections, all the line sections are connected end to end, the length proposal is not less than 1km, and expressway space position data are formed, wherein the expressway space position data specifically comprise road numbers, road names, road section numbers, starting point longitude and latitude, and ending point longitude and latitude. Sample data are shown in table 2.
TABLE 2 Highway spatial location data sample
Highway base station data:
Collecting, calibrating and arranging communication base station data along a target expressway through field test, and establishing an expressway base station database corresponding to an expressway road section and a base station, wherein the database comprises road codes, road names, road section numbers, base stations LAC, base stations CELLID, base stations longitude and latitude and advancing directions; specifically, the results are shown in Table 3. According to the specific calibration method, a tester can hold communication network test equipment, such as an adabase station APP, and conduct bidirectional repeated traveling along a highway, and at least 5 times of communication base station information along the highway recorded by the test equipment are recommended, so that the sequence calibration of the highway base station is completed.
TABLE 3 Highway base station data sample
High speed toll station base station data:
Through field test, the base station corresponding to each toll station in the expressway network is collected, calibrated and arranged, a toll station base station database corresponding to the toll station and the base station is established, and the database comprises toll station codes, toll station names, toll station longitudes and latitudes, base stations LAC, base stations CELLID and base station longitudes and latitudes, and is specifically shown in table 4. The calibration method can be used for enabling a tester to hold communication network test equipment, such as an adabase station APP, to walk back and forth in each expressway toll station and stay, so that all positions of the toll station are covered as much as possible, and all communication base station information connected with a user in the toll station can be acquired through base station information recorded by the test equipment.
Table 4 high speed toll station base station data sample
Creating a database:
In S2, base station position matching is performed on the original (mobile) signaling data, so as to form signaling track data.
Construction of a basic signaling database:
Matching the original signaling data table 1 with the expressway base station data table 3 provided by the operator through keywords LAC and CELLID; and adding the field longitude and latitude into the original signaling data table to obtain a signaling track data table, wherein the sample table mainly comprises a user ID, LAC, CELLID, a date, time and longitude and latitude. And for the abnormal signaling track data with the repetition partially, searching according to the user ID, date, time, LAC and CELLID fields, and deleting the completely repeated signaling data. Finally, the signaling track data are ordered according to the sequence of the user numbers and the time sequence to form a basic signaling database D, and concrete examples are shown in a table 5.
Table 5 signalling track data table
Constructing a highway base station database:
the highway base station data and the highway toll station base station data form a highway base station database GD.
In S3, the judgment that the user is driving into the expressway is made:
if the interactive base station with a certain signaling data belongs to the expressway base station database GD, the user is regarded as a suspected entering user.
For a suspected entering user P, if the ith signaling data and the 1 st data generated after 10min belong to the expressway base station database GD and the distance between the ith signaling data and the 1 st data is greater than 5km, determining that the user P enters an expressway, and turning to the next step; if not, let i=i+1, repeat this step until the ith signaling data does not belong to the highway base station database GD.
TABLE 6 entry Highway user Signaling data sheet
The i-th signaling data for confirming entry to the user P is calculated as distances from all the toll stations, and the toll station with the smallest distance is used as the entry point of the user P. Finally, a user information table of the entering expressway is generated, wherein the user information table specifically comprises user IDs, time, toll station codes, toll station names and toll station longitude and latitude, and the sample is shown in table 7.
TABLE 7 Inlet Highway user information List
Travel path matching based on mobile phone signaling data:
The expressway base station database is compared with user signaling data, and whether the user enters the expressway is judged, the expressway trip path is matched step by step, and whether the user leaves the expressway is judged. Judging whether the user enters the expressway or not according to the S3, if the signaling data generated by the user do not belong to the expressway base station database, deleting the signaling data directly; and (5) judging whether the user leaves the expressway or not by adopting the step (S5), and if the signaling data generated by the user still belong to the expressway base station database, returning to the step (S4) to continue road section matching.
Expressway travel section matching:
for the signaling data of each target user, there are the following three matching scenarios as shown in fig. 1.
In the scenario 1, the communication base station 1 covers only the road 3, and in this case, processing is easier, and matching can be performed directly.
In the scenario 2, the communication base station 1 corresponding to a certain piece of signaling data covers the road 1 and the road 3, and it is temporarily impossible to determine which road the signaling data matches. Therefore, first, the base station 2 corresponding to the next signaling data is matched, and if there is a unique link for 2 to match with, the base station 1 corresponding to the signaling data is matched to the link consistent with the base station 2, and the link 3 is obtained.
In the scene 3, the base station 1 and the base station 2 corresponding to two continuous signaling data of a certain user cover a plurality of roads, and the public road 3 with the largest occurrence number is determined as the target road matched with the two signaling data.
And a detailed matching step:
1) And (3) matching the CELLID with the expressway base station database GD by using the LAC, and writing in a calibration road section where each piece of signaling data can travel.
2) And (3) searching a common calibration section of the i and the i+1 data from the ith signaling data, wherein if the i signaling data calibration section is R001001, R001002 and R001003, the i+1 signaling data calibration section is R001002 and R001003, the common calibration section is R001002 and R001003, and then the next step is carried out.
3) If the public calibration section exists, the public calibration section of the (i+2) th signaling data and the (i+1) th signaling data is continuously judged, if the (i+1) th signaling data and the (i+1) th signaling data are calibrated sections of R001002 and R001003, the (i+2) th signaling data are calibrated sections of R001002 and R001004, and if the (i+2) th signaling data and the (i+1) th signaling data are calibrated sections of R001002 and R001004, the public calibration section is the section of R001002.
4) Gradually iterating until the ith to the (i+n-1) th signaling data and the (i+n) th signaling data have no public road sections, and calculating the occurrence times of all the calibrated road sections in the ith to the (i+n-1) th signaling data.
5) And the public calibration road sections with the largest occurrence number are regarded as the matching road sections of the ith to the i+n-1 signaling data.
Table 8 shows a matching example, where each of the 1 st to 4 th signaling data can be marked to the road segment R001002, and the 5 th signaling data is no longer marked to R001002, so that the number of occurrences of each road segment in the 1 st to 4 th signaling data is calculated, and the number of occurrences of R001002 is found to be the largest, so that the 1 st to 4 th signaling data is matched to the road segment R001002.
Table 8 travel segment matching
The final result of the user path matching to the expressway is shown in table 9.
TABLE 9 road segment Table Re where entry into Highway subscribers are located
Judgment of user driving off expressway:
in S5, the determination that the user is driving off the expressway includes the following steps:
1) For each user driving into the expressway, when the interaction base station of the ith signaling data does not belong to the expressway base station database any more, the 1 st signaling data generated after 5min does not belong to the expressway base station database either, and all signaling data generated in the subsequent 5min of the signaling data does not belong to the expressway base station database, the user is judged to drive out of the expressway, and the next step 2 is carried out. If not, the i=i+1 is set, whether the data is the signaling data of the expressway outbound site is judged, and if not, path matching is carried out until the signaling data of the expressway outbound site is obtained. The drive-off result is shown in table 10.
Meter 10 off Highway determination
2) For the i-th signaling data confirming the driving away from the user P, the distances between the i-1-th signaling data and all toll stations are calculated, and the toll station closest to the i-1-th signaling data is regarded as the expressway outbound station.
TABLE 11 off Highway user information List
/>
In S6, the travel speed of each matching section of the expressway is calculated:
The dark dots in fig. 2 represent the location points when the user travels on the road to generate signaling data, the circles represent the coverage area of the base station, and the circle center is the base station location a n.
Determining the time when the user enters and leaves the service range of each base station:
And aiming at the user track data road section matching result table Re shown in table 9, judging the signaling data points under the same base station one by one, determining the time point of entering the base station and the time point of leaving the base station, and extracting the time corresponding to the 1 st signaling data of the user entering each base station service range and the last signaling data before leaving the base station service range as the origin-destination time of the user entering and leaving each base station service range. If the user generates only 1 piece of signaling data under a certain base station, the piece of data is skipped.
Calculating the position and time of a key point C n of a user in the service range of each base station:
After the time of entering and exiting the base station by the user is determined, any two ab points on the straight line section of the highway are selected by combining the coordinates of the road section, and a straight line equation L ab is established as
X a, a point longitude, y a, a point latitude, x b, b point longitude, y b, b point latitude.
At the same time, the perpendicular L 1 of the ab section passing through the point A 1 of the base station is
X a, a point longitude, y a, a point latitude, x b, b point longitude, y b, b point latitude,-Base station a 1 longitude,/>-Base station a 1 latitude.
The intersection point of the two straight lines can be obtained by combining the straight line L 1 and the straight line L ab equation, namely, the drop foot point (key point) is
/>
The drop foot of the base station A 2 and the straight line L ab can be obtained by the same calculation
Because the signaling data of the user in the coverage area of one base station is relatively dense in the 4G environment, the foot drop time can be estimated by using the time of the user entering and exiting the base station, and is the time of the middle moment of the user entering and exiting the base station:
Time at c 1,/> Time at a 1,/>-A 2 time
Time at c 2,/>Time at a 3,/>-A 4 time.
Determining a user traveling direction:
For the travelling direction of the user, determining by using the key point of the user at the base station and the position point of the first piece of data of the user entering the base station. Direction of abscissa, if Judging that the user is going eastward, forward, vertical, if/>The user is judged to be traveling north and is forward.
Calculating the speed between two consecutive key points:
with vertical foot coordinate points Its corresponding time point/>Based on this, the velocity V 1 between the user segments is calculated. The specific calculation formula is as follows:
DISTANCE-a function of the DISTANCE between two coordinate points of a plane, V 1 -a speed between segments c 1-c2.
The formula of the function DISTANCE is:
M＝sin(LatA)*sin(LatB)*coS(LonA-LonB)
N＝cos(LatA)*CoS(LatB)
DISTANCE([LonA，LatA]，[LonB，LatB])＝R*Arccos(M+N)*π/180
Wherein R is the earth radius 6371000m; [ LonA, latA ] and [ LonB, latB ] are longitude and latitude coordinates of the point A and the point B respectively; pi is a circumference ratio constant 3.141593.
And calculating the average speed among the rest base stations according to the steps, and removing the data with the speed greater than the speed limit according to the speed limit condition V Limiting the limit of the matched road. The results are for example the following:
table 12 table of results of speed calculation between base stations to which a certain user is connected
Calculating the running speed of each road section:
With table 12, the average running speed of the user on each road section is further calculated, and the expressway section and the coverage area of the base station are not in one-to-one correspondence, so that the analysis is performed in 3 cases.
① A certain road section comprises a plurality of base station drop feet
As shown in fig. 3, the highway section ab includes 3 base station foot hanging points, foot hanging points of the No. 1 and No. 5 base stations are stored on two sides of the highway section, the corresponding distance and speed are X 1～X4、V1～V4, the distance between the road section endpoint a and the base station 2 foot hanging point is X a2, and the distance between the road section endpoint b and the base station 4 foot hanging point is X b4. The speed V ab of the road section ab is calculated as follows:
② A certain road section comprises a base station drop foot
As shown in fig. 4, the road section ab includes 1 base station foot drop points, the outside of the ab has 1 and 3 base station foot drop points, the corresponding distance and speed are X 1～X2、V1～V2, the distance between the road section endpoint a and the base station 2 foot drop point is X a2, and the distance between the road section endpoint b and the base station 2 foot drop point is X b2. The road section ab speed V ab
The calculation formula is as follows:
③ The road not comprising a base station drop foot
As shown in FIG. 5, the road section ab comprises 0 base station foot drop points, the outside of the ab is provided with 1, 2 base station foot drop points, and the corresponding distance and speed are X 1、V1 respectively, so that the speed V of the road section ab ab
The calculation formula is as follows:
Vab＝V1
in S7, the squaring user determination based on the dynamic time warping algorithm:
Because the invention is based on the calculation of the carbon emission of the vehicle on the expressway, and the mobile phone signaling data is used for counting the related information about the users, two or more users need to be counted to take the same vehicle. According to the user path matching result of table 12, if there are two different users generating signaling track data very similar, it may belong to the same user; the signaling data of different users riding different vehicles are quite different, so that the users cannot belong to the same-riding users.
The sharing users can be distinguished according to the running speeds and the time of the two users on each road section, the running speed of each road section can be calculated according to S6, and the running speeds of the sharing users on each road section are also high in similarity due to the fact that the mobile phone signaling data generated by the sharing users are very similar, so that Dynamic Time Warping (DTW) is adopted for judging. The algorithm solving thought is as follows: searching an optimal regular path, if each speed point in one speed track sequence can be subjected to similar matching with the speed point in the other speed track sequence, forming a regular path based on the position relation of the speed points of the tracks, wherein the distance sum of the matching points is the accumulated distance value of the regular path, namely the similarity value of the two tracks. The smaller the cumulative distance of the regular path is, the higher the similarity is; and vice versa. The invention firstly extracts users with the same driving path and less than 5min of time interval when the users enter and leave the expressway, and identifies whether the users take the vehicles together or not.
In the time period t 1-t2, the users 1 and 2 obtain the same running paths of the two users according to S3, S4 and S5 in the expressway section i, and calculate the running speeds of the two users in each section according to S6, so that the signaling data track speed sequence of the user 1Signalling track speed sequence of user 2/> The concrete expression is as follows:
Track speed/> The DTW distance value between the adjacent regular path points is the minimum value of the distance value between the i and j-th matching points.
Track speed/>Distance function between.
If the DTW distance value of the two-user track speed sequence is smaller than the threshold value 100 (in practice, the two users can be judged as the same-user, and if the DTW distance value of the two-user track speed sequence is far greater than the threshold value, the two users can be considered as not being the same-user.
In S8, the carbon emission of the motor vehicle at each section of the highway is calculated:
The mobile phone signaling data can identify that the number of users on the expressway is m in the appointed time, and then according to the quantity of the motor vehicles stored on the expressway in each province, the ratio of passenger to trucks is R 1：R2, the energy supply mode of the passenger is mainly gasoline, and the energy supply mode of the trucks is mainly diesel. And calculating the total carbon emission of all users of the expressway in the designated time according to the obtained driving characteristic parameters, the vehicle self characteristic parameters and the carbon emission parameters, and multiplying the total carbon emission by the corresponding passenger-cargo ratio to obtain the carbon emission corresponding to the expressway passenger-cargo vehicle in the designated time.
Total carbon emission calculation for all vehicles in the highway at time t 1 to t 2:
/>
-carbon emissions of highway network passenger car in time t 1 to t 2.
-Carbon emissions of highway network trucks in the time t 1 to t 2.
-Total carbon emission of the highway in the time period t 1 to t 2.
CL x —energy consumption per kilometer (L/km), x=1, fuel is gasoline; x=2, the fuel is diesel; from IPCC national greenhouse gas inventory guidelines in 2006, the fuel is gasoline, and the query value is 0.088; the fuel is diesel oil, and the query value is 0.4.
Ρ x —energy density (kg/L), x is the same as above; from IPCC national greenhouse gas inventory guidelines in 2006, the fuel is gasoline, and the query value is 0.725; the fuel is diesel oil, and the query value is 0.835.
Q x -energy equivalent value (Tj/kg), x is as above; from IPCC national greenhouse gas inventory guidelines in 2006, the fuel is gasoline, and the query value is 0.0000443; the fuel is diesel oil, and the query value is 0.000043.
E x -energy consumption co 2 emission factor (kg/Tj), as above, inquiry can be made 69300; the fuel is diesel oil, and the query value is 0.835; the fuel is diesel oil, and the query value is 74100.
V l,i -travel speed of the first user on the i-th road section (km/h).
T l,i -travel time (h) of the first user on the i-th road section.
Total number of first user travel road segments in time n-t 1 to t 2.
Total motor vehicle amount on the highway in the time from m-t 1 to t 2.
Example 1:
Taking signaling data of 44min of 1 user as an example, the carbon emission calculation of the motor vehicle on the expressway is carried out:
step 1: and (3) acquisition of travel data and construction of a database:
And obtaining the mobile phone signaling data and the expressway base station data of the user from the E-business companies such as mobile and Unicom, and performing field test by a tester to obtain the base station data of the expressway toll station. The collected data are classified and stored according to the number and time sequence of the user, a basic signaling database is built, and part of basic signaling data of the user for 44 minutes is displayed in a table 13.
Table 13 basic signaling data
Step 2: judgment of user entering expressway
The interactive base station with a certain signaling data of the user belongs to the expressway base station database GD, and the user is regarded as a suspected entering user.
For a suspected entering user P, if the ith signaling data and the 1 st data generated after 10min belong to the expressway base station database GD and the distance between the ith signaling data and the 1 st data is greater than 5km, determining that the user P enters an expressway, and turning to the next step; if not, let i=i+1, repeat this step until the ith signaling data does not belong to the highway base station database GD.
TABLE 14 entry Highway user Signaling data sheet
The i-th signaling data for confirming entry to the user P is calculated as distances from all the toll stations, and the toll station with the smallest distance is used as the entry point of the user P. Finally, a user information table of the entering expressway is generated, and specifically comprises a user ID, time, a toll station code, a toll station name and a toll station longitude and latitude, and the user ID, the time, the toll station code, the toll station name and the toll station longitude and latitude are shown in table 15.
TABLE 15 Inlet Highway user information List
From this, it can be derived that this user is at 8:28 from the high speed toll station into the high speed road section.
Step 3: travel path matching based on mobile phone signaling data
1) And (3) matching the CELLID with the expressway base station database GD by using the LAC, and writing in a calibration road section where each piece of signaling data can travel.
2) And (3) searching a common calibration section of the i and the i+1 data from the ith signaling data, wherein if the i signaling data calibration section is S001001, S001002 and S001003 and the i+1 signaling data calibration section is S001002 and S001003, the common calibration section is S001002 and S001003, and then carrying out the next step.
3) If the public calibration section exists, the public calibration section of the (i+2) th signaling data and the (i+1) th signaling data is continuously judged, if the (i+1) th signaling data and the (i+1) th signaling data are calibrated sections of S001002 and S001003, the (i+2) th signaling data are calibrated sections of S001002 and S001004, and if the (i+2) th signaling data and the (i+1) th signaling data are calibrated sections of S001002 and S001002, the public calibration section is the section of S001002.
4) Gradually iterating until the ith to the (i+n-1) th signaling data and the (i+n) th signaling data have no public road sections, and calculating the occurrence times of all the calibrated road sections in the ith to the (i+n-1) th signaling data.
5) And the public calibration road sections with the largest occurrence number are regarded as the matching road sections of the ith to the i+n-1 signaling data.
6) The final partial match results are shown in table 16.
Table 16 entry Highway user section Table
Step 4: judgment of user driving off expressway:
1) For each user entering the expressway, when the interaction base station of the ith signaling data does not belong to the expressway base station database any more, the 1 st signaling data generated after 5min does not belong to the expressway base station database either, and all signaling data generated in the subsequent 5min of the ith signaling data does not belong to the expressway base station database, the user is judged to leave the expressway, the next step 2) is carried out, otherwise, i=i+1 is judged, whether the signaling data is the expressway outbound site signaling data or not is judged, if the signaling data is not the expressway outbound site signaling data, the step 3 is returned to carry out path matching until the expressway outbound site signaling data is obtained.
2) For the i-th signaling data confirming the driving away from the user P, the distances between the i-1-th signaling data and all toll stations are calculated, and the toll station closest to the i-1-th signaling data is regarded as the expressway outbound station. From Table 17, it is known that the user has traveled off the highway at 8 points 58, and Table 18 is a table of off highway user information.
Meter 17 off highway determination
Table 18 entry highway user information table
Step 5: calculating the running speed of each section of the expressway:
And aiming at the user track data section matching result table shown in the table 16, judging signaling data points under the same base station one by one, and determining the time point of entering the base station and the time point of leaving the base station. If the user generates only 1 piece of signaling data under a certain base station, the piece of data is skipped.
1. Calculating the position and time of a key point (C n) of a user in the service range of each base station:
After the time of entering and exiting the base station by the user is determined, any two ab points on the straight line section of the highway are selected by combining the coordinates of the road section, and a straight line equation L ab is established as follows:
×106.7025)＝2.03x-190.021319
Wherein x a-a is longitude, y a -a is latitude, x b -b is longitude, y b -b is latitude.
Meanwhile, the perpendicular L 1 of the ab section passing through the point A 1 of the base station is as follows:
×106.691388)＝2.03x-190.0149066
Wherein x a -a latitude, y a -a latitude, x b -b latitude, y b -b latitude, x A1 -base station a 1 longitude, y A1 -base station a 1 latitude.
The intersection point of the two straight lines can be obtained by combining the equation of the straight line L 1 and the straight line L ab, namely, the drop foot point is
/>
The drop foot of the base station A 2 and the straight line L ab can be obtained by the same calculationIs (106.686808, 26.554533).
2. Determining a user traveling direction:
For the travelling direction of the user, determining by using the key point of the user at the base station and the position point of the first piece of data of the user entering the base station. Direction of abscissa, if Judging that the user is going eastward, forward, vertical, if/>The user is judged to be traveling north and is forward.
This user's abscissa direction is used to determine,
The user is known to travel in the west direction, in the ordinate direction,The user is known to travel in the southbound direction.
3. Calculating the speed between two consecutive key points:
because the signaling data of the user in the coverage area of one base station is relatively dense in the 4G environment, the foot drop time can be estimated by using the time of the user entering and exiting the base station, and is the time of the middle moment of the user entering and exiting the base station:
Wherein, Time of placement,/>Time of placement,/>Time at.
Wherein,Time of placement,/>Time of placement,/>Time at.
With vertical foot coordinate pointsIts corresponding time point/>Based on this, the velocity V 1 between the user segments is calculated. The specific calculation formula is as follows: /(I)
DISTANCE-a function of the DISTANCE between two coordinate points of a plane, V 1 -a speed between segments c 1—c2.
The formula of the function DISTANCE is:
M＝sin(LatA)*sin(latB)*cos(LonA-LonB)
M＝sin(26.566045)*sin(26.554533)*cos(106.693283-106.686808)
＝0.199933
N＝cos(LatA)*cos(LatB)
N＝cos(26.566045)*cos(26.554533)＝0.800066
DISTANCE([LonA,LonA],[LonB,LatB])＝R*Arccos(M+N)*π/180
＝6371000×Arccos(0.199933+0.800066)×π/180
＝9009.95589
Wherein R is the earth radius 6371000m; [ LonA, latA ] and [ LonB, latB ] are longitude and latitude coordinates of the point A and the point B respectively; pi is a circumference ratio constant 3.141593.
And calculating the average speed among the rest base stations according to the steps, and removing the data with the speed greater than the speed limit according to the speed limit condition V Limiting the limit of the matched road. The results are for example the following:
table 18 table of speed calculation results between base stations to which a certain user is connected
4. Calculating the running speed of each road section:
The average speed of the user traveling over each section of the road is further calculated using table 18. Both the S001001 and S001002 road segments contain 3 base station footholds, whereby the user has already traveled the S001001 and S001002 road segments, so that the speeds of the two segments can be averaged.
/>
Step 6: calculating the carbon emission of motor vehicles on each section of the expressway:
calculating the carbon emissions for this user, passenger car, so x=1, at 8 hours 28 minutes to 8 hours 58 minutes:
so that the user discharged 7.279728kg of carbon dioxide in total in 30min.
The above embodiments are merely preferred embodiments of the present invention, the protection scope of the present invention is not limited thereto, and any simple changes or equivalent substitutions of technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention disclosed in the present invention belong to the protection scope of the present invention.

Claims (9)

1. The method for calculating the carbon emission of the motor vehicle on the expressway based on the mobile phone signaling data is characterized by comprising the following steps of:
acquiring mobile phone signaling data, spatial position data, base station data along a target expressway and base station data corresponding to each toll station in an expressway network, and establishing an expressway base station database;
Base station position matching is carried out on mobile phone signaling data to form signaling track data, and the signaling track data are sequenced according to a user numbering sequence and a time sequence to obtain a basic signaling database;
If the basic signaling database of the user has a certain interaction base station of signaling data belonging to the expressway base station database, the user is regarded as a suspected entering user;
Matching the position area number LAC of the suspected entering user with the cell number CELID and the expressway base station database to obtain a possible traveling calibration road section, searching a calibration road section common to all signaling data, and taking the common calibration road section with the largest occurrence number as a matching road section;
Calculating the average speed of the users in the base station according to the key point positions and the time in each base station, and weighting according to the obtained average speed among each base station and the corresponding distance to obtain the average running speed among each matched road section of the expressway;
judging a co-riding user based on a dynamic time warping algorithm, calculating carbon emission of only one user ID for the co-riding user, and obtaining respective carbon emission parameters according to the self characteristics of different motor vehicles;
Calculating the carbon emission of each matched road section of different motor vehicles in a specified time according to the average running speed, the self characteristics of the motor vehicles and the carbon emission parameters, and calculating the overall carbon emission of all the motor vehicles in the time;
the dynamic time warping algorithm is used for judging the co-riding users, and only one user ID carbon emission is calculated for the co-riding users, and the method comprises the following steps:
extracting users with the same driving path and less than 5 minutes of time interval when the users enter and leave the expressway, and identifying whether the users take the same vehicle or not;
acquiring signaling data track speed sequence of user 1 Signaling track speed sequence with user 2And calculates the track velocity/>The DTW distance value between the two is expressed as follows:
Track speed/> The DTW distance value between every two adjacent regular path points is added with the minimum value between the ith and the jth matching points;
Track speed/> A distance function between;
if the DTW distance value of the two-user track speed sequence is smaller than the threshold value 100, the two users can be judged as the same-time users.
2. The method for calculating the carbon emission of the motor vehicle on the expressway based on the mobile phone signaling data as set forth in claim 1, wherein said establishing an expressway base station database comprises the steps of:
Acquiring mobile phone signaling data of user travel by using space-time characteristic data obtained from communication companies such as Unicom, mobile and the like, wherein the mobile phone signaling data comprises 4G signaling data and communication base station data; the 4G signaling data is used to represent the user ID, LAC, CELLID, date, time; the communication base station data are used for representing longitude and latitude and service information of the base station;
Collecting, calibrating and arranging communication base station data along a target expressway through field test, and establishing an expressway base station database corresponding to an expressway road section and a base station, wherein the expressway base station database comprises road codes, road names, road section numbers, base stations LAC, base stations CELLID, base station longitude and latitude and advancing directions;
Through field test, the base station corresponding to each toll station in the expressway network is collected, calibrated and arranged, and a toll station base station database corresponding to the toll station and the base station is established, wherein the toll station base station database comprises toll station codes, toll station names, toll station longitudes and latitudes, base stations LAC, base stations CELLID and base station longitudes and latitudes.
3. The method for calculating the carbon emission of the motor vehicle on the expressway based on the mobile phone signaling data according to claim 1, wherein the base station position matching is performed on the mobile phone signaling data to form signaling track data, and the method comprises the following steps:
Performing base station position matching on mobile phone signaling data through key words LAC and CELLID;
and adding the field longitude and latitude into the mobile phone signaling data to obtain signaling track data.
4. The method for calculating carbon emission of motor vehicle on expressway based on mobile phone signaling data as set forth in claim 1, wherein if the interactive base station with a certain signaling data belongs to the expressway base station database, the user is regarded as a suspected entering user, comprising the following steps:
Judging the ith signaling data of the suspected entering user P and the 1 st data generated after 10 min;
if the two data are all in the expressway base station database and the distance between the two data is more than 5km, determining that the user P enters the expressway;
If not, making i=i+1, repeating the step until the ith signaling data does not belong to the expressway base station database;
for the i-th signaling data of the confirmed entering user P, the distances between the i-th signaling data and all toll stations are calculated, and the toll station with the smallest distance is taken as the entering point of the user P to generate an entering expressway user information table.
5. The method for calculating the carbon emission of the motor vehicle on the expressway based on the mobile phone signaling data according to claim 1, wherein the matching between the location area number LAC of the suspected entering user and the cell number CELLID and the expressway base station database is performed to obtain a calibration road section of possible traveling, and the common calibration road section with the greatest occurrence number is searched, and the method specifically comprises the following steps:
Matching with the expressway base station database by using LAC and CELLID, and writing in a calibration road section where each piece of signaling data is likely to travel;
Starting from the ith signaling data, searching a calibration road section common to the i and the i+1 data;
if the public calibration section exists, continuing to judge the public calibration section of the (i+2) th signaling data and the (i+1) th signaling data;
gradually iterating until no public road section exists between the ith signaling data to the (i+n-1) th signaling data and the (i+n) th signaling data, and calculating the occurrence times of all calibration road sections in the ith signaling data to the (i+n-1) th signaling data;
the public calibration road section with the largest occurrence number is regarded as a matching road section of the ith to the (i+n-1) th signaling data;
Judging whether the user leaves the expressway, if the signaling data generated by the user does not belong to the expressway base station database, deleting the signaling data directly, and if not, continuing to match the road sections.
6. The method for calculating the carbon emission of the motor vehicle on the expressway based on the mobile phone signaling data as set forth in claim 1, wherein the method for judging that the user leaves the expressway comprises the following steps:
judging whether the interactive base station of the ith signaling data of the user belongs to a highway base station database or not;
If the ith signaling data does not belong to the expressway base station database, and meanwhile the 1 st signaling data generated after 5min does not belong to the expressway base station database, and all signaling data generated in the subsequent 5min of the ith signaling data do not belong to the expressway base station database, judging that the user drives away from the expressway;
If not, the i=i+1 is made, whether the signaling data is the signaling data of the station out of the expressway is judged, and if not, path matching is carried out;
And (3) after confirming the ith signaling data of the driving-away user P, calculating the distances between the ith-1 signaling data and all toll stations, and regarding the toll station closest to the ith signaling data as the expressway outbound station.
7. The method for calculating the carbon emission of the motor vehicle on the expressway based on the mobile phone signaling data according to claim 1, wherein the average speed of the users in the base stations is calculated according to the positions and the time of the key points in the base stations, and the average running speed of each matched road section of the expressway is obtained by weighting according to the obtained average speed among the base stations and the corresponding distance, and the method specifically comprises the following steps:
Extracting the time corresponding to the 1 st signaling data of the user entering each base station service range and the last signaling data before leaving the base station service range, and taking the time as the origin-destination time of the user entering and leaving each base station service range;
calculating the position and time of a key point C n of a user in the service range of each base station;
Determining the travelling direction of the user according to the key point of the user at the base station and the position point of the first piece of data of the user entering the base station;
calculating the speed between key points according to the ratio of the distance between key points of the service ranges of two continuous base stations and the corresponding time to obtain the average speed between the base stations;
and calculating the running speed of each matched road section of the highway by weighting the distance between the end point of the matched road section and the adjacent base station and the average speed between the base stations.
8. The method for calculating the carbon emission of the motor vehicle on the expressway based on the mobile phone signaling data according to claim 1, wherein the calculation expression of the carbon emission of the motor vehicle in a specified time is as follows according to the average running speed, the self-characteristics of the motor vehicle and the carbon emission parameters:
wherein, x=1, the fuel is gasoline, x=2, the fuel is diesel;
For the carbon emission quantity of the highway network passenger car within the time from t 1 to t 2,/> The carbon emission of the highway network truck in the time from t 1 to t 2 is CL x, the energy consumption L/km per kilometer, ρ x, the energy density kg/L, q x, the energy equivalent value Tj/kg, e x, the energy consumption co 2 emission factor kg/Tj, v l,i, the running speed km/h of the first user in the i-th road section, t l,i, the running time h of the first user in the i-th road section, n, the total number of the running road sections of the first user in the time from t 1 to t 2, and m, the total number of motor vehicles of the highway in the time from t 1 to t 2.
9. The method for calculating the carbon emission of motor vehicles on the expressway based on the mobile phone signaling data as set forth in claim 8, wherein the method for calculating the total carbon emission of all motor vehicles on the expressway in the time comprises the following steps:
The total carbon emissions for all vehicles in the highway at time t 1 to t 2 were calculated as:
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