CN104217126A - Road routing method based on low air pollution exposure risk - Google Patents

Abstract

The invention discloses a road routing method based on a low air pollution exposure risk. Firstly, a high temporal-spatial resolution multiple linear regression cartographic model is constructed, and a trend surface for the hour concentration of all air pollutants in a to-be- searched regional extent refreshed dynamically is generated; secondly, roads are scattered into road sections according to optimal intervals, with the combination of the trend surface for the hour concentration of the air pollutants, the air pollutant exposure concentration is extracted, with the consideration of driving time on the road sections, the air pollution exposure risk weights of all road sections are calculated, and through accumulation and summation, evaluation to the corresponding air pollution exposure risk weights of the roads is performed; finally, a road pathway with the low air pollution exposure risk is selected on the basis of real-time dynamic evaluation to the corresponding air pollution exposure risks of the roads, and through the combination of speed-limiting data to the roads, expected driving time can be calculated, whether updating routing is required or not can be judged. Therefore a suggestion for routing roads prevented from air pollution risk can be provided for the public accurately and in time.

Description

A kind of based on low air Pollution exposure risk road routing resource

Technical field

The present invention relates to environmental risk assessment and traffic intelligent navigation field, particularly a kind of based on low air Pollution exposure risk road routing resource.

Background technology

Global air pollution has worldwide caused a series of environment and health problems, makes the mankind's survival and development be subject to stern challenge.Particularly, as the China of developing country, pollute among 10 the most serious cities in the world, have nearly 7 to be positioned at China, its air-polluting situation allows of no optimist; According to WHO: the rising of the increase of pollutants in air concentration and death rate is closely related, average daily PM ₁₀the every rising 10ug/m of pollution concentration ³, crowd's general mortality rate will rise 0.6%, especially for sensitive group (such as: the patient of the diseases such as chronic bronchitis, lung cancer) air-polluting harm is especially remarkable; In the situation that world's majority state takes to reduce discharge of pollutant sources, reduce air quality concentration to the measure short-term of WHO regulation safety value and be difficult to prove effective, from ensureing healthy angle, there is important theory significance and more practical value.

Daily walking or the trip air amount pollutant that rides public transportation means are public's air pollution exposure one of topmost approach of originating.Therefore, based on air pollution concentration high-spatial and temporal resolution drawing, optimization walking along the street footpath, be, one of a kind of public of guidance important means of effectively evading the relevant Health cost of air pollution.

Yet in the travel route choice method having existed at present, most methods is only considered the factors such as between the Origin And Destination of road routing, distance is the shortest, road conditions are best, the time is minimum, price is minimum, landscape is the most beautiful, or provides real-time optimal path based on dynamic road vehicle flowrate situation for resident; Consider above factor, reduce the road routing resource that air pollution exposure Health cost is object and there is not yet report.

Therefore, utilize the auxiliary datas such as interior each monitoring station position air pollutants hour concentration Monitoring Data of regional extent to be searched and traffic route space distribution, the distribution of airborne dust spatial surface, the density of population, land use pattern number percent, temperature, wind speed, humidity, take least square rule as basis, build high-spatial and temporal resolution multiple linear regression cartographic model, generate in real time and dynamically update each air pollutants hour concentration trend surface in regional extent to be searched; Secondly, according to optimal spacing by road discrete be road segment segment, in conjunction with each air pollutants hour concentration trend surface within the scope of district to be repaired, extract and upgrade each air pollutants exposure concentrations of road segment segment, and the exposure response relation of usining between itself and fatal rate is as air pollution exposure coefficient, take the running time of the vehicles in road segment segment into account, calculate each road segment segment air pollution exposure weight, and on the basis of Cumulative sum, the air pollution exposure weight that each road is corresponding is carried out to real-time dynamic estimation; Finally, air pollution exposure weight based on each road is corresponding after real-time dynamic estimation, selection has low air Pollution exposure risk road path, and calculate and estimate running time in conjunction with road speed limit data, with this, judge whether to upgrade this routing, thus can be in time, to the public, provide the road routing suggestion of evading air pollution risk exactly.

Summary of the invention

In order to realize in time, to provide to the public traffic route routing suggestion of evading air pollution risk exactly, to formulate scientific and effective prevention and control measure object, technical scheme of the present invention is that a kind of road routing resource of low air Pollution exposure risk, comprises the following steps:

Step 1: the locus based on each monitoring station in regional extent to be searched and each air pollutants hour concentration dynamic monitoring data, spatial distribution data, envirment factor data in conjunction with the traffic route after spatialization and airborne dust earth's surface, the high-spatial and temporal resolution multiple linear regression cartographic model of structure based on criterion of least squares, estimates each air pollutants hour concentration trend surface in regional extent to be searched;

Step 2: based on air pollutants hour concentration trend surface in step 1 regional extent to be searched, calculating, apart from the related coefficient between the nearest traffic route discrete point in monitoring station position and each air pollutants hour concentration of monitoring station, is then determined the discrete optimal spacing of road according to the related coefficient obtaining, and according to optimal spacing by road discrete be road segment segment, in conjunction with each air pollutants hour concentration trend surface within the scope of district to be repaired, extract each air pollutants of road segment segment hour exposure concentrations, with the exposure response relation between each air pollutants hour exposure concentrations and fatal rate, calculate each air pollutants exposure coefficient, then utilize air pollutants exposure coefficient and the vehicles running time in road segment segment, calculate each road segment segment air pollution exposure weight, finally by each road segment segment air pollution exposure weight Cumulative sum is obtained to each road air Pollution exposure Risk rated ratio,

Step 3: each calculating based on step 2 road air Pollution exposure Risk rated ratio, carry out the selection in low air Pollution exposure risk road path; Then according to road speed limit data, calculating is travelled according to the low air Pollution exposure risk road path of selecting, the time that needs cost, and in conjunction with zero hour of low air Pollution exposure risk road routing and each monitoring station air pollutants hour concentration updated time, judgement is when each monitoring station air pollutants hour concentration upgrades, can people or transport facility reach home, with this, determine whether to reselect path, thereby, realize the real-time Dynamic Selection in low air Pollution exposure risk road path.

The road routing resource of described a kind of low air Pollution exposure risk, determining of the discrete optimal spacing of road segment segment described in step 2, comprises the following steps:

Step 1): because each monitoring station air pollutant concentration is according to a hour renewal, therefore, road routing all based on integral point constantly; If the selection in road path is T constantly, obtain the integral point moment t that T is corresponding constantly; For example: the selection in road path is T=16:32 constantly, integral point corresponding to T is 16 points constantly constantly;

Step 2): at t constantly according to fixing discrete interval d ^t, each road in regional extent to be searched is discrete for having many road segment segment of equal length d, and according to the middle point coordinate of each discrete rear road segment segment, generate a series of traffic route discrete points;

Step 3): take each monitoring station as the center of circle, the traffic route discrete point nearest apart from monitoring station position mated with it, and based on t moment air pollutants hour concentration trend surface in regional extent to be searched, extract the rear traffic route discrete point position t of these couplings each air pollutant concentration of correspondence constantly, according to following formula, calculate its related coefficient:

$R^t=\frac{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}({Y_i}^{j,t}-\stackrel{\‾}{{Y_i}^t})(C_i^{j,t}-\stackrel{\‾}{C_i^t})}{\sqrt{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{({Y_i}^{j,t}-\stackrel{\‾}{{Y_i}^t})}^2\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{(C_i^{j,t}-\stackrel{\‾}{C_i^t})}^2}}$

Wherein, Y _i ^j,trepresent the t monitoring concentration of monitoring station j position air pollutants type i constantly, represent the t mean value of the monitoring concentration of monitoring station position air pollutants type i constantly, the monitoring concentration that represents the traffic route discrete point j position air pollutants type i that historical t is constantly nearest apart from monitoring station position, the mean value that represents the monitoring concentration of the traffic route discrete point air pollutants type i that t is constantly nearest apart from monitoring station position, n represents monitoring station number, monitoring station number is consistent with the traffic route discrete point number nearest apart from monitoring station position, m represents the air pollutants species number of monitoring station monitoring, R ^tfor t moment related coefficient, R ^tthe larger expression of the absolute value t constantly correlativity of each air pollutant concentration of monitoring station and traffic route discrete point each air pollutants nearest apart from monitoring station position is stronger, road discrete point representativeness after road is discrete is stronger, road discrete interval is better, and span is between [1,1];

Step 4): in 100m-1000m interval range, change road discrete interval d ^t, and repeat above-mentioned steps 1)-3), all coefficient R of acquisition at t, constantly calculated ^tin, choose maximum value corresponding discrete interval as the discrete optimal spacing of t moment road segment segment.

The road routing resource of described a kind of low air Pollution exposure risk, after the discrete optimal spacing of definite road described in step 2, the calculating of each road air Pollution exposure Risk rated ratio comprises the following steps:

Step 1): based on the discrete optimal spacing of t moment road segment segment according to the title of each road, respectively that each road in regional extent to be searched is discrete for having equal length d ₀road segment segment, and record the traffic route of discrete rear road segment segment representative;

Step 2): calculate the t corresponding air pollution exposure of each road segment segment coefficient constantly:

$R_k^{j,t}=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{M}_i*\frac{(C_{i,k}^{j,t}-C_i^0)}{10}$

Wherein, represent constantly air pollution exposure coefficient corresponding to road segment segment k of road j after discrete of t, M _irepresent that air pollutants type is the fatal rate of i, represent the exposure concentrations that the t air pollutants type that the discrete rear road segment segment k of road j extracts at point midway is constantly i, represent that air pollutants type is the exposure threshold concentration of i, m represents air pollutants species number; Fatal rate is as M ₁, M ₂, M ₃, M ₄, M ₅, M ₆represent respectively air pollutants PM _2.5, PM ₁₀, SO ₂, CO, NO ₂, O ₃fatal rate.

Step 3): calculate the t corresponding air pollution exposure of each road segment segment weight constantly:

$W_k^{j,t}=\frac{d_0^t}{V^j}*R_k^{j,t}$

Wherein, represent constantly air pollution exposure weight corresponding to road segment segment k of road j after discrete of t, represent the t discrete optimal spacing of road segment segment constantly, V ^jthe maximum limit speed that represents traffic route j;

Step 4): calculate each road air Pollution exposure Risk rated ratio:

$W^{j,t}=\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{W}_k^{j,t}$

Wherein, W ^j,trepresent constantly road j air pollution exposure weight of t, n represents the road hop count of road j after discrete.

The road routing resource of described a kind of low air Pollution exposure risk, step 3 low-to-medium altitude gas Pollution exposure risk road path dynamic selection method, comprises the following steps:

Step 1): take the residing position of people or transport facility is the center of circle, and build and take the buffer zone that r is radius, and increase gradually the radius of buffer zone, stop while comprising the end points of any road within the scope of buffer zone, and the starting point using the end points of this road as road routing; In like manner, take the position that people or transport facility need to arrive is the center of circle, structure with and build and take the buffer zone that r is radius, and increase gradually the radius of buffer zone, while comprising the end points of any road within the scope of buffer zone, stop, and the terminal using the end points of this road as road routing; T based on calculating in step 2 is each road air Pollution exposure Risk rated ratio constantly, with the starting point of road routing and all roads between terminal, builds undirected complete connection layout;

Step 2): the starting point of establishing t moment road routing is and by this attribute flags, be (Start, 0); If the terminal of road routing is zequin to and its there is the minimum value of air pollution exposure weight between all road end points of annexation, and the corresponding road end points of this minimum value of mark is attribute flags is:

$(N_{\mathrm{start}}^t,\underset{N_{\mathrm{start}}}{\overset{N_i}{\mathrm{\Σ}}}{W}^t)$

Expression via to road end points between the minimum value of air pollution exposure weight be and because road end points is directly be connected with starting point, in above-mentioned attribute flags, have relation:

$\underset{N_{\mathrm{start}}}{\overset{N_i}{\mathrm{\Σ}}}{W}^t=W^{i,t};$

Except above-mentioned road end points, the road end points of all the other road end points for not being labeled, arranges two set with store respectively the road end points that above-mentioned t has been labeled constantly and the road end points not being labeled, that is to say: $U_1^t=\{N_{\mathrm{start}}^t,N_i^t\},U_2^t=\{N_1^t,N_2^t,\·\·\·,N_j^t,\·\·\·,N_{\mathrm{end}}^t\};$

Step 3): establishing road end points is with there is annexation; T is constantly via the road end points being labeled starting point from road routing to the road end points not being labeled between all road end points form the minimum value of path corresponding air pollution exposure weight summation;

$\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t=\min (\underset{N_{\mathrm{start}}}{\overset{N_i}{\mathrm{\Σ}}}{W}^t+W^{j,t})$

Wherein, represent that the t moment is from the starting point of road routing to road end points between all road end points form the minimum value of path corresponding air pollution exposure weight summation, represent that the t moment is from the starting point of road routing to road end points between all road end points in road path form the minimum value of path corresponding air pollution exposure weight summation, W ^j,trepresent t road end points constantly to road end points between road j air pollution exposure weight; Between the road end points that has been labeled and has not been labeled as fruit part, there is not annexation, by the weighted value W of air pollution exposure between these end points ^j,tbe made as infinity;

Step 4): build dynamic set O simultaneously ^t, repeat above-mentioned steps 3) and calculate successively and store t constantly via set in the road end points that has been labeled or the starting point of road routing to road Extreme points set in all road end points that are not labeled between the minimum value of path corresponding air pollution exposure weight summation;

$O^t=\{\underset{N_{\mathrm{start}}}{\overset{N_1}{\mathrm{\Σ}}}{W}^t,\underset{N_{\mathrm{start}}}{\overset{N_2}{\mathrm{\Σ}}}{W}^t,\·\·\·,\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t,\·\·\·\underset{N_{\mathrm{start}}}{\overset{N_{\mathrm{end}}}{\mathrm{\Σ}}}{W}^t\}$

Step 5): ask for above-mentioned t dynamic set O constantly ^tin minimum value:

$\min \left(O^t\right)=\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t$

Step 6): above-mentioned dynamic set O ^tin minimum value corresponding road end points be labeled as:

$(N_i^t,\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t)$

Wherein, this label table is shown: t is constantly via road end points the starting point of selecting from road to the road end points being labeled between all road end points form the minimum value of path corresponding air pollution exposure weight summation;

Step 7): upgrade set with $U_1^t=\{N_{\mathrm{start}}^t,N_i^t,N_j^t\},U_2^t=\{N_1^t,N_2^t,\·\·\·,N_{\mathrm{end}}^t\};$

Step 8): repeating step 1)-7), until the terminal of t moment road routing be labeled and think:

$(N_k^t,\underset{N_{\mathrm{start}}}{\overset{N_{\mathrm{end}}}{\mathrm{\Σ}}}{W}^t)$

Road routing finishes; Search forward the terminal of road routing previous corresponding road end points and search successively, until get back to the t starting point of road routing constantly thereby finally determine the order of connection of all mark road end points, with this, determine the road routing scheme with minimum air Pollution exposure risk;

Step 9): the road path of the minimum air Pollution exposure risk of selecting according to above-mentioned steps, in conjunction with the speed limit data of each road in this path, calculate people or transport facility and estimate running time;

$\mathrm{\Δt}=\underset{j=1}{\overset{u}{\mathrm{\Σ}}}\frac{S^j}{V^j}$

Wherein, Δ t represents to estimate running time, S ^jthe length that represents traffic route j, V ^jthe maximum limit speed that represents traffic route j, u represents the number of all roads in road routing result;

Step 10): as step 2), if the selection in road path is T constantly, obtain the integral point moment that T+ Δ t is corresponding, when this is t constantly, advance in the path that expression people or transport facility are selected according to current time t road, before t+1 each monitoring station position air pollutants hour concentration Monitoring Data issue constantly, can reach home, low air Pollution exposure risk road routing finishes, when this is t+1 constantly, advance in the path that expression people or transport facility are selected according to current time t road, before t+1 each monitoring station position air pollutants hour concentration Monitoring Data issue constantly, fail to reach home, need again to using t+1 moment people or transport facility present position as starting point, the position that people or transport facility need to arrive is as terminal, according to t+1 each air pollutant concentration trend surface of the moment after step 1 real-time update, and repeating step 2, step 3, to dynamically update in real time t+1 each road air Pollution exposure Risk rated ratio constantly, carry out the t+1 real-time Dynamic Selection in low air Pollution exposure risk road path constantly.

Compared with the conventional method, technique effect of the present invention is: (1) this method be based on each monitoring station position air pollutants hour concentration Monitoring Data in regional extent to be searched and path space distribute, the envirment factor auxiliary data such as the distribution of airborne dust spatial surface and the density of population, land use pattern number percent, temperature, wind speed, humidity, take least square rule as basis, by arithmetic of linearity regression, a kind of high-spatial and temporal resolution air pollutant concentration drafting method of invention; (2) the exposure response relation that this method is usingd between each air pollution exposure concentrations of road segment segment and fatal rate is simultaneously as each air pollutants exposure coefficient, calculate each road segment segment air pollution exposure weight, and on the basis of Cumulative sum, the air pollution exposure weight that a kind of road of invention is corresponding is carried out real-time dynamic estimation method; (3) this method or a kind of low air Pollution exposure risk road routing resource, needed technology and data can Real-time Obtainings, algorithm is comparatively efficient, can be in time, to the public, provide the road routing suggestion of evading air pollution risk exactly.

Accompanying drawing explanation

Fig. 1 shows low-to-medium altitude gas Pollution exposure risk road of the present invention path dynamic selection method process flow diagram;

Fig. 2 shows the discrete optimal spacing of road in the present invention and determines schematic diagram;

Fig. 3 shows the multiple air pollutants exposure concentrations of road segment segment schematic diagram calculation in the present invention;

Fig. 4 shows low-to-medium altitude gas Pollution exposure risk road of the present invention path dynamic selection method schematic diagram;

Fig. 5 shows the low air Pollution exposure risk road path Dynamic Selection result figure according to the embodiment of the present invention;

Embodiment:

To a preferred embodiment of the invention below, the detailed description of carrying out by reference to the accompanying drawings.

1, air pollutant concentration trend surface dynamic estimation, in the regional extent to be searched that the present invention adopts, the step of air pollutant concentration trend surface dynamic estimation comprises:

First, (pollutant comprises: PM to obtain the locus of each monitoring station in regional extent to be searched and each air pollutants hour concentration dynamic monitoring data _2.5, PM ₁₀, SO ₂, CO, NO ₂, O ₃); Meanwhile, obtain the spatial distribution data on traffic route and airborne dust earth's surface, and collect envirment factor data in regional extent to be searched, comprising: the density of population, land use pattern number percent, temperature, wind speed, humidity;

Secondly, spacing distance z is set, by region of search, according to the grid partition of z * z size, be grid region, according to the above-mentioned traffic route obtaining and airborne dust spatial surface distributed data, the distance on any grid positions in space and traffic route, airborne dust earth's surface in difference computation grid region, and using these distances as area variable, realize the spatialization of traffic route, airborne dust spatial surface distributed data; Meanwhile, to the density of population, temperature, wind speed, humidity data in the regional extent to be searched of collecting, adopt the method for space interpolation to carry out spatialization; Because land use pattern number percent data have been the data after spatialization, do not need to process;

Again, using multiple air pollutant concentration hour dynamic monitoring data as dependent variable, using the spatial distribution data on the traffic route after above-mentioned spatialization and airborne dust earth's surface, envirment factor data as independent variable, in each monitoring station position, build the high-spatial and temporal resolution multiple linear regression cartographic model based on criterion of least squares; On this basis, the characteristic variable that any grid positions in space in grid region is extracted is input to above-mentioned model, estimates each air pollutant concentration trend surface in regional extent to be searched; Concrete computation process is as follows:

1) build the regression model between dependent variable and independent variable, be shown below:

Y＝a ₀+a ₁X ₁+a ₂X ₂+…+a _iX _i+…+a _nX _n+u

In formula, Y is dependent variable (air pollutants hour concentration Monitoring Data), X _iindependent variable (spatial distribution data on spatial distribution data, airborne dust earth's surface and envirment factor data), the number that n is independent variable, a _ifor unknown parameter, u stochastic error;

2) according to least square method rule, determine a _iand u;

$\left\{\begin{array}{c}Q(a_0,a_1,\·\·\·,a_i,\·\·\·,a_n)=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}(Y^j-a_0-a_1{X}_1-a_2{X}_2-\·\·\·-a_i{X}_i+\·\·\·-a_n{X}_n)\\ \underset{a_0,a_1,\·\·\·{,a}_i,\·\·\·,a_n}{\min }Q\\ E\left(u\right)=0\\ \mathrm{Var}\left(u\right)={\mathrm{\σ}}^2\end{array}\right.$

3) according to above step (2) a that tries to achieve _ifor unknown parameter and u stochastic error, the air pollutants hour concentration estimated data's of any grid positions of computer memory place estimated value C, formula is as follows:

C＝a ₀+a ₁x ₁+a ₂x ₂+…+a _ix _i+…+a _nx _n

Wherein, C is that the air pollutants hour concentration data at any grid positions place, space obtain estimated value, x _iit is the measured value for any grid positions characteristic variable in space (spatial distribution data on spatial distribution data, airborne dust earth's surface and envirment factor data);

Finally, according to each air pollutants hour concentration dynamic monitoring data of monitoring station position, (pollutant comprises: PM _2.5, PM ₁₀, SO ₂, CO, NO ₂, O ₃), according to above-mentioned steps, each air pollutants hour concentration trend surface in dynamic estimation regional extent to be searched.

2, determining of the discrete optimal spacing of road segment segment, the discrete optimal spacing determining step of road segment segment that the present invention adopts comprises:

First, because each monitoring station air pollutant concentration is according to a hour renewal, so road routing all based on integral point constantly; If the selection in road path is T constantly, obtain the integral point moment t of T constantly; For example: the selection in road path is T=16:32 constantly, integral point corresponding to T is 16 points constantly constantly;

Secondly, as shown in Figure 2, at t constantly according to fixing discrete interval d ^t, each road in regional extent to be searched is discrete for having many road segment segment of equal length d, and according to the middle point coordinate of each discrete rear road segment segment, generate a series of traffic route discrete points;

Again, take each monitoring station as the center of circle, the traffic route discrete point nearest apart from monitoring station position mated with it, and based on t moment air pollutants hour concentration trend surface in regional extent to be searched, extract the rear traffic route discrete point position t of these couplings each air pollutant concentration of correspondence constantly, according to following formula, calculate its related coefficient;

$R^t=\frac{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}({Y_i}^{j,t}-\stackrel{\‾}{{Y_i}^t})(C_i^{j,t}-\stackrel{\‾}{C_i^t})}{\sqrt{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{({Y_i}^{j,t}-\stackrel{\‾}{{Y_i}^t})}^2\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{(C_i^{j,t}-\stackrel{\‾}{C_i^t})}^2}}$

Wherein, Y _i ^j,trepresent the t monitoring concentration of monitoring station j position air pollutants type i constantly, represent the t mean value of the monitoring concentration of monitoring station position air pollutants type i constantly, the monitoring concentration that represents the traffic route discrete point j position air pollutants type i that historical t is constantly nearest apart from monitoring station position, the mean value that represents the monitoring concentration of the traffic route discrete point air pollutants type i that t is constantly nearest apart from monitoring station position, n represents monitoring station number, monitoring station number is consistent with the traffic route discrete point number nearest apart from monitoring station position, m represents the air pollutants species number of monitoring station monitoring, R ^tfor t moment related coefficient, R ^tthe larger expression of the absolute value t constantly correlativity of each air pollutant concentration of monitoring station and traffic route discrete point each air pollutants nearest apart from monitoring station position is stronger, road discrete point representativeness after road is discrete is stronger, road discrete interval is better, and span is between [1,1];

Finally, in 100m-1000m interval range, change road discrete interval d ^t, and repeat above-mentioned steps, at t, constantly calculate all coefficient R of acquisition ^tin, choose maximum value corresponding discrete interval as the discrete optimal spacing of t moment road segment segment.

3, the dynamic estimation of road air Pollution exposure Risk rated ratio, the step of the road air Pollution exposure Risk rated ratio dynamic estimation that the present invention adopts comprises:

First, based on the definite discrete optimal spacing of t moment road segment segment of above-mentioned steps 2 according to the title of each road, respectively that each road in regional extent to be searched is discrete for having equal length d ₀road segment segment, and record the traffic route of discrete rear road segment segment representative;

Secondly, calculate the t corresponding air pollution exposure of each road segment segment coefficient constantly:

$R_k^{j,t}=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{M}_i*\frac{(C_{i,k}^{j,t}-C_i^0)}{10}$

Wherein, represent constantly air pollution exposure coefficient corresponding to road segment segment k of road j after discrete of t, M _irepresent that air pollutants type is the fatal rate of i, represent the exposure concentrations that the t air pollutants type that the discrete rear road segment segment k of road j extracts at point midway is constantly i, represent that air pollutants type is the exposure threshold concentration of i, m represents air pollutants species number; Fatal rate is as M ₁, M ₂, M ₃, M ₄, M ₅, M ₆represent respectively air pollutants PM _2.5, PM ₁₀, SO ₂, CO, NO ₂, O ₃fatal rate

Again, calculate the t corresponding air pollution exposure of each road segment segment weight constantly:

$W_k^{j,t}=\frac{d_0^t}{V^j}*R_k^{j,t}$

Wherein, represent constantly air pollution exposure weight corresponding to road segment segment k of road j after discrete of t, represent the t discrete optimal spacing of road segment segment constantly, V ^jthe maximum limit speed that represents traffic route j;

Finally, calculate each road air Pollution exposure Risk rated ratio:

$W^{j,t}=\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{W}_k^{j,t}$

Wherein, W ^j,trepresent constantly road j air pollution exposure weight of t, n represents the road hop count of road j after discrete.

4, low air Pollution exposure risk road path dynamic selection method, the low air Pollution exposure risk road path Dynamic Selection step that the present invention adopts comprises:

First, as shown in Figure 4, take the residing position of people or transport facility is the center of circle, and build and take the buffer zone that r is radius, and increase gradually the radius of buffer zone, while comprising the end points of any road within the scope of buffer zone, stop, and the starting point using the end points of this road as road routing; In like manner, take the position that people or transport facility need to arrive is the center of circle, structure with and build and take the buffer zone that r is radius, and increase gradually the radius of buffer zone, while comprising the end points of any road within the scope of buffer zone, stop, and the terminal using the end points of this road as road routing;

Then, as shown in Figure 4, the t calculating based on above-mentioned steps 2 is each road air Pollution exposure Risk rated ratio constantly, with the starting point of road routing and all roads between terminal, builds undirected complete connection layout;

Finally, as shown in Figure 4, based on the not dynamic change of each road air Pollution exposure Risk rated ratio in the same time, carry out road path Dynamic Selection, concrete computation process is as follows:

1) establish t constantly the starting point of road routing be and by this attribute flags, be (Start, 0); If the terminal of road routing is zequin to and its there is the minimum value of air pollution exposure weight between all road end points of annexation, and the corresponding road end points of this minimum value of mark; Here establishing this road end points is attribute flags is:

$(N_{\mathrm{start}}^t,\underset{N_{\mathrm{start}}}{\overset{N_i}{\mathrm{\Σ}}}{W}^t)$

Expression via to road end points between the minimum value of air pollution exposure weight be and because road end points is directly be connected with starting point, in above-mentioned attribute flags, have relation:

$\underset{N_{\mathrm{start}}}{\overset{N_i}{\mathrm{\Σ}}}{W}^t=W^{i,t};$

Except above-mentioned road end points, the road end points of all the other road end points for not being labeled, arranges two set with store respectively the road end points that above-mentioned t has been labeled constantly and the road end points not being labeled, that is to say: $U_1^t=\{N_{\mathrm{start}}^t,N_i^t\},U_2^t=\{N_1^t,N_2^t,\·\·\·,N_j^t,\·\·\·,N_{\mathrm{end}}^t\};$

2) t is constantly via the road end points being labeled starting point from road routing to the road end points not being labeled between all road end points form the minimum value of path corresponding air pollution exposure weight summation;

$\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t=\min (\underset{N_{\mathrm{start}}}{\overset{N_i}{\mathrm{\Σ}}}{W}^t+W^{j,t})$

Wherein, represent that the t moment is from the starting point of road routing to road end points between all road end points form the minimum value of path corresponding air pollution exposure weight summation, represent that the t moment is from the starting point of road routing to road end points between all road end points in road path form the minimum value of path corresponding air pollution exposure weight summation, W ^j,trepresent t road end points constantly to road end points between road j air pollution exposure weight; Between the road end points that has been labeled and has not been labeled as fruit part, there is not annexation, by the weighted value W of air pollution exposure between these end points ^j,tbe made as infinity;

3) build dynamic set O simultaneously ^t, repeat above-mentioned steps 2) and calculate successively and store t constantly via set in the road end points that has been labeled or the starting point of road routing to road Extreme points set in all road end points that are not labeled between the minimum value of path corresponding air pollution exposure weight summation;

$O^t=\{\underset{N_{\mathrm{start}}}{\overset{N_1}{\mathrm{\Σ}}}{W}^t,\underset{N_{\mathrm{start}}}{\overset{N_2}{\mathrm{\Σ}}}{W}^t,\·\·\·,\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t,\·\·\·\underset{N_{\mathrm{start}}}{\overset{N_{\mathrm{end}}}{\mathrm{\Σ}}}{W}^t\}$

4) ask for above-mentioned t dynamic set O constantly ^tin minimum value:

$\min \left(O^t\right)=\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t$

5) above-mentioned dynamic set O ^tin minimum value corresponding road end points be labeled as:

$(N_i^t,\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t)$

Wherein, this label table is shown: t is constantly via road end points the starting point of selecting from road to the road end points being labeled between all road end points form the minimum value of path corresponding air pollution exposure weight summation;

6) upgrade set with $U_1^t=\{N_{\mathrm{start}}^t,N_i^t,N_j^t\},U_2^t=\{N_1^t,N_2^t,\·\·\·,N_{\mathrm{end}}^t\};$

7) repeating step 1)-6), until the terminal of t moment road routing be labeled and think:

$(N_k^t,\underset{N_{\mathrm{start}}}{\overset{N_{\mathrm{end}}}{\mathrm{\Σ}}}{W}^t)$

Road routing finishes; Search forward road routing terminal previous corresponding road end points and search successively, until get back to the t starting point of road routing constantly thereby finally determine the order of connection of all mark road end points, with this, determine the road routing scheme with minimum air Pollution exposure risk;

8) the road path of the minimum air Pollution exposure risk of selecting according to above-mentioned steps, in conjunction with the speed limit data of each road in this path, calculates people or transport facility and estimates running time;

$\mathrm{\Δt}=\underset{j=1}{\overset{u}{\mathrm{\Σ}}}\frac{S^j}{V^j}$

Wherein, Δ t represents to estimate running time, S ^jthe length that represents traffic route j, V ^jthe maximum limit speed that represents traffic route j, u represents the number of all roads in road routing result;

9) as described in step 2, if the selection in road path is T constantly, obtain the integral point moment that T+ Δ t is corresponding, when this is t constantly, advance in the path that expression people or transport facility are selected according to current time t road, before t+1 each monitoring station position air pollutants hour concentration Monitoring Data issue constantly, can reach home, low air Pollution exposure risk road routing finishes, when this is t+1 constantly, advance in the path that expression people or transport facility are selected according to current time t road, before t+1 each monitoring station position air pollutants hour concentration Monitoring Data issue constantly, fail to reach home, need again to using t+1 moment people or transport facility present position as starting point, the position that people or transport facility need to arrive is as terminal, according to t+1 each air pollutant concentration trend surface of the moment after step 1 real-time update, and repeating step 2, step 3, to dynamically update in real time t+1 each road air Pollution exposure Risk rated ratio constantly, carry out the t+1 real-time Dynamic Selection in low air Pollution exposure risk road path constantly.

In above to detailed introduction of the present invention, to have applied specific case principle of the present invention and embodiment have been set forth, the explanation of above embodiment is just for helping to understand method of the present invention and core concept thereof., for one of ordinary skill in the art, according to thought of the present invention, all will change in specific embodiments and applications, in sum, this description should not be construed as limitation of the present invention meanwhile.

Claims (4)

1. a road routing resource for low air Pollution exposure risk, is characterized in that, comprises the following steps:

Step 1: the locus based on each monitoring station in regional extent to be searched and each air pollutants hour concentration dynamic monitoring data, spatial distribution data, envirment factor data in conjunction with the traffic route after spatialization and airborne dust earth's surface, the high-spatial and temporal resolution multiple linear regression cartographic model of structure based on criterion of least squares, estimates each air pollutants hour concentration trend surface in regional extent to be searched;

Step 2: based on air pollutants hour concentration trend surface in step 1 regional extent to be searched, calculating, apart from the related coefficient between the nearest traffic route discrete point in monitoring station position and each air pollutants hour concentration of monitoring station, is then determined the discrete optimal spacing of road according to the related coefficient obtaining, and according to optimal spacing by road discrete be road segment segment, in conjunction with each air pollutants hour concentration trend surface within the scope of district to be repaired, extract each air pollutants of road segment segment hour exposure concentrations, with the exposure response relation between each air pollutants hour exposure concentrations and fatal rate, calculate each air pollutants exposure coefficient, then utilize air pollutants exposure coefficient and the vehicles running time in road segment segment, calculate each road segment segment air pollution exposure weight, finally by each road segment segment air pollution exposure weight Cumulative sum is obtained to each road air Pollution exposure Risk rated ratio,

Step 3: each calculating based on step 2 road air Pollution exposure Risk rated ratio, carry out the selection in low air Pollution exposure risk road path; Then according to road speed limit data, calculating is travelled according to the low air Pollution exposure risk road path of selecting, the time that needs cost, and in conjunction with zero hour of low air Pollution exposure risk road routing and each monitoring station air pollutants hour concentration updated time, judgement is when each monitoring station air pollutants hour concentration upgrades, can people or transport facility reach home, with this, determine whether to reselect path, thereby, realize the real-time Dynamic Selection in low air Pollution exposure risk road path.

2. the road routing resource of a kind of low air Pollution exposure risk according to claim 1, is characterized in that, determining of the discrete optimal spacing of road segment segment described in step 2, comprises the following steps:

Step 1): because each monitoring station air pollutant concentration is according to a hour renewal, therefore, road routing all based on integral point constantly; If the selection in road path is T constantly, obtain the integral point moment t that T is corresponding constantly;

Step 2): at t constantly according to fixing discrete interval d ^t, each road in regional extent to be searched is discrete for having many road segment segment of equal length d, and according to the middle point coordinate of each discrete rear road segment segment, generate a series of traffic route discrete points;

Step 3): take each monitoring station as the center of circle, the traffic route discrete point nearest apart from monitoring station position mated with it, and based on t moment air pollutants hour concentration trend surface in regional extent to be searched, extract the rear traffic route discrete point position t of these couplings each air pollutant concentration of correspondence constantly, according to following formula, calculate its related coefficient:

$R^t=\frac{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}({Y_i}^{j,t}-\stackrel{\‾}{{Y_i}^t})(C_i^{j,t}-\stackrel{\‾}{C_i^t})}{\sqrt{\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{({Y_i}^{j,t}-\stackrel{\‾}{{Y_i}^t})}^2\underset{i=1}{\overset{m}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{(C_i^{j,t}-\stackrel{\‾}{C_i^t})}^2}}$

Wherein, Y _i ^j,trepresent the t monitoring concentration of monitoring station j position air pollutants type i constantly, represent the t mean value of the monitoring concentration of monitoring station position air pollutants type i constantly, the monitoring concentration that represents the traffic route discrete point j position air pollutants type i that historical t is constantly nearest apart from monitoring station position, the mean value that represents the monitoring concentration of the traffic route discrete point air pollutants type i that t is constantly nearest apart from monitoring station position, n represents monitoring station number, monitoring station number is consistent with the traffic route discrete point number nearest apart from monitoring station position, m represents the air pollutants species number of monitoring station monitoring, R ^tfor t moment related coefficient, R ^tthe larger expression of the absolute value t constantly correlativity of each air pollutant concentration of monitoring station and traffic route discrete point each air pollutants nearest apart from monitoring station position is stronger, road discrete point representativeness after road is discrete is stronger, road discrete interval is better, and span is between [1,1];

Step 4): in 100m-1000m interval range, change road discrete interval d ^t, and repeat above-mentioned steps 1)-3), all coefficient R of acquisition at t, constantly calculated ^tin, choose maximum value corresponding discrete interval as the discrete optimal spacing of t moment road segment segment.

3. the road routing resource of a kind of low air Pollution exposure risk according to claim 1, is characterized in that, after the discrete optimal spacing of definite road described in step 2, the calculating of each road air Pollution exposure Risk rated ratio comprises the following steps:

Step 1): based on the discrete optimal spacing of t moment road segment segment according to the title of each road, respectively that each road in regional extent to be searched is discrete for having equal length d ₀road segment segment, and record the traffic route of discrete rear road segment segment representative;

Step 2): calculate the t corresponding air pollution exposure of each road segment segment coefficient constantly:

$R_k^{j,t}=\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{M}_i*\frac{(C_{i,k}^{j,t}-C_i^0)}{10}$

Wherein, represent constantly air pollution exposure coefficient corresponding to road segment segment k of road j after discrete of t, M _irepresent that air pollutants type is the fatal rate of i, represent the exposure concentrations that the t air pollutants type that the discrete rear road segment segment k of road j extracts at point midway is constantly i, represent that air pollutants type is the exposure threshold concentration of i, m represents air pollutants species number;

Step 3): calculate the t corresponding air pollution exposure of each road segment segment weight constantly:

$W_k^{j,t}=\frac{d_0^t}{V^j}*R_k^{j,t}$

Wherein, represent constantly air pollution exposure weight corresponding to road segment segment k of road j after discrete of t, represent the t discrete optimal spacing of road segment segment constantly, V ^jthe maximum limit speed that represents traffic route j;

Step 4): calculate each road air Pollution exposure Risk rated ratio:

$W^{j,t}=\underset{k=1}{\overset{n}{\mathrm{\Σ}}}{W}_k^{j,t}$

Wherein, W ^j,trepresent constantly road j air pollution exposure weight of t, n represents the road hop count of road j after discrete.

4. the road routing resource of a kind of low air Pollution exposure risk according to claim 1, is characterized in that, step 3 low-to-medium altitude gas Pollution exposure risk road path dynamic selection method, comprises the following steps:

Step 1): take the residing position of people or transport facility is the center of circle, and build and take the buffer zone that r is radius, and increase gradually the radius of buffer zone, stop while comprising the end points of any road within the scope of buffer zone, and the starting point using the end points of this road as road routing; In like manner, take the position that people or transport facility need to arrive is the center of circle, structure with and build and take the buffer zone that r is radius, and increase gradually the radius of buffer zone, while comprising the end points of any road within the scope of buffer zone, stop, and the terminal using the end points of this road as road routing; T based on calculating in step 2 is each road air Pollution exposure Risk rated ratio constantly, with the starting point of road routing and all roads between terminal, builds undirected complete connection layout;

Step 2): the starting point of establishing t moment road routing is and by this attribute flags, be (Start, 0); If the terminal of road routing is zequin to and its there is the minimum value of air pollution exposure weight between all road end points of annexation, and the corresponding road end points of this minimum value of mark is attribute flags is:

$(N_{\mathrm{start}}^t,\underset{N_{\mathrm{start}}}{\overset{N_i}{\mathrm{\Σ}}}{W}^t)$

Expression via to road end points between the minimum value of air pollution exposure weight be and because road end points is directly be connected with starting point, in above-mentioned attribute flags, have relation:

$\underset{N_{\mathrm{start}}}{\overset{N_i}{\mathrm{\Σ}}}{W}^t=W^{i,t};$

Except above-mentioned road end points, the road end points of all the other road end points for not being labeled, arranges two set with store respectively the road end points that above-mentioned t has been labeled constantly and the road end points not being labeled, that is to say: $U_1^t=\{N_{\mathrm{start}}^t,N_i^t\},U_2^t=\{N_1^t,N_2^t,\·\·\·,N_j^t,\·\·\·,N_{\mathrm{end}}^t\};$

Step 3): establishing road end points is with there is annexation; T is constantly via the road end points being labeled starting point from road routing to the road end points not being labeled between all road end points form the minimum value of path corresponding air pollution exposure weight summation;

$\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t=\min (\underset{N_{\mathrm{start}}}{\overset{N_i}{\mathrm{\Σ}}}{W}^t+W^{j,t})$

Wherein, represent that the t moment is from the starting point of road routing to road end points between all road end points form the minimum value of path corresponding air pollution exposure weight summation, represent that the t moment is from the starting point of road routing to road end points between all road end points in road path form the minimum value of path corresponding air pollution exposure weight summation, W ^j,trepresent t road end points constantly to road end points between road j air pollution exposure weight; Between the road end points that has been labeled and has not been labeled as fruit part, there is not annexation, by the weighted value W of air pollution exposure between these end points ^j,tbe made as infinity;

Step 4): build dynamic set O simultaneously ^t, repeat above-mentioned steps 3) and calculate successively and store t constantly via set in the road end points that has been labeled or the starting point of road routing to road Extreme points set in all road end points that are not labeled between the minimum value of path corresponding air pollution exposure weight summation;

$O^t=\{\underset{N_{\mathrm{start}}}{\overset{N_1}{\mathrm{\Σ}}}{W}^t,\underset{N_{\mathrm{start}}}{\overset{N_2}{\mathrm{\Σ}}}{W}^t,\·\·\·,\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t,\·\·\·\underset{N_{\mathrm{start}}}{\overset{N_{\mathrm{end}}}{\mathrm{\Σ}}}{W}^t\}$

Step 5): ask for above-mentioned t dynamic set O constantly ^tin minimum value:

$\min \left(O^t\right)=\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t$

Step 6): above-mentioned dynamic set O ^tin minimum value corresponding road end points be labeled as:

$(N_i^t,\underset{N_{\mathrm{start}}}{\overset{N_j}{\mathrm{\Σ}}}{W}^t)$

Wherein, this label table is shown: t is constantly via road end points the starting point of selecting from road to the road end points being labeled between all road end points form the minimum value of path corresponding air pollution exposure weight summation;

Step 7): upgrade set with $U_1^t=\{N_{\mathrm{start}}^t,N_i^t,N_j^t\},U_2^t=\{N_1^t,N_2^t,\·\·\·,N_{\mathrm{end}}^t\};$

Step 8): repeating step 1)-7), until the terminal of t moment road routing be labeled and think:

$(N_k^t,\underset{N_{\mathrm{start}}}{\overset{N_{\mathrm{end}}}{\mathrm{\Σ}}}{W}^t)$

Road routing finishes; Search forward the terminal of road routing previous corresponding road end points and search successively, until get back to the t starting point of road routing constantly thereby finally determine the order of connection of all mark road end points, with this, determine the road routing scheme with minimum air Pollution exposure risk;

Step 9): the road path of the minimum air Pollution exposure risk of selecting according to above-mentioned steps, in conjunction with the speed limit data of each road in this path, calculate people or transport facility and estimate running time;

$\mathrm{\Δt}=\underset{j=1}{\overset{u}{\mathrm{\Σ}}}\frac{S^j}{V^j}$

Wherein, Δ t represents to estimate running time, S ^jthe length that represents traffic route j, V ^jthe maximum limit speed that represents traffic route j, u represents the number of all roads in road routing result;

Step 10): as step 2), if the selection in road path is T constantly, obtain the integral point moment that T+ Δ t is corresponding, when this is t constantly, advance in the path that expression people or transport facility are selected according to current time t road, before t+1 each monitoring station position air pollutants hour concentration Monitoring Data issue constantly, can reach home, low air Pollution exposure risk road routing finishes, when this is t+1 constantly, advance in the path that expression people or transport facility are selected according to current time t road, before t+1 each monitoring station position air pollutants hour concentration Monitoring Data issue constantly, fail to reach home, need again to using t+1 moment people or transport facility present position as starting point, the position that people or transport facility need to arrive is as terminal, according to t+1 each air pollutant concentration trend surface of the moment after step 1 real-time update, and repeating step 2, step 3, to dynamically update in real time t+1 each road air Pollution exposure Risk rated ratio constantly, carry out the t+1 real-time Dynamic Selection in low air Pollution exposure risk road path constantly.