CN116468152A - Multi-situation regional prediction method for pollutant discharge amount of highway transportation - Google Patents
Multi-situation regional prediction method for pollutant discharge amount of highway transportation Download PDFInfo
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Abstract
The invention discloses a method for predicting the pollutant discharge amount of highway transportation and transportation in multiple scenic areas, which comprises the following steps: acquiring a traffic distribution observation data set of a highway based on an entrance ramp charging system in a prediction space-time range; generating a policy regulation profile combination based on different emission standard updating schemes; measuring and calculating a reference pollutant emission factor, and acquiring an emission factor data set under each scene; predicting the holding quantity of vehicles in different scenes in the future through a seasonal SARIMA-SVR model, and estimating the pollutant discharge quantity of the months in the future of each scene; and evaluating the combined emission reduction effect of each emission standard regulation policy. According to the invention, under the condition of considering different emission standard updating regulation policies, quantitative evaluation of the emission reduction effect of the multi-scenario highway traffic pollutants is realized, seasonal prediction accuracy is improved by using the SARIMA-SVR model, and technical support is provided for reasonably formulating the carbon peak reaching policy.
Description
Technical Field
The invention belongs to the technical field of carbon emission prediction, and particularly relates to a method for predicting the emission of pollutants in highway transportation.
Background
The carbon emission of the transportation industry is 15% of the carbon emission of the terminal in China, and the carbon emission of the transportation industry is still in an increasing trend due to the fact that the improvement of the fuel efficiency of the transportation departments is insufficient to meet the increasing demands. Wherein, the carbon emission of the road transportation accounts for 74% of the total carbon emission of the transportation industry, and the carbon emission of the heavy goods vehicle accounts for 54% of the total carbon emission of the road transportation. In recent years, with the rapid increase of the conservation amount of motor vehicles and the rapid development of the express transportation industry, the transportation amount of the expressway is geometrically increased, and the carbon emission of the expressway becomes an important source of the carbon emission of the expressway.
It follows that it is of great importance to study the predictions of carbon emissions in highway traffic in order to reduce the negative effects of carbon emissions.
According to the existing literature, most researches construct regression relations between predicted influence factors and carbon emission by adopting linear regression methods such as least square regression, ridge regression, quantile regression and the like and machine learning methods such as a support vector machine, a neural network and the like based on macroscopic influence factors obtained by decomposition of an IPAT model, a Kaya identity, an STIRPAT and an expansion model thereof, so as to predict the traffic carbon emission of an area.
In addition, some studies have been made to estimate future vehicle carbon emissions by predicting important vehicle operating parameters such as vehicle inventory growth, vehicle technology development, vehicle mileage, fuel efficiency, etc., and applying bottom-up carbon emission measurement methods (e.g., LEAP models, emission factor models such as COPERT, MOVES, GEI, etc.). The prior art discloses a carbon emission prediction method and equipment (application number 202210631985.2) based on transportation, which calculates the total carbon emission of transportation by presetting a transportation carbon emission factor library and carries out stabilization correction and optimization on the total carbon emission of transportation based on transportation volume data sets of different transportation carbon emission chains, transportation modes and activity characteristics in a specified geographic range, and establishes a comprehensive autoregressive moving average model after optimization to predict a change curve of carbon emission. The prior art also discloses a prediction method and a prediction system (application number 202110923903.7) for the carbon dioxide emission of road traffic, which are used for predicting the spatial change of a weighted road network based on urban construction land and population space data sets, predicting the fuel vehicle holding quantity based on a vehicle survival curve and new energy vehicle sales permeability, distributing the fuel vehicle holding quantity to a space grid according to the weighted road network density, and predicting the carbon dioxide emission of the space grid of the road traffic in the coming year by establishing a functional relation between the fuel vehicle quantity and the carbon dioxide emission.
The method is used for comprehensively researching and applying at home and abroad, and predicting the total carbon emission level of traffic by depending on macroscopic indexes such as population, GDP, energy intensity, total consumption, total social output and the like, and has the problems of insufficient data precision, low industry subdivision degree and the like. Prediction research based on a bottom-up measurement model makes an attempt on improving the accuracy of carbon emission factors and influence factor prediction in the measurement model, but the influence of regulation policies of different emission standards on the formation of expressway traffic fleets and the change of pollutant emission factors is not fully considered, so that the emission reduction effect of various emission standard regulation policy combination scenes cannot be well evaluated, and the accuracy of a prediction result is further influenced.
Disclosure of Invention
To solve at least one of the above technical problems, according to an aspect of the present invention, there is provided a method for predicting a multi-scenario zone of pollutant discharge amount in highway transportation, comprising the steps of:
s1, determining a space-time range of the emission prediction of pollutants on the expressway, and obtaining a traffic distribution observation data set of each expressway;
specifically, the space-time range includes the geographical area in which the highway is located and the representative year-month and target year-month range; aiming at expressways in the area to be detected representing the year and month, acquiring a traffic distribution observation data set of each expressway, wherein the traffic distribution observation data set comprises the length of an observation interval, the number of passing vehicles in the observation interval and attribute data such as corresponding vehicle types, sizes, uses, emission standards and the like of the passing vehicles;
based on the vehicle maintenance data and the new vehicle registration data representing months in the geographic space boundary, the vehicle-passing emission standard attribute in the data set is obtained by calculating the survival amount and the total amount ratio of various vehicles and combining with the updated policy of the emission standard, wherein the survival amount of various vehicles is calculated as follows:
wherein, SVP n,i,t To register the number of non-scrapped vehicles of type i vehicles with month n when estimating month t; RP (RP) n,m Registering the number of i-type vehicles with month n; SR (SR) n,i,t To register the vehicle survival rate of an i-type vehicle with month n when estimating month t; t (T) n,i And k n,i Is a characteristic parameter.
S2, generating a policy regulation contextual model combination updated based on emission standards; the policy regulation scenario comprises a change rate combination of pollutant emission factors, an emission standard updating interval and the early rejection years of old standard vehicles;
the change rate combination of the pollutant emission factors is set into two scenes of uniform change A1 and fluctuation change A2; the emission standard updating interval is set to three scenes of high frequency B1, general B2 and low frequency B3; the advanced year of the old standard vehicle is set as three scenes of natural elimination C1, final elimination C2 and minor elimination C3;
the three scenes are randomly combined, 18 policy regulation scene mode combinations Pr (r=1, 2,3,.. 18) are correspondingly generated, and AaBbCc (a=1, 2; b, c=1, 2, 3) are respectively correspondingly combined.
S3, measuring and calculating reference pollutant emission factors, and acquiring emission factor change data sets under different policy regulation situations;
the baseline pollutant emission factor is expressed as BEF i,j,s,p Wherein i represents a pollution source vehicle type, including minibuses, medium buses, large buses, minivans, medium vans, large vans, container vehicles; j represents the fuel type of the pollution source vehicle, including gasoline, diesel oil and mixed new energy; s represents the emission standard of pollution source vehicles, including the prior state one, state two, state three, state four, state five, state six and state seven, state eight, state nine, state ten and state eleven under the future planned situations; p represents a contaminant type including NOX, VOC, PM 2.5, PM 10, CO2, etc.;
according to COPERT, the basic emission factors (BEF, basic Emission Factors) measured under the standard environment are simulated, the local conditions and the highway road conditions are combined, the correction parameters reflecting the driving conditions and the environmental factors are used for localization, meanwhile, the emission factors corresponding to the emission standards of the future planned situations are obtained based on the change rates of the emission factors under the future different policy regulation situations, and the calculation formula is as follows:
wherein EF is i,j,s,p,r Under the policy regulation scene Pr, j is the emission factor/(g x km-1) of p pollutants emitted by an i-type vehicle with an emission standard s; BEF (BEF) i,j,s,p Is the corresponding basic emission factor/(g-km-1);the environment correction factors comprise temperature and humidity; omega i,j,s,p Is a corresponding velocity correction factor; delta i,j,s,p Other corresponding correction coefficients comprise load coefficients, lubricating oil parameters, oil quality and the like; θ i,j,s,p,a Under the policy regulation situation Pr, namely the change rate combination Aa of pollutant emission factors corresponds to the rowRate of change of the discharge factor;
s4, determining age distribution of various vehicles representing months, and predicting the storage quantity of the vehicles in different future scenes through a SARIMA-SVR model;
the specific process for determining the age distribution of various vehicles representing months is as follows:
based on the vehicle holding amount data representing the month and the new vehicle registration data, according to the survival rate of the vehicles of each vehicle type, the age distribution of the vehicles of each vehicle type representing the month is obtained, and the calculation formula is as follows:
wherein, SVP n,i,t To register the number of non-scrapped vehicles of type i vehicles with month n when estimating month t; RP (RP) n,m Registering the number of i-type vehicles with month n; SR (SR) n,i,t To register the vehicle survival rate of an i-type vehicle with month n when estimating month t; t (T) n,i And k n,i Is a characteristic parameter;
the specific acquisition process for predicting the vehicle holding quantity under different future scenes by using the SARIMA-SVR model comprises the following steps of;
based on month traffic volume data representing a highway observation interval of a month to-be-measured area, obtaining month traffic vehicle numbers of different vehicle types, fuel types and emission standards according to the vehicle age distribution and the implementation year of the vehicle emission standard;
ADF inspection and white noise inspection are carried out on different vehicle types and the number of monthly passing vehicles of the fuel types in the expressway observation interval of the time-series to-be-detected area so as to judge whether the time series is stable, if so, the subsequent step is carried out, otherwise, d-step difference is carried out on the time series so as to lead the time series to be stable;
determining optimal parameters of an SARIMA (P, D, Q) x (P, D, Q) s model by using grid search according to a red pool information criterion AIC, wherein P, D, Q are non-seasonal autoregressive terms, moving average hysteresis terms and differential orders, P, D, Q are seasonal autoregressive terms, moving average hysteresis terms and differential orders, and s is a seasonal period of a time sequence;
training the SARIMA model by adopting a stable time sequence training sample, and predicting by using a prediction function of the SARIMA model to obtain a fitting sequence of the time sequenceCorresponding residual sequence R i,j,t The method comprises the steps of carrying out a first treatment on the surface of the R is set by sliding window i,j,t Reconstructing the sequence into a sequence with the order of u, and training and predicting the residual sequence by using an SVR model to obtain a residual fitting sequence +.>SARIMA-SVR model predicts the number of monthly passing vehicles of different vehicle types and fuel types +.>Is->And->And (3) summing.
Predicting the number of new vehicles registered in a target month according to an exponential smoothing method, acquiring the vehicle age and emission standard distribution of the target month of each scene by combining the emission standard updating frequency Bb and the early scrapping scheme Cc of old emission standard vehicles under the policy regulation scene Pr, and passing the number of vehicles in the monthDistributed according to emission standards, and the target month passing vehicle quantity of different vehicle types, fuel types and emission standards under the scene Pr is obtained>
And 5, estimating pollutant emission of future months of each scene, and evaluating the emission reduction effect of the emission standard adjustment policy adopted by each scene.
According to the average vehicle mileage of the month of the expressway observation interval of the area to be measured representing the month, predicting the average vehicle mileage of the month of the target month by adopting a secondary exponential smoothing method
Obtained by combiningAnd predicting monthly pollutant discharge amount of highway transportation in each scenario of the target month +.>And emission factor EF i,j,s,p,r The calculation formula for estimating the p-type pollutant emission amount of the vehicle with the month of t under the scene Pr is as follows:
and evaluating the emission reduction effect of the adopted emission standard adjustment policy by analyzing the predicted emission change trend of the p-th pollutant of each scene in the target month.
According to another aspect of the present invention there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method for predicting the amount of pollutant emissions in highway traffic.
According to a further aspect of the present invention there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the steps in the method for multi-situational inter-regional prediction of pollutant emissions from highway traffic according to the present invention.
Compared with the prior art, the invention has at least the following beneficial effects:
the prediction accuracy and generalization capability of the seasonal model are improved through SARIMA-SVR model prediction based on historical highway traffic data; meanwhile, the influence of policy combination situations of pushing different emission standard updating forces, updating intervals and advanced scrapping policies on the emission amount of future pollutants is considered, and technical support is provided for reasonably formulating the carbon peak reaching policy in order to solve the future pollutant emission trend of the expressway.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following brief description of the drawings of the embodiments will make it apparent that the drawings in the following description relate only to some embodiments of the present invention and are not limiting of the present invention.
FIG. 1 is a flow chart of the prediction of carbon emissions from highway traffic.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
Example 1:
the method for predicting the multi-scenario areas of the emission amount of the pollutants in the highway transportation, as shown in fig. 1, comprises the following specific steps of 1-5:
s1, determining a space-time range of the emission prediction of pollutants on the expressway, and obtaining a traffic distribution observation data set of each expressway;
specifically, cities, city groups and the like in the administrative division sense are taken as geographic space boundaries, and successive year and month intervals are taken as representative year and month intervals and target year and month intervals respectively.
And then, aiming at the expressway of the area to be measured representing the year and month, acquiring a traffic distribution observation data set of each expressway, wherein the traffic distribution observation data set comprises the length of an observation interval, the number of passing vehicles in the observation interval and the corresponding attribute data such as vehicle types, sizes, purposes, emission standards and the like of the passing vehicles.
Based on the vehicle maintenance data representing months and the new vehicle registration data in the geographic space boundary, the emission standard attribute of passing vehicles in the data set is obtained by calculating the survival amount of various vehicles and combining with the updated policy of the emission standard, and the survival amount of various vehicles is calculated as follows:
wherein, SVP n,i,t To register the number of non-scrapped vehicles of type i vehicles with month n when estimating month t; RP (RP) n,m Registering the number of i-type vehicles with month n; SR (SR) n,i,t To register the vehicle survival rate of an i-type vehicle with month n when estimating month t; t (T) n,i And k n,i Is a characteristic parameter.
In one embodiment, a city group is taken as an observation space, the representative annual month interval is 1 month in 2004 to 12 months in 2020, and the target annual month interval is 2021 month in 1 to 12 months in 2030. And according to the holding quantity of the large truck in the representative year and the registration quantity of the new truck, combining with the emission standard updating policy of the urban mass in the prediction area to obtain the emission standard duty ratio distribution of the observed vehicles. Taking a large truck passing through the observation area as an example, the duty ratio distribution of each emission standard of the large truck is shown in table 1.
TABLE 1 Large truck emission Standard Duty distribution
| Year of year | Country 0 | Country 1 | Country 2 | Country 3 | Country 4 | Country 5 | Country 6 |
| 2004 | 0.2797 | 0.7203 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 2005 | 0.2442 | 0.6293 | 0.1265 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 2006 | 0.2262 | 0.5850 | 0.1888 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 2007 | 0.2007 | 0.5230 | 0.2763 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 2008 | 0.1748 | 0.4625 | 0.3627 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| 2009 | 0.1552 | 0.4220 | 0.3345 | 0.0883 | 0.0000 | 0.0000 | 0.0000 |
| 2010 | 0.1049 | 0.2985 | 0.2415 | 0.3552 | 0.0000 | 0.0000 | 0.0000 |
| 2011 | 0.0632 | 0.1933 | 0.1623 | 0.5812 | 0.0000 | 0.0000 | 0.0000 |
| 2012 | 0.0422 | 0.1435 | 0.1282 | 0.6861 | 0.0000 | 0.0000 | 0.0000 |
| 2013 | 0.0284 | 0.1130 | 0.1113 | 0.7473 | 0.0000 | 0.0000 | 0.0000 |
| 2014 | 0.0162 | 0.0800 | 0.0909 | 0.6293 | 0.1836 | 0.0000 | 0.0000 |
| 2015 | 0.0081 | 0.0542 | 0.0754 | 0.5498 | 0.3126 | 0.0000 | 0.0000 |
| 2016 | 0.0033 | 0.0331 | 0.0608 | 0.4819 | 0.4209 | 0.0000 | 0.0000 |
| 2017 | 0.0012 | 0.0202 | 0.0538 | 0.4830 | 0.4419 | 0.0000 | 0.0000 |
| 2018 | 0.0002 | 0.0087 | 0.0371 | 0.3990 | 0.3719 | 0.1830 | 0.0000 |
| 2019 | 0.0000 | 0.0031 | 0.0240 | 0.3288 | 0.3169 | 0.3272 | 0.0000 |
| 2020 | 0.0000 | 0.0008 | 0.0138 | 0.2616 | 0.2667 | 0.3669 | 0.0902 |
S2, generating a policy regulation contextual model combination updated based on the emission standard. The policy regulation scenario comprises a change rate combination of pollutant emission factors, an emission standard updating interval and the early rejection years of old standard vehicles.
The change rate combination of the pollutant emission factors is set into two scenes of uniform change A1 and fluctuation change A2; the emission standard updating interval is set to three scenes of high frequency B1, general B2 and low frequency B3; the advanced year of the old standard vehicle is set as three scenes of natural elimination C1, final elimination C2 and minor elimination C3;
randomly combining the three scenes to correspondingly generate 18 policy regulation scene mode combinations Pr (r=1, 2,3,.. 18), wherein the combinations aabbbc (a=1, 2; b, c=1, 2, 3) are respectively corresponding to the combinations;
in this embodiment, the policy regulatory profile combinations in 18 are shown in table 2.
| Contextual model | Corresponding combination | Rate of change combination A | Standard update interval B | Advanced age C |
| P 1 | A 1 B 1 C 1 | Uniform variation A 1 | 2(B 1 ) | 0(C 1 ) |
| P 2 | A 1 B 1 C 2 | Uniform variation A 1 | 2(B 1 ) | 1(C 2 ) |
| P 3 | A 1 B 1 C 3 | Uniform variation A 1 | 2(B 1 ) | 2(C 3 ) |
| P 4 | A 2 B 1 C 1 | Fluctuation variation A 2 | 2(B 1 ) | 0(C 1 ) |
| P 5 | A 2 B 1 C 2 | Fluctuation variation A 2 | 2(B 1 ) | 1(C 2 ) |
| P 6 | A 2 B 1 C 3 | Fluctuation variation A 2 | 2(B 1 ) | 2(C 3 ) |
| P 7 | A 1 B 2 C 1 | Uniform variation A 1 | 3(B 2 ) | 0(C 1 ) |
| P 8 | A 1 B 2 C 2 | Uniform variation A 1 | 3(B 2 ) | 1(C 2 ) |
| P 9 | A 1 B 2 C 3 | Uniform variation A 1 | 3(B 2 ) | 2(C 3 ) |
| P 10 | A 2 B 2 C 1 | Fluctuation variation A 2 | 3(B 2 ) | 0(C 1 ) |
| P 11 | A 2 B 2 C 2 | Fluctuation variation A 2 | 3(B 2 ) | 1(C 2 ) |
| P 12 | A 2 B 2 C 3 | Fluctuation variation A 2 | 3(B 2 ) | 2(C 3 ) |
| P 13 | A 1 B 3 C 1 | Uniform variation A 1 | 4(B 3 ) | 0(C 1 ) |
| P 14 | A 1 B 3 C 2 | Uniform variation A 1 | 4(B 3 ) | 1(C 2 ) |
| P 15 | A 1 B 3 C 3 | Uniform variation A 1 | 4(B 3 ) | 2(C 3 ) |
| P 16 | A 2 B 3 C 1 | Fluctuation variation A 2 | 4(B 3 ) | 0(C 1 ) |
| P 17 | A 2 B 3 C 2 | Fluctuation variation A 2 | 4(B 3 ) | 1(C 2 ) |
| P 18 | A 2 B 3 C 3 | Fluctuation variation A 2 | 4(B 3 ) | 2(C 3 ) |
S3, measuring and calculating reference pollutant emission factors, and acquiring emission factor change data sets under different policy regulation situations;
the baseline pollutant emission factor is expressed as BEF i,j,s,p Wherein i represents a pollution source vehicle type, including minibuses, medium buses, large buses, minivans, medium vans, large vans, container vehicles; j represents the fuel type of the pollution source vehicle, including gasoline, diesel oil and mixed new energy; s represents the emission standard of pollution source vehicles, including the prior state one, state two, state three, state four, state five, state six and state seven, state eight, state nine, state ten and state eleven under the future planned situations; p represents a contaminant type including NOX, VOC, PM 2.5, PM 10, CO2, etc.
The method is characterized in that local conditions and highway road conditions are combined, correction parameters reflecting driving conditions and environmental factors are used for localization, meanwhile, based on the change rate of the emission factors in different future policy regulation scenes, the emission factors corresponding to each emission standard of the future planned scenes are obtained, and the calculation formula is as follows:
wherein EF is i,j,s,p,r Under the policy regulation scene Pr, j is the emission factor/(g x km-1) of p pollutants emitted by an i-type vehicle with an emission standard s; BEF (BEF) i,j,s,p Is the corresponding basic emission factor/(g-km-1);the environment correction factors comprise temperature and humidity; omega i,j,s,p Is a corresponding velocity correction factor; delta i,j,s,p Other corresponding correction coefficients comprise load coefficients, lubricating oil parameters, oil quality and the like; θ i,j,s,p,a Under the condition of policy regulation PrI.e. the combination of the rates of change of the pollutant emission factors Aa corresponds to the rate of change of the emission factors.
In this example, a COPERT carbon emission factor library was selected, simulating the basic emission factor (BEF, basic Emission Factors) measured under standard conditions. The emission of various pollutants produced by a large truck with the fuel type of diesel oil running for 1000km is used as a reference emission factor, and the reference emission factors corresponding to various emission standards are shown in Table 3.
TABLE 3 Large truck benchmark NOx emission factors
| Emission standard | Before the country | Guoyi (Chinese character) | Guoguan II | Guosan (Chinese character of three) | Guoqiu (four countries) | Guowu (five countries) | Guohui (Chinese six) |
| NO X [t] | 0.01240 | 0.00872 | 0.00924 | 0.00742 | 0.00525 | 0.00236 | 0.00016 |
And (3) carrying out localization simulation on an environment correction factor, a speed correction factor and other correction factors according to the average speed and the load of the expressway of the large truck, and presetting the factor change rate of the future emission standard according to the class A scene generated in the step (2), wherein the factor change rate is shown in a table 4.
TABLE 4 Large truck NOx emission factors for different emission factor rate scenarios
| Emission standard | Guoqi (seven kinds of Chinese character) | Guoba (Chinese eight) | Guojiu (Chinese nine) | Guozheng (ten countries) | Guoyang (national eleventh code) |
| Uniform variation A 1 | 0.00020 | 0.00013 | 0.00008 | 0.00005 | 0.00003 |
| Fluctuation variation A 2 | 0.00034 | 0.00027 | 0.00019 | 0.00009 | 0.00001 |
S4, predicting the quantity of the vehicle in different future scenes through an SARIMA-SVR model;
specifically, ADF inspection and white noise inspection are carried out on different vehicle types and the number of monthly passing vehicles of the fuel type in the expressway observation interval of the time-series to-be-detected area so as to judge whether the time series is stable, if so, the subsequent step is carried out, otherwise, d-step difference is carried out on the time series so as to lead the time series to be stable;
determining optimal parameters of an SARIMA (P, D, Q) x (P, D, Q) s model by using grid search according to a red pool information criterion AIC, wherein P, D, Q are non-seasonal autoregressive terms, moving average hysteresis terms and differential orders, P, D, Q are seasonal autoregressive terms, moving average hysteresis terms and differential orders, and s is a seasonal period of a time sequence;
training the SARIMA model by adopting a stable time sequence training sample, and predicting by using a prediction function of the SARIMA model to obtain a fitting sequence of the time sequenceCorresponding residual sequence R i,j,t The method comprises the steps of carrying out a first treatment on the surface of the R is set by sliding window i,j,t Reconstructing the sequence into a sequence with the order of u, and training and predicting the residual sequence by using an SVR model to obtain a residual fitting sequence +.>SARIMA-SVR model predicts the number of monthly passing vehicles of different vehicle types and fuel types +.>Is thatAnd->And (3) summing.
Predicting the number of new vehicles registered in a target month according to an exponential smoothing method, acquiring the vehicle age and emission standard distribution of the target month of each scene by combining the emission standard updating frequency Bb and the early scrapping scheme Cc of old emission standard vehicles under the policy regulation scene Pr, and passing the number of vehicles in the monthDistributed according to emission standards, and the target month passing vehicle quantity of different vehicle types, fuel types and emission standards under the scene Pr is obtained>
In this embodiment, data from 1 month in 2004 to 12 months in 2018 are taken as a model training set, and data from 1 month in 2019 to 12 months in 2020 are taken as a model test set. After the first-order trend difference and the seasonal difference, ADF test and white noise test results show that the test statistics are smaller than the threshold value, the condition that p is smaller than 0.05 is satisfied, the original time sequence is a stable sequence and is not a white noise sequence, and the difference order d is determined to be 1. According to the drawn original sequence autocorrelation diagram and partial correlation diagram, the preset autoregressive term number p can be 1 and 2, and the preset moving average hysteresis term number can be 1 and 2. In addition, the data clearly shows that 12 months is a seasonal period, and s is 12. The optimal combination of parameters for the SARIMA (P, D, Q) x (P, D, Q) s model is determined to be SARIMA (2, 1, 2) x (1, 2) 12 using a grid search according to AIC criteria.
And training an SARIMA model by adopting an original time sequence, predicting a predicted fitting sequence from 2019 1 month to 2020 12 months and a corresponding residual sequence, segmenting the residual sequence through a sliding window with the order of 24 months, and training and fitting by using an SVR model to obtain the residual fitting sequence. And adding the SARIMA prediction fitting sequence and the SVR residual error fitting sequence to obtain a SARIMA-SVR final predicted value, and obtaining the monthly traffic vehicle quantity predicted values of different vehicle types and fuel types from 2021 month to 2030 month 12. . The mean percent error (MAPE) and the mean absolute percent error of Symmetry (SMAPE) of the SARIMA single model and the SARIMA-SVR integrated model are compared in Table 5. As can be obtained from Table 5, the SARIMA-SVR model significantly reduces the prediction error and effectively improves the prediction accuracy.
TABLE 5 comparison of prediction accuracy of single SARIMA model and SAIRMA-SVR model
| Model | SARIMA | SARIMA-SVR |
| MAPE | 7.06％ | 1.27％ |
| SMAPE | 6.54％ | 1.21％ |
According to the new vehicle registration number of the target month predicted by an exponential smoothing method, the vehicle age and the emission standard distribution duty ratio of the target month of each scene are obtained by combining the emission standard updating frequency Bb under the policy regulation scene Pr and the advanced scrapping scheme Cc of the old emission standard vehicle, the month passing vehicle number of 2021 month 1 month to 2030 month predicted by the SARIMA-SVR model is distributed according to the emission standard distribution duty ratio, and the target month passing vehicle number of different vehicle types, fuel types and emission standards under the scene Pr is obtained.
S5, estimating pollutant discharge amounts of future months of each scene, and evaluating the emission reduction effect of the discharge standard adjustment policy adopted by each scene.
Specifically, according to the average vehicle mileage of the month of the expressway observation area representing the month to-be-measured area, the average vehicle mileage of the month of the target month is predicted by adopting a secondary exponential smoothing method
Obtained by combiningAnd predicting monthly pollutant discharge amount of highway transportation in each scenario of the target month +.>And emission factor EF i,j,s,p,r The calculation formula for estimating the p-type pollutant emission amount of the vehicle with the month of t under the scene Pr is as follows:
and evaluating the emission reduction effect of the adopted emission standard adjustment policy by analyzing the predicted emission change trend of the p-th pollutant of each scene in the target month.
In the present embodiment, taking PM 2.5 discharged by a large truck whose fuel type is diesel as an example, during 1 month 2021 to 12 months 2030, the number of vehicles passing monthly in each scenario predicts. The prediction result shows that, regarding the emission reduction effect of PM 2.5, the regulation effect of the emission standard update frequency (regulation scenario B) on pollutant emission is most obvious, the influence of the emission standard update frequency on the pollutant emission is gradually enhanced with the time, and the high-frequency emission standard update frequency has the optimal emission reduction effect with policies (regulation scenario A2B1C 3) such as fluctuating update with technical innovation and stagnation, reporting the old and useless standard vehicles 2 years in advance, and the like.
The method for predicting the multi-situation area of the emission of the pollutants in the highway transportation has the beneficial effects that: the prediction accuracy and generalization capability of the seasonal model are improved through SARIMA-SVR model prediction based on historical highway traffic data; meanwhile, the influence of policy combination situations of pushing different emission standard updating forces, updating intervals and advanced scrapping policies on the emission amount of future pollutants is considered, and technical support is provided for reasonably formulating the carbon peak reaching policy in order to solve the future pollutant emission trend of the expressway.
Example 2:
the computer-readable storage medium of the present embodiment has stored thereon a computer program which, when executed by a processor, implements the steps in the highway transportation pollutant discharge amount multi-scenario inter-zone prediction method of embodiment 1.
The computer readable storage medium of the present embodiment may be an internal storage unit of the terminal, for example, a hard disk or a memory of the terminal; the computer readable storage medium of the present embodiment may also be an external storage device of the terminal, for example, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. provided on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.
The computer-readable storage medium of the present embodiment is used to store a computer program and other programs and data required for a terminal, and the computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Example 3:
the computer device of this embodiment includes a memory, a processor, and a computer program stored on the memory and executable on the processor, which when executed implements the steps in the method for multi-scenario inter-regional prediction of pollutant emissions from highway traffic of embodiment 1.
In this embodiment, the processor may be a central processing unit, or may be other general purpose processors, digital signal processors, application specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like, where the general purpose processor may be a microprocessor or the processor may also be any conventional processor, or the like; the memory may include read only memory and random access memory, and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory, e.g., the memory may also store information of the device type.
It will be appreciated by those skilled in the art that the embodiment(s) disclosure may be provided as a method, system, or computer program product. Thus, the present approach may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present aspects may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present aspects are described with reference to flowchart illustrations and/or block diagrams of methods and computer program products according to embodiments of the invention, it being understood that each flowchart illustration and/or block diagram illustration, and combinations of flowcharts and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions; these computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-only memory (ROM), a random access memory (RandomAccessMemory, RAM), or the like.
The examples of the present invention are merely for describing the preferred embodiments of the present invention, and are not intended to limit the spirit and scope of the present invention, and those skilled in the art should make various changes and modifications to the technical solution of the present invention without departing from the spirit of the present invention.
Claims (8)
1. The method for predicting the multi-situation area of the emission of the pollutants in the highway transportation is characterized by comprising the following steps:
s1, determining a space-time range of the prediction of the pollutant discharge amount of the expressway, and acquiring a traffic distribution observation data set of the expressway based on an entrance ramp charging system;
s2, generating a policy regulation contextual model combination updated based on emission standards;
s3, measuring and calculating reference pollutant emission factors, and acquiring emission factor change data sets under different policy regulation situations;
s4, predicting the quantity of the vehicle in different future scenes through an SARIMA-SVR model;
s5, estimating pollutant discharge amounts of future months of each scene, and evaluating the emission reduction effect of the discharge standard adjustment policy adopted by each scene.
2. The method according to claim 1, wherein step S1 is specifically as follows: the space-time range comprises the geographical area of the expressway and the representative year-month and target year-month range; aiming at the expressway of the area to be measured representing the year and month, acquiring a traffic distribution observation data set of the expressway based on an entrance ramp charging system, wherein the traffic distribution observation data set comprises the length of an observation interval, the number of passing vehicles in the observation interval and corresponding attribute data of the passing vehicles;
based on the vehicle maintenance data and the new vehicle registration data representing months in the geographic space boundary, the vehicle-passing emission standard attribute in the data set is obtained by calculating the survival amount and the total amount ratio of various vehicles and combining with the updated policy of the emission standard, wherein the survival amount of various vehicles is calculated as follows:
wherein, SVP n,i,t To register the number of non-scrapped vehicles of type i vehicles with month n when estimating month t; RP (RP) n,m Registering the number of i-type vehicles with month n; SR (SR) n,i,t To register the vehicle survival rate of an i-type vehicle with month n when estimating month t; t (T) n,i And k n,i Is a characteristic parameter.
3. The method according to claim 1, characterized in that step S2 is specifically as follows: the policy regulation scenario comprises a change rate combination of pollutant emission factors, an emission standard updating interval and the early scrapping year of old standard vehicles;
the change rate combination of the pollutant emission factors is set into two scenes of uniform change A1 and fluctuation change A2; the emission standard updating interval is set to three scenes of high frequency B1, general B2 and low frequency B3; the advanced year of the old standard vehicle is set as three scenes of natural elimination C1, final elimination C2 and minor elimination C3;
the three scenes are randomly combined, 18 policy regulation scene mode combinations Pr (r=1, 2,3,.. 18) are correspondingly generated, and AaBbCc (a=1, 2; b, c=1, 2, 3) are respectively correspondingly combined.
4. A method according to claim 3, wherein step S3 is specifically as follows: the baseline pollutant emission factor is expressed as BEF i,j,s,p Wherein i represents a pollution source vehicle type including minibuses, medium buses, large buses, minivans, medium vans, large vans and container vehicles; j represents the fuel type of the pollution source vehicle, including gasoline, diesel oil and mixed new energy; s represents the emission standard of pollution source vehicles, including the existing first, second, third, fourth, fifth, sixth and seventh, eighth, ninth, tenth and eleventh situations under future planning; p represents a contaminant type;
according to COPERT simulation, basic emission factors measured under standard environments are combined with local conditions and highway road conditions, correction parameters reflecting driving conditions and environmental factors are used for localization, meanwhile, emission factors corresponding to all emission standards of future planned scenes are obtained based on the change rate of the emission factors under different policy regulation scenes in the future, and the calculation formula is as follows:
in the formula, EF i,j,s,p,r Under the policy regulation scene Pr, j is the emission factor/(g x km-1) of p pollutants emitted by an i-type vehicle with an emission standard s; BEF (BEF) i,j,s,p Is the corresponding basic emission factor/(g-km-1);the environment correction factors comprise temperature and humidity; omega i,j,s,p Is a corresponding velocity correction factor; delta i,j,s,p Other corresponding correction coefficients comprise load coefficients, lubricating oil parameters, oil quality and the like; θ i,j,s,p,a Under the policy regulation situation Pr, namely the pollutant emission factorThe combination of rates of change Aa for the sub-corresponds to the rate of change of the emission factor.
5. The method according to claim 4, wherein step S4 is specifically as follows: ADF inspection and white noise inspection are carried out on different vehicle types and the number of monthly passing vehicles of the fuel types in the expressway observation interval of the time-series to-be-detected area so as to judge whether the time series is stable, if so, the subsequent step is carried out, otherwise, d-step difference is carried out on the time series so as to lead the time series to be stable;
determining optimal parameters of an SARIMA (P, D, Q) x (P, D, Q) s model by using grid search according to a red pool information criterion AIC, wherein P, D, Q are non-seasonal autoregressive terms, moving average hysteresis terms and differential orders, P, D, Q are seasonal autoregressive terms, moving average hysteresis terms and differential orders, and s is a seasonal period of a time sequence;
training the SARIMA model by adopting a stable time sequence training sample, and predicting by using a prediction function of the SARIMA model to obtain a fitting sequence of the time sequenceCorresponding residual sequence R i,j,t The method comprises the steps of carrying out a first treatment on the surface of the R is set by sliding window i,j,t Reconstructing the sequence into a sequence with the order of u, and training and predicting the residual sequence by using an SVR model to obtain a residual fitting sequenceSARIMA-SVR model predicts the number of monthly passing vehicles of different vehicle types and fuel types +.>Is->And (3) withAnd (3) summing;
predicting the number of new vehicles registered in a target month according to an exponential smoothing method, acquiring the vehicle age and emission standard distribution of the target month of each scene by combining the emission standard updating frequency Bb and the early scrapping scheme Cc of old emission standard vehicles under the policy regulation scene Pr, and passing the number of vehicles in the monthDistributed according to emission standards, and the target month passing vehicle quantity of different vehicle types, fuel types and emission standards under the scene Pr is obtained>
6. The method according to claim 5, wherein step S5 is specifically: according to the average vehicle mileage of the month of the expressway observation interval of the area to be measured representing the month, predicting the average vehicle mileage of the month of the target month by adopting a secondary exponential smoothing method
Obtained by combiningAnd predicting monthly pollutant emissions for highway traffic in each scenario of the target monthAnd emission factor EF i,j,s,p,r The calculation formula for estimating the p-type pollutant emission amount of the vehicle with the month of t under the scene Pr is as follows:
and evaluating the emission reduction effect of the adopted emission standard adjustment policy by analyzing the predicted emission change trend of the p-th pollutant of each scene in the target month.
7. A computer-readable storage medium having stored thereon a computer program, characterized by: the program when executed by a processor implements the steps in the method for predicting the amount of pollutant discharge in highway traffic according to any one of claims 1 to 6.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps in the method for multi-situational inter-regional prediction of pollutant emissions in highway traffic according to any of claims 1 to 6.
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