CN116910383A - Traffic energy consumption source sink identification method based on automobile track big data - Google Patents
Traffic energy consumption source sink identification method based on automobile track big data Download PDFInfo
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Abstract
The invention relates to the field of energy planning, and discloses a traffic energy consumption source sink identification method based on big data of automobile track, which generates traffic track data with time resolution of 1 hour based on GPS point location information of a vehicle; combining with the field actual measurement data, obtaining the energy consumption coefficient value of the vehicle in unit time on different time periods and different road types, and calculating to obtain the energy consumption value of the vehicle on the corresponding time period and the corresponding road type; according to the hour scale and the day scale, the vehicle energy consumption value is counted, and drawing is carried out according to the time period; classifying all the obtained track data according to urban internal roads, suburban roads and highways, and calculating vehicle energy consumption values by combining unit values of vehicle energy consumption of different roads in different time periods to obtain traffic artificial heat emission sources and traffic artificial heat emission sinks; and respectively drawing the identified vehicle energy sources and the vehicle energy sinks according to the constructed vehicle energy consumption source sink index.
Description
Technical Field
The invention relates to the field of energy planning, in particular to a traffic energy consumption source sink identification method based on big data of automobile tracks.
Background
The key of the carbon emission reduction of the traffic is to control the energy consumption of the automobile, namely to realize the reduction of the traffic energy consumption source and the improvement of the sink. Therefore, a scientific and effective identification method of traffic energy consumption sources and sinks is needed to be proposed.
However, the existing traffic energy data is mostly based on statistical yearbook data, and is subject to the characteristic that the space-time resolution of the original data is low (space is the minimum unit of county and time is the minimum unit of year), and the following application bottleneck (1) exists, because the statistical yearbook data is the minimum space unit of county, the traffic energy heterogeneity in county cannot be characterized. (2) The traffic energy has obvious heterogeneity on the time scale, and the statistical yearbook data takes the year as the minimum time unit, so that the change characteristics of the traffic energy source collection on other time scales such as hours, days, quarters and the like can not be obtained. Because the bottleneck exists, the effectiveness of the specified measure for reducing the emission of the traffic energy is greatly reduced, and therefore, a method capable of accurately expressing the traffic energy source and sink under the multi-time space scale is required to be constructed, and therefore, the method for identifying the traffic energy consumption source and sink based on the big data of the automobile track is provided.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a traffic energy consumption source sink identification method based on big data of automobile track, which solves the problems.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions: a traffic energy consumption source sink identification method based on big data of automobile track comprises the following steps:
the first step: based on the GPS point position information of the vehicle, generating traffic track data with time resolution of 1 hour;
and a second step of: combining with the field actual measurement data, obtaining the energy consumption coefficient value of the vehicle in unit time on different time periods and different road types, and calculating to obtain the energy consumption value of the vehicle on the corresponding time period and the corresponding road type;
and a third step of: according to the hour scale and the day scale, the vehicle energy consumption value is counted, and drawing is carried out according to the time period;
fourth step: classifying all the obtained track data according to urban internal roads, suburban roads and highways, and calculating vehicle energy consumption values by combining unit values of vehicle energy consumption of different roads in different time periods to obtain traffic artificial heat emission sources and traffic artificial heat emission sinks;
fifth step: and respectively drawing the identified vehicle energy sources and the vehicle energy sinks according to the constructed vehicle energy consumption source sink index.
Preferably, the vehicle energy consumption value in the second step has a calculation formula of e=l×εx;
e is vehicle energy consumption, L is vehicle track length, and εx is the energy consumption coefficient value of the vehicle in unit time on different periods and different road types.
Preferably, the drawing is performed at 0-6 hours, 6-12 hours, 12-14 hours, 14-18 hours, and 18-23 hours in the third step.
Preferably, the calculation formula of the vehicle energy consumption value in the fourth step is as follows:
AHR in v Representing artificial heat emission t of different types of vehicle traffic 0 To t 1 Time period change value, AHR i,t1 Representing an i-grid t 1 Traffic people at momentAHR is a heat emission value i,t0 Representing the traffic artificial heat emission value at the moment of the grid t 0;
O v representing t 0 To t 1 The quantity change value, O, of the initial points of the tracks of the vehicles of different types in time periods i,t1 Representing an i-grid t 1 Number of starting points of vehicle track at moment, O i,t0 Representing an i-grid t 0 The number of starting points of the track of the vehicle at the moment;
D v representing t 0 To t 1 The change value of the number of the end points of the track of the vehicles of different types in time periods, D i,t1 Representing an i-grid t 1 Number of vehicle track end points at moment, D i,t0 Representing an i-grid t 0 Number of vehicle track end points at the moment;
when AHR v With O v All exhibit rapid growth, this region is defined as the source of traffic artificial thermal emissions, when AHR v And D v All exhibit rapid growth, this region is defined as the sink of traffic artificial heat emission.
(III) beneficial effects
Compared with the prior art, the invention provides a traffic energy consumption source sink identification method based on big data of automobile track, which has the following beneficial effects:
1. the traffic energy consumption source sink identification method based on the big data of the automobile track combines different time periods and running tracks to calculate, so that the data result of the scheme has high space-time resolution and simultaneously has the characteristic representation capability of multiple time-space scales.
2. According to the traffic energy consumption source sink identification method based on the big data of the automobile track, the data of the automobile track is introduced, the data of the point location information of the automobile at fixed time intervals is obtained, and all the points to which the data belong are constructed into a complete track according to the number of the automobile, so that the method has high time resolution.
3. The traffic energy consumption source and sink identification method based on the big data of the automobile track has the advantages of stable identification result of the characteristics of the vehicle energy consumption source and sink and high scientificity. The method provides important technical method support for realizing the double-carbon target.
Drawings
FIG. 1 is a schematic diagram of a test road selection;
FIG. 2 is a schematic diagram of a multi-time-space scale change feature of vehicle energy consumption;
fig. 3 is a schematic diagram of a spatial distribution of energy sources and sinks of a vehicle.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, a method for identifying traffic energy consumption sources and sinks based on big data of automobile track includes the following steps:
the first step: vehicle track data is introduced for limiting conditions of low space-time resolution of customer service statistics annual-discrimination data, the vehicle track data refers to data of vehicle point location information at fixed time intervals by means of a vehicle-mounted GPS, all points to which the vehicle point location information belongs are constructed into a complete track according to vehicle numbers, and the vehicle track data has the characteristics of high time resolution (point location information is recorded once in 5 minutes), good data continuity (continuous acquisition) and the like.
And a second step of: and carrying out statistics of traffic energy consumption per unit time in different time periods and different running speeds by combining with field test data, namely selecting an urban internal road (road 1), a suburban road (road 2) and a highway (road 3), running according to a selected path in early peak (8:000-10:00), off-peak period (13:00-15:00) and late peak (17:00-19:00), and calculating the traffic energy consumption per unit time according to a vehicle-mounted diagnosis system.
And a third step of: and constructing a vehicle energy statistical data set, classifying all acquired track data according to the urban internal roads, suburban roads and highways, and calculating vehicle energy consumption values by combining unit values of vehicle energy consumption of different roads in different time periods. The calculation formula is as follows:
wherein AHR represents traffic artificial heat emission, f (concar) matrix represents different running states of different types of vehicles, f (engyf) matrix represents energy consumption coefficients of different types of vehicles in different running states, and f (engyf) matrix represents energy consumption values of different types of vehicles in standard running states.
Fourth step: defining a traffic energy source and sink, comprehensively considering the starting point and the energy consumption of a vehicle track, defining the area as a source of the vehicle energy consumption when the starting point and the vehicle energy consumption of the vehicle track of the area are changed greatly, and defining the area as the source of the vehicle energy consumption when the ending point and the vehicle energy consumption of the vehicle track of the area are changed greatly, wherein the calculation formula is as follows:
AHR in v Representing artificial heat emission t of different types of vehicle traffic 0 To t 1 Time period change value, AHR i,t1 Representing an i-grid t 1 Time-of-day traffic artificial heat emission value, AHR i,t0 Representing the traffic artificial heat emission value at the moment of the grid t 0; o (O) v Representing t 0 To t 1 The quantity change value, O, of the initial points of the tracks of the vehicles of different types in time periods i,t1 Representing an i-grid t 1 Number of starting points of vehicle track at moment, O i,t0 Representing an i-grid t 0 The number of starting points of the track of the vehicle at the moment; d (D) v Representing t 0 To t 1 Different types of vehicle trajectories over timeEnd point number change value, D i,t1 Representing an i-grid t 1 Number of vehicle track end points at moment, D i,t0 Representing an i-grid t 0 Number of vehicle track end points at time.
When AHR v With O v All exhibit rapid growth, this region is defined as the source of traffic artificial thermal emissions, when AHR v And D v All exhibit rapid growth, this region is defined as the sink of traffic artificial heat emission.
Fifth step: and respectively drawing the identified vehicle energy sources and the vehicle energy sinks according to the constructed vehicle energy consumption source sink index.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. The traffic energy consumption source sink identification method based on the big data of the automobile track is characterized by comprising the following steps:
the first step: based on the GPS point position information of the vehicle, generating traffic track data with time resolution of 1 hour;
and a second step of: combining with the field actual measurement data, obtaining the energy consumption coefficient value of the vehicle in unit time on different time periods and different road types, and calculating to obtain the energy consumption value of the vehicle on the corresponding time period and the corresponding road type;
and a third step of: according to the hour scale and the day scale, the vehicle energy consumption value is counted, and drawing is carried out according to the time period;
fourth step: classifying all the obtained track data according to urban internal roads, suburban roads and highways, and calculating vehicle energy consumption values by combining unit values of vehicle energy consumption of different roads in different time periods to obtain traffic artificial heat emission sources and traffic artificial heat emission sinks;
fifth step: and respectively manufacturing the identified vehicle energy sources and the vehicle energy sinks according to the constructed vehicle energy consumption source sink index.
2. The method for identifying traffic energy consumption sources and sinks based on big data of automobile track according to claim 1, wherein the method comprises the following steps: the calculation formula of the vehicle energy consumption value in the second step is E=L×εx;
e is vehicle energy consumption, L is vehicle track length, and εx is the energy consumption coefficient value of the vehicle in unit time on different periods and different road types.
3. The method for identifying traffic energy consumption sources and sinks based on big data of automobile track according to claim 1, wherein the method comprises the following steps: in the third step, the drawing is performed according to the time of 0-6, 6-12, 12-14, 14-18 and 18-23.
4. The method for identifying traffic energy consumption sources and sinks based on big data of automobile track according to claim 1, wherein the method comprises the following steps: the calculation formula of the vehicle energy consumption value in the fourth step is as follows:
AHR in v Representing artificial heat emission t of different types of vehicle traffic 0 To t 1 Time period change value, AHR i,t1 Representing an i-grid t 1 Time-of-day traffic artificial heat emission value, AHR i,t0 And representing the traffic artificial heat emission value at the moment of the grid t 0.
5. The method for identifying traffic energy consumption sources and sinks based on big data of automobile track according to claim 4, wherein the method comprises the following steps: o (O) v Representing t 0 To t 1 The quantity change value, O, of the initial points of the tracks of the vehicles of different types in time periods i,t1 Representing an i-grid t 1 Number of starting points of vehicle track at moment, O i,t0 Representing an i-grid t 0 The number of starting points of the track of the vehicle at the moment;
D v representing t 0 To t 1 The change value of the number of the end points of the track of the vehicles of different types in time periods, D i,t1 Representing an i-grid t 1 Number of vehicle track end points at moment, D i,t0 Representing an i-grid t 0 Number of vehicle track end points at the moment;
when AHR v With O v All exhibit rapid growth, this region is defined as the source of traffic artificial thermal emissions, when AHR v And D v All exhibit rapid growth, this region is defined as the sink of traffic artificial heat emission.
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- 2023-01-16 CN CN202310078568.4A patent/CN116910383A/en active Pending
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| US20150175003A1 (en) * | 2013-12-24 | 2015-06-25 | Yuan Ze University | Power-saving apparatus and method for transportation vehicle |
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