Embodiment
Below in conjunction with drawings and the specific embodiments, the present invention is further detailed explanation.
Consulting shown in Fig. 1, is the operation process chart of passenger stock illegal parking of the present invention place recognition methods preferred embodiment.
Step S401, obtains the gps data of multiple vehicles.Particularly, from the GPS of multiple even magnanimity vehicles, obtain data.
Step S402, according to the gps data obtaining, identifies the parking aggregation zone of described multiple vehicles.
Particularly: first, described gps data is carried out to pre-service.Long-distance passenger transportation GPS track data in certain period is carried out to pre-service, remove various invalid records, comprise null value, signal drift etc.
Then, extract Parking.Extract the stop of travel speed v=0, continuous 2 above stops are labeled as to Parking one time.Calculate the spatial dimension of all stops in this Parking, for example, if longitude or latitude scope exceed certain threshold value (, being greater than 0.5 degree), be labeled as the invalid Parking that error in data causes.For the effective Parking after screening, the residence time using the time interval of initial stop as Parking, the geographic coordinate using the average longitude and latitude of stop as Parking.
Finally, find parking aggregation zone.According to the spacial distribution density of the Parking of multiple vehicles, find parking aggregation zone.This step adopts the clustering methodology based on density, filters out the region that parking density is greater than certain threshold value.Specific as follows:
The present embodiment adopts cuclear density analytic approach to extract the accurate shape of parking aggregation zone.Analyzed area is divided into grid cell by described cuclear density analytic approach, then calculate each grid cell around want vegetarian refreshments, the i.e. density of the coordinate points of Parking.Described grid cell is simply the most direct spatial data structure, refers to earth surface is divided into the evenly tight adjacent grid array of size, and each grid is as a basic space cell.According to cuclear density method, each vegetarian refreshments top of wanting is all covered with a smooth surface.Point position place face value is the highest, along with reducing gradually with the increase face value of the distance of point, is zero at the position face value that equals search radius with the distance of point.The volume in the space that the plane of curved surface and below surrounds equals the total amount of the event generation of this point, occurs in the Parking total amount of this coordinate points.The density of each output grid unit is the value sum on all cores surface that is superimposed upon grid cell, raster cell center.Computing method as shown in Equation 1.
Formula 1
Wherein, K is kernel function, x
1, x
2... x
nfor Parking sample set, n is sample size, and h is bandwidth (search radius).Conventional kernel function K comprises Uniform, Epanechikov, Quartic, Gaussian etc.The present embodiment adopts Epanechikov function, as shown in Equation 2.In practicing, can select corresponding kernel function according to actual conditions.
Formula 2
Wherein, u=(x-xi)/h.
Use in the process of cuclear density analysis, it is a committed step that bandwidth h is set.Arranging of different bandwidth can cause density Estimation result difference, and then the aggregation zone that causes stopping extracts result difference.In concrete application, should test bandwidth by many experiments, select the bandwidth of suitable applications scene.
After using cuclear density analysis to estimate the continuous density distribution plan of Parking, aggregation degree density threshold is set, extracts the grid cell that is greater than this threshold value, continuous grid cell forms a parking aggregation zone.And then can extract comparatively accurately the shape of illegal parking aggregation zone.In concrete application, should test aggregation degree density threshold by many experiments, select the density threshold of suitable applications scene.
Step S403, screens the parking aggregation zone of the vehicle identifying, and setting threshold is to obtain suspicious parking area.Concrete steps are as follows:
Coach there will be various rational parking scenes in operation process, comprises through charge station, waits traffic lights, blocks up, oiling, auto repair, driver and conductor have a rest etc.Thereby, in the parking aggregation zone extracting, comprise various rational stop parking lot scape.How distinguishing illegal parking region is a committed step of the present invention with reasonable parking area.
The present embodiment adopts exclusive method to realize the discovery in parking offense region, and four kinds of main exclusion programs are as follows:
(a) get rid of planning carrying point.Collect the distributing position of planning carrying point, will exclude with planning the have living space parking area of overlapping relation of carrying point region.Wherein, planning carrying point comprises regular passenger station and joins objective point.
(b) get rid of and wait traffic lights the parking aggregation zone causing.Collect traffic lights distributing position, will necessarily wait for that apart from traffic lights the parking area in distance excludes.The present embodiment is waited for apart from testing traffic lights by many experiments, is selected the wait distance of suitable applications scene.
(c) get rid of the jogging region that blocks up.Extract on expressway, major trunk roads and be along road the parking area that strip distributes, travel speed before and after calculated target point, if average overall travel speed is lower than certain speed threshold value in region, judges that it,, for the jogging region that blocks up, excludes this region.
(d) get rid of other reasonable parking areas.Collect the distributing position of other reasonable parking sites, the parking area that has space intersection relation with various reasonable stops is excluded.Wherein, described other reasonable parking areas comprise charge station, inspection post, joint inspection station, motor vehicle inspection station, maintenance factory, automobile fitting, car detailing shop etc.
Step S404, carries out suspicious intensity grade division to suspicious parking area.Be high suspicious by near the region division common illegal parking terrestrial references such as passenger station, bus station, subway station, parking lot, refuelling station, travel agency, have same vehicle repeatedly suspicious in being set to of Parking in remaining area, all the other region divisions are low suspicious.Concrete steps are as follows:
Verify the period for emphasis, first according to one day 24 period, calculate the stop frequency in each period in month.Suppose that Parking follows Poisson distribution, adopt formula 3 to calculate the probability of finding at least 1 Parking in each period.In the time that a random occurrence occurs at random and independently with the average momentary rate λ (or claiming density) fixing, the number of times that this event occurs within the unit interval (area or volume) so or number are just obeyed Poisson distribution approx.According to finding the probability of Parking and the needs of practical application, filter out emphasis and verify the period.
Formula 3
Verify car plate for emphasis, this project is calculated in each suspicious region total stop frequency of each car plate in month.By maximum total stop frequency and have at least the car plate of 2 times to be labeled as emphasis to verify car plate.
Consulting shown in Fig. 2, is the hardware structure figure of passenger stock illegal parking location identifying system of the present invention.This system comprises acquisition module, identification module, the screening module of mutual electric connection and divides module.
Described acquisition module is for obtaining the gps data of vehicle.Particularly, described acquisition module obtains data from be arranged on the GPS of each vehicle.
Described identification module, for according to the gps data obtaining, is identified the parking aggregation zone of vehicle.Specific as follows:
First, described identification module carries out pre-service to described gps data.Long-distance passenger transportation GPS track data in certain period is carried out to pre-service, remove various invalid records, comprise null value, signal drift etc.
Then, described identification module extracts Parking.Extract the stop of travel speed v=0, continuous 2 above stops are labeled as to Parking one time.Calculate the spatial dimension of all stops in this Parking, for example, if longitude or latitude scope exceed certain threshold value (, being greater than 0.5 degree), be labeled as the invalid Parking that error in data causes.For the effective Parking after screening, the residence time using the time interval of initial stop as Parking, the geographic coordinate using the average longitude and latitude of stop as Parking.
Finally, described identification module is found parking aggregation zone.According to the spacial distribution density of the Parking of multiple vehicles, find the region of stopping and assembling.This step adopts the clustering methodology based on density, filters out the region that parking density is greater than certain threshold value.Specific as follows:
The present embodiment adopts cuclear density analytic approach to extract the accurate shape of parking aggregation zone.Analyzed area is divided into grid cell by described cuclear density analytic approach, then calculate each grid cell around want vegetarian refreshments, the i.e. density of the coordinate points of Parking.Described grid cell is simply the most direct spatial data structure, refers to earth surface is divided into the evenly tight adjacent grid array of size, and each grid is as a basic space cell.According to cuclear density method, each vegetarian refreshments top of wanting is all covered with a smooth surface.Point position place face value is the highest, along with reducing gradually with the increase face value of the distance of point, is zero at the position face value that equals search radius with the distance of point.The volume in the space that the plane of curved surface and below surrounds equals the total amount of the event generation of this point, occurs in the Parking total amount of this coordinate points.The density of each output grid unit is the value sum on all cores surface that is superimposed upon grid cell, raster cell center.Computing method as shown in Equation 1.
Formula 1
Wherein, K is kernel function, x
1, x
2... x
nfor Parking sample set, n is sample size, and h is bandwidth (search radius).Conventional kernel function K comprises Uniform, Epanechikov, Quartic, Gaussian etc.The present embodiment adopts Epanechikov function, as shown in Equation 2.In practicing, can select corresponding kernel function according to actual conditions.
Formula 2
Wherein, u=(x-xi)/h.
Use in the process of cuclear density analysis, it is a committed step that bandwidth h is set.Arranging of different bandwidth can cause density Estimation result difference, and then the aggregation zone that causes stopping extracts result difference.In concrete application, should test bandwidth by many experiments, select the bandwidth of suitable applications scene.
After using cuclear density analysis to estimate the continuous density distribution plan of Parking, aggregation degree density threshold is set, extracts the grid cell that is greater than this threshold value, continuous grid cell forms a parking aggregation zone.And then can extract comparatively accurately the shape of illegal parking aggregation zone.In concrete application, should test aggregation degree density threshold by many experiments, select the density threshold of suitable applications scene.
Described screening module is for the parking aggregation zone of the vehicle identifying is screened, and setting threshold is to obtain suspicious parking area.Specific as follows:
Coach there will be various rational parking scenes in operation process, comprises through charge station, waits traffic lights, blocks up, oiling, auto repair, driver and conductor have a rest etc.Thereby, in the parking aggregation zone extracting, comprise various rational stop parking lot scape.How distinguishing illegal parking region is a committed step of the present invention with reasonable parking area.
The present embodiment adopts exclusive method to realize the discovery in parking offense region, and four kinds of main exclusion programs are as follows:
(a) get rid of planning carrying point.Collect the distributing position of planning carrying point, will exclude with planning the have living space parking area of overlapping relation of carrying point region.Wherein, planning carrying point comprises regular passenger station and joins objective point.
(b) get rid of and wait traffic lights the parking aggregation zone causing.Collect traffic lights distributing position, will necessarily wait for that apart from traffic lights the parking area in distance excludes.The present embodiment is waited for apart from testing traffic lights by many experiments, is selected the wait distance of suitable applications scene.
(c) get rid of the jogging region that blocks up.Extract on expressway, major trunk roads and be along road the parking area that strip distributes, travel speed before and after calculated target point, if average overall travel speed is lower than certain speed threshold value in region, judges that it,, for the jogging region that blocks up, excludes this region.
(d) get rid of other reasonable parking areas.Collect the distributing position of other reasonable parking sites, the parking area that has space intersection relation with various reasonable stops is excluded.Wherein, described other reasonable parking areas comprise charge station, inspection post, joint inspection station, motor vehicle inspection station, maintenance factory, automobile fitting, car detailing shop etc.
Described division module is for carrying out suspicious intensity grade division to suspicious parking area.Be high suspicious by near the region division common illegal parking terrestrial references such as passenger station, bus station, subway station, parking lot, refuelling station, travel agency, have same vehicle repeatedly suspicious in being set to of Parking in remaining area, all the other region divisions are low suspicious.Specific as follows:
Verify the period for emphasis, first according to one day 24 period, calculate the stop frequency in each period in month.Suppose that Parking follows Poisson distribution, adopt formula 3 to calculate the probability of finding at least 1 Parking in each period.In the time that a random occurrence occurs at random and independently with the average momentary rate λ (or claiming density) fixing, the number of times that this event occurs within the unit interval (area or volume) so or number are just obeyed Poisson distribution approx.According to finding the probability of Parking and the needs of practical application, filter out emphasis and verify the period.
Formula 3
Verify car plate for emphasis, this project is calculated in each suspicious region total stop frequency of each car plate in month.By maximum total stop frequency and have at least the car plate of 2 times to be labeled as emphasis to verify car plate.
Although the present invention is described with reference to current preferred embodiments; but those skilled in the art will be understood that; above-mentioned preferred embodiments is only used for illustrating the present invention; not be used for limiting protection scope of the present invention; any within the spirit and principles in the present invention scope; any modification of doing, equivalent replacement, improvement etc., within all should being included in the scope of the present invention.