CN103245347B - Based on intelligent navigation method and the system of road condition predicting - Google Patents
Based on intelligent navigation method and the system of road condition predicting Download PDFInfo
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Abstract
The present invention relates to a kind of intelligent navigation method based on road condition predicting and system.The method comprises the following steps: obtain the starting point of user's input, terminal and travel time; Between each adjacent node between acquisition origin-to-destination, section is at the predicted congestion weights of travel time, and from basic road network information, obtain navigation cost and the running time in section between each adjacent node between origin-to-destination; Calculate total navigation valuation cost of this node to the valuation cost of terminal according to described predicted congestion weights, navigation cost and node, and calculate according to travel time and running time the time arriving this node, calculate total navigation valuation cost of each node successively and reach the time of each node; Determine to obtain the best navigation way from origin-to-destination according to the section between total navigation valuation cost of each node, the time arriving each node and each adjacent node.The accuracy of dirigibility and navigation way can be improve.
Description
[technical field]
The present invention relates to navigational system, particularly a kind of intelligent navigation method based on road condition predicting and system.
[background technology]
In navigation road network, plan that best driving route is a basic function of map.Conventional navigation strategy of driving has shortest time, minimal path etc., and these algorithms are generally carry out route search in static road network.
Actual optimum driving route is often very large with the relation of road conditions, and traditional optimization method selects optimal route in conjunction with real-time road, and its method of work is: according to starting point and the destination acquisition alternative route of user; Obtain the weights of the build-in attribute of relevant road segments; According to real-time monitor message, scoring is weighted to the route searched, selects traffic route according to weighted scoring.
But because obtaining real-time road, cannot carry out route planning ahead of time according to the travel time determined, lack dirigibility, because real-time road can only obtain current road conditions, and road conditions can change when driving, then precalculated route is not accurate enough.
[summary of the invention]
Based on this, be necessary that providing a kind of can arrange the navigation way of trip and the intelligent navigation method based on road condition predicting of raising navigation way accuracy flexibly.
Based on an intelligent navigation method for road condition predicting, comprise the following steps:
Obtain the starting point of user's input, terminal and travel time;
Between each adjacent node between acquisition origin-to-destination, section is at the predicted congestion weights of travel time, and from basic road network information, obtain navigation cost and the running time in section between each adjacent node between origin-to-destination;
Calculate total navigation valuation cost of this node to the valuation cost of terminal according to described predicted congestion weights, navigation cost and node, and calculate according to travel time and running time the time arriving this node, calculate total navigation valuation cost of each node successively and reach the time of each node;
The best navigation way from origin-to-destination is determined according to the section between total navigation valuation cost of each node, the time arriving each node and each adjacent node.
Preferably, also step is comprised:
According to section between each adjacent node of road conditions statistics generation at the predicted congestion weights of predetermined point of time;
Section is obtained between each adjacent node between described origin-to-destination at the predicted congestion weights of travel time from the predicted congestion weights of described predetermined point of time;
Describedly generate section between each adjacent node according to road conditions statistics and comprise in the concrete steps of the predicted congestion weights of predetermined point of time:
In advance road conditions are blocked up and carry out classification, and set according to classification the weights that block up accordingly;
Add up the history traffic information in section between each adjacent node, and to obtain between each adjacent node section and to block up weights in the corresponding history of predetermined point of time;
The weights that block up of the history in section between described each adjacent node are assigned weight, and to try to achieve between each adjacent node section at the predicted congestion weights of predetermined point of time by weighted mean.
Preferably, also step is comprised: timing to upgrade between described each adjacent node section at the predicted congestion weights of predetermined point of time.
Preferably, the described total navigation valuation cost calculating this node according to described predicted congestion weights, navigation cost and node to the valuation cost of terminal, and calculate the step of the time arriving this node according to travel time and running time and be specially:
Obtain the actual cost arriving predecessor node, the time arriving predecessor node and between predecessor node and present node section at the predicted congestion weights of time arriving predecessor node;
Navigation cost and the running time in section between predecessor node and present node is obtained from basic road network information;
By the navigation cost in section between predecessor node and present node and the quadrature of predicted congestion weights, more long-pending summation with the actual cost of predecessor node obtains the actual cost of present node by this;
Calculate the valuation cost of present node, and the valuation cost of present node and actual cost summation are obtained total navigation valuation cost of present node;
By the predicted congestion weights in section between predecessor node and present node and running time quadrature, then temporal summation that is this is long-pending and arrival predecessor node, obtain the time arriving present node.
Preferably, obtain arrive predecessor node actual cost, arrive predecessor node time and between predecessor node and present node section arrive predecessor node time predicted congestion weights step before, also comprise:
Create the first table for storing node current to be investigated and the second table for storing the node investigated, and initialization first is shown and the second table;
Judge whether described first table is empty, if so, then terminates, if not, then from described first table, take out the node of total navigation valuation Least-cost, using described node as predecessor node;
Judge whether this predecessor node is terminal, if, then determine the best navigation way from origin-to-destination according to section between total navigation valuation cost of each node, the time arriving node and each adjacent node, and terminate, if not, then the next adjacent node of this predecessor node is taken out as present node;
In the total navigation valuation cost obtaining present node with after the time reaching present node, also comprise step:
Judge whether described predecessor node also exists adjacent node, if so, then turn back to the step of next adjacent node as present node of this predecessor node of taking-up, if not, then described predecessor node is added in described second table.
Preferably, when judging whether described predecessor node also exists adjacent node, also step is comprised:
When described present node is in described first table, whether total navigation valuation cost of the described present node relatively calculated is less than total navigation valuation cost of the described present node in described first table, if so, then the time of total navigation valuation cost of the described present node calculated and arrival present node is updated in described first table;
When described present node is in described second table, whether total navigation valuation cost of the described present node relatively calculated is less than total navigation valuation cost of the described present node in described second table, if so, then the time of total navigation valuation cost of the described present node calculated and arrival present node is updated in described second table;
When described present node is not in described first table and the second table, present node is inserted in described first table.
In addition, there is a need to provide a kind of and can arrange the navigation way of trip flexibly and the intelligent guidance system based on road condition predicting of raising navigation way accuracy.
Based on an intelligent guidance system for road condition predicting, comprising:
Load module, for obtaining starting point, terminal and the travel time of user's input;
Enquiry module, for obtaining between each adjacent node between origin-to-destination section at the predicted congestion weights of travel time, and obtains navigation cost and the running time in section between each adjacent node between origin-to-destination from basic road network information;
Processing module, for calculating total navigation valuation cost of this node to the valuation cost of terminal according to described predicted congestion weights, navigation cost and node, and calculate according to travel time and running time the time arriving this node, calculate total navigation valuation cost of each node successively and reach the time of each node;
Navigation module, for determining the best navigation way from origin-to-destination according to section between total navigation valuation cost of each node, the time arriving each node and each adjacent node.
Preferably, also comprise generation module, for generating the predicted congestion weights in predetermined point of time in section between each adjacent node according to road conditions statistics;
Described enquiry module also for obtaining between each adjacent node between described origin-to-destination section at the predicted congestion weights of travel time from the predicted congestion weights of described predetermined point of time;
Described generation module comprises:
Stage unit, carries out classification for blocking up to road conditions in advance, and sets according to classification the weights that block up accordingly;
Statistic unit, for adding up the history traffic information in section between each adjacent node, and to obtain between each adjacent node section and to block up weights in the corresponding history of predetermined point of time;
Weights predicting unit, assigns weight for the weights that block up to the history in section between described each adjacent node, and to try to achieve between each adjacent node section at the predicted congestion weights of predetermined point of time by weighted mean.
Preferably, also comprise update module, to upgrade between described each adjacent node section at the predicted congestion weights of predetermined point of time for timing.
Preferably, described processing module comprises:
Acquiring unit, for obtain the actual cost arriving predecessor node, the time arriving predecessor node and between predecessor node and present node section at the predicted congestion weights of time arriving predecessor node;
Extraction unit, for obtaining navigation cost and the running time in section between predecessor node and present node from basic road network information;
First computing unit, for by the navigation cost in section between predecessor node and present node and the quadrature of predicted congestion weights, more long-pending summation with the actual cost of predecessor node obtains the actual cost of present node by this;
Second computing unit, for calculating the valuation cost of present node, and obtains total navigation valuation cost of present node by the valuation cost of present node and actual cost summation;
Time calculating unit, for by the predicted congestion weights in section between predecessor node and present node and running time quadrature, then by this long-pending temporal summation with arriving predecessor node, obtain the time arriving present node.
Preferably, described processing module also comprises creating unit, judging unit and updating block;
Described creating unit is for creating the first table for storing node current to be investigated and the second table for storing the node investigated, and initialization first is shown and the second table;
Described judging unit is for judging whether described first table is empty;
Described acquiring unit also for judging that at described judging unit described first table is not for time empty, takes out the node of total navigation valuation Least-cost from described first table, and using described node as predecessor node;
Described judging unit is also for judging whether this predecessor node is terminal, if, then described navigation module determines the best navigation way from origin-to-destination according to section between total navigation valuation cost of each node, the time arriving node and each adjacent node, if not, then described acquiring unit takes out the next adjacent node of this predecessor node as present node;
In the total navigation valuation cost obtaining present node with after the time reaching present node, described judging unit is also for judging whether described predecessor node also exists adjacent node, if, then described acquiring unit retrieves the next adjacent node of this predecessor node as present node, if not, then described predecessor node adds in described second table by described updating block.
Preferably, when described judging unit judges that described present node is in described first table, and the total navigation valuation cost comparing the described present node calculated is when being less than the total navigation valuation cost of described present node in described first table, described updating block is also for being updated in described first table by total navigation valuation cost of the described present node calculated and time of arriving present node;
When described judging unit judges that described present node is in described second table, and the total navigation valuation cost comparing the described present node calculated is less than total navigation valuation cost of the described present node in described second table, described updating block is also for being updated in described second table by the time of total navigation valuation cost of the described present node calculated and arrival present node;
When described judging unit judges described present node not in described first table and the second table, described updating block is also for inserting present node in described first table.
The above-mentioned intelligent navigation method based on road condition predicting and system, adopt reading according to the predicted congestion weights in section between each adjacent node of road conditions statistics generation, and the navigation cost in section and running time between each adjacent node in basic road network information, calculate total navigation valuation cost of each node respectively and reach the time of each node, thus determine to obtain the best navigation way from origin-to-destination, so can carry out route planning ahead of time according to the travel time determined, do not need to face and before travel could obtain real-time road and carry out route planning, improve dirigibility, because the road condition change of prediction can not be very large, improve the accuracy of navigation way.
[accompanying drawing explanation]
Fig. 1 is the process flow diagram based on the intelligent navigation method of road condition predicting in an embodiment;
Fig. 2 is operation interface schematic diagram;
Fig. 3 is the particular flow sheet generating the predicted congestion weights in section between each adjacent node in an embodiment according to road conditions statistics;
Fig. 4 is the total navigation valuation cost calculating this node according to this predicted congestion weights, navigation cost and node to the valuation cost of terminal, and calculates the particular flow sheet of the step of the time arriving this node according to travel time and running time;
Fig. 5 is the total navigation valuation cost calculating this node according to this predicted congestion weights, navigation cost and node to the valuation cost of terminal, and calculates another particular flow sheet of the step of the time arriving this node according to travel time and running time;
Fig. 6 is the inner structure schematic diagram based on the intelligent guidance system of road condition predicting in an embodiment;
Fig. 7 is the inner structure schematic diagram based on the intelligent guidance system of road condition predicting in another embodiment;
Fig. 8 is the inner structure schematic diagram of generation module in Fig. 7;
Fig. 9 is the inner structure schematic diagram of processing module in Fig. 7.
[embodiment]
Be described in detail based on the intelligent navigation method of road condition predicting and the technical scheme of system below in conjunction with specific embodiment and accompanying drawing.
As shown in Figure 1, in one embodiment, a kind of intelligent navigation method based on road condition predicting, comprises the following steps:
Step S110, obtains the starting point of user's input, terminal and travel time.
User inputs the time of starting point, terminal and trip on operation interface, as shown in Figure 2, starting point is Beijing-Tong Hui He Beilu, terminal is Beijing-Dongdan, travel time is midweek 17:00, also can select not consider to avoid blocking up, avoiding blocking up and avoiding blocking up by road condition predicting by current road conditions.
Step S120, between each adjacent node between acquisition origin-to-destination, section is at the predicted congestion weights of travel time, and from basic road network information, obtain navigation cost and the running time in section between each adjacent node between origin-to-destination.
Basis road network information comprises section and node, and there is attribute such as length, category of roads, route coordinates point string, traffic status etc. in each section, show also the annexation between node, and section and node forms a digraph simultaneously.Meanwhile, basic road network information comprises navigation cost and the running time in section between adjacent node.
In one embodiment, also comprised before step S120: according to section between each adjacent node of road conditions statistics generation at the predicted congestion weights of predetermined point of time; Section is obtained between each adjacent node between described origin-to-destination at the predicted congestion weights of travel time from the predicted congestion weights of predetermined point of time.
Concrete, section is generated between each adjacent node at the predicted congestion weights of predetermined point of time in advance according to road conditions statistics, wherein, predetermined point of time refers to one group of time of statistics, as 0 o'clock, 0: 30, be divided into one group of time at interval with 30, predetermined point of time also can be one group of time of arbitrary time span.Generate the predicted congestion weights in predetermined point of time, therefrom can inquire about the predicted congestion weights obtaining the travel time, also can inquire about the predicted congestion weights obtaining random time.
In one embodiment, as shown in Figure 3, generate section between each adjacent node according to road conditions statistics to comprise in the concrete steps of the predicted congestion weights of predetermined point of time:
Step S310, blocks up to road conditions in advance and carries out classification, and sets according to classification the weights that block up accordingly.
Congestion can be judged according to the average velocity of driving vehicle on all sections obtained.In statistics, the average velocity of the time period that traffic status is best is as the reference speed of a motor vehicle, using monitoring current average speed divided by reference speed as unobstructed degree index, classification is carried out to this unobstructed degree, namely be block up to road conditions to carry out classification, then according to classification, imparting carried out to monitoring road conditions and to block up weights.As shown in table 1, the weights that block up that different unobstructed degree is corresponding different, describe different road conditions, this table is only a kind of weights that block up describing the setting of road conditions congestion, can sets itself as required.
Table 1
| Unimpeded degree | Block up weights | Describe |
| ＞0.8 | 1.0 | Smooth the way |
| 0.6～0.8 | 1.5 | Vehicle is slightly many |
| 0.3～0.6 | 3.0 | Congested in traffic |
| 0.1～0.3 | 5.0 | Block up very much |
| 0～0.1 | 10.0 | Block completely |
In addition, the different instrument of testing the speed such as camera, vehicle GPS (GlobalPositioningSystem, GPS) can be adopted to test the speed.
Step S320, adds up the history traffic information in section between each adjacent node, and to obtain between each adjacent node section and to block up weights in the corresponding history of predetermined point of time.
Add up the history traffic information in section between each adjacent node, section can be obtained between each adjacent node and to block up weights in the corresponding history of predetermined point of time.
Step S330, assigns weight to the weights that block up of the history in section between this each adjacent node, and to try to achieve between each adjacent node section at the predicted congestion weights of predetermined point of time by weighted mean.
In one embodiment, denoising is carried out to the history weights that block up of statistics, be less than certain threshold value by some probability and get rid of than the larger history of the average difference weights that block up, then trying to achieve between each adjacent node section at the predicted congestion weights of predetermined point of time by weighted mean.As established predetermined point of time d to have n history to block up weights, then the block up formula of weights of computational prediction is:
In formula (1),
during for time d, the predicted congestion weights in section between adjacent node X and node Y, W
xY(d-i) for the several times history after denoising is blocked up weights;
a
ibe that i-th history is blocked up the weight of weights, usually early data can referential better, the weight of distribution is large.
Because long-term road condition predicting has certain stability, because road condition predicting results change can not be very fast, this step does not need according to monitored results real-time update, regularly can to upgrade between each adjacent node section at the predicted congestion weights of predetermined point of time.
Rule of thumb show with statistics, the congestion of road has obvious Time-distribution in the period distribution of every day, and road conditions common comparison the in some section, peak period as on and off duty in working day blocks up; Have periodically on longer time dimension simultaneously, the road conditions of such as day Saturday and working day road conditions present different features, but each week has certain similarity on the same day.In the present embodiment with a week for predetermined period, to be per half an hour a predicted time point, to blocking up, weights process.Finally a predicted congestion weight matrix is obtained for each section, as shown in table 2.
Table 2
In addition, can generate trying to achieve the predicted congestion weights in predetermined point of time of section between each adjacent node the weights file that blocks up, setting up index and navigation circuit planning provides query interface.In the present embodiment, using the identification number in each section as inquiry key assignments, use the weight matrix that blocks up to set up index, the predicted congestion weights of the road conditions of given section preset time can be obtained in constant time.Because blocking up, weight matrix can only be mapped to limited predetermined point of time, can choose and the predicted congestion weights of the predicted congestion weights of the immediate predetermined point of time of query time point as this query time point during inquiry.
Step S130, calculate total navigation valuation cost of this node to the valuation cost of terminal according to this predicted congestion weights, navigation cost and node, and calculate according to travel time and running time the time arriving this node, calculate total navigation valuation cost of each node successively and reach the time of each node.
In order to obtain best navigation way, adopting the A* algorithm of heuristic search, all searching for from current optimum position when each step search, being communicated with route until find.In A* algorithm, trace utilization cost evaluation function weighs current optimum position, if total navigation valuation cost of node n is f (n), then calculating total navigation valuation cost formula is:
f(n)＝g(n)+h(n)(2)
In formula (2), g (n) is the actual cost from starting point to node n in conjunction with real-time road, and h (n) is for node n is to the valuation cost of terminal.In the present embodiment, valuation cost use straight line connect and road conditions unobstructed time cost.
Step S140, determines the best navigation way from origin-to-destination according to section between total navigation valuation cost of each node, the time arriving each node and each adjacent node.
From the off, after determining a certain node on best navigation way, select the adjacent node of adjacent node as this node of total navigation valuation Least-cost of each adjacent node of a certain node, the like, determine the node on all best navigation ways, form best navigation way in conjunction with section between each adjacent node.
In further embodiment, as shown in Figure 4, step S130 is specially:
Step S410, obtain the actual cost arriving predecessor node, the time arriving predecessor node and between predecessor node and present node section at the predicted congestion weights of time arriving predecessor node.
Predecessor node is best navigation circuit adjoins with present node previous by the node determined.Such as predecessor node is m, and present node is n, and the time arriving predecessor node is T
m, directly can obtain actual cost g (m) of predecessor node, between predecessor node m and present node n, section (m, n) is at T
mtime predicted congestion weight w (T
m, m, n).
Step S420, obtains navigation cost and the running time in section between predecessor node and present node from basic road network information.
Between predecessor node and present node, the navigation cost in section is Cost (m, n), and running time is t (m, n).
Step S430, by the navigation cost in section between predecessor node and present node and the quadrature of predicted congestion weights, more long-pending summation with the actual cost of predecessor node obtains the actual cost of present node by this.
The actual cost of present node namely from starting point to present node in conjunction with the actual cost of real-time road.The formula calculating the actual cost of present node is:
g(n)＝g(m)+w(T
x，m，n)*Cost(m，n)(3)
Wherein, w (T
m, m, n) and be the predicted congestion weights in section between node m and node n, Cost (m, n) is the navigation cost in section between node m and node n obtained according to basic road network information.
Step S440, calculates the valuation cost of present node, and the valuation cost of present node and actual cost summation are obtained total navigation valuation cost of present node.
The valuation cost calculating present node is h (n), then calculates total navigation valuation cost of present node according to formula (2).In the present embodiment, valuation cost use straight line connect and road conditions unobstructed time cost.If the latitude and longitude coordinates of present node n is (lon
n, lat
n), the latitude and longitude coordinates of terminal is (lon
end, lat
end), calculate the angle A ngle (n, end) of the major circle of a sphere angle of present node n and terminal, and obtain the distance Dist (n, end) (wherein RADIUS be earth radius) of present node n to terminal further:
Angle(n，end)＝arccos(sin(lat
n)*sin(lat
end)+cos(lat
n)*cos(lat
end)*cos(lon
n-lon
end))(4)
Dist(n，end)＝RADIUS*π/180.0*Angle(n，end)(5)
If average traveling cost during road unimpeded according to statistics and empirical data acquisition unit length is Cost
avg, then the computing formula of valuation cost is:
h(n)＝Cost
avg*Dist(n，end)(6)
Step S450, by the predicted congestion weights between predecessor node and present node and running time quadrature, then temporal summation that is this is long-pending and arrival predecessor node, obtain the time arriving present node.
Calculate the time T arriving present node
n=T
m+ t (m, n) * w (T
m, m, n).
So calculate total navigation valuation cost of each node successively according to step S410 to 450 and reach the time of each node.
In further embodiment, as shown in Figure 5, step S130 is specially:
Step S501, creates the first table for storing node current to be investigated and the second table for storing the node investigated, and initialization first is shown and the second table.
First table is the OPEN table in A* algorithm, and for storing node current to be investigated, the second table is the CLOSE table in A* algorithm, for storing the node investigated.Initialization first is shown and the second table, adds in the first table by starting point, and the second table is put sky.
Step S502, judges whether this first table is empty, if so, then terminates, and if not, then performs step S503.
If the first table is for empty, the failure of mark route search.
Step S503, takes out the node of total navigation valuation Least-cost, using this node as predecessor node from this first table.
From the first table, take out the node of total navigation valuation Least-cost as a node on the best navigation way determined, and it can be used as predecessor node, so that total navigation valuation cost of subsequent calculations adjacent node.
Step S504, judges whether this predecessor node is terminal, if so, then performs step S505, if not, then performs step S506.
Step S505, determines the best navigation way from origin-to-destination according to section between total navigation valuation cost of each node, the time arriving node and each adjacent node, and terminates.
From the off, after determining a certain node on best navigation way, select the adjacent node of adjacent node as this node of total navigation valuation Least-cost of each adjacent node of a certain node, the like, determine the node on all best navigation ways, form best navigation way in conjunction with section between each adjacent node.
Step S506, takes out the next adjacent node of this predecessor node as present node.
Step S507, calculates total navigation valuation cost of present node and arrives the time of present node.
Concrete calculating is described by step S410 to step S450.
Step S508, judges whether this predecessor node also exists adjacent node, if so, then turns back to step S506, if not, then performs step S509.
Step S509, adds this predecessor node in this second table, and returns step S502.
In further embodiment, also step was comprised: when this present node is in this first table before step S508, whether total navigation valuation cost of this present node relatively calculated is less than total navigation valuation cost of the described present node in described first table, if so, then the time of total navigation valuation cost of this present node calculated and arrival present node is updated in described first table; Maybe when this present node is in described second table, whether total navigation valuation cost of this present node relatively calculated is less than total navigation valuation cost of this present node in this second table, if so, then the time of total navigation valuation cost of this present node calculated and arrival present node is updated in described second table; Maybe when this present node is not in this first table and the second table, present node is inserted in this first table.
As shown in Figure 6, in one embodiment, a kind of intelligent guidance system based on road condition predicting, comprises load module 610, enquiry module 620, processing module 630 and navigation module 640.Wherein,
Load module 610 is for obtaining starting point, terminal and the travel time of user's input.The time that user inputs starting point on operation interface, namely terminal goes on a journey, as shown in Figure 2, starting point is Beijing-Tong Hui He Beilu, terminal is Beijing-Dongdan, travel time is midweek 17:00, also can select not consider to avoid blocking up, avoiding blocking up and avoiding blocking up by road condition predicting by current road conditions.
Enquiry module 620 is for obtaining the predicted congestion weights in the travel time between each adjacent node between origin-to-destination, and the navigation cost obtained from basic road network information between each adjacent node between origin-to-destination and running time.
Basis road network information comprises section and node, and there is attribute such as length, category of roads, route coordinates point string, traffic status etc. in each section, show also the annexation between node, and section and node forms a digraph simultaneously.Meanwhile, basic road network information comprises navigation cost and the running time in section between adjacent node.
Processing module 630 is for calculating total navigation valuation cost of this node to the valuation cost of terminal according to described predicted congestion weights, navigation cost and node, and calculate according to travel time and running time the time arriving this node, calculate total navigation valuation cost of each node successively and reach the time of each node.In order to obtain best navigation way, adopting the A* algorithm of heuristic search, all searching for from current optimum position when each step search, being communicated with route until find.In A* algorithm, trace utilization cost evaluation function weighs current optimum position, if total navigation valuation cost of node n is f (n), then calculating total navigation valuation cost formula is:
f(n)＝g(n)+h(n)(2)
In formula (2), g (n) is the actual cost from starting point to node n in conjunction with real-time road, and h (n) is for node n is to the valuation cost of terminal.In the present embodiment, valuation cost use straight line connect and road conditions unobstructed time cost.
Navigation module 640 is for determining the best navigation way from origin-to-destination according to total navigation valuation cost of each node and time of arriving each node.From the off, after determining a certain node on best navigation way, select the adjacent node of adjacent node as this node of total navigation valuation Least-cost of each adjacent node of a certain node, the like, determine the node on all best navigation ways, form best navigation way in conjunction with section between each adjacent node.
As shown in Figure 7, in one embodiment, the above-mentioned intelligent guidance system based on road condition predicting, except comprising load module 610, enquiry module 620, processing module 630 and navigation module 640, also comprises generation module 650 and update module 660.Wherein,
Generation module 650 is for generating between each adjacent node section at the generation module of the predicted congestion weights of predetermined point of time according to road conditions statistics.Concrete, section is generated between each adjacent node at the predicted congestion weights of predetermined point of time in advance according to road conditions statistics, wherein, predetermined point of time refers to one group of time of statistics, as 0 o'clock, 0: 30, be divided into one group of time at interval with 30, predetermined point of time also can be one group of time of arbitrary time span.
Enquiry module 620 also for obtaining between each adjacent node between described origin-to-destination section at the predicted congestion weights of travel time from the predicted congestion weights of predetermined point of time.Generate the predicted congestion weights in predetermined point of time, enquiry module 620 therefrom can inquire about the predicted congestion weights obtaining the travel time, also can inquire about the predicted congestion weights obtaining random time.
In one embodiment, as shown in Figure 8, generation module 650 comprises stage unit 651, statistic unit 653 and weights predicting unit 655.Wherein,
Stage unit 651 carries out classification for blocking up to road conditions in advance, and sets according to classification the weights that block up accordingly.Congestion can be judged according to the average velocity of driving vehicle on all sections obtained.In statistics, the average velocity of the time period that traffic status is best is as the reference speed of a motor vehicle, using monitoring current average speed divided by reference speed as unobstructed degree index, classification is carried out to this unobstructed degree, namely be block up to road conditions to carry out classification, then according to classification, imparting carried out to monitoring road conditions and to block up weights.As shown in table 1, the weights that block up that different unobstructed degree is corresponding different, describe different road conditions, this table is only a kind of weights that block up describing the setting of road conditions congestion, can sets itself as required.
Statistic unit 653 for adding up the historical information between each adjacent node, and obtains blocking up weights in the corresponding history of predetermined point of time between each adjacent node.
Weights predicting unit 655 assigns weight for the weights that block up to the history between this each adjacent node, and tries to achieve the predicted congestion weights in predetermined point of time between each adjacent node by weighted mean.Denoising is carried out to the history weights that block up of statistics, be less than certain threshold value by some probability and get rid of than the larger history of the average difference weights that block up, then trying to achieve between each adjacent node section at the predicted congestion weights of predetermined point of time by weighted mean.As established predetermined point of time d to have n history to block up weights, then the block up formula of weights of computational prediction is:
In formula (1),
during for time d, the predicted congestion weights in section between adjacent node X and node Y, W
xY(d-i) for the several times history after denoising is blocked up weights;
a
ibe that i-th history is blocked up the weight of weights, usually early data can referential better, the weight of distribution is large.
Weights predicting unit 655 is carried out prediction to each section and is obtained a predicted congestion weight matrix, as shown in table 2.
In addition, generation module 650 can generate trying to achieve the predicted congestion weights in predetermined point of time of section between each adjacent node the weights file that blocks up, and sets up index and navigation circuit planning provides query interface.In the present embodiment, using the identification number in each section as inquiry key assignments, use the weight matrix that blocks up to set up index, the predicted congestion weights of the road conditions of given section preset time can be obtained in constant time.Because blocking up, weight matrix can only be mapped to limited predetermined point of time, can choose and the predicted congestion weights of the predicted congestion weights of the immediate predetermined point of time of query time point as this query time point during inquiry.
Update module 660 upgrades the predicted congestion weights in predetermined point of time between described each adjacent node for timing.
In further embodiment, as shown in Figure 9, processing module 630 comprises acquiring unit 631, extraction unit 632, first computing unit 633, second computing unit 634, time calculating unit 635, creating unit 636, judging unit 637 and updating block 638.
Acquiring unit 631 arrive for obtaining predecessor node actual cost, arrive predecessor node time and between predecessor node and present node at the predicted congestion weights of time arriving predecessor node.Predecessor node is best navigation circuit adjoins with present node previous by the node determined.Such as predecessor node is m, and present node is n, and the time arriving predecessor node is T
m, directly can obtain actual cost g (m) of predecessor node, between predecessor node m and present node n, section (m, n) is at T
mtime predicted congestion weight w (T
m, m, n).
Extraction unit 632 for obtaining navigation cost between predecessor node and present node and running time from basic road network information.Between predecessor node and present node, the navigation cost in section is Cost (m, n), and running time is t (m, n).
First computing unit 633 is for by the navigation cost between predecessor node and present node and the quadrature of predicted congestion weights, more long-pending summation with the actual cost of predecessor node obtains the actual cost of present node by this.The actual cost of present node namely from starting point to present node in conjunction with the actual cost of real-time road.The formula calculating the actual cost of present node is:
g(n)＝g(m)+w(T
x，m，n)*Cost(m，n)
Wherein, w (T
m, m, n) and be the predicted congestion weights in section between node m and node n, Cost (m, n) is the navigation cost in section between node m and node n obtained according to basic road network information.
The valuation cost of present node and actual cost summation for calculating the valuation cost of present node, and are obtained total navigation valuation cost of present node by the second computing unit 634.The valuation cost calculating present node is h (n), specifically calculates as formula (4) (5) (6), then calculates total navigation valuation cost of present node according to formula (2).
Time calculating unit 635 for by the predicted congestion weights between predecessor node and present node and running time quadrature, then by this long-pending temporal summation with arriving predecessor node, obtain the time arriving present node.Calculate the time T arriving present node
n=T
m+ t (m, n) * w (T
m, m, n).
The unit of processing module 630 like this can calculate total navigation valuation cost of each node successively and reach the time of each node.
Creating unit 636 is for creating the first table for storing node current to be investigated and the second table for storing the node investigated, and initialization first is shown and the second table.First table is the OPEN table in A* algorithm, and for storing node current to be investigated, the second table is the CLOSE table in A* algorithm, for storing the node investigated.Initialization first is shown and the second table, adds in the first table by starting point, and the second table is put sky.
Judging unit 637 is for judging whether this first table is empty.If the first table is for empty, the failure of mark route search.
Acquiring unit 631 also for judging that at judging unit 637 this first table is not for time empty, takes out the node of total navigation valuation Least-cost from this first table, and using this node as predecessor node.From the first table, take out the node of total navigation valuation Least-cost as a node on the best navigation way determined, and it can be used as predecessor node, so that total navigation valuation cost of subsequent calculations adjacent node.
Judging unit 637 is also for judging whether this predecessor node is terminal.
Navigation module 640, for when judging unit 637 judges that this predecessor node is terminal, determines the best navigation way from origin-to-destination according to section between total navigation valuation cost of each node, the time arriving node and each adjacent node.From the off, after determining a certain node on best navigation way, select the adjacent node of adjacent node as this node of total navigation valuation Least-cost of each adjacent node of a certain node, the like, determine the node on all best navigation ways, form best navigation way in conjunction with section between each adjacent node.
Acquiring unit 631 is for judging this predecessor node not for terminal during at judging unit 637, take out the next adjacent node of this predecessor node as present node.
In the total navigation valuation cost obtaining present node with after the time reaching present node, judging unit 637 is also for judging whether described predecessor node also exists adjacent node, if, then acquiring unit 631 retrieves the next adjacent node of this predecessor node as present node, if not, then this predecessor node adds in described second table by updating block 638.
In further embodiment, when judging unit 637 judges that this present node is in this first table, and the total navigation valuation cost comparing the described present node calculated is when being less than the total navigation valuation cost of this present node in this first table, updating block 638 is also for being updated to total navigation valuation cost of the described present node calculated and time of arriving present node in described first table.
When judging unit 637 judges that this present node is in this second table, and the total navigation valuation cost comparing this present node calculated is less than total navigation valuation cost of this present node in this second table, updating block 638 is also for being updated to the time of total navigation valuation cost of this present node calculated and arrival present node in described second table.
When judging unit 637 judges this present node not in this first table and the second table, present node inserts in this first table by updating block 638.
The above-mentioned intelligent navigation method based on road condition predicting and system, adopt reading according to the predicted congestion weights in section between each adjacent node of road conditions statistics generation, and the navigation cost in section and running time between each adjacent node in basic road network information, calculate total navigation valuation cost of each node respectively and reach the time of each node, thus determine to obtain the best navigation way from origin-to-destination, so can carry out route planning ahead of time according to the travel time determined, do not need to face and before travel could obtain real-time road and carry out route planning, improve dirigibility, because the road condition change of prediction can not be very large, improve the accuracy of navigation way.
In addition, the history weights that block up according to statistics assign weight, then the predicted congestion weights obtained by weighted mean are comparatively accurate; Timing upgrades predicted congestion weights, further increases the accuracy of prediction; Adopt the predicted congestion weights according to section between predecessor node and present node, navigation cost and running time, calculate total navigation valuation cost of present node, preferably navigation way can be obtained, and do not increase computation complexity.
The above embodiment only have expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (10)
1., based on an intelligent navigation method for road condition predicting, comprise the following steps:
Obtain the starting point of user's input, terminal and travel time;
Between each adjacent node between acquisition origin-to-destination, section is at the predicted congestion weights of travel time, and from basic road network information, obtain navigation cost and the running time in section between each adjacent node between origin-to-destination;
Obtain the actual cost arriving predecessor node, the time arriving predecessor node and between predecessor node and present node section at the predicted congestion weights of time arriving predecessor node;
Navigation cost and the running time in section between predecessor node and present node is obtained from basic road network information;
By the navigation cost in section between predecessor node and present node and the quadrature of predicted congestion weights, more long-pending summation with the actual cost of predecessor node obtains the actual cost of present node by this;
Calculate the valuation cost of present node, and the valuation cost of present node and actual cost summation are obtained total navigation valuation cost of present node;
By the predicted congestion weights in section between predecessor node and present node and running time quadrature, then temporal summation that is this is long-pending and arrival predecessor node, obtain the time arriving present node;
The best navigation way from origin-to-destination is determined according to the section between total navigation valuation cost of each node, the time arriving each node and each adjacent node.
2. the intelligent navigation method based on road condition predicting according to claim 1, is characterized in that, also comprise step:
According to section between each adjacent node of road conditions statistics generation at the predicted congestion weights of predetermined point of time;
Section is obtained between each adjacent node between described origin-to-destination at the predicted congestion weights of travel time from the predicted congestion weights of described predetermined point of time;
Describedly generate section between each adjacent node according to road conditions statistics and comprise in the concrete steps of the predicted congestion weights of predetermined point of time:
In advance road conditions are blocked up and carry out classification, and set according to classification the weights that block up accordingly;
Add up the history traffic information in section between each adjacent node, and to obtain between each adjacent node section and to block up weights in the corresponding history of predetermined point of time;
The weights that block up of the history in section between described each adjacent node are assigned weight, and to try to achieve between each adjacent node section at the predicted congestion weights of predetermined point of time by weighted mean.
3. the intelligent navigation method based on road condition predicting according to claim 2, is characterized in that, also comprise step: timing to upgrade between described each adjacent node section at the predicted congestion weights of predetermined point of time.
4. the intelligent navigation method based on road condition predicting according to claim 1, it is characterized in that, obtain arrive predecessor node actual cost, arrive predecessor node time and between predecessor node and present node section arrive predecessor node time predicted congestion weights step before, also comprise:
Create the first table for storing node current to be investigated and the second table for storing the node investigated, and initialization first is shown and the second table;
Judge whether described first table is empty, if so, then terminates, if not, then from described first table, take out the node of total navigation valuation Least-cost, using described node as predecessor node;
Judge whether this predecessor node is terminal, if, then determine the best navigation way from origin-to-destination according to section between total navigation valuation cost of each node, the time arriving node and each adjacent node, and terminate, if not, then the next adjacent node of this predecessor node is taken out as present node;
In the total navigation valuation cost obtaining present node with after the time reaching present node, also comprise step:
Judge whether described predecessor node also exists adjacent node, if so, then turn back to the step of next adjacent node as present node of this predecessor node of taking-up, if not, then described predecessor node is added in described second table.
5. the intelligent navigation method based on road condition predicting according to claim 4, is characterized in that, when judging whether described predecessor node also exists adjacent node, also comprises step:
When described present node is in described first table, whether total navigation valuation cost of the described present node relatively calculated is less than total navigation valuation cost of the described present node in described first table, if so, then the time of total navigation valuation cost of the described present node calculated and arrival present node is updated in described first table;
When described present node is in described second table, whether total navigation valuation cost of the described present node relatively calculated is less than total navigation valuation cost of the described present node in described second table, if so, then the time of total navigation valuation cost of the described present node calculated and arrival present node is updated in described second table;
When described present node is not in described first table and the second table, present node is inserted in described first table.
6., based on an intelligent guidance system for road condition predicting, comprising:
Load module, for obtaining starting point, terminal and the travel time of user's input;
Enquiry module, for obtaining between each adjacent node between origin-to-destination section at the predicted congestion weights of travel time, and obtains navigation cost and the running time in section between each adjacent node between origin-to-destination from basic road network information;
Processing module, for calculating total navigation valuation cost of this node to the valuation cost of terminal according to described predicted congestion weights, navigation cost and node, and calculate according to travel time and running time the time arriving this node, calculate total navigation valuation cost of each node successively and reach the time of each node;
Navigation module, for determining the best navigation way from origin-to-destination according to section between total navigation valuation cost of each node, the time arriving each node and each adjacent node;
Described processing module comprises:
Acquiring unit, for obtain the actual cost arriving predecessor node, the time arriving predecessor node and between predecessor node and present node section at the predicted congestion weights of time arriving predecessor node;
Extraction unit, for obtaining navigation cost and the running time in section between predecessor node and present node from basic road network information;
First computing unit, for by the navigation cost in section between predecessor node and present node and the quadrature of predicted congestion weights, more long-pending summation with the actual cost of predecessor node obtains the actual cost of present node by this;
Second computing unit, for calculating the valuation cost of present node, and obtains total navigation valuation cost of present node by the valuation cost of present node and actual cost summation;
Time calculating unit, for by the predicted congestion weights in section between predecessor node and present node and running time quadrature, then by this long-pending temporal summation with arriving predecessor node, obtain the time arriving present node.
7. the intelligent guidance system based on road condition predicting according to claim 6, is characterized in that, also comprise generation module, for generating between each adjacent node section according to road conditions statistics at the predicted congestion weights of predetermined point of time;
Described enquiry module also for obtaining between each adjacent node between described origin-to-destination section at the predicted congestion weights of travel time from the predicted congestion weights of described predetermined point of time;
Described generation module comprises:
Stage unit, carries out classification for blocking up to road conditions in advance, and sets according to classification the weights that block up accordingly;
Statistic unit, for adding up the history traffic information in section between each adjacent node, and to obtain between each adjacent node section and to block up weights in the corresponding history of predetermined point of time;
Weights predicting unit, assigns weight for the weights that block up to the history in section between described each adjacent node, and to try to achieve between each adjacent node section at the predicted congestion weights of predetermined point of time by weighted mean.
8. the intelligent guidance system based on road condition predicting according to claim 7, is characterized in that, also comprise update module, to upgrade between described each adjacent node section at the predicted congestion weights of predetermined point of time for timing.
9. the intelligent guidance system based on road condition predicting according to claim 6, is characterized in that, described processing module also comprises creating unit, judging unit and updating block;
Described creating unit is for creating the first table for storing node current to be investigated and the second table for storing the node investigated, and initialization first is shown and the second table;
Described judging unit is for judging whether described first table is empty;
Described acquiring unit also for judging that at described judging unit described first table is not for time empty, takes out the node of total navigation valuation Least-cost from described first table, and using described node as predecessor node;
Described judging unit is also for judging whether this predecessor node is terminal, if, then described navigation module determines the best navigation way from origin-to-destination according to section between total navigation valuation cost of each node, the time arriving node and each adjacent node, if not, then described acquiring unit takes out the next adjacent node of this predecessor node as present node;
In the total navigation valuation cost obtaining present node with after the time reaching present node, described judging unit is also for judging whether described predecessor node also exists adjacent node, if, then described acquiring unit retrieves the next adjacent node of this predecessor node as present node, if not, then described predecessor node adds in described second table by described updating block.
10. the intelligent guidance system based on road condition predicting according to claim 9, is characterized in that,
When described judging unit judges that described present node is in described first table, and the total navigation valuation cost comparing the described present node calculated is when being less than the total navigation valuation cost of described present node in described first table, described updating block is also for being updated in described first table by total navigation valuation cost of the described present node calculated and time of arriving present node;
When described judging unit judges that described present node is in described second table, and the total navigation valuation cost comparing the described present node calculated is less than total navigation valuation cost of the described present node in described second table, described updating block is also for being updated in described second table by the time of total navigation valuation cost of the described present node calculated and arrival present node;
When described judging unit judges described present node not in described first table and the second table, described updating block is also for inserting present node in described first table.
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