CN1710624A - Method for obtaining average speed of city rode traffic low region - Google Patents
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Abstract
Based on considering each error existed in data received by GPS, features of roads in GIS, and artificial factors of drivers synthetically, the invention uses a method of nearest neighbor to obtain preliminary matched result in map. Accurate positioning location of vehicle is obtained from following steps: through A* algorithm finds out shortcut between two points; recovering run locus of vehicle in real time and intelligence; considering relation between fore and after as well as load condition of data of vehicle, the method carries out modification for doubtful matching points and formed run locus of vehicle. Finally, average up going speed, down going speed and average speed in mixed-use area are calculated from run locus of vehicle, and displayed on digital map. Features are: high real time and precision, without need of angular transducer etc. hardware facilities so as to lower system cost and complexity of designing system, and raising system performance
Description
Technical field
The present invention relates to a kind of acquisition methods of average speed of city rode traffic low region, be used in real time, dynamically obtain vehicle accurately location and driving trace from GPS (GPS) data, and combining geographic information system (GIS) carries out the calculating of the two-way section mean speed of urban traffic flow, belongs to the intelligent transport technology field.
Background technology
Intelligent transportation system (ITS) is the ideal solution of numerous traffic problems of bringing of economic development, and it has represented the development trend of traffic system.Along with reaching its maturity of micro embedded technology, gps satellite location technology, the communication technology and Geographic Information System (GIS), based on the exploitation of the urban traffic information system of GPS/GIS be applied in the intelligent transportation system field and come into one's own just day by day, and demonstrate huge economic and social benefit.Urban traffic information system supports and information platform for intelligent transportation provides important technology; It in real time, dynamically reflects the overall state of traffic flow in monitor vehicle and city, is vehicle monitoring and dynamic navigation, the traffic congestion analysis of causes, and decision-making is induced in traffic flow, and making rational planning for of urban road provides important actual foundation.Obtaining of average speed of city rode traffic low region is a gordian technique in the urban traffic information system, the basis that the accurate location of vehicle and track following are follow-up work, and be one of important parameter of traffic flow based on this section mean speed, can be dynamic navigation, traffic dispersion provides directly foundation reliably.
The Data Source obtain manner of city road network traffic flow velocity information has multiple, mainly contains at the underground inductive coil monitor (as Australian SCATS system) of burying underground, and video detecting device etc. is installed.These researchs have obtained success in some aspects and have had its practical value, but have limitation in the calculating of city road network traffic flow speed: 1) the inductive coil monitor can obtain multiple traffic flow parameter, velocity estimation is the indirect calculation result, and precision is not high, calculation of complex; Again it is big to lay the inductive coil difficulty, acquires the cost of equipment height, and is subjected to serviceable life the artificial destruction factor affecting big.2) Video Detection is had relatively high expectations to hardware device, and is subjected to weather effect big.3) problem that all exists sensing range influenced by the hardware construction conditions can only detect the city main trunk road.Adopt the GPS vehicle location can effectively address the above problem as Data Source.The gps signal broad covered area is subjected to weather effect little, and hardware only need be acquired cheap GPS vehicle positioning system; And domestic existing city is used for vehicle dispatch system with the GPS vehicle now, and this makes the GPS vehicle data conveniently to obtain.
Section mean speed may be defined as 1) ratio or 2 of the vehicle ' distance average running time corresponding with this distance) mean value of all vehicle location speed on a certain moment highway section.Wherein define 2) the more realistic velocity distribution of statistical distribution of the speed observation that obtains, and be more suitable for being used for the big city road network of the volume of traffic.Section mean speed has been taken all factors into consideration the influence and the road section information of different vehicle transport condition, traffic lights, and velocity information that can macroscopic view reflection road can be congestion in road situation, traffic pressure assessment, the travel time is estimated and dynamic navigation provides direct data foundation.Calculate the accurately direct application section mean speed of location and driving trace of vehicle in real time by gps data, simple and practical, domestic do not have concrete achievement in research and real application systems at present as yet.
Summary of the invention
The objective of the invention is to above-mentioned deficiency and actual needs at prior art, a kind of method from GPS (GPS) data acquisition average speed of city rode traffic low region is provided, provides necessary base data and important parameter for the estimation of urban transportation informix in real time and exactly.
For realizing such purpose, the various errors that the present invention occurs in taking all factors into consideration the gps data reception, among the GIS on the basis of roadway characteristic and driver's human factor, adopt nearest neighbor method to obtain preliminary map matching result, by the A* algorithm, find out the shortest path between per two points, in real time, the intelligence restoration vehicle driving trace, and the front and back of considering vehicle data are got in touch and road conditions, successively suspicious match point and established vehicle driving trace are revised, obtaining vehicle accurately locatees, last average speed uplink by the vehicle driving trace unit of calculating highway section, downstream rate and comprehensive section mean speed, and be shown on the numerical map.
Method of the present invention comprises following step:
1, carries out map match with nearest neighbor method
Real-time GPS locator data and the GIS information of input city vehicle, the position data of vehicle GPS is considered as diffusing point data road vertical projection towards periphery, and calculating projector distance, if wherein the shortest projector distance of certain diffusing point data is greater than the threshold value that sets in advance, then think error matching points, it is filtered out, otherwise getting the pairing road of its shortest projector distance is the travel at vehicle place, corresponding subpoint is the position after the vehicle coupling, obtain PRELIMINARY RESULTS, finish from putting the map match of line.
The map match of nearest neighbor method is mainly the error that overcomes GPS locator data and GIS data.The GPS location provides vehicle three-dimensional position, three-dimensional velocity and temporal information in real time, but three-dimensional all can make gps data produce drift phenomenon to the transformed error of two-dimensional coordinate system and the outside sudden change of the internal system sum of errors error of gps signal, bigger deviation occurs even lose, this has caused wrong gps data.GIS road data quality not only is subjected to the influence of self precision, data age, map scale, map projection etc.These errors make on the road of the inaccurate GIS of being in of position data of GPS of vehicle, and are in around the road.Hypothesis based on map match: vehicle is adapted on the road of vehicle ' with the GPS locator data of nearest neighbor method with vehicle always in travels down.
2. the formation of track of vehicle
To the dispersion vehicle match point that obtains in the step 1, time sequencing according to match point adopts the A* algorithm, the vehicle match point of previous time is considered as start node, the vehicle match point of next time is considered as destination node, search for the whole road network space diagram that may reach, till finding destination node, recall searching route, just obtain two shortest paths between the point, note.All match points that connect each car as stated above successively form track of vehicle, finish the coupling of whole vehicle driving trace line and urban road.All vehicles are handled equally, obtained the driving trace of all vehicles in the road network.
This step is isolated the single vehicle trace information at the dispersion vehicle match point result that step 1 obtains, and in conjunction with a hypothesis of map match: vehicle ' has continuity.The A* algorithm belongs to heuristic search algorithm, and heuristic search is preferentially gone down along enlightenment and node searching with customizing messages, and these nodes are the nodes that reach on the optimal path of destination node.
3, the differentiation of uncertain locator data and processing
At first according to the confidence level of the information calculations match point of point of proximity place road on projector distance and the same vehicle driving trace, the big more match point confidence level of projector distance is low more, and the match point confidence level that is isolated to point of proximity place road also is made as low.Another directly perceived and crucial evaluation principle is: the behavior of turning back at the crossing, promptly can directly list uncertain vehicle location data point in by the match point of twice of same paths continuously.Secondly the vehicle driving trace place road that uncertain vehicle location data point is obtained in step 2 carries out projection, and projector distance is revised successfully in threshold value, according to the vehicle driving trace of new this section of match point correction; Otherwise, revise failure, think that the original driving trace coupling of vehicle is correct.All vehicles are handled equally, obtained the revised driving trace of all vehicles in the road network.
Uncertain vehicle location data point mainly is because the Primary Location data that nearest neighbor method obtains are not considered the front and back contact of vehicle data, it mainly appears at intersection, and the vehicle of this crossing intersection part coupling is the emphasis and the difficult point of map match always.This step is based on following hypothesis: between two match points, and normal vehicle operation, promptly vehicle can select shortest path path or simple path to travel.Simple path refers to that vehicle is minimum through the road circuit node between two GPS vehicle positioning data points that record, and the structure of driving path is the simplest.This step is a good basis with existing Primary Location and track of vehicle data, further contemplates the historical data and the follow-up data of vehicle, uncertain vehicle location data point is handled and is corrected, and obtain corrected more accurate track of vehicle.
4, the road network section mean speed is calculated
At first vehicle position information and the place road information that is provided by revised single vehicle driving trace in the step 3 obtains the distance value of vehicle on track, utilize the temporal information in the GPS vehicle data again, both are divided by and obtain vehicle in driving trace up stroke speed.Each automobile-used same procedure in the road network is obtained the travel speed of all vehicles on driving trace.Be base unit with the highway section then, find out all vehicles that travel at the appointed time specifying on the highway section, Filtering Processing, weighting value are on average recovered the overall section mean speed of road network; Consider the difference of vehicle, obtain the different travel directions of vehicle on road, thereby the velocity information of vehicle is classified, obtain average speed uplink, the downstream rate of road, and dynamically be shown on the GIS map by the road node sequence.
The present invention can provide the basic status information and the distribution situation of information of vehicles and road network situation in real time, exactly in based on the urban traffic information system of GIS and GPS technique construction.The road network section mean speed vividly describes big city vehicle " tide current " phenomenon, and real data and decision references are provided for the monitoring of public transport scheduling, automobile dynamic navigation, congestion in road and eliminating, Urban Traffic Planning etc., avoid the blindness of these field classic methods and empirical, had bigger economic benefits and social effect.Native system is based on Shanghai City real data design implementation, and is real-time, precision is high, excellent in efficiency, can implement easily under the condition that does not need hardware facilities such as angular transducer.
Description of drawings
Fig. 1 is urban traffic information system structural framing figure.
Fig. 2 is nearest neighbor method ultimate principle figure.
Fig. 3 forms the typical classification synoptic diagram of preceding match point distribution situation for track of vehicle.
Fig. 4 is uncertain locator data correction synoptic diagram.
Wherein, Fig. 4 (a) is wrong some match map, and Fig. 4 (b) is the later figure as a result of corresponding data correction.
Fig. 5 is the map match of the bustling location selected parts in the city center and track following result's example.
Fig. 6 is the displayed map of road network average travel speed in the urban transportation information platform system.
Embodiment
Below in conjunction with accompanying drawing and specific embodiment technical scheme of the present invention is further described.
Embodiment is with Shanghai City GIS data, and 3000 left and right sides taxi gps datas that popular taxi company provides are that instance data is handled, and handling period T is 10 minutes, and the cycle selects mainly to consider the cyclical variation of traffic lights periodicity and traffic behavior.The GPS locator data of taxi data is 20 seconds at interval in actual the advancing, and data are comparatively sparse, and may stride across one or more highway sections between continuous data, and the directional data that does not have gyroscope to provide can't be used the DR algorithm; The road spacing is little in the city road network, topological structure is complicated, information is diversified.
The present invention adopts treatment scheme as shown in Figure 1, with real-time GPS vehicle-mounted data and GIS data as the input data, the unified nearest neighbor method of using carries out map match, carrying out track of vehicle by the A* algorithm follows the tracks of, according to the front and back contact and the road conditions of vehicle data, uncertain locator data is differentiated again, handled obtaining revised vehicle driving trace, and average speed uplink, downstream rate and the comprehensive section mean speed in unit of account highway section in view of the above, be shown on the numerical map.Among the present invention, the intermediate data of processing and last unification as a result are placed in the database preserves, and can be used for systems such as traffic guidance, dynamic navigation.
The concrete implementation step of the present invention is as follows:
1, carries out map match with nearest neighbor method
10 minutes taxi gps data is read in simultaneously, and the position data point data that is considered as loosing is carried out unified map match with nearest neighbor method, the GPS locator data of vehicle is adapted on the road of vehicle '.Ultimate principle during concrete enforcement promptly will be searched for one section road as shown in Figure 2, makes a P the shortest to its projector distance, and subpoint is the match point of being asked.During practical operation, should select the interior highway section of certain area coverage, accelerate algorithm speed as candidate roads.Among Fig. 2, some P is a point to be matched, and L1, L2 are candidate roads on every side, and P1, P2 are subpoint.If A is (x
1, y
1), B (x
2, y
2) be the two-end-point of candidate roads, k is the road slope, point coordinate to be matched (x, y), subpoint coordinate (x
p, y
p).As Fig. 2, when representing the line segment equation with two point form, the projector distance formula can be expressed as:
The computing formula of subpoint is:
It is 10m that the shortest projector distance here is provided with threshold value, when it is worth when excessive, thinks error matching points, filters out, and obtains PRELIMINARY RESULTS.The density and the gps data error of city road network taken all factors into consideration in the threshold value setting.The subpoint of nearest neighbor method correspondence is the position after the vehicle coupling, finishes from putting the map match of line.
2, the formation of track of vehicle
To the dispersion vehicle match point that obtains in the step 1, time sequencing according to match point adopts the A* algorithm, the vehicle match point of previous time is considered as start node, the vehicle match point of next time is considered as destination node, search for the whole road network space diagram that may reach, till finding destination node, recall searching route, just obtain two shortest paths between the point, note.The concrete condition classification for the situation in same highway section shown in Fig. 3 (a) and (b) and continuous highway section, is handled simpler as shown in Figure 3; For the complex situations of striding the block as Fig. 3 (c), mainly solve with the A* algorithm, discuss as follows:
The road network structure of system is obtain in advance changeless, belong to static shortest path and calculate, so the present invention has adopted the higher A* algorithm of efficient in the static shortest path calculating.The A* algorithm belongs to heuristic search algorithm, and search procedure is searched for the whole state space graph that may reach from given original state, till finding dbjective state.Heuristic search is preferentially gone down along enlightenment and node searching with customizing messages, and these nodes may be the nodes that reaches on the optimal path of destination node.In heuristic search, can the design of heuristic function will determine when search consumption and search out optimum solution, be the key of algorithm.The evaluation function of A* algorithm is defined as among the present invention:
f(n)＝g(n)+h(n) (3)
Wherein, 1) f (n) is the evaluation function that arrives impact point from initial point by node n;
2) g (n) is the actual cost from start node to the n node in state space, and the definition of g (n) is:
G (n)=d (n)+α c (n) (4) d (n) is the length of starting point to node n actual path, and c (n) is the expansion number of plies of node n, and α is both scale factors.
3) h (n) is the estimation cost from node n to the destination node optimal path.It has determined the efficient and the admissibility of search.For how much road networks, can get the point-to-point transmission Euclidean distance as assessment values since assessment values h (n)≤n to destination node apart from actual value, algorithm has admissibility, can obtain optimum solution.
Above formula is made amendment, can obtain several different evaluation functions that Practical significance is arranged:
1) when d (n)=0, f (n)=g (n)+h (n)=c (n)+h (n), assessment level mean that vehicle is selected the intersection and the less simple road driving of situation of turning.
2) when c (n)=0, f (n)=g (n)+h (n)=d (n)+h (n), assessment level mean that vehicle selects the shortest road driving of path distance.
3) as d (n)=0, h (n)=0, algorithm is in the nature the breadth First algorithm.
All match points that connect each car as stated above successively form track of vehicle, finish the coupling of whole vehicle driving trace line and urban road.All vehicles are handled equally, obtained the driving trace of all vehicles in the road network.
3, the differentiation of uncertain locator data and processing
According to the confidence level of the information calculations match point of point of proximity place road on projector distance and the same vehicle driving trace, the big more match point confidence level of projector distance is low more, and the match point confidence level that is isolated to point of proximity place road also is made as low.Another evaluation principle directly perceived crucial is: the behavior of turning back at the crossing, promptly can directly list uncertain vehicle location data point in by the match point of twice of same paths continuously.Secondly the vehicle driving trace place road that uncertain vehicle location data point is obtained in step 2 carries out projection, and projector distance is revised successfully in threshold value, according to the vehicle driving trace of new this section of match point correction; Otherwise, revise failure, think that the original driving trace coupling of vehicle is correct.As shown in Figure 4, some A, B, C are respectively the match point of arranging in chronological order on the vehicle driving trace, judge that B is uncertain match point.Among Fig. 4 (a), do not consider to find out the path T of an A with the A* algorithm by a B earlier to C
AC, finish a B then to T
ACProjection because projector distance in threshold value, is revised successfully, according to the vehicle driving trace of new this section of match point correction, is revised vehicle driving trace as Fig. 4 (b) again.Fig. 5 is the map match of the bustling location selected parts in the city center and track following result's example, and the position of circles mark is according to the example of the uncertain locator data of above step modification among the figure.All vehicles are handled equally, obtained the revised driving trace of all vehicles in the road network.
Uncertain vehicle location data point mainly is because the Primary Location data that nearest neighbor method obtains are not considered the front and back contact of vehicle data, it mainly appears at intersection, and the vehicle of this crossing intersection part coupling is the emphasis and the difficult point of map match always.This step is based on following hypothesis: between two match points, and normal vehicle operation, promptly vehicle can select shortest path path or simple path to travel.Simple path refers to that vehicle is minimum through the road circuit node between two GPS vehicle positioning data points that record, and the structure of driving path is the simplest.This step is a good basis with existing Primary Location and track of vehicle data, further contemplates the historical data and the follow-up data of vehicle, uncertain vehicle location data point is handled and is corrected, and obtain corrected more accurate track of vehicle.
4, the road network section mean speed is calculated
At first vehicle position information and the place road information that is provided by revised single vehicle driving trace in the step 3 obtains the distance value of vehicle on track, utilize the temporal information in the GPS vehicle data again, both are divided by and obtain vehicle in driving trace up stroke speed.The road junction segmentation that comprises the traffic flow cycle information is got in the estimation of travel speed, speed and time when estimating vehicle by road junction according to track of vehicle.Each automobile-used same procedure in the road network is obtained the travel speed of all vehicles on driving trace.Be base unit with the highway section then, find out all vehicles that travel at the appointed time specifying on the highway section, Filtering Processing, weighting value are on average recovered the overall section mean speed of road network; Consider the difference of vehicle, obtain the different travel directions of vehicle on road, thereby the velocity information of vehicle is classified, obtain average speed uplink, the downstream rate of road by the road node sequence.Dynamically be shown on the GIS map, as shown in Figure 6, urban traffic information system is distinguished the road network average travel speed with different colors and is shown.
Claims (1)
1, a kind of acquisition methods of average speed of city rode traffic low region is characterized in that comprising the steps:
1) carries out map match with nearest neighbor method: real-time GPS locator data and the Geographic Information System information of input city vehicle, the position data of vehicle is considered as diffusing point data road vertical projection towards periphery, and calculating projector distance, if wherein the shortest projector distance of certain diffusing point data is greater than the threshold value that sets in advance, then think error matching points, it is filtered out, otherwise getting the pairing road of its shortest projector distance is the travel at vehicle place, corresponding subpoint is the position after the vehicle coupling, obtain PRELIMINARY RESULTS, finish from putting the map match of line;
2) formation of track of vehicle: to the dispersion vehicle match point that obtains in the step 1, time sequencing according to match point adopts the A* algorithm, the vehicle match point of previous time is considered as start node, the vehicle match point of next time is considered as destination node, search for the whole road network space diagram that may reach, till finding destination node, recall searching route, obtain two shortest paths between the point, note; All match points that connect each car as stated above successively form track of vehicle, finish the coupling of whole vehicle driving trace line and urban road; All vehicles are handled equally, obtained the driving trace of all vehicles in the road network;
3) differentiation of uncertain locator data and processing: according to the confidence level of the information calculations match point of point of proximity place road on projector distance and the same vehicle driving trace, and will be continuously directly list uncertain vehicle location data point in by the match point of twice of same paths, the vehicle driving trace place road that uncertain vehicle location data point is obtained in step 2 carries out projection, projector distance is in threshold value, revise successfully, according to the vehicle driving trace of new this section of match point correction; Otherwise, revise failure, think that the original driving trace coupling of vehicle is correct; All vehicles are handled equally, obtained the revised driving trace of all vehicles in the road network;
4) the road network section mean speed is calculated: the vehicle position information and the place road information that are provided by revised single vehicle driving trace in the step 3 obtain the distance value of vehicle on track, utilize the temporal information in the GPS vehicle data, obtain vehicle in driving trace up stroke speed, and obtain the travel speed of all vehicles on driving trace in the road network with same procedure; Be base unit with the highway section then, find out all vehicles that travel at the appointed time specifying on the highway section, Filtering Processing, weighting value are on average recovered the overall section mean speed of road network; By the difference of vehicle by the road node sequence, obtain the different travel directions of vehicle on road, thereby the velocity information of vehicle is classified, obtain average speed uplink, the downstream rate of road, and dynamically be shown on the Geographic Information System map.
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