CN105628033A - Map matching method based on road connection relationship - Google Patents

Abstract

The invention provides a map matching method based on a road connection relationship. The method comprises the steps that S1, GPS positioning data is preprocessed; S2, road matching rules are established according to the distance factor and the direction factor, then a candidate matching road set is obtained, similarity between a GPS track and candidate matching roads is calculated based on road connection, and then the candidate matching road most similar to the GPS track is selected as the matching road of the GPS track; S3, the GPS track is subjected to coordinate correction. According to the method, corresponding detection and processing methods are proposed based on error data of different error types, and therefore high efficiency and accuracy of the map matching method are ensured; correlation between historical data is considered, and the influence of a certain point with a large error on direction calculation is lowered; constraint of historical matching information is also considered, so that the accuracy rate of the matching result is high; compared with a model in the prior art, the overall technical scheme is simpler, and continuity and the matching effect are better.

Description

A kind of map-matching method being connected relation based on road

Technical field

The present invention relates to a kind of map-matching method, particularly relate to a kind of map-matching method being connected relation based on road.

Background technology

In recent years, satellite-navigation technology, especially Global Positioning System (GlobalPositioningSystem, GPS) being widely applied in road traffic location, the utilization of GPS in traffic location comprises path navigation, vehicle scheduling, accident emergency reaction, location tracking and traffic information acquisition etc. Along with these are based on location technology location-based service (LocationBasedService, LBS) fast development, a large amount of vehicle track data constantly produce by, these track datas are analyzed, so excavate useful information, better provide Information services be current one research focus.

But, owing to GPS positioning data often exists error, often there is certain deviation with actual geographic position in GPS positioning result, this causes difficulty to location Based service, therefore, needing taking the relatively high electronics map of precision as benchmark, matched by GPS positioning track on road that vehicle truly runs, this is exactly map match.

Through the analysis to data with existing, GPS positioning error data mainly show as following three kinds of forms: one, data redundancy, when vehicle is in a certain position during static or low cruise, vehicle can record the identical GPS point of a series of coordinate in same place, the locating information of these redundancies can affect the effect of map match, sometimes even there will be mistake situation; Two, data disappearance, when occurring that signal shielding, signal are bad, cold start-up, instrument failure, power supply exhaust, the situation such as mishandle time, may producing the data disappearance of a time period, data disappearance causes the discontinuous of GPS positioning track, can reduce map match effect equally; Three, data wander, near the starting point, terminal of driving path or in other situations, it is possible to GPS positioning data can be occurred not meet the phenomenon of logic, serious skew vehicle physical location, i.e. data wander. Drift data is a kind of noise, can map match and display effect be made a big impact.

For above-mentioned gps data error condition, it is necessary to find corresponding treatment process, and the GPS track after process is carried out map match, by GPS track data correction to physical location, to meet the needs of location information service.

At present, achievement in research about map match is existing a lot; There is fairly simple matching process, such as nearest point match method; Also there is complicated matching process, such as fuzzy logic algorithm, method based on cost function, the method based on D-S evidence reasoning and the method etc. based on neural network; Nearest Point matching method judges according to the distance of locating point and road, and simply, easily realize, but matching effect is poor for the method; The main thought of weighting factor algorithm, fuzzy logic algorithm and pattern recognition algorithm finds the matching result of the road maximum with GPS positioning track similarity as this track in electronic map road net, although the effect of these methods has a distinct increment compared with nearest Point matching method, but model is comparatively complicated, system-computed expense is bigger.

Summary of the invention

Technical problem to be solved by this invention needs to provide a kind of model comparatively simple, and continuity is good and the better map-matching method of matching effect.

To this, the present invention provides a kind of map-matching method being connected relation based on road, comprises the following steps:

Step S1, carries out pre-treatment to GPS positioning data;

Step S2, road coupling rule is set up according to two, Distance geometry direction factor, and then obtain candidate matches road set, calculate the similarity between GPS track and candidate matches road based on road path connectedness, and then select similarity the maximum as the coupling road of GPS track;

Step S3, carries out coordinate adjustment to GPS track.

Further improvement of the present invention is, in described step S1, carries out removing or interpolation operation respectively for data redundancy, data disappearance and data wander, it is achieved to the pre-treatment of GPS positioning data.

Further improvement of the present invention is, in described step S1, when GPS positioning data exists repetition phenomenon, the GPS positioning data that will repeat is deleted; When vehicle stops and producing the identical GPS positioning data of more than two position coordinates, then retain the GPS positioning data of nearest time point and record this vehicle stand-by time; When lacking GPS positioning data between two adjacent GPS point, carry out interpolation processing; When detecting out drift data, drift data is carried out delete processing.

Further improvement of the present invention is, if the time difference �� t between two adjacent GPS point_pFormula �� t is met with sampling interval �� t_p=t_i-t_i+1 �� �� t, then represent to there is missing data between these two adjacent GPS point, and then according to formula $\left\{\begin{array}{c}{x}_{i_n}=\frac{\mathrm{n\Δt}(x_{i+1}-x_i)}{{\mathrm{\Δt}}_p}\\ y_{i_n}=\frac{\mathrm{n\Δt}(y_{i+1}-y_i)}{{\mathrm{\Δt}}_p}\end{array}\right.(n=1,...\frac{{\mathrm{\Δt}}_p}{\mathrm{\Δt}}-1)$ Carry out interpolation processing; For the trilateral �� ABC that three continuous GPS point A, B and C are formed, the area s of this trilateral �� ABC isWherein, p is the half of trilateral �� ABC girth, and a, b and c are respectively three length of sides of trilateral �� ABC, if GPS point is positioned at the B point of trilateral �� ABC, and B point is satisfied to the distance of line between 2, its adjacent front and back A point and C pointWherein, d_errorWhat represent is the maximum value allowing skew, and this allows that the maximum value of skew is equipment positioning precision and the road width sum of GPS point, now, shows that B point is for drift point, then delete the GPS positioning data of this point.

Further improvement of the present invention is, in described step S2, set up road coupling rule for carrying out, according to two, Distance geometry direction factor, the structure that topology relation is connected relation with section according to two, Distance geometry direction factor, and the topological relation between the section connection relation of structure and dotted line is saved in serializing file.

Further improvement of the present invention is, described step S2 also comprises following sub-step:

Step S201, by GPS positioning error probability distribution and road width factor miscalculation region, gathers the candidate matches section of all sections dropped in error band as this GPS point;

Step S202, is marked to candidate matches section by the distance between GPS point and section and the direction difference between GPS track and section, and the integrate score in candidate matches section is distance score and direction score sum;

Step S203, is connected relation by the matching result of a upper GPS point with road, selects Optimum Matching road gathering from the candidate matches section marked.

Further improvement of the present invention is, in described step S201, by formula R=r+w miscalculation region, and then all sections obtained in border circular areas that radius is R are as the candidate matches road set of this GPS point, and wherein, w is road width, $r=c=d={\widehat{\mathrm{\δ}}}_0\sqrt{\frac{1}{2({\mathrm{\δ}}_x^2+{\mathrm{\δ}}_y^2)}}={\widehat{\mathrm{\δ}}}_0\·{\mathrm{\δ}}_x={\widehat{\mathrm{\δ}}}_0\·{\mathrm{\δ}}_y,$ R is the radius calculation value of candidate matches road set, c and d represents the axial length of error ellipse respectively,WithFor the standard value of positioning error,For the initial value of positioning error; And each parameter of error ellipse is: $c=\widehat{\mathrm{\δ}}\sqrt{\frac{{\mathrm{\δ}}_x^2+v_y^2+\sqrt{{\mathrm{\δ}}_x^2-{\mathrm{\δ}}_y^2+4{\mathrm{\δ}}_{xy}^2}}{2}},$ $d=\widehat{\mathrm{\δ}}\sqrt{\frac{{\mathrm{\δ}}_x^2+{\mathrm{\δ}}_y^2-\sqrt{{\mathrm{\δ}}_x^2+{\mathrm{\δ}}_y^2+4{\mathrm{\δ}}_{xy}^2}}{2}}$ With $\mathrm{\θ}=\frac{\mathrm{\π}}{2}-\frac{1}{2}arctan\left(\frac{2{\mathrm{\δ}}_{xy}}{{\mathrm{\δ}}_x^2-{\mathrm{\δ}}_y^2}\right),$ Wherein, �� represents the angle of error ellipse major axis and y-axis,The posteriori error of representation unit weights,Represent covariance.

Further improvement of the present invention is, in described step S202, if the vertical projection point Q' of GPS point Q to section AB drops on this section, then the distance in this GPS point and section is the distance d between Q and Q' (Q, Q '); Otherwise, for GPS point is to the smaller of this section two-end-point distance; Pass through W_dRepresent distance score, then GPS point p_iWith target road section e_jDistance must be divided into:Wherein ��=0, �� equals candidate roads zone radius R; Its direction score passes through formula W_a=cos �� can obtain, and wherein, note v is direction of vehicle movement, and g is road direction mark, and g=0 represents two-way passing road, and g=1 represents along GPS point number order one-way trip, and g=2 represents along GPS point number order backward one-way trip, p_iAnd p_i+1Be respectively the rear and front end point of section to be matched along GPS point numbering direction, then vehicle operating direction and road direction angle �� are:

Further improvement of the present invention is, in described step S203, by formula W=k_d��W_d+k_����W_aThe integrate score in calculated candidate coupling section, wherein, k_dFor distance weighting, k_aFor direction weight, $\left\{\begin{array}{cc}if(v<5),& k_d=0.4,k_{\mathrm{\&alpha;}}=0.6\\ else,& k_d=0.6,k_{\mathrm{\&alpha;}}=0.4\end{array}\right..$

Further improvement of the present invention is, in described step S3, the process that realizes that GPS track carries out coordinate adjustment is: when the subpoint of the fitting a straight line end points of GPS track drops on candidate matches section, then distance between the projection of this fitting a straight line end points is part to be matched; If outside the subpoint of fitting a straight line end points drops on candidate matches section, then the corresponding end points in candidate matches section should being selected to mate, the coordinate that coordinate adjustment process obtains after the geometric transformation of translation, Rotation and Zoom is final matching results.

Compared with prior art, the useful effect of the present invention is: the wrong type summarizing data redundancy in GPS positioning data, data disappearance and data wander, and wrong data for different mistake type propose corresponding detection and treatment method respectively, ensure that the high efficiency of described map-matching method and accuracy; And use least square fitting linear method when obtaining direction of vehicle movement, both considered the dependency of historical data, and again reduced the bigger point of indivedual error to the impact of direction calculating; On this basis, it is also contemplated that the binding character of history match information, therefore matching result accuracy rate is higher, and overall technical architecture is more simpler than the model of prior art, continuity and matching effect are better.

Accompanying drawing explanation

Fig. 1 is the workflow schematic diagram of an embodiment of the present invention;

The detailed operation schematic flow sheet of Fig. 2 an embodiment of the present invention;

Fig. 3 is the recognition methods schematic diagram of the GPS drift data of an embodiment of the present invention;

The road that Fig. 4 is an embodiment of the present invention is connected relation schematic diagram;

Fig. 5 is the error ellipse schematic diagram of an embodiment of the present invention;

Fig. 6 is the schematic diagram of the acquisition candidate matches road set of an embodiment of the present invention;

Fig. 7 is the least square fitting straight line schematic diagram of an embodiment of the present invention;

Fig. 8 is the calculating section conversion probability process schematic diagram of an embodiment of the present invention;

Fig. 9 is the result schematic diagram of the calculating section conversion probability of an embodiment of the present invention;

Figure 10 is the coordinate adjustment schematic diagram of an embodiment of the present invention;

Figure 11 is the analogous diagram of the Optimum Matching road that an embodiment of the present invention obtains by mating when choosing simple road;

Figure 12 is the emulation result figure after GPS track is corrected to Optimum Matching road when choosing simple road by an embodiment of the present invention;

Figure 13 is the analogous diagram of the Optimum Matching road that an embodiment of the present invention obtains by mating when choosing complicated road;

Figure 14 is the emulation result figure after GPS track is corrected to Optimum Matching road when choosing complicated road by an embodiment of the present invention;

The experimental result analogous diagram of geometrical similarity matching process in Figure 15 prior art;

Figure 16 is the experimental result analogous diagram of the map match of an embodiment of the present invention.

Embodiment

Below in conjunction with accompanying drawing, the preferably embodiment of the present invention is described in further detail.

As depicted in figs. 1 and 2, this example provides a kind of map-matching method being connected relation based on road, comprises the following steps:

Step S1, carries out pre-treatment to GPS positioning data;

Step S2, road coupling rule is set up according to two, Distance geometry direction factor, and then obtain candidate matches road set, calculate the similarity between GPS track and candidate matches road based on road path connectedness, and then select similarity the maximum as the coupling road of GPS track;

Step S3, carries out coordinate adjustment to GPS track, for GPS track is corrected to the coupling road of correspondence according to dependency rule.

In this routine described step S1, carry out removing or interpolation operation respectively for data redundancy, data disappearance and data wander, it is achieved to the pre-treatment of GPS positioning data; In described step S1, when GPS positioning data exists repetition phenomenon, the GPS positioning data that will repeat is deleted; When vehicle stops and producing the identical GPS positioning data of more than two position coordinates, then retain the GPS positioning data of nearest time point and record this vehicle stand-by time; When lacking GPS positioning data between two adjacent GPS point, carry out interpolation processing; When detecting out drift data, drift data is carried out delete processing.

The GPS positioning data of GPS track is carried out pre-treatment by described step S1, imports in database by GPS warehouse-in instrument by original positioning data and builds index in a database, to improve efficiency data query.

If the time difference �� t between two adjacent GPS point_pFormula �� t is met with sampling interval �� t_p=t_i-t_i+1�� �� t, then represent to there is missing data between these two adjacent GPS point, and then according to formula $\left\{\begin{array}{c}{x}_{i_n}=\frac{n\mathrm{\&Delta;}t(x_{i+1}-x_i)}{{\mathrm{\&Delta;t}}_p}\\ y_{i_n}=\frac{n\mathrm{\&Delta;}t(y_{i+1}-y_i)}{{\mathrm{\&Delta;t}}_p}\end{array}\right.,(n=$ $1,...\frac{{\mathrm{\&Delta;t}}_p}{\mathrm{\&Delta;}t}-1)$ Carry out interpolation processing.

As shown in Figure 3, for the trilateral �� ABC that three continuous GPS point A, B and C are formed, the area s of this trilateral �� ABC isWherein, p is the half of trilateral �� ABC girth, and a, b and c are respectively three length of sides of trilateral �� ABC, if GPS point is positioned at the B point of trilateral �� ABC, and B point is satisfied to the distance of line between 2, its adjacent front and back A point and C pointWherein, d_errorWhat represent is the maximum value allowing skew, and this allows that the maximum value of skew is equipment positioning precision and the road width sum of GPS point, now, shows that B point is for drift point, then delete the GPS positioning data of this point.

In this routine described step S2, set up road coupling rule for carrying out, according to two, Distance geometry direction factor, the structure that topology relation is connected relation with section according to two, Distance geometry direction factor, and the topological relation between the section connection relation of structure and dotted line is saved in serializing file.

Relation builds the topological relation that this routine described step S2 carries out road network with being connected, and formation sequence file; According to the connection relation of road network intermediate node and line, build dotted line topology relation; As shown in Figure 4, according to the connection relation between section, build 1,2,3 grade of run-through large space, connection property uses road expansion rank to measure, being expanded road itself is 0 grade of connected set C0, its expansion one-level can obtain 1 grade of connected set C1, continue expansion can obtain 2 grades connected set C2 ..., n level connected set Cn. By to gps data analysis, build between section the requirement that 3 grades of run-through large space can meet map match. The topological relation being connected in the section of structure between relation and dotted line is saved in serializing file, just serializing file can be directly read like this when needing the topological relation using between road network, avoid again building topology relation every time and being connected relation, to improve matching efficiency.

Road is chosen and is also comprised following sub-step by this routine described step S2:

Step S201, by GPS positioning error probability distribution and road width factor miscalculation region, gathers the candidate matches section of all sections dropped in error band as this GPS point;

Step S202, is marked to candidate matches section by the distance between GPS point and section and the direction difference between GPS track and section, and the integrate score in candidate matches section is distance score and direction score sum;

Step S203, is connected relation by the matching result of a upper GPS point with road, selects Optimum Matching road gathering from the candidate matches section marked.

This routine described step S201 is for obtaining candidate matches road set, road net One's name is legion, it is impossible to by alternatively section, all sections, it is thus desirable to formulate Rules Filtering to go out possible candidate roads set. Owing to GPS positioning error meets probability-statistics rule, positioning data, all the time centered by physical location, is distributed in an error ellipse region, and as shown in Figure 5, according to Probability Statistics Theory, error ellipse parameter is: $c=\widehat{\mathrm{\&delta;}}\sqrt{\frac{{\mathrm{\&delta;}}_x^2+{\mathrm{\&delta;}}_y^2\sqrt{{\mathrm{\&delta;}}_x^2-{\mathrm{\&delta;}}_y^2+4{\mathrm{\&delta;}}_{xy}^2}}{2}},$ $d=\widehat{\mathrm{\&delta;}}\sqrt{\frac{v_x^2+{\mathrm{\&delta;}}_y^2-\sqrt{{\mathrm{\&delta;}}_x^2+{\mathrm{\&delta;}}_y^2+4{\mathrm{\&delta;}}_{xy}^2}}{2}}$ With $\mathrm{\&theta;}=\frac{\mathrm{\&pi;}}{2}-\frac{1}{2}arctan\left(\frac{2{\mathrm{\&delta;}}_{xy}}{{\mathrm{\&delta;}}_x^2-{\mathrm{\&delta;}}_y^2}\right),$ Wherein, c and d represents the axial length of error ellipse respectively,WithFor the standard value of positioning error, �� represents the angle of error ellipse major axis and y-axis,The posteriori error of representation unit weights,Represent covariance.

Assume that space two-dimensional coordinate x and y that station-keeping system obtains is separate, and x direction is identical with the variance in y direction, then elliptic region turns into border circular areas, and its radius formula is calculated by following formula: In addition, it is contemplated that to the impact of road width factor, candidate matches road area radius R value is R=r+w, and wherein, w is road width, and r is the radius calculation value of candidate matches road set,For the initial value of positioning error, the initial value of this positioning error is distinguished to some extent according to different position determining equipments, it is possible to measured by reality and checking is done difference and obtained.

Above-mentioned candidate matches road area radius R value has considered GPS positioning error probability distribution and road width factor, drops on centered by GPS point, and R is all sections in the border circular areas of radius is the candidate road section set of this GPS point; The quantity of candidate road section can be greatly reduced like this, it is to increase matching efficiency. As shown in Figure 6, section l1, l2, l₃Dropping on taking GPS point pi as the center of circle, R is in the circle of radius, therefore, and l₁��l₂��l₃For p_iCandidate matches road.

This routine described step S202 is for marking to candidate matches road, owing to the distance of GPS point and section is more little, between GPS track and section, angle is more little, then this section is that the possibility in GPS track coupling section is more big, therefore can be marked to candidate matches road by GPS point and the distance in section and the direction difference between GPS track and section.

The distance of GPS point and section is had to give a definition: if the vertical projection point Q' of GPS point Q to section AB drops on this section, then the distance in this GPS point and section is the distance d between Q and Q' (Q, Q '); Otherwise, for GPS point is to the smaller of this section two-end-point distance.

Pass through $d(Q,AB)=\left\{\begin{array}{c}d(Q,Q^{\&prime;}),Q^{\&prime;}\&Element;AB\\ \min \{d\left(Q,A\right),d\left(Q,B\right)\},others\end{array}\right.$ It will be seen that be the probability density function that the relation between the possibility of GPS point match objects meets normal distribution according to the distance of data analysis, GPS point and target road section and this section, therefore, distance score can represent with the probability density function of normal distribution.

Pass through W_dRepresent distance score, then GPS point p_iWith target road section e_jDistance must be divided into: Wherein ��=0, �� equals candidate roads zone radius R.

Direction score described in this example passes through formula W_a=cos �� can obtain, and wherein, note v is direction of vehicle movement, and g is road direction mark, and g=0 represents two-way passing road, and g=1 represents along GPS point number order one-way trip, and g=2 represents along GPS point number order backward one-way trip, p_iAnd p_i+1Be respectively the rear and front end point of section to be matched along GPS point numbering direction, then vehicle operating direction and road direction angle �� are:

Most map-matching method all using GPS point course data or by the direction of consecutive point line as direction of vehicle movement. But, single GPS point course may be subject to the impact of the situation such as inertia or sideslip; Adjacent GPS point line only determines direction of motion according to two points, and accuracy depends on positioning data precision. In view of the foregoing, the present invention adopts least square fitting straight-line method to obtain direction of vehicle movement, and as shown in Figure 7, the direction of the straight line obtained using linear least squares fit is as direction of vehicle movement. This kind of method had both taken the dependency of historical data into account, turn avoid the impact that indivedual error is more a little bigger.

As shown in Figure 7, so-called least square fitting straight line, by a series of measurement point p₁(x₁,y₁)��p₂(x₂,y₂)������p_n(x_n,y_n) track replace with a straight line y=ax+b is approximate. Wherein the calculation formula of a, b is as follows: $\left\{\begin{array}{c}a=\frac{{\mathrm{\&Sigma;}}_{i=1}^n{x}_i{\mathrm{\&Sigma;}}_{i=1}^n{x}_i{y}_i-{\mathrm{\&Sigma;}}_{i=1}^n{y}_i{\mathrm{\&Sigma;}}_{i=1}^n{x}_i^2}{{\left({\mathrm{\&Sigma;}}_{i=1}^n{x}_i\right)}^2-{\mathrm{n\&Sigma;}}_{i=1}^n{x}_i^2}\\ b=\frac{{\mathrm{\&Sigma;}}_{i=1}^n{x}_i{\mathrm{\&Sigma;}}_{i=1}^n{y}_i-{\mathrm{n\&Sigma;}}_{i=1}^n{x}_i{y}_i}{{\left({\mathrm{\&Sigma;}}_{i=1}^n{x}_i\right)}^2-{\mathrm{n\&Sigma;}}_{i=1}^n{x}_i^2}\end{array}\right.;$ Straight line y=ax+b along the direction of GPS point number order namely as the direction of vehicle movement.

Direction of vehicle movement and candidate roads direction are more close, and the possibility of its coupling is then more big, and namely vehicle movement direction then corresponding direction fractional value more little of road direction angle is more big. Angle scope be 0 to 180 spend time, cosine function character meets the feature of this direction score just. When having multiple section in the conversion section at intersection mouth place or turning, owing to vehicle movement direction is relatively big to the matching constraint of GPS point, the weight that therefore direction score accounts for also should be bigger.

In this routine described step S203, by formula W=k_d��W_d+k_����W_aThe integrate score in calculated candidate coupling section, wherein, k_dFor distance weighting, k_��For direction weight, $\left\{\begin{array}{cc}if(v<5),& k_d=0.4,k_{\mathrm{\&alpha;}}=0.6\\ else,& k_d=0.6,k_{\mathrm{\&alpha;}}=0.4\end{array}\right..$

If Current GPS point is in simple straight road, then distance weighting k_dBigger; If GPS point is in intersection mouth, turning or other are relatively in Complicated Road Network, then D-factor is relatively big to the matching constraint of GPS point, direction weight k_��Should be bigger.

Owing to, in section crossing, turning or other Complicated Road Networks, generally there are three kinds of running statuses in vehicle: stop wait, keep straight on by, turn round. Now in three kinds of situations, the speed of vehicle is all less, therefore by judging that speed value carries out weight coefficient assignment (speed threshold value is set to 5m/s) herein: $\left\{\begin{array}{cc}if(v<5),& k_d=0.4,k_{\mathrm{\&alpha;}}=0.6\\ else,& k_d=0.6,k_{\mathrm{\&alpha;}}=0.4\end{array}\right.,$ The integrate score calculated accordingly is more high, and the possibility that this candidate roads is GPS track match objects is more big.

This routine described step S203 is used for realizing route and chooses. Consider that the matching result of a upper GPS point is connected sexual factor with road, concentrate from the candidate matches road marked and select Optimum Matching road. As shown in Figure 8 and Figure 9, concrete method of calculation are as follows.

The first, conversion probability in section between road run-through large space; Owing to vehicle operating track has time and space continuity feature, so the coupling section corresponding to adjacent GPS point should have connection relation, therefore conversion probability between adjacent GPS point candidate roads can be weighed with the topology connectivity between road. This kind of connection relation can use road expansion rank to measure.

The floating car data sampling time interval that this example uses is 40s, and according to the analysis to experimental data, most of adjacent GPS point is on same or one-level connection road, and part is present in two grades of roads, is greater than three grades and is connected the less of roads. Therefore conversion probability between adjacent segments is set as follows: current road is connected with next road zero level, and probability is 1, and one-level connected probability 0.9, two grades of connected probability 0.8, are more than or equal to three grades and are connected, and probability is 0.4; Change probability between road run-through large space to represent and be: $T(e_{i-1}^j\&RightArrow;e_i^k)=\left\{\begin{array}{c}1,r\&Element;C_0\\ 0.9,r\&Element;C_1\\ 0.8,r\&Element;C_2\\ 0.4,r\&Element;C_3\end{array}\right..$

Two, Optimum Matching path is obtained, order based on integrate score and candidate road section conversion probability For GPS point p_iCandidate roads set,For candidate roadsIntegrate score,Represent p_i-1Jth bar candidate roads is to p_iThe conversion probability of kth bar candidate roads, obtain the Optimum Matching path of vehicle track based on integrate score and candidate roads conversion probability.

If GPS point p_iCorresponding Optimum Matching pathFinal must be divided intoAs i=1,Namely the initially final of path must be divided into section self integrate score), otherwise the product of the conversion probability of the final Optimum Matching path that a GPS point must be divided into corresponding of current path candidate to current path candidate and current path candidate integrate score, namely $W_i^k\&CenterDot;T(e_{i-1}^j\&RightArrow;e_i^k).$

The final score the maximum in all candidate matches paths is Current GPS point p_iOptimum Matching path be $S\left(e_i^k\right)=$ $max\{W_i^k\&CenterDot;T(e_{i-1}^j\&RightArrow;e_i^k\left)\right\},(i\&NotEqual;0).$

As shown in Figure 10, in this routine described step S3, the process that realizes that GPS track carries out coordinate adjustment is: when the subpoint of the fitting a straight line end points of GPS track drops on candidate matches section, then distance between the projection of this fitting a straight line end points is part to be matched; If outside the subpoint of fitting a straight line end points drops on candidate matches section, then the corresponding end points in candidate matches section should being selected to mate, the coordinate that coordinate adjustment process obtains after the geometric transformation of translation, Rotation and Zoom is final matching results.

In described step 3, GPS point being carried out coordinate adjustment, each GPS point obtains and uniquely mates section, is projected by GPS point, it is possible to obtain the correction point coordinate of GPS point in corresponding road section on this section. Concrete steps are as follows:

Step S301, if GPS track point p₁,p₂,��,p_nFitting a straight line AB terminal A, B subpoint a, b drop on coupling section l on, then line segment ab is part to be matched.

Step S302, if outside a or b drop on l, then should select the corresponding end points of l to mate.

If the original coordinates matrix of GPS track point set P is $P=\left[\begin{array}{ccc}{x}_0& y_0& 1\\ x_1& y_1& 1\\ ...& ...& ...\\ x_n& y_n& 1\end{array}\right],$ Coordinate adjustment process puts T3 tri-geometric transformation through translation T1, rotation T2 and contracting, and transformation matrix is as follows respectively: $T_1=\left[\begin{array}{ccc}1& 0& 0\\ 0& 1& 0\\ x_a-x_A& y_a-y_A& 1\end{array}\right],T_2=\left[\begin{array}{ccc}\cos \mathrm{\&theta;}& \sin \mathrm{\&theta;}& 0\\ -\sin \mathrm{\&theta;}& \cos \mathrm{\&theta;}& 0\\ 0& 0& 1\end{array}\right]$ With $T_3=\left[\begin{array}{ccc}\frac{x_B-x_A}{x_b-x_a}& 0& 0\\ 0& \frac{y_B-y_A}{y_b-y_a}& 0\\ 0& 0& 1\end{array}\right].$

Then GPS track point correct after coordinates matrix $P^{\&prime;}=\left[\begin{array}{ccc}{x}_0^{\&prime;}& y_0^{\&prime;}& 1\\ x_1^{\&prime;}& y_1^{\&prime;}& 1\\ ...& ...& ...\\ x_n^{\&prime;}& y_n^{\&prime;}& 1\end{array}\right]=P\&CenterDot;T_1\&CenterDot;T_2\&CenterDot;T_3,$ P ' is final matching results.

This example summarizes the wrong type of data redundancy in GPS positioning data, data disappearance and data wander, and wrong data for different mistake type propose corresponding detection and treatment method respectively, for the high efficiency and accuracy ensureing described map-matching method provides precondition; And use least square fitting linear method when obtaining direction of vehicle movement, both considered the dependency of historical data, and again reduced the bigger point of indivedual error to the impact of direction calculating; On this basis, it is also contemplated that the binding character of history match information, therefore matching result accuracy rate is higher, and overall technical architecture is more simpler than the model of prior art, continuity and matching effect are better.

Finally, being the interpretation of application the present invention: based on above map-matching algorithm step, this example gives two groups of tests and result has been analyzed.

First group is choose simple road conditions to carry out Matching Experiment, and shown in Figure 11 is for being mated, according to this routine described map-matching method, the optimum road obtained, and shown in Figure 12 is the result after GPS track is corrected to this coupling road by this routine described map-matching method.

Selecting more complicated road conditions to carry out Matching Experiment for 2nd group, mated, according to this routine described map-matching method, the optimum road obtained shown in Figure 13, shown in Figure 14 is the result after GPS track is corrected to this coupling road by this routine described map-matching method.

By test-results it may be seen that simple road conditions and complicated road conditions are all had good matching effect by this routine described map-matching method, all GPS track point can be corrected on correct road. In addition, the matching process experimental result according to traditional geometrical factor similarity it is respectively shown in Figure 15 and Figure 16 and this example is carried the local comparison diagram of map-matching method experimental result. As shown in figure 15, for traditional method experimental result, owing to traditional geometric match method only chooses similarity the maximum as final match objects according to factors such as distance, directions from candidate matches road set, and do not consider that road is connected sexual factor, therefore there will be the illogical situation of matching result. Being the map-matching method experimental result that this example is carried as shown in figure 16, in same link situation, be connected sexual factor owing to this paper method considers road, add the constraint factor of road conversion probability in the matching process, therefore matching result is more accurate.

Above content is in conjunction with concrete preferred implementation further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations. For general technical staff of the technical field of the invention, without departing from the inventive concept of the premise, it is also possible to make some simple deduction or replace, all should be considered as belonging to protection scope of the present invention.

Claims (10)

1. one kind is connected the map-matching method of relation based on road, it is characterised in that, comprise the following steps:

Step S1, carries out pre-treatment to GPS positioning data;

Step S2, road coupling rule is set up according to two, Distance geometry direction factor, and then obtain candidate matches road set, calculate the similarity between GPS track and candidate matches road based on road path connectedness, and then select similarity the maximum as the coupling road of GPS track;

Step S3, carries out coordinate adjustment to GPS track.

2. the map-matching method being connected relation based on road according to claim 1, it is characterised in that, in described step S1, carry out removing or interpolation operation respectively for data redundancy, data disappearance and data wander, it is achieved to the pre-treatment of GPS positioning data.

3. the map-matching method being connected relation based on road according to claim 2, it is characterised in that, in described step S1, when GPS positioning data exists repetition phenomenon, the GPS positioning data that will repeat is deleted; When vehicle stops and producing the identical GPS positioning data of more than two position coordinates, then retain the GPS positioning data of nearest time point and record this vehicle stand-by time; When lacking GPS positioning data between two adjacent GPS point, carry out interpolation processing; When detecting out drift data, drift data is carried out delete processing.

4. the map-matching method being connected relation based on road according to claim 3, it is characterised in that, if the time difference �� t between two adjacent GPS point_pFormula �� t is met with sampling interval �� t_p=t_i-t_i+1�� �� t, then represent to there is missing data between these two adjacent GPS point, and then according to formula $\left\{\begin{array}{c}{x}_{i_n}=\frac{n\mathrm{\&Delta;}t\left(x_{i+1}-x_i\right)}{{\mathrm{\&Delta;t}}_p}\\ y_{i_n}=\frac{n\mathrm{\&Delta;}t\left(y_{i+1}-y_i\right)}{{\mathrm{\&Delta;t}}_p}\end{array}\right.,(n=1,\mathrm{...}\frac{{\mathrm{\&Delta;t}}_p}{\mathrm{\&Delta;}t}-$ $1)$ Carry out interpolation processing; For the trilateral �� ABC that three continuous GPS point A, B and C are formed, the area s of this trilateral �� ABC is $s=\sqrt{p(p-a)(p-b)(p-c)},$ Wherein, p is the half of trilateral �� ABC girth, and a, b and c are respectively three length of sides of trilateral �� ABC, if GPS point is positioned at the B point of trilateral �� ABC, and B point is satisfied to the distance of line between 2, its adjacent front and back A point and C pointWherein, d_errorWhat represent is the maximum value allowing skew, and this allows that the maximum value of skew is equipment positioning precision and the road width sum of GPS point, now, shows that B point is for drift point, then delete the GPS positioning data of this point.

5. according to Claims 1-4 any one based on road be connected relation map-matching method, it is characterized in that, in described step S2, set up road coupling rule for carrying out, according to two, Distance geometry direction factor, the structure that topology relation is connected relation with section according to two, Distance geometry direction factor, and the topological relation between the section connection relation of structure and dotted line is saved in serializing file.

6. the map-matching method being connected relation based on road according to claim 5, it is characterised in that, described step S2 also comprises following sub-step:

Step S201, by GPS positioning error probability distribution and road width factor miscalculation region, gathers the candidate matches section of all sections dropped in error band as this GPS point;

Step S202, is marked to candidate matches section by the distance between GPS point and section and the direction difference between GPS track and section, and the integrate score in candidate matches section is distance score and direction score sum;

Step S203, is connected relation by the matching result of a upper GPS point with road, selects Optimum Matching road gathering from the candidate matches section marked.

7. the map-matching method being connected relation based on road according to claim 6, it is characterized in that, in described step S201, by formula R=r+w miscalculation region, and then all sections obtained in border circular areas that radius is R are as the candidate matches road set of this GPS point, wherein, w is road width R is the radius calculation value of candidate matches road set, c and d represents the axial length of error ellipse respectively,WithFor the standard value of positioning error,For the initial value of positioning error; And each parameter of error ellipse is: $c=\widehat{\mathrm{\&delta;}}\sqrt{\frac{{\mathrm{\&delta;}}_x^2+{\mathrm{\&delta;}}_y^2+\sqrt{{\mathrm{\&delta;}}_x^2-{\mathrm{\&delta;}}_y^2+4{\mathrm{\&delta;}}_{xy}^2}}{2}},d=\widehat{\mathrm{\&delta;}}\sqrt{\frac{{\mathrm{\&delta;}}_x^2+{\mathrm{\&delta;}}_y^2+\sqrt{{\mathrm{\&delta;}}_x^2+{\mathrm{\&delta;}}_y^2+4{\mathrm{\&delta;}}_{xy}^2}}{2}}$ WithWherein, �� represents the angle of error ellipse major axis and y-axis,The posteriori error of representation unit weights,Represent covariance.

8. the map-matching method being connected relation based on road according to claim 7, it is characterized in that, in described step S202, if the vertical projection point Q' of GPS point Q to section AB drops on this section, then the distance in this GPS point and section is the distance d between Q and Q' (Q, Q '); Otherwise, for GPS point is to the smaller of this section two-end-point distance; Pass through W_dRepresent distance score, then the distance of GPS point pi and target road section ej must be divided into: Wherein ��=0, �� equals candidate roads zone radius R; Its direction score passes through formula W_a=cos �� can obtain, and wherein, note v is direction of vehicle movement, and g is road direction mark, and g=0 represents two-way passing road, and g=1 represents along GPS point number order one-way trip, and g=2 represents along GPS point number order backward one-way trip, p_iAnd p_i+1Be respectively the rear and front end point of section to be matched along GPS point numbering direction, then vehicle operating direction and road direction angle �� are:

9. the map-matching method being connected relation based on road according to claim 8, it is characterised in that, in described step S203, by formula W=k_d��W_d+k_����W_aThe integrate score in calculated candidate coupling section, wherein, k_dFor distance weighting, k_��For direction weight, $\left\{\begin{array}{cc}if(v<5),& k_d=0.4,k_{\mathrm{\&alpha;}}=0.6\\ else,& k_d=0.6,k_{\mathrm{\&alpha;}}=0.4\end{array}\right..$

10. the map-matching method being connected relation based on road according to claim 5, it is characterized in that, in described step S3, the process that realizes that GPS track carries out coordinate adjustment is: when the subpoint of the fitting a straight line end points of GPS track drops on candidate matches section, then distance between the projection of this fitting a straight line end points is part to be matched; If outside the subpoint of fitting a straight line end points drops on candidate matches section, then the corresponding end points in candidate matches section should being selected to mate, the coordinate that coordinate adjustment process obtains after the geometric transformation of translation, Rotation and Zoom is final matching results.