CN111582580B - Travel path planning method considering population pollutant exposure - Google Patents

Travel path planning method considering population pollutant exposure Download PDF

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CN111582580B
CN111582580B CN202010382166.XA CN202010382166A CN111582580B CN 111582580 B CN111582580 B CN 111582580B CN 202010382166 A CN202010382166 A CN 202010382166A CN 111582580 B CN111582580 B CN 111582580B
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吴亦政
王宇昕
宋国华
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Abstract

The embodiment of the invention provides a travel path planning method considering crowd pollutant exposure, which is characterized in that Vehicle emission data of a road section is obtained by calculating Vehicle Specific Power (VSP) distribution; on the basis, a Gaussian steady-state smoke plume equation is applied to establish a diffusion distribution model of pollutants of the motor vehicles on the urban road, and the time-space variation characteristics of the concentration of the pollutants on each road section are estimated in real time; and finally, carrying out gridding treatment on the pollutant concentration distribution parameters, further calculating the pollutant exposure parameters of characteristic crowds in the grid by using a crowd pollutant exposure model, establishing a low pollutant exposure path optimization method by using the pollutant exposure parameters as weights and applying an operation research method, and outputting to obtain an optimal path, an alternative path, corresponding travel time and a pollutant exposure value. The embodiment of the invention is based on the perspective of travelers, fully considers the coordination relationship between travel time and pollutant exposure, can provide a travel path with low pollutant exposure for travelers who walk and ride, and provides a reference basis for the travel decision of travelers.

Description

Travel path planning method considering population pollutant exposure
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a travel path planning method considering crowd pollutant exposure.
Background
The low population pollutant exposure path planning belongs to a new research direction at home and abroad at present, is mainly based on an atmospheric pollutant emission and diffusion model, a population pollutant exposure evaluation system, an operation research method and the like, and simulates the actual urban road network environment so as to analyze and obtain the population pollutant exposure level of a traveler passing through a road section and finally output an optimal path by applying the operation research method.
The atmospheric pollutant discharge model mainly has several directions, such as: an average speed type model, a driving condition type model and a microscopic engine model. The average speed model is limited to the average speed index, so that the data accuracy is low, and the model is not suitable for urban road simulation. The model is mainly applied to driving condition models at present, can reflect energy consumption emission under a dynamic traffic state, and can realize analysis of urban microscopic air pollutants. The atmospheric diffusion model focuses on the application of a steady-state smoke plume model, which is mature at present.
The population pollutant exposure evaluation mainly comprises a direct monitoring method and an indirect simulation method, wherein the direct monitoring method is limited by equipment and is suitable for small-range high-precision calculation, and the indirect simulation method is used for carrying out simulation calculation based on the concentration of pollutants in a traffic microenvironment and combining various indexes of a human body.
At present, certain research has been carried out on the low population pollutant exposure path planning at home and abroad, but the research in the prior art mainly adopts the basic data of an air monitoring station for obtaining the concentration of the atmospheric pollutants, and the actual values of the same-path network have larger difference, so that the pollutant distribution of the urban path network scale cannot be well reflected; in addition, the traffic data prediction method in the prior art mainly adopts static prediction, and the travel origin-destination point is analyzed through the land use function, so that the data such as the road network flow and the like are indirectly acquired, certain errors exist, and the real-time path optimization is not facilitated.
Disclosure of Invention
The embodiment of the invention provides a travel path planning method considering population pollutant exposure, which aims to overcome the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A travel path planning method considering population pollutant exposure comprises the following steps:
step S1, acquiring various traffic parameters related to the road section based on the floating vehicle speed data, the traffic data collected by the traffic collection equipment of the road section and the road network geographic information table data, and constructing a road network traffic state model and a motor vehicle characteristic database;
step S2, based on the road network traffic state model and the motor vehicle characteristic database, obtaining real-time motor vehicle specific power VSP interval distribution according to the motor vehicle specific power model, and calculating and establishing a road motor vehicle various pollutant emission rate database;
step S3, based on the road section motor vehicle various pollutant discharge rate database and the local meteorological database, applying a Gaussian steady-state smoke plume equation to calculate the pollutant diffusion conditions of the road section and the surrounding area thereof, and calculating the pollutant concentration distribution data of the road section and the surrounding area thereof;
step S4, carrying out gridding processing on the pollutant concentration distribution data of the road section and the peripheral area thereof, calculating the pollutant concentration mean value in each grid, and calculating the characteristic crowd pollutant exposure parameter in each grid by adopting a crowd pollutant exposure model according to the pollutant concentration mean value in each grid and the behavior characteristics of each crowd type;
and step S5, planning the optimal travel path of the user by adopting an operation research method according to the characteristic population pollutant exposure parameters in each grid and combining user information, travel time and a road network traffic state model.
Preferably, the step S2 specifically includes:
step S21: basic VSP data of all vehicle types of the road section are obtained through calculation based on a road network traffic state model, and the calculation formula of the basic VSP data of all vehicle types of the road section is as follows:
Figure GDA0003488312390000031
wherein
Figure GDA0003488312390000032
For a section i0The specific power of the upper motor vehicle,
Figure GDA0003488312390000033
for a section i0Speed of motor vehicle on road section, a is acceleration of motor vehicle, A, B, C, m0F is a constant related to the vehicle type, and a specific numerical value is obtained from a motor vehicle characteristic database;
integrating basic VSP data of all vehicle types of the existing road section to construct an existing VSP database;
step S22: generating a real-time VSP distribution database of the road section according to road sections such as the type of the road section, the average speed interval, the type of the vehicle weight and the like detected in real time and vehicle characteristic data by utilizing basic VSP data of various vehicle types of the road section;
step S23: generating a real-time emission rate database under the vehicle type composition condition according to the vehicle type composition detected in real time by utilizing the existing vehicle emission rate database;
step S24: generating a real-time VSP distribution-emission rate database according to the real-time emission rate database and the real-time VSP distribution database of the road sections, and calculating the average emission rate AvgER of each road section in a specific average speed interval, wherein the calculation formula is as follows:
Figure GDA0003488312390000034
among them, VSPBINFrequencyi，jRepresents the distribution frequency of the jth VSP interval under the average speed interval i; n represents n VSP intervals under the average speed interval; ER (VSPBin)i，j，v) The emission rate of certain emission pollutants in the jth VSP interval of the vehicle of the v vehicle type under the average speed interval i is represented by the following unit: g/s;
step S25: calculating the discharge amount of each road section in the acquired updating time period T according to the average discharge rate AvgER of each road section in the specific average speed interval, and constructing a road section motor vehicle pollutant discharge rate database, wherein the calculation formula is as follows:
Figure GDA0003488312390000041
wherein, PiThe pollutant emission amount of the road section i is shown, T is a time interval, T is an updating time interval, and n is a vehicle type;
and the pollutant discharge rate database of the motor vehicles at the road sections stores the pollutant discharge amount of each road section at different time.
Preferably, the step S3 specifically includes:
based on the road section motor vehicle various pollutant emission rate database, combining the actual meteorological data of the area where the road section is located and applying a Gaussian steady-state smoke plume equation to establish a pollutant concentration distribution model of the road section and the surrounding area, wherein the pollutant concentration distribution model of the road section and the surrounding area comprises;
1: under the condition of a stable boundary layer, the calculation formula of the pollutant concentration diffusion is as follows:
Figure GDA0003488312390000042
Figure GDA0003488312390000043
wherein, Cs(x, y, z) is the pollutant concentration at the coordinate (x, y, z) under the condition of stable boundary layer acquired according to the discharge rate database of various pollutants of the motor vehicles on the road section, u is the wind speed, F is the wind speedyIs a transverse distribution function, zieffIs effectively stabilized the height of the mixed layer, sigmazsIs the vertical diffusion coefficient, hesIs the height of the plume, σyIs the horizontal dispersion coefficient;
2: under the condition of a convection boundary layer, the calculation formula of the pollutant concentration diffusion is as follows:
Cc(x，y，z)＝Cd(x，y，z)+Cp(x，y，z)+Cr(x，y，z)
Cc(x, y, z) is the pollutant concentration at the coordinate (x, y, z) under the condition of the convection boundary layer acquired according to the database of the discharge rates of various pollutants of the motor vehicles on the road section, Cd(x, y, z) is the direct plume diffusion concentration at coordinate (x, y, z), Cp(x, y, z) is the osmotic source plume diffusion concentration at coordinate (x, y, z), Cr(x, y, z) is the indirect plume diffusion concentration at coordinate (x, y, z);
3: the formula for calculating the diffusion concentration of the direct smoke plume is as follows:
Figure GDA0003488312390000051
Figure GDA0003488312390000052
wherein, Cd(x, y, z) is the direct plume diffusion concentration,. psidjIs the height of the plume, Δ hdPlume elevation height, h, of a direct plume diffusion sourcesIs to consider the height of the sinking pollution source, z is the height of the monitoring point, { z ═ zr，zpJ is 1, 2, and represents an ascending air flow when being equal to 1, and represents a descending air flow when being equal to 2;
4: the formula for calculating the diffusion concentration of the indirect smoke plume is as follows:
Figure GDA0003488312390000053
ψrj＝ψdj-Δhi
wherein C isr(x, y, z) is the indirect plume diffusion concentration, Δ hiIs the indirect pollution source plume lifting height;
5: the calculation formula of the diffusion concentration of the smoke plume of the penetration source is as follows:
Figure GDA0003488312390000054
wherein C isp(x, y, z) is the diffusion concentration of the osmotic source plume, zieffIs effectively stabilized the height of the mixed layer, sigmazpIs the osmotic diffusion coefficient.
Preferably, the S4 specifically includes:
step S41: dividing the road section and the peripheral area thereof into a plurality of grids, and calculating the pollutant concentration in each grid based on the pollutant concentration distribution model of the road section and the peripheral area thereof, wherein the calculation formula is as follows:
Figure GDA0003488312390000055
wherein i is the grid number, j is the number of sampling points in the grid, CiIs the average contaminant concentration of grid i, CiThe concentration of the pollutants at a sampling point j in a grid i is shown;
step S42: based on the population pollutant exposure model, calculating individual exposure dose of pollutants of each grid characteristic population, wherein the calculation formula is as follows:
Ep，i＝Cp，i×Bk×Ti
wherein E isp，iIs the individual exposure dose of contaminant p within grid i, Cp，iIs the concentration of contaminant p in grid i, BkCharacterised by the respiratory rate, T, of the population kiExposure time within grid i;
step S43: based on the travel characteristics of the crowd, the calculation formula of the exposure time is as follows:
Figure GDA0003488312390000061
wherein, TiIs the exposure time in grid i, LiFor mesh i inner road network length, vk，nThe average moving speed of the traffic mode m is adopted for the characteristic population k.
Preferably, the S5 specifically includes:
s51, inputting a starting point and an end point of a user, obtaining the acceptable maximum travel distance extension ratio p of the user, and determining the type of the crowd to which the user belongs;
s52, calculating and determining the weight of each road section according to the user information, the exposure dose of the crowd pollutants and the road network traffic state model, wherein the calculation formula is as follows:
Figure GDA0003488312390000062
0≤w≤1
wherein CostiIs the weight of grid i; ep，i(ii) individual exposure doses for contaminants p within grid i; epmaxMaximum pollutant exposure in the accessible grid; lijThe traffic distance in the grid i is represented by j being 1, 2 and 3, which respectively represent straight running, left turning and right turning; l is the grid width; w is the relative specific gravity, the calculation only takes the road length influence into consideration when w is 0, and the calculation only takes the exposure dose influence into consideration when w is 1;
s53, taking w as 0, and calculating the shortest path length L based on each path weight by applying Dijkstra algorithmmin；
S54, taking w as 1, and calculating the maximum path length L by applying the maximum travel distance extension ratio pmaxThe calculation formula is as follows:
Lmax＝(1+p)Lmin
s55, determining an optimal path based on the weight of each segment by applying a Dijkstra algorithm, and calculating the length L of the optimal path;
s56, if L is less than LmaxStopping calculation and outputting an optimal path; if L is greater than or equal to LmaxLet w be w-0.1, and return to step S55.
Preferably, the generating a VSP distribution database of the real-time road segment according to the road segment type, the average speed section, the vehicle weight type and other road segments detected in real time and the vehicle characteristic data by using the basic VSP data of each vehicle type of the road segment in the step S22 specifically includes:
acquiring the type of a road section, acquisition time and average speed per minute from flow acquisition data, and determining the average speed interval of the road section;
coding the acquired road section type and the average speed interval according to the coding rule of the existing VSP database;
and generating a real-time VSP distribution database of the road section according to the frequency distribution of the road section by inquiring the conventional VSP database according to the road section type code and the average speed interval code.
Preferably, the generating of the real-time emission rate database under the vehicle type composition condition by using the existing vehicle emission rate database in the step S23 according to the vehicle type composition detected in real time includes:
acquiring the proportion, standard vehicle weight, fuel type and emission standard of each vehicle type according to vehicle type data in the flow acquisition data;
according to vehicle type classification and coding rules, bringing various types of acquired vehicle information into a real-time vehicle type composition database;
and forming a database according to the real-time vehicle types, calculating the discharge amount of vehicles of each vehicle type on the road section at different speeds through data coupling with the existing discharge rate database, and generating a real-time discharge rate database, wherein the real-time discharge rate database stores the pollutant discharge value of the vehicles of each vehicle type on each road section at different average speeds in unit time.
Preferably, the flow rate collecting device includes: a remote traffic microwave detector RTMS or a coil or video acquisition device.
Preferably, the road section related traffic parameters include: road section name, number, driving direction, speed, flow rate and time.
According to the technical scheme provided by the embodiment of the invention, the travel path planning method based on population pollutant exposure provided by the embodiment of the invention starts from the perspective of travelers, fully considers the coordination relationship between travel time and pollutant exposure, can provide a travel path with low pollutant inhalation for travelers walking and riding, and provides a new reference mode for trip decision of travelers.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a travel path planning method considering exposure of pollutants to a crowd according to an embodiment of the present invention;
FIG. 2 is a flow chart of a real-time VSP distribution data calculation provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a calculation of pollutant emission of a motor vehicle on a road according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a convective boundary layer of a Gaussian plume model provided in an embodiment of the present invention;
fig. 5 is a flow chart of a low population pollutant exposure path planning method according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The embodiment of the invention provides a travel path planning method considering crowd pollutant exposure, which is characterized in that motor vehicle pollutant distribution models of urban roads and surrounding areas thereof are obtained through simulation calculation, crowd pollutant exposure values possibly generated at different positions of a user during traveling are calculated, low pollutant exposure is brought into a path decision factor, and a healthier travel path is provided for the user on the premise of ensuring that the detour distance does not exceed the limit, so that the user can conveniently make a travel decision and the travel path planning method is more suitable for the actual life requirements.
Fig. 1 is a schematic flow chart of a travel path planning method considering population pollutant exposure according to an embodiment of the present invention, which includes the following steps:
and step S1, acquiring various traffic parameters related to the road sections based on the speed data detected by the floating cars, the flow data collected by the flow acquisition equipment of the road sections and the road network geographic information table data, and constructing a road network traffic state model and a motor vehicle characteristic database. The floating vehicle is a motor vehicle which is provided with a vehicle-mounted positioning device and normally runs on a road.
The flow collection devices include, but are not limited to: RTMS (Remote transport Microwave Sensor) or coil or video acquisition equipment.
The traffic data includes, but is not limited to: detecting road section number, detecting time, traffic flow, average speed and vehicle type proportion.
The road network geographic information table data includes but is not limited to: road section number, road section name, road section type, road section origin-destination coordinate, road section length, road section width and road section lane number.
The road network traffic state model is constructed based on the acquired data, which includes but is not limited to: a geographic information system model constructed based on the geographic coordinates and the linear information of the road network and a traffic information connection data table realized based on the road number.
The vehicle characteristic database is constructed based on measured values of road segments, including but not limited to: the data of the motor vehicle equipment, the data of the motor vehicle type proportion, the data of the motor vehicle age and the data of the motor vehicle fuel.
Step S2, acquiring VSP (vehicle specific power) interval distribution according to a vehicle specific power model based on a road network traffic state model and a vehicle characteristic database, calculating and establishing a road section vehicle various pollutant discharge rate database;
the classes of contaminants include, but are not limited to: PM2.5, PM10, CO, NOx、HC。
And S3, calculating the pollutant diffusion conditions of the road section and the surrounding area thereof by applying a Gaussian steady-state smoke plume equation based on the road section motor vehicle various pollutant emission rate database and the local meteorological database, and establishing a pollutant concentration distribution model of the road section and the surrounding area thereof.
The local weather database includes, but is not limited to: surface meteorological data, sounding meteorological data.
Step S4, carrying out gridding processing on pollutant concentration distribution data of the road section and the peripheral area thereof, calculating a pollutant concentration mean value in each grid, taking the calculated mean value as an index, combining behavior characteristics of each crowd type, and calculating the pollutant exposure parameter of each grid characteristic crowd by adopting a crowd pollutant exposure model;
and step S5, according to the exposure parameters of the crowd pollutants of each grid characteristic, combining user information and a road network traffic state model, establishing a low-pollutant exposure path optimization method by adopting an operation research method, overlapping the exposure parameters of the crowd pollutants and travel time as weights, comparing the weights of all determined paths to determine an optimal path, and outputting the predicted optimal path, alternative path, corresponding pollutant exposure level and travel time predicted value.
Further, the step S2 specifically includes:
step S21: the specific power is a comprehensive index for measuring the dynamic performance of the automobile, and particularly refers to the ratio of the maximum power of an automobile engine to the total mass of the automobile. Generally, for the same type of automobile, the higher the specific power, the better the dynamic performance of the automobile.
Calculating and acquiring basic VSP data of each vehicle type of a road section based on a road network traffic state model;
the calculation formula of the basic VSP data of each vehicle type of the road section is as follows:
Figure GDA0003488312390000111
wherein
Figure GDA0003488312390000112
For a section i0The specific power of the upper motor vehicle,
Figure GDA0003488312390000113
for a section i0Speed of motor vehicle on road section, a is acceleration of motor vehicle, A, B, C, m0F is a constant related to the vehicle type, and a specific numerical value is obtained from a motor vehicle characteristic database;
and integrating basic VSP data of all vehicle types of the existing road section to construct an existing VSP database. Data stored in existing VSP databases include, but are not limited to: link number, link name, link type, acquisition time, vehicle type and proportion, average speed, and VSP interval.
The road section number, the road section name, the road section type, the acquisition time, the vehicle type, the proportion, the average speed and the like are consistent with the number and the representation mode in the road network traffic state model, and the VSP interval is divided at intervals according to the actual requirement and is coded according to the average speed value of the VSP interval. When database data is consulted, positioning and searching are carried out according to numbers of corresponding road sections, vehicle types and the like so as to determine the road section VSP value at a specific speed.
Step S22: FIG. 2 is a flow chart of a real-time VSP distribution data calculation provided by an embodiment of the present invention. The method comprises the following steps of generating a real-time VSP distribution database of a road section according to road sections such as the type of the road section, the average speed section and the type of the vehicle weight detected in real time and vehicle characteristic data by utilizing basic VSP data of various vehicle types of the road section, and specifically comprises the following steps:
a: acquiring the type of a road section, acquisition time and average speed per minute from flow acquisition data, and determining the average speed interval of the road section;
b: coding the acquired road section type and the average speed interval according to the coding rule of the existing VSP database;
c: and c, generating a real-time VSP distribution database of the road section by inquiring the frequency distribution according to the road section from the existing VSP database according to the road section type code and the average speed interval code acquired in the step b.
The data stored in the real-time VSP profile database includes, but is not limited to: the road section number, the road section name, the road section type, the acquisition time, the vehicle type and proportion, the average speed, the VSP interval and the proportion of the VSP interval. Wherein, except the VSP interval, the other data are processed by referring to the coding rule of the existing VSP database; the ratio of the VSP interval is represented by the frequency obtained by actual calculation. When database data is consulted, positioning and searching are carried out according to the corresponding numbers so as to determine the distribution value of the specific VSP interval.
Step S23: generating a real-time emission rate database under the vehicle type composition condition according to the vehicle type composition detected in real time by utilizing the existing vehicle emission rate database;
a: acquiring the proportion, standard vehicle weight, fuel type and emission standard of each vehicle type according to vehicle type data in the flow acquisition data;
b: according to vehicle type classification and coding rules, bringing various types of acquired vehicle information into a real-time vehicle type composition database;
c: and c, forming a database according to the real-time vehicle models generated in the step b, calculating the discharge amount of vehicles of each vehicle model on the road section at different speeds through data coupling with the existing discharge rate database, and generating the real-time discharge rate database. The real-time emission rate database mainly stores pollutant emission values which can be generated in unit time when vehicles of various types on various sections have different average speeds. Data within the real-time emission rate database includes, but is not limited to: road section number, road section name, acquisition time, vehicle type number, average speed, pollutant discharge rate.
Step S24: fig. 3 is a schematic diagram illustrating calculation of pollutant emission amount of a motor vehicle on a road section according to an embodiment of the present invention. Generating a real-time VSP distribution-emission rate database according to the real-time emission rate database and the real-time VSP distribution database, and calculating the average emission rate AvgER in a specific average speed interval of each road section, wherein the calculation formula is as follows:
Figure GDA0003488312390000131
wherein the content of the first and second substances,
Figure GDA0003488312390000132
is shown in the average speed interval i1Lower j (th)2Distribution frequency of VSP bins; n represents n VSP intervals under the average speed interval;
Figure GDA0003488312390000133
is shown in the average speed interval i1Next, the j-th vehicle of the v-th vehicle type2The unit of the emission rate of certain emission pollutants in each VSP interval is as follows: g/s;
step S25: calculating the discharge amount of each road section in the acquired updating time period T according to the average discharge rate AvgER of each road section in the specific average speed interval, and constructing a road section motor vehicle pollutant discharge rate database, wherein the calculation formula is as follows:
Figure GDA0003488312390000134
wherein the content of the first and second substances,
Figure GDA0003488312390000135
for a section i0T is the time interval, T is the update time interval, n1The vehicle type is adopted;
the road section motor vehicle pollutant discharge rate database mainly stores the pollutant discharge amount of each road section at different time, and the data comprises but is not limited to: road section number, road section name, acquisition time and emission rate.
Further, the step S3 specifically includes:
based on the discharge rate database of various pollutants of motor vehicles on the road section, a Gaussian steady-state smoke plume equation is applied in combination with actual meteorological data of the area where the road section is located to establish a pollutant concentration distribution model of the road section and the surrounding area, and the model is calculated according to the atmospheric stability and the boundary layer position. The pollutant concentration distribution model of the road section and the peripheral area thereof comprises:
(1) under the condition of stabilizing the boundary layer, the calculation formula of the pollutant concentration diffusion is as follows:
Figure GDA0003488312390000141
Figure GDA0003488312390000142
wherein, Cs(x, y, z) is the pollutant concentration at the coordinate (x, y, z) under the condition of stable boundary layer acquired according to the discharge rate database of various pollutants of the motor vehicles on the road section, u is the wind speed, F is the wind speedyIs a transverse distribution function, zieffIs effectively stabilized the height of the mixed layer, sigmazsIs the vertical diffusion coefficient, hesIs the height of the plume (stack height plus plume elevation height), σ, under stable boundary layer conditionsyIs the horizontal dispersion coefficient.
(2) Fig. 4 is a schematic diagram of a convective boundary layer of a gaussian plume model according to an embodiment of the present invention. Under the condition of a convection boundary layer, the pollutant concentration diffusion calculation formula is as follows:
Cc(x，y，z)＝Cd(x，y，z)+Cp(x，y，z)+Cr(x，y，z)
Cc(x, y, z) is maneuvering according to the road segmentPollutant concentration C at coordinates (x, y, z) under convection boundary layer conditions acquired from various pollutant discharge rate databasesd(x, y, z) is the direct plume diffusion concentration at coordinate (x, y, z), Cp(x, y, z) is the osmotic source plume diffusion concentration at coordinate (x, y, z), Cr(x, y, z) is the indirect plume diffusion concentration at coordinate (x, y, z).
(3) The direct smoke plume diffusion concentration calculation formula is as follows:
Figure GDA0003488312390000143
Figure GDA0003488312390000144
wherein, Cd(x, y, z) is the direct plume diffusion concentration,. psidjIs the direct diffusion height of the smoke plume, delta h, under the condition of a convection boundary layerdPlume elevation height, h, of a direct plume diffusion sourcesIs to consider the height of the sinking pollution source, z is the height of the monitoring point, { z ═ zr，zpAnd j is 1 and 2, and represents ascending air flow when j is 1 and represents descending air flow when j is 2.
(4) The indirect smoke plume diffusion concentration calculation formula is as follows:
Figure GDA0003488312390000151
ψrj＝ψdj-Δhi
wherein C isr(x, y, z) is the indirect plume diffusion concentration, Δ hiIs the indirect pollution source plume lifting height.
(5) The calculation formula of the diffusion concentration of the smoke plume of the penetration source is as follows:
Figure GDA0003488312390000152
wherein C isp(x, y, z) is the diffusion concentration of the osmotic source plume, zieffIs effectively stabilized the height of the mixed layer, sigmazpIs the osmotic diffusion coefficient.
Further, the step S4 specifically includes:
step S42: based on the population pollutant exposure model, calculating the individual exposure dose of each grid characteristic population, wherein the calculation formula is as follows:
(1) dividing the road section and the peripheral area thereof into a plurality of grids, and calculating the pollutant concentration in each grid, wherein the calculation formula is as follows:
Figure GDA0003488312390000153
wherein i is a grid number, j0Numbering the sampling points in the grid, CiIs the average contaminant concentration of grid i, Cij0For sampling point j in grid i0Treating the concentration of the pollutant;
(2) based on the population pollutant exposure model, calculating individual exposure dose of pollutants of each grid characteristic population, wherein the calculation formula is as follows:
Ep，i＝Cp，i×Bk×Ti
wherein E isp，iIs the individual exposure dose of contaminant p within grid i, Cp，iIs the concentration of contaminant p in grid i, BkCharacterised by the respiratory rate, T, of the population kiExposure time within grid i;
(3) based on the travel characteristics of the crowd, the calculation formula of the exposure time is as follows:
Figure GDA0003488312390000161
wherein, TiIs the exposure time in grid i, LiFor the length of the road network within the mesh i,
Figure GDA0003488312390000162
adopting a traffic mode n for a characteristic crowd k0Average moving rate of (d);
further, fig. 5 is a flow chart of a low population pollutant exposure path planning method according to an embodiment of the present invention. The step S5 specifically includes:
s51, inputting the starting point and the end point of the user, and acquiring the acceptable maximum travel distance extension ratio p of the user0Determining the type of the crowd to which the user belongs;
s52, calculating and determining the weight of each road section according to the user information, the individual exposure dose of pollutants and the road network traffic state model, wherein the calculation formula is as follows:
Figure GDA0003488312390000163
0≤w≤1
wherein CostiIs the weight of grid i; ep，i(ii) individual exposure doses for contaminants p within grid i; epmaxMaximum pollutant exposure in the accessible grid;
Figure GDA0003488312390000164
is the traffic distance, j, within the grid i 11, 2 and 3 respectively represent straight running, left turning and right turning; lcellIs the grid width; w is the relative gravity, and the user information includes but is not limited to: travel tendency, age, sex, travel time, travel mode and the like.
S53, taking w as 0, applying Dijkstra algorithm based on each segment weight CostiCalculate shortest path length Lmin；
Where the Dijkstra algorithm divides all vertices in the weighted graph into two sets S and V-S. And storing the vertex with the found shortest path in the set S, and storing the vertex without the found shortest path in the V-S. The algorithm will add the elements in the V-S set to the S set one by one in order of increasing shortest path length until all vertices enter the S set. The specific algorithm idea is as follows:
(1) initially, only the source point v is included in the set S0Collection, collectionThe combined V-S comprises a source removing point V0All other vertices, v0The path length to each vertex in V-S is either a certain weight (if there are arcs connected between them) or infinity (no arcs connected);
(2) selecting a vertex V from the set V-S in order of increasing shortest path length0Vertex v with shortest path lengthkAdding the obtained product into an S set;
(3) adding vkThen, to find the next shortest path, the slave v must be modified0To all remaining vertices V in the set V-SiThe shortest path of (2). If v is added to the pathkAfter that, let v0To viHas a path length longer than that of the original path length without v addedkIf the path length is short, v is corrected0To viOf which the path length is shorter;
(4) the above steps are repeated until all vertices in the set V-S are added to the set S.
S54, taking w as 1 and applying the maximum travel distance extension ratio p0Calculating the maximum path length LmaxThe calculation formula is as follows:
Lmax＝(1+p0)Lmmin
s55, determining an optimal path based on the weight of each segment by applying a Dijkstra algorithm, and calculating the length L of the optimal path;
s56, if L is less than LmaxStopping calculation and outputting an optimal path; if L is greater than or equal to LmaxLet w be w-0.1, and return to step S55.
In summary, the embodiment of the present invention provides a method for acquiring vehicle emission data of a road segment by calculating Vehicle Specific Power (VSP) distribution based on urban road traffic basic information; on the basis, a Gaussian steady-state smoke plume equation is applied to establish a diffusion distribution model of pollutants of the motor vehicles on the urban road, and the characteristics of pollutant concentration change and the like of each road section are reflected in real time; and finally, carrying out gridding treatment on the pollutant distribution model, calculating the pollutant exposure index of the characteristic crowd in the grid based on the crowd pollutant exposure model, establishing a low pollutant exposure path navigation optimization method by applying an operation research method by taking the pollutant exposure index as a weight, and outputting to obtain an optimal path, an alternative path, corresponding travel time and a pollutant exposure condition.
The embodiment of the invention is based on the perspective of travelers, fully considers the coordination relationship between travel time and pollutant exposure, can provide a low-pollutant-suction travel path for travelers who walk and ride, and provides a new reference mode for the travel decision of travelers.
The embodiment of the invention effectively solves the problems that in the prior art, only travel time and travel distance are considered, exposure of pollutants possibly encountered by a traveler in a travel process is not considered, and high risk of pollutants possibly existing in the shortest distance is not considered, so that the travel health cost of the traveler is increased.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A travel path planning method considering population pollutant exposure is characterized by comprising the following steps:
step S1, acquiring various traffic parameters related to the road section based on the floating vehicle speed data, the traffic data collected by the traffic collection equipment of the road section and the road network geographic information table data, and constructing a road network traffic state model and a motor vehicle characteristic database;
step S2, based on the road network traffic state model and the motor vehicle characteristic database, obtaining real-time motor vehicle specific power VSP interval distribution according to the motor vehicle specific power model, and calculating and establishing a road motor vehicle various pollutant emission rate database;
step S3, based on the road section motor vehicle various pollutant discharge rate database and the local meteorological database, applying a Gaussian steady-state smoke plume equation to calculate the pollutant diffusion conditions of the road section and the surrounding area thereof, and calculating the pollutant concentration distribution data of the road section and the surrounding area thereof;
step S4, carrying out gridding processing on the pollutant concentration distribution data of the road section and the peripheral area thereof, calculating the pollutant concentration mean value in each grid, and calculating the characteristic crowd pollutant exposure parameter in each grid by adopting a crowd pollutant exposure model according to the pollutant concentration mean value in each grid and the behavior characteristics of each crowd type;
step S5, planning the optimal travel path of the user by adopting an operation research method according to the exposure parameters of the characteristic crowd pollutants in each grid and combining user information, travel time and a road network traffic state model, wherein the step S3 specifically comprises the following steps:
based on the road section motor vehicle various pollutant emission rate database, combining the actual meteorological data of the area where the road section is located and applying a Gaussian steady-state smoke plume equation to establish a pollutant concentration distribution model of the road section and the surrounding area, wherein the pollutant concentration distribution model of the road section and the surrounding area comprises;
1: under the condition of a stable boundary layer, the calculation formula of the pollutant concentration diffusion is as follows:
Figure FDA0003505258390000011
Figure FDA0003505258390000021
wherein, Cs(x, y, z) is the pollutant concentration at the coordinate (x, y, z) under the condition of stable boundary layer acquired according to the discharge rate database of various pollutants of the motor vehicles on the road section, u is the wind speed, F is the wind speedyIs a transverse distribution function, zieffIs effectively stabilized the height of the mixed layer, sigmazsIs the vertical diffusion coefficient, hesIs the height of the plume, sigma, under the condition of stable boundary layeryIs the horizontal dispersion coefficient;
2: under the condition of a convection boundary layer, the calculation formula of the pollutant concentration diffusion is as follows:
Cc(x，y，z)＝Cd(x，y，z)+Cp(x，y，z)+Cr(x，y，z)
Cc(x, y, z) is the pollutant concentration at the coordinate (x, y, z) under the condition of the convection boundary layer acquired according to the database of the discharge rates of various pollutants of the motor vehicles on the road section, Cd(x，y, z) is the direct plume diffusion concentration at coordinate (x, y, z), Cp(x, y, z) is the osmotic source plume diffusion concentration at coordinate (x, y, z), Cr(x, y, z) is the indirect plume diffusion concentration at coordinate (x, y, z);
3: the formula for calculating the diffusion concentration of the direct smoke plume is as follows:
Figure FDA0003505258390000022
Figure FDA0003505258390000023
wherein, Cd(x, y, z) is the direct plume diffusion concentration,. psidjIs the direct diffusion height of the smoke plume, delta h, under the condition of a convection boundary layerdPlume elevation height, h, of a direct plume diffusion sourcesConsidering the height of a sunken pollution source, wherein z is the height of a monitoring point, j is 1 and 2, and represents ascending airflow when being equal to 1 and represents descending airflow when being equal to 2;
4: the formula for calculating the diffusion concentration of the indirect smoke plume is as follows:
Figure FDA0003505258390000024
ψrj＝ψdj-Δhi
wherein C isr(x, y, z) is the indirect plume diffusion concentration, Δ hiIs the indirect pollution source plume lifting height;
5: the calculation formula of the diffusion concentration of the smoke plume of the penetration source is as follows:
Figure FDA0003505258390000031
wherein C isp(x, y, z) is the diffusion concentration of the osmotic source plume, zieffIs an effective stable mixed layer height;
the S4 specifically includes:
step S41: dividing the road section and the peripheral area thereof into a plurality of grids, and calculating the pollutant concentration in each grid based on the pollutant concentration distribution model of the road section and the peripheral area thereof, wherein the calculation formula is as follows:
Figure FDA0003505258390000032
wherein i is a grid number, j0Numbering the sampling points in the grid, CiIs the average contaminant concentration of grid i, Cij0For sampling point j in grid i0Treating the concentration of the pollutant;
step S42: based on the population pollutant exposure model, calculating the individual exposure dose of each grid characteristic population, wherein the calculation formula is as follows:
Ep，i＝Cp，i×Bk×Ti
wherein E isp，iIs the individual exposure dose of contaminant p within grid i, Cp，iIs the concentration of contaminant p in grid i, BkCharacterised by the respiratory rate, T, of the population kiExposure time within grid i;
step S43: based on the travel characteristics of the crowd, the calculation formula of the exposure time is as follows:
Figure FDA0003505258390000033
wherein, TiIs the exposure time in grid i, LiFor the length of the road network within the mesh i,
Figure FDA0003505258390000034
adopting a traffic mode n for a characteristic crowd k0Average moving rate of (d);
the S5 specifically includes:
s51, inputting the starting point and the end point of the user, and acquiring the acceptable maximum travel distance extension ratio p of the user0Determining the type of the crowd to which the user belongs;
s52, calculating and determining the weight of each road section according to the user information, the individual exposure dose of pollutants and the road network traffic state model, wherein the calculation formula is as follows:
Figure FDA0003505258390000035
0≤w≤1
wherein CostiIs the weight of grid i; ep，i(ii) individual exposure doses for contaminants p within grid i; epmaxMaximum pollutant exposure in the accessible grid;
Figure FDA0003505258390000044
is the traffic distance, j, within the grid i11, 2 and 3 respectively represent straight running, left turning and right turning; lcellIs the grid width; w is the relative specific gravity, the calculation only takes the road length influence into consideration when w is 0, and the calculation only takes the exposure dose influence into consideration when w is 1;
s53, taking w as 0, and calculating the shortest path length L based on each path weight by applying Dijkstra algorithmmin；
S54, taking w as 1 and applying the maximum travel distance extension ratio p0Calculating the maximum path length LmaxThe calculation formula is as follows:
Lmax＝(1+p0)Lmin
s55, determining an optimal path based on the weight of each segment by applying a Dijkstra algorithm, and calculating the length L of the optimal path;
s56, if L is less than LmaxStopping calculation and outputting an optimal path; if L is greater than or equal to LmaxLet w be w-0.1, and return to step S55.
2. The method according to claim 1, wherein the step S2 specifically includes:
step S21: basic VSP data of all vehicle types of the road section are obtained through calculation based on a road network traffic state model, and the calculation formula of the basic VSP data of all vehicle types of the road section is as follows:
Figure FDA0003505258390000041
wherein
Figure FDA0003505258390000042
For a section i0The specific power of the upper motor vehicle,
Figure FDA0003505258390000043
for a section i0Speed of motor vehicle on road section, a is acceleration of motor vehicle, A, B, C, m0F is a constant related to the vehicle type, and a specific numerical value is obtained from a motor vehicle characteristic database;
integrating basic VSP data of all vehicle types of the existing road section to construct an existing VSP database;
step S22: generating a real-time VSP distribution database of the road section according to the road section detected in real time and the vehicle characteristic data by using the basic VSP data of each vehicle type of the road section;
step S23: generating a real-time emission rate database under the vehicle type composition condition according to the vehicle type composition detected in real time by utilizing the existing vehicle emission rate database;
step S24: generating a real-time VSP distribution-emission rate database according to the real-time emission rate database and the real-time VSP distribution database of the road sections, and calculating the average emission rate AvgER of each road section in a specific average speed interval, wherein the calculation formula is as follows:
Figure FDA0003505258390000051
wherein the content of the first and second substances,
Figure FDA0003505258390000052
is shown in the average speed interval i1Lower j (th)2Distribution frequency of VSP bins; n represents the average speedN VSP intervals are provided under the interval;
Figure FDA0003505258390000053
is shown in the average speed interval i1Next, the j-th vehicle of the v-th vehicle type2The unit of the emission rate of certain emission pollutants in each VSP interval is as follows: g/s;
step S25: calculating the discharge amount of each road section in the acquired updating time period T according to the average discharge rate AvgER of each road section in the specific average speed interval, and constructing a road section motor vehicle pollutant discharge rate database, wherein the calculation formula is as follows:
Figure FDA0003505258390000054
wherein the content of the first and second substances,
Figure FDA0003505258390000055
for a section i0T is the time interval, T is the update time interval, n1The vehicle type is adopted;
and the pollutant discharge rate database of the motor vehicles at the road sections stores the pollutant discharge amount of each road section at different time.
3. The method according to claim 2, wherein the step S22 of generating the real-time VSP distribution database of the road segments according to the road segments detected in real time and the vehicle characteristic data by using the basic VSP data of each vehicle type of the road segments specifically comprises:
acquiring the type of a road section, acquisition time and average speed per minute from flow acquisition data, and determining the average speed interval of the road section;
coding the acquired road section type and the average speed interval according to the coding rule of the existing VSP database;
and generating a real-time VSP distribution database of the road section according to the frequency distribution of the road section by inquiring the conventional VSP database according to the road section type code and the average speed interval code.
4. The method according to claim 2, wherein the step S23 of generating the real-time emission rate database under the vehicle type composition condition according to the vehicle type composition detected in real time by using the existing vehicle emission rate database comprises:
acquiring the proportion, standard vehicle weight, fuel type and emission standard of each vehicle type according to vehicle type data in the flow acquisition data;
according to vehicle type classification and coding rules, bringing various types of acquired vehicle information into a real-time vehicle type composition database;
and forming a database according to the real-time vehicle types, calculating the discharge amount of vehicles of each vehicle type on the road section at different speeds through data coupling with the existing discharge rate database, and generating a real-time discharge rate database, wherein the real-time discharge rate database stores the pollutant discharge value of the vehicles of each vehicle type on each road section at different average speeds in unit time.
5. The method of claim 1, wherein the flow collection device comprises: a remote traffic microwave detector RTMS or a coil or video acquisition device.
6. The method of claim 1, wherein the road segment-related traffic parameters comprise: road section name, number, driving direction, speed, flow rate and time.
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