CN105809370A - Scheduling method of accident loss service staff - Google Patents

Abstract

The invention discloses a scheduling method of accident loss service staff.The method comprises the steps that an accident occurrence possibility index and a road jam index are determined for each road segment and each road junction according to accident occurrence historical data and road jam historical data in a traffic planning area; according to the accident occurrence possibility indexes, on the basis of the principle of ensuring optimal accident loss service time, standby sites are selected, and a loss area and the number of loss service staff corresponding to each standby site are determined; according to the accident occurrence possibility indexes and the road jam indexes, on the basis of the principle that the poorer the road condition is, the smaller the number of flowing patrolling staff is and the principle that the higher the accident occurrence rate is, the larger the number of patrolling staff is, the flowing patrolling proportion of the loss service staff of each standby site is determined through a fuzzy reasoning method; for each standby site, loss service staff is scheduled for patrolling according to the flowing patrolling proportion and the number of loss service staff; when an accident occurs, the nearest loss staff is scheduled for processing.By the adoption of the scheduling method, loss service response time can be shortened, and scheduling efficiency can be improved.

Description

A kind of accident is in danger the dispatching method of service personal

Technical field

The present invention relates to Computer Applied Technology, particularly relate to a kind of accident and be in danger the dispatching method of service personal.

Background technology

Along with constantly perfect, the people's living standard of urban road construction and being greatly improved of the consuming capacity, the motor vehicles recoverable amount of China is significantly enhanced, motor vehicles have become one of main traffic instrument of goods transport and go off daily, play an important role in daily life.Meanwhile, increasing substantially of the rhythm of life constantly accelerated and driving vehicle number, also cause car accident incidence rate to have certain lifting.In car accident processing procedure, whether reliably service response time of being in danger (interval time when arriving the scene of the accident with the service personal that is in danger occurs accident) and efficiency are the evaluation accident service of being in danger major criterions, delayed, the low of efficiency of service response time of being in danger all can cause the wasting of resources of a large amount of social times, and therefore how formulating efficient scheduling scheme for the service personal that is in danger has important practical significance.

The principle that service response time is minimum although the service dispatch that is in danger in Vehicles Collected from Market is based on being in danger mostly, but generally adopt after learning place where the accident occurred by forms such as phones, the mode that the reselection distance nearest leisure service personnel in place where the accident occurred go to place where the accident occurred to carry out accident treatment, the scheduling behavior of service personal is to occur at the period after accident occurs by it, and service response time places one's entire reliance upon service personal's self-position.So, will the delayed situation of presence service response time, and then the inefficiency of the service personal that causes being in danger, the requirement of the accident service of being in danger cannot be met.

As can be seen here, above-mentioned traditional accident service personal's scheduling scheme that is in danger exists and is in danger the problem that service response time is delayed, dispatching efficiency is low.

Summary of the invention

In view of this, present invention is primarily targeted at and provide a kind of accident to be in danger the dispatching method of service personal, the method be conducive to shortening be in danger service response time, improve dispatching efficiency.

In order to achieve the above object, the technical scheme that the present invention proposes is:

A kind of accident is in danger the dispatching method of service personal, including:

According to accident generation historical data in traffic programme region and congestion in road historical data, for each section in described traffic programme region and crossing, it is determined that accident possibility occurrence index and congestion in road index；

Transportation network according to described accident possibility occurrence index and described traffic programme region, according to guaranteeing that accident is in danger optimum selection principle service time, selects the ready position for arranging the service personal that is in danger from candidate's ready position；And determine be in danger region and the service personal's number that is in danger that each described ready position is corresponding；

According to described accident possibility occurrence index and described congestion in road index, utilize fuzzy reasoning method, street number is patrolled more less and the more many principle of accident rate Yue Gaoxun street number, it is determined that street ratio is patrolled in the flowing of the service personal that is in danger arranged on each described ready position according to the more poor flowing of road conditions；

For each described ready position, patrol street ratio and the described service personal's number that is in danger according to corresponding described flowing, dispatch the service personal that is in danger and carry out patrolling street in the region of being in danger that this ready position is corresponding；

When having an accident in described traffic programme region, described accident is processed by the personnel that are in danger dispatching distance accident location nearest.

In sum, the accident that the present invention proposes is in danger the dispatching method of service personal, based on accident generation historical data in traffic programme region and congestion in road historical data, according to guaranteeing that accident is in danger optimum selection principle service time, select to be in danger the ready position of service personal, and utilize fuzzy reasoning method, street number is patrolled more less and the more many principle of accident rate Yue Gaoxun street number according to the more poor flowing of road conditions, determine that street ratio is patrolled in the flowing of the service personal that is in danger arranged on each described ready position, so, can effectively shorten service response time of being in danger, ensure to be in danger the work efficiency of service personal, improve dispatching efficiency.

Accompanying drawing explanation

Fig. 1 is the method flow schematic diagram of the embodiment of the present invention one.

Detailed description of the invention

For making the object, technical solutions and advantages of the present invention clearly, the present invention is described in further detail below in conjunction with the accompanying drawings and the specific embodiments.

The core concept of the present invention is: the present invention is based on history accident generation data and urban traffic situation data, be in danger service response time and efficiency for target with raising accident, on the basis of abundant analysis accident pests occurrence rule, be in danger service personal's ready position selection strategy with instruct scheduling be in danger service personal go to the best ready position, ensure the optimum that accident is in danger service time, adopt simultaneously and patrol street number more less and the more many principle of accident rate Yue Gaoxun street number according to the more poor flowing of road conditions, determine that street ratio is patrolled in the flowing of the service personal that is in danger arranged on each described ready position, to instruct the service state dispatching the service personal that is in danger, ensure to be in danger the work efficiency of service personal.

Fig. 1 is the schematic flow sheet of the embodiment of the present invention one, as it is shown in figure 1, this embodiment specifically includes that

Step 101, according to accident generation historical data in traffic programme region and congestion in road historical data, for each section in described traffic programme region and crossing, it is determined that accident possibility occurrence index and congestion in road index.

This step, for based on accident generation historical data in traffic programme region and congestion in road historical data, obtaining accident possibility occurrence index and the congestion in road index at Nei Ge section, region and crossing.So, by the historical traffic data in analyzed area, the data of the accident pests occurrence rule embodying each section and crossing can be obtained, such that it is able to select on this basis in subsequent step suitable ready position and instruct scheduling be in danger service personal go to the best ready position, it is ensured that the optimum that accident is in danger service time.

Specifically, for each section or crossing, described accident possibility occurrence index and congestion in road index can adopt the calculating of existing computational methods to obtain.For example, it is possible to according toCalculating accident possibility occurrence index p_i, wherein, n is the natural law that vehicle accident occurs in historical data correspondence timing statistics section, and N is total natural law of timing statistics section corresponding to historical data；Can be according toCalculate congestion in road index q_i, wherein, z represents the path index issued in the timing statistics section that historical data is corresponding, z_maxMaximum for path index.p_iIt is worth more big, then it represents that the probability that accident occurs is more big.q_iIt is worth more big, then it represents that congestion in road situation is more serious.

Step 102, transportation network according to described accident possibility occurrence index and described traffic programme region, according to guaranteeing that accident is in danger optimum selection principle service time, from candidate's ready position, select the ready position for arranging the service personal that is in danger, and determine be in danger region and the service personal's number that is in danger that each described ready position is corresponding.

In this step, by according to guaranteeing that accident is in danger optimum selection principle service time, it is determined that go out the ready position for arranging the service personal that is in danger, so, it can be ensured that for arranging that the ready position of the service personal that is in danger is conducive to optimization accident to be in danger service time.

It is preferred that this step can adopt following method to realize:

Step 1021, described candidate's ready position being combined, obtain w group candidate's ready position, wherein, often group comprises m candidate's ready position, and the ready position that m is default arranges quantity, and w is the quantity of default candidate's ready position group.

In this step, it is necessary to candidate's ready position is grouped, in order to select in subsequent step and be in danger a group of service time as the ready position for arranging the service personal that is in danger most beneficial for optimization accident.

Here, described w and m can be configured according to actual needs by those skilled in the art.The mode that described candidate's ready position is carried out permutation and combination can be adopted in actual applications to obtain above-mentioned w group candidate's ready position.Namely, it is assumed that described candidate's ready position add up to M, then from M candidate's ready position, take the combination of m candidate's ready position obtain one group of candidate's ready position, the number w of obtained candidate's ready position group be the quantity that likely combines namely

Step 1022, for often organizing candidate ready position Y, within the scope of this group candidate's ready position, for each crossing x in described traffic programme region_i, calculate the distance Dist (x between nearest candidate's ready position in this crossing and this group_i,Y)；According to described Dist (x_i, Y) and crossing x_iDescribed accident possibility occurrence index p_i, according to D (X, Y)=Max (p_i×Dist(x_i, Y)), i ∈ 1,2 ..., and n}, calculate worst incident response ability value D (X, Y) corresponding to this group candidate ready position Y；Wherein, n is the crossing sum in described traffic programme region.

In this step, for often organizing candidate ready position Y, it is necessary to first calculate each crossing distance Dist (x to candidate's ready position closest in this group_i, Y), then further according to the Dist (x at all crossings_i, Y) and accident possibility occurrence index p_i, according to D (X, Y)=Max (p_i×Dist(x_i, Y)), calculate worst incident response ability value D (X, Y) corresponding to this group candidate ready position Y.Here, p_i×Dist(x_i, Y) maximum mean correspondence crossing be the place that in this group candidate ready position Y, incident response ability is worst.

Step 1023, described D (X, Y) according to all groups of candidate's ready positions, select one group of minimum for D (X, Y) candidate ready position Y* as the described ready position for arranging the service personal that is in danger.

In this step, by one group of minimum for selection incident response ability worst-case value candidate ready position Y^*As the described ready position for arranging the service personal that is in danger.So, by assuring that be used for arranging that the incident response ability worst-case value of one group of ready position of the service personal that is in danger is minimum so that the longest response time of accident reduces as much as possible, control effectively service time such that it is able to accident is in danger and optimizes.

Utilize above-mentioned steps 1021～1023, it may be determined that go out one group of ready position Y* for arranging the service personal that is in danger, it can be ensured that the longest response time of accident is the shortest.In actual applications, particle cluster algorithm can be utilized in order to improve operation efficiency, solve described candidate ready position group Y^*。

Step 1024, for described Y^*In each ready positionAccording toCalculate the service personal number C that is in danger that ready position is corresponding_j, and will meet:Region corresponding to all crossings, as this ready positionRegion of being in danger；Wherein, round [] is downward bracket function, and g (α, β) is discriminant function, as α=β, g (α, β)=1, otherwise, and g (α, β)=0.

This step, is used for calculating each ready positionThe middle service personal quantity C that is in danger needing to arrange_j, specifically according toIt is calculated obtaining hereIn time, meansIt is to say,For the above-mentioned one group of ready position Y being defined as arranging the service personal that is in danger^*In to crossing x_iNearest ready position, so,Meaning will with ready positionAccident possibility occurrence index p for all crossings of nearest ready position_iSummation, as such, it is desirable to the service personal quantity C that is in danger arranged_jIt is the value that this summed result rounds downwards.And with ready positionCrossing for nearest ready position means, when actual being in danger, to dispatch this ready positionIn personnel may insure that the response time that is in danger is the shortest, therefore, by region corresponding for these crossings, as this ready positionRegion of being in danger, and at ready positionMiddle layout is in danger service personal accordingly, it is possible to reach effectively to shorten service response time of being in danger, improve the purpose of dispatching efficiency.It addition, when the accident possibility occurrence index at crossing is more high, the quantity required of the personnel that are in danger is also many, therefore, the personnel configured here according to accident possibility occurrence index also can be more many, it can be seen that, adopt this step to determine the service personal quantity C that is in danger_jMore meet the traffic conditions needs of reality so that the configuration of service personal's quantity of being in danger is more reasonable, it is possible to be effectively improved dispatching efficiency.

Step 103, according to described accident possibility occurrence index and described congestion in road index, utilize fuzzy reasoning method, street number is patrolled more less and the more many principle of accident rate Yue Gaoxun street number, it is determined that street ratio is patrolled in the flowing of the service personal that is in danger arranged on each described ready position according to the more poor flowing of road conditions.

In this step, in order to improve the work efficiency of the service personal that is in danger, above-mentioned accident possibility occurrence index and described congestion in road index will be utilized, adopt fuzzy reasoning method, patrol street number more less and the more many principle of accident rate Yue Gaoxun street number according to the more poor flowing of road conditions, determine that street ratio is patrolled in the flowing of the service personal that is in danger on each ready position.So, according to road conditions and accident rate, it is possible to the service status of simple and effective ground specified services personnel.

It is preferred that this step can adopt following method, determine that street ratio is patrolled in the flowing of the service personal that is in danger arranged on each described ready position:

Step 1031, for each described ready position, the average of the described accident possibility occurrence index in each section this ready position being in danger in region, it is determined that for accident possibility occurrence index in the region of this ready position；The average of the described congestion in road index in each section this ready position being in danger in region, it is determined that for congestion in road index in the region of this ready position.

In this step, it needs to be determined that go out the road conditions feature in the region of being in danger of each ready position, i.e. congestion in road index in accident possibility occurrence index and region in region, these parameters are also determine according to the accident possibility occurrence index and congestion in road index that obtain each section in step 101.

Step 1032, for each described ready position, according to accident possibility occurrence index in described region and congestion in road index in described region, stick with paste the membership function of each element in set according to the membership function of each element in the membership function of each element, default accident possibility occurrence fuzzy set in default fuzzy rule, default traffic fuzzy set and default personnel's proportional die of making an inspection tour, adopt Mamdani inference method to make inferences；And utilizing centroid method that described the reasoning results is carried out de-fuzzy process, street ratio is patrolled in the flowing of the service personal that is in danger obtaining this ready position.

In this step, for accident possibility occurrence index in the region obtaining each ready position with in region congestion in road index basis, according to the membership function that default fuzzy rule and above-mentioned fuzzy set are corresponding, Mamdani inference method is adopted to make inferences, after completing reasoning, with centroid method, the reasoning results is carried out de-fuzzy process again, such that it is able to street ratio is patrolled in the flowing obtaining the service personal that is in danger of this ready position.

In actual applications, on the basis of membership function corresponding to given fuzzy rule and above-mentioned fuzzy set, Mamdani inference method is adopted to make inferences, and utilize centroid method that described the reasoning results is carried out de-fuzzy process, and then the flowing obtaining the service personal that is in danger of each ready position patrols the concrete grammar of street ratio, grasped by those skilled in the art, do not repeated them here.

It is preferred that above-mentioned fuzzy rule specifically can include following rule:

If traffic congestion situation is unimpeded in region, accident rate is higher, then send major part service personal's flowing to patrol street；

If traffic congestion situation is unimpeded in region, accident rate is moderate, then send approximately half of service personal flowing to patrol street；

If traffic congestion situation is normal in region, accident rate is higher, then send approximately half of service personal flowing to patrol street；

If traffic congestion situation is normal in region, accident rate is moderate, then send approximately half of service personal flowing to patrol street；

If traffic congestion situation heavy congestion in region, then service personal's flowing of minority is sent to patrol street；

If accident rate is relatively low in region, then service personal's flowing of minority is sent to patrol street.

It is preferred that the described traffic fuzzy set in this step is normally made up of with heavy congestion unobstructed, traffic congestion situation；Wherein,

Described unobstructed membership function QA is:

$\begin{array}{cc}QA=\{\stackrel{\&OverBar;}{q},{\mathrm{\&mu;}}_{QA}\left(\stackrel{\&OverBar;}{q}\right)|\stackrel{\&OverBar;}{q}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{QA}\left(\stackrel{\&OverBar;}{q}\right)=\left\{\begin{array}{cc}1& \stackrel{\&OverBar;}{q}<0.2\\ 1-5(\stackrel{\&OverBar;}{q}-0.2)& 0.2\&le;\stackrel{\&OverBar;}{q}\&le;0.4\\ 0& \stackrel{\&OverBar;}{q}\&GreaterEqual;0.4\end{array}\right.\end{array};$

The normal membership function QB of described traffic congestion situation is:

$\begin{array}{cc}QB=\{\stackrel{\&OverBar;}{q},{\mathrm{\&mu;}}_{QB}\left(\stackrel{\&OverBar;}{q}\right)|\stackrel{\&OverBar;}{q}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{QB}\left(\stackrel{\&OverBar;}{q}\right)=\left\{\begin{array}{cc}1-\frac{|\stackrel{\&OverBar;}{q}-0.5|}{0.3}& |\stackrel{\&OverBar;}{q}-0.5|\&le;0.3\\ 0& |\stackrel{\&OverBar;}{q}-0.5|>0.3\end{array}\right.\end{array};$

The membership function QC of described heavy congestion is:

$\begin{array}{cc}QC=\{\stackrel{\&OverBar;}{q},{\mathrm{\&mu;}}_{QC}\left(\stackrel{\&OverBar;}{q}\right)|\stackrel{\&OverBar;}{q}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{QC}\left(\stackrel{\&OverBar;}{q}\right)=\left\{\begin{array}{cc}1& \stackrel{\&OverBar;}{q}<0.6\\ 1-5(0.8-\stackrel{\&OverBar;}{q})& 0.6\&le;\stackrel{\&OverBar;}{q}\&le;0.8\\ 1& \stackrel{\&OverBar;}{q}\&GreaterEqual;0.8\end{array}\right.\end{array}.$

It is preferred that the described accident possibility occurrence fuzzy set in this step is by relatively low, normal and higher composition；Wherein,

Described relatively low membership function PA is:

$\begin{array}{cc}PA=\{\stackrel{\&OverBar;}{p},{\mathrm{\&mu;}}_{PA}\left(\stackrel{\&OverBar;}{p}\right)|\stackrel{\&OverBar;}{p}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{PA}\left(\stackrel{\&OverBar;}{p}\right)=\left\{\begin{array}{cc}1& \stackrel{\&OverBar;}{p}<0.2\\ 1-5(\stackrel{\&OverBar;}{p}-0.2)& 0.2\&le;\stackrel{\&OverBar;}{p}\&le;0.4\\ 0& \stackrel{\&OverBar;}{p}\&GreaterEqual;0.4\end{array}\right.\end{array};$

Described normal membership function PB is:

$\begin{array}{cc}PB=\{\stackrel{\&OverBar;}{p},{\mathrm{\&mu;}}_{PB}\left(\stackrel{\&OverBar;}{p}\right)|\stackrel{\&OverBar;}{p}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{PB}\left(\stackrel{\&OverBar;}{p}\right)=\left\{\begin{array}{cc}1-\frac{|\stackrel{\&OverBar;}{p}-0.5|}{0.3}& |\stackrel{\&OverBar;}{p}-0.5|\&le;0.3\\ 0& |\stackrel{\&OverBar;}{p}-0.5|>0.3\end{array}\right.\end{array};$

Described higher membership function PC is:

$\begin{array}{cc}PC=\{\stackrel{\&OverBar;}{p},{\mathrm{\&mu;}}_{PC}\left(\stackrel{\&OverBar;}{p}\right)|\stackrel{\&OverBar;}{p}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{PC}\left(\stackrel{\&OverBar;}{p}\right)=\left\{\begin{array}{cc}0& \stackrel{\&OverBar;}{p}<0.6\\ 1-5(0.8-\stackrel{\&OverBar;}{p})& 0.6\&le;\stackrel{\&OverBar;}{p}\&le;0.8\\ 1& \stackrel{\&OverBar;}{p}\&GreaterEqual;0.8\end{array}\right.\end{array}.$

Form with major part it is preferred that described tour personnel's proportional die sticks with paste set by about minority, half；Wherein,

The membership function RA of described minority is:

$\begin{array}{cc}RA=\{r,{\mathrm{\&mu;}}_{RA}\left(r\right)|r\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{RA}\left(r\right)=\left\{\begin{array}{cc}1& \stackrel{\&OverBar;}{p}<0.2\\ 1-\frac{10}{3}(\stackrel{\&OverBar;}{p}-0.2)& 0.2\&le;\stackrel{\&OverBar;}{p}\&le;0.5\\ 0& \stackrel{\&OverBar;}{p}\&GreaterEqual;0.5\end{array}\right.\end{array};$

Membership function RB about described half is:

$\begin{array}{cc}RB=\{r,{\mathrm{\&mu;}}_{RB}\left(r\right)|r\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{RB}\left(r\right)=\left\{\begin{array}{cc}1-\frac{|r-0.5|}{0.2}& |\stackrel{\&OverBar;}{p}-0.5|\&le;0.2\\ 0& |\stackrel{\&OverBar;}{p}-0.5|>0.2\end{array}\right.\end{array};$

Described most membership function RC is:

$\begin{array}{cc}RC=\{r,{\mathrm{\&mu;}}_{RC}\left(r\right)|r\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{RC}\left(r\right)=\left\{\begin{array}{cc}0& \stackrel{\&OverBar;}{p}<0.5\\ 1-\frac{10}{3}(0.8-r)& 0.5\&le;\stackrel{\&OverBar;}{p}\&le;0.8\\ 1& \stackrel{\&OverBar;}{p}\&GreaterEqual;0.8\end{array}\right.\end{array}.$

Step 104, for each described ready position, patrol street ratio and the described service personal's number that is in danger according to corresponding described flowing, the service personal that is in danger carries out patrolling street in the region of being in danger that this ready position is corresponding in scheduling.

Step 105, when having an accident in described traffic programme region, scheduling the distance nearest personnel that are in danger in accident location process described accident.

Be can be seen that by such scheme, the present invention is by based on accident generation historical data in traffic programme region and congestion in road historical data, according to guaranteeing that accident is in danger optimum selection principle service time, select to be in danger the ready position of service personal, and utilize fuzzy reasoning method, street number is patrolled more less and the more many principle of accident rate Yue Gaoxun street number according to the more poor flowing of road conditions, determine that street ratio is patrolled in the flowing of the service personal that is in danger arranged on each described ready position, can effectively shorten service response time of being in danger, ensure to be in danger the work efficiency of service personal, improve dispatching efficiency.

In sum, these are only presently preferred embodiments of the present invention, be not intended to limit protection scope of the present invention.All within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention.

Claims (7)

1. an accident is in danger the dispatching method of service personal, it is characterised in that including:

According to accident generation historical data in traffic programme region and congestion in road historical data, for each section in described traffic programme region and crossing, it is determined that accident possibility occurrence index and congestion in road index；

Transportation network according to described accident possibility occurrence index and described traffic programme region, according to guaranteeing that accident is in danger optimum selection principle service time, from candidate's ready position, select the ready position for arranging the service personal that is in danger, and determine be in danger region and the service personal's number that is in danger that each described ready position is corresponding；

According to described accident possibility occurrence index and described congestion in road index, utilize fuzzy reasoning method, street number is patrolled more less and the more many principle of accident rate Yue Gaoxun street number, it is determined that street ratio is patrolled in the flowing of the service personal that is in danger arranged on each described ready position according to the more poor flowing of road conditions；

For each described ready position, patrol street ratio and the described service personal's number that is in danger according to corresponding described flowing, dispatch the service personal that is in danger and carry out patrolling street in the region of being in danger that this ready position is corresponding；

When having an accident in described traffic programme region, described accident is processed by the personnel that are in danger dispatching distance accident location nearest.

2. method according to claim 1, it is characterised in that the described ready position selected from candidate's ready position for arranging the service personal that is in danger, and determine each described ready position corresponding be in danger region and the service personal's number that is in danger includes:

Described candidate's ready position is divided into w group, and wherein, often group comprises m candidate's ready position, and the ready position that m is default arranges quantity；

For often organizing candidate ready position Y, within the scope of this group candidate's ready position, for each crossing x in described traffic programme region_i, calculate the distance Dist (x between nearest candidate's ready position in this crossing and this group_i,Y)；According to described Dist (x_i, Y) and crossing x_iDescribed accident possibility occurrence index p_i, according to D (X, Y)=Max (p_i×Dist(x_i, Y)), i ∈ 1,2 ..., and n}, calculate worst incident response ability value D (X, Y) corresponding to this group candidate ready position Y；Wherein, n is the crossing sum in described traffic programme region；

Described D (X, Y) according to all groups of candidate's ready positions, selects one group of minimum for D (X, Y) candidate ready position Y^*As the described ready position for arranging the service personal that is in danger；

For described Y^*In each ready positionAccording toCalculate the service personal number C that is in danger that ready position is corresponding_j, and will meet:Region corresponding to all crossings, as this ready positionRegion of being in danger；Wherein, round [] is downward bracket function, and g (α, β) is discriminant function, as α=β, g (α, β)=1, otherwise, and g (α, β)=0.

3. method according to claim 1, it is characterised in that the described flowing determining the service personal that is in danger arranged on each described ready position is patrolled street ratio and included:

For each described ready position, the average of the described accident possibility occurrence index in each section this ready position being in danger in region, it is determined that for accident possibility occurrence index in the region of this ready position；The average of the described congestion in road index in each section this ready position being in danger in region, it is determined that for congestion in road index in the region of this ready position；

For each described ready position, according to accident possibility occurrence index in described region and congestion in road index in described region, stick with paste the membership function of each element in set according to the membership function of each element in the membership function of each element, default accident possibility occurrence fuzzy set in default fuzzy rule, default traffic fuzzy set and default personnel's proportional die of making an inspection tour, adopt Mamdani inference method to make inferences；And utilizing centroid method that described the reasoning results is carried out de-fuzzy process, street ratio is patrolled in the flowing of the service personal that is in danger obtaining this ready position.

4. method according to claim 3, it is characterised in that described fuzzy rule includes:

If traffic congestion situation is unimpeded in region, accident rate is higher, then send major part service personal's flowing to patrol street；

If traffic congestion situation is unimpeded in region, accident rate is moderate, then send approximately half of service personal flowing to patrol street；

If traffic congestion situation is normal in region, accident rate is higher, then send approximately half of service personal flowing to patrol street；

If traffic congestion situation is normal in region, accident rate is moderate, then send approximately half of service personal flowing to patrol street；

If traffic congestion situation heavy congestion in region, then service personal's flowing of minority is sent to patrol street；

If accident rate is relatively low in region, then service personal's flowing of minority is sent to patrol street.

5. method according to claim 3, it is characterised in that described traffic fuzzy set is normally made up of with heavy congestion unobstructed, traffic congestion situation；Wherein,

Described unobstructed membership function QA is:

$\begin{array}{cc}QA=\{\stackrel{\&OverBar;}{q},{\mathrm{\&mu;}}_{QA}\left(\stackrel{\&OverBar;}{q}\right)|\stackrel{\&OverBar;}{q}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{QA}\left(\stackrel{\&OverBar;}{q}\right)=\left\{\begin{array}{cc}1& \stackrel{\&OverBar;}{q}<0.2\\ 1-5(\stackrel{\&OverBar;}{q}-0.2)& 0.2\&le;\stackrel{\&OverBar;}{q}\&le;0.4\\ 0& \stackrel{\&OverBar;}{q}\&GreaterEqual;0.4\end{array}\right.\end{array};$

The normal membership function QB of described traffic congestion situation is:

$\begin{array}{cc}QB=\{\stackrel{\&OverBar;}{q},{\mathrm{\&mu;}}_{QB}\left(\stackrel{\&OverBar;}{q}\right)|\stackrel{\&OverBar;}{q}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{QB}\left(\stackrel{\&OverBar;}{q}\right)=\left\{\begin{array}{cc}1-\frac{|\stackrel{\&OverBar;}{q}-0.5|}{0.3}& |\stackrel{\&OverBar;}{q}-0.5|\&le;0.3\\ 0& |\stackrel{\&OverBar;}{q}-0.5|>0.3\end{array}\right.\end{array};$

The membership function QC of described heavy congestion is:

$\begin{array}{cc}QC=\{\stackrel{\&OverBar;}{q},{\mathrm{\&mu;}}_{QC}\left(\stackrel{\&OverBar;}{q}\right)|\stackrel{\&OverBar;}{q}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{QC}\left(\stackrel{\&OverBar;}{q}\right)=\left\{\begin{array}{cc}0& \stackrel{\&OverBar;}{q}<0.6\\ 1-5\left(0.8-\stackrel{\&OverBar;}{q}\right)& 0.6\&le;\stackrel{\&OverBar;}{q}\&le;0.8\\ 1& \stackrel{\&OverBar;}{q}\&GreaterEqual;0.8\end{array}\right.\end{array}.$

6. method according to claim 3, it is characterised in that described accident possibility occurrence fuzzy set by relatively low, normal and higher composition；Wherein,

Described relatively low membership function PA is:

$\begin{array}{cc}PA=\{\stackrel{\&OverBar;}{p},{\mathrm{\&mu;}}_{PA}\left(\stackrel{\&OverBar;}{p}\right)|\stackrel{\&OverBar;}{p}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{PA}\left(\stackrel{\&OverBar;}{p}\right)=\left\{\begin{array}{cc}1& \stackrel{\&OverBar;}{p}<0.2\\ 1-5(\stackrel{\&OverBar;}{p}-0.2)& 0.2\&le;\stackrel{\&OverBar;}{p}\&le;0.4\\ 0& \stackrel{\&OverBar;}{p}\&GreaterEqual;0.4\end{array}\right.\end{array};$

Described normal membership function PB is:

$\begin{array}{cc}PB=\{\stackrel{\&OverBar;}{p},{\mathrm{\&mu;}}_{PB}\left(\stackrel{\&OverBar;}{p}\right)|\stackrel{\&OverBar;}{p}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{PB}\left(\stackrel{\&OverBar;}{p}\right)=\left\{\begin{array}{cc}1-\frac{|\stackrel{\&OverBar;}{p}-0.5|}{0.3}& |\stackrel{\&OverBar;}{p}-0.5|\&le;0.3\\ 0& |\stackrel{\&OverBar;}{p}-0.5|>0.3\end{array}\right.\end{array};$

Described higher membership function PC is:

$\begin{array}{cc}PC=\{\stackrel{\&OverBar;}{p},{\mathrm{\&mu;}}_{PC}\left(\stackrel{\&OverBar;}{p}\right)|\stackrel{\&OverBar;}{p}\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{PC}\left(\stackrel{\&OverBar;}{p}\right)=\left\{\begin{array}{cc}0& \stackrel{\&OverBar;}{p}<0.6\\ 1-5(0.8-\stackrel{\&OverBar;}{p})& 0.6\&le;\stackrel{\&OverBar;}{p}\&le;0.8\\ 1& \stackrel{\&OverBar;}{p}\&GreaterEqual;0.8\end{array}\right.\end{array}.$

7. method according to claim 3, it is characterised in that described tour personnel's proportional die is stuck with paste set and formed with major part by about minority, half；Wherein,

The membership function RA of described minority is:

$\begin{array}{cc}RA=\{r,{\mathrm{\&mu;}}_{RA}\left(r\right)|r\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{RA}\left(r\right)=\left\{\begin{array}{cc}1& \stackrel{\&OverBar;}{p}<0.2\\ 1-\frac{10}{3}(\stackrel{\&OverBar;}{p}-0.2)& 0.2\&le;\stackrel{\&OverBar;}{p}\&le;0.5\\ 0& \stackrel{\&OverBar;}{p}\&GreaterEqual;0.5\end{array}\right.\end{array};$

Membership function RB about described half is:

$\begin{array}{cc}RB=\{r,{\mathrm{\&mu;}}_{RB}\left(r\right)|r\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{RB}\left(r\right)=\left\{\begin{array}{cc}1-\frac{|r-0.5|}{0.2}& |\stackrel{\&OverBar;}{p}-0.5|\&le;0.2\\ 0& |\stackrel{\&OverBar;}{p}-0.5|>0.2\end{array}\right.\end{array};$

Described most membership function RC is:

$\begin{array}{cc}RC=\{r,{\mathrm{\&mu;}}_{RC}\left(r\right)|r\&Element;\&lsqb;0,1\&rsqb;\}& {\mathrm{\&mu;}}_{RC}\left(r\right)=\left\{\begin{array}{cc}0& \stackrel{\&OverBar;}{p}<0.5\\ 1-\frac{10}{3}(0.8-r)& 0.5\&le;\stackrel{\&OverBar;}{p}\&le;0.8\\ 1& \stackrel{\&OverBar;}{p}\&GreaterEqual;0.8\end{array}\right.\end{array}.$