CN103903438A - Method and system for recognizing illegal parking positions of passenger service vehicles - Google Patents
Method and system for recognizing illegal parking positions of passenger service vehicles Download PDFInfo
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Abstract
The invention relates to a method for recognizing illegal parking positions of passenger service vehicles. The method comprises the step of obtaining GPS data of the vehicles, the step of recognizing the parking aggregating areas of the vehicles according to the obtained GPS data, the step of screening the recognized parking aggregating areas to obtain suspicious parking areas and the step of grading the suspicious parking areas according to the suspicious degree. The invention further relates to a system for recognizing the illegal parking positions of the passenger service vehicles. According to the method and system, misjudgment can be greatly reduced, the recognition accuracy for the suspicious illegal parking positions can be improved, and labor cost for monitoring the illegal parking is reduced.
Description
Technical field
The present invention relates to a kind of passenger stock illegal parking place recognition methods and system.
Background technology
Under national economy sustainable growth, resident grows with each passing day to the demand of intercity trip, and expressway construction is also rapidly developed, and it is flourish that coach passenger transport market has correspondingly obtained continuing.But coach is in when operation, the phenomenon of ubiquity illegal parking.Illegal parking attracts customers outside normally standing, and this behavior exists huge potential safety hazard: on the one hand, outer the attracting customers of standing easily causes overload, and overload is a main cause that causes traffic hazard; On the other hand, the outside upper visitor's that stands luggage, through the investigation of regular upper visitor's point, does not likely comprise the violated article that carry, and then forms serious potential safety hazard.Therefore, the behavior of supervision illegal parking is the important process in long-distance passenger transportation industry.
For a long time, domestic relevant departments mainly rely on on-the-spot method of scouting illegal parking is investigated and enforced the law.In recent years, the watch-dog that each main cities must have been installed GPS as long-distance passenger transportation vehicle, has progressively started the coach supervisory system based on GPS.This type systematic has vehicle location tracking, driving trace playback, overspeed alarming, cross the border warning, the overtime warning of stopping etc. conventionally for monitoring the function of unlawful practice at present.Wherein, be mainly to cross the border to report to the police and two of the overtime warnings of stopping with the closely-related function of discovery illegal parking behavior.The warning function that crosses the border need to arrange the planning travel route of coach, once find that it departs from planning travel route certain distance, reports to the police.The overtime warning function that stops need to arrange threshold value down time, once find to exceed setting threshold its down time, reports to the police.
Carry out on-the-spot scouting for illegal parking and need to rely on law enfrocement official's protracted experience, and expend a large amount of manpowers and time cost.For illegal parking behavior, all there is obvious deficiency with the overtime warning function that stops in the warning function that crosses the border in existing coach supervisory system.One, departs from planning travel route certain distance once the warning function that crosses the border is found vehicle in use, reports to the police.But coach is in actual travel, while especially travelling in city, the traffic (for example, traffic jam, traffic control etc.) changing can cause driver and conductor to select non-programme path to travel, and then causes erroneous judgement, interference management work, reduces system actual utility.Its two, in reality operation, the outer behaviors of attracting customers in a large amount of stations occur in programme path on the way, the warning function that crosses the border cannot be monitored the behavior of the type.Its three, outer phenomenon down time under many circumstances very of short duration (for example, tens seconds or one or two minute) that attracts customers of standing.The time of fire alarming threshold value setting of overtime warning function higher (for example, 30 minutes) if stopped, outside so a large amount of stations, attracting customers stops can't cause the overtime warning of stopping.And the time of fire alarming threshold value setting of the overtime warning function that stops lower (for example, 2 minutes), so a large amount of reasonable parkings (for example, waiting traffic lights) can cause erroneous judgement.Therefore there is a large amount of erroneous judgements and careless omission in existing illegal parking place recognition technology.
Summary of the invention
In view of this, be necessary to provide a kind of passenger stock illegal parking place recognition methods and system.
The invention provides a kind of passenger stock illegal parking place recognition methods, the method comprises the steps: that a. obtains the gps data of multiple vehicles; B. according to the gps data obtaining, identify the parking aggregation zone of described multiple vehicles; C. the described parking aggregation zone identifying is screened and obtains suspicious parking area; D. suspicious parking area is carried out to suspicious intensity grade division.
Wherein, described step b comprises: described gps data is carried out to pre-service; Extract Parking according to pretreated gps data; Find parking aggregation zone according to the Parking extracting.
Described finds that according to the Parking extracting parking aggregation zone adopts cuclear density analytic approach.
Described step c comprises: get rid of planning carrying point; Get rid of and wait traffic lights the parking aggregation zone causing; Get rid of the jogging region that blocks up; Get rid of other reasonable parking areas.
Described suspicious intensity grade comprise high suspicious, in suspicious, low suspicious.
The present invention also provides a kind of passenger stock illegal parking location identifying system, comprises acquisition module, identification module, the screening module of mutual electric connection and divides module, wherein: described acquisition module is for obtaining the gps data of multiple vehicles; Described identification module, for according to the gps data obtaining, is identified the parking aggregation zone of described multiple vehicles; Described screening module is for screening and obtain suspicious parking area the described parking aggregation zone identifying; Described division module is for carrying out suspicious intensity grade division to suspicious parking area.
Wherein, described identification module, specifically for described gps data is carried out to pre-service, extracts Parking according to pretreated gps data, and finds parking aggregation zone according to the Parking extracting.
Described finds that according to the Parking extracting parking aggregation zone adopts cuclear density analytic approach.
Described screening module, specifically for getting rid of planning carrying point, is got rid of and is waited traffic lights the parking aggregation zone causing, and gets rid of the jogging region that blocks up, and gets rid of other reasonable parking areas.
Described suspicious intensity grade comprise high suspicious, in suspicious, low suspicious.
Passenger stock illegal parking place recognition methods of the present invention and system, assemble the higher rule of the suspicious degree in place of stopping according to multiple vehicles, from the driving trace of numerous vehicles, identify suspicious parking and assemble place and feature, can significantly reduce erroneous judgement, improve the recognition accuracy in suspicious illegal parking place, reduce the human cost of supervision illegal parking.
Brief description of the drawings
Fig. 1 is the process flow diagram of passenger stock illegal parking place recognition methods of the present invention;
Fig. 2 is the hardware structure figure of passenger stock illegal parking location identifying system of the present invention.
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.
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.
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.
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.
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.
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.
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.
Claims (10)
1. the recognition methods of passenger stock illegal parking place, is characterized in that, the method comprises the steps:
A. obtain the gps data of multiple vehicles;
B. according to the gps data obtaining, identify the parking aggregation zone of described multiple vehicles;
C. the described parking aggregation zone identifying is screened and obtains suspicious parking area;
D. suspicious parking area is carried out to suspicious intensity grade division.
2. the method for claim 1, is characterized in that, described step b comprises:
Described gps data is carried out to pre-service;
Extract Parking according to pretreated gps data;
Find parking aggregation zone according to the Parking extracting.
3. method as claimed in claim 2, is characterized in that, described finds that according to the Parking extracting parking aggregation zone adopts cuclear density analytic approach.
4. method as claimed in claim 1 or 2, is characterized in that, described step c comprises:
Get rid of planning carrying point;
Get rid of and wait traffic lights the parking aggregation zone causing;
Get rid of the jogging region that blocks up;
Get rid of other reasonable parking areas, described other reasonable parking areas comprise charge station, inspection post, joint inspection station, motor vehicle inspection station, maintenance factory, automobile fitting, car detailing shop.
5. method as claimed in claim 4, is characterized in that, described suspicious intensity grade comprise high suspicious, in suspicious, low suspicious.
6. a passenger stock illegal parking location identifying system, is characterized in that, this system comprises acquisition module, identification module, screening module and the division module of mutual electric connection, wherein:
Described acquisition module is for obtaining the gps data of multiple vehicles;
Described identification module, for according to the gps data obtaining, is identified the parking aggregation zone of described multiple vehicles;
Described screening module is for screening and obtain suspicious parking area the described parking aggregation zone identifying;
Described division module is for carrying out suspicious intensity grade division to suspicious parking area.
7. system as claimed in claim 6, is characterized in that, described identification module, specifically for described gps data is carried out to pre-service, extracts Parking according to pretreated gps data, and finds parking aggregation zone according to the Parking extracting.
8. system as claimed in claim 7, is characterized in that, described finds that according to the Parking extracting parking aggregation zone adopts cuclear density analytic approach.
9. the system as described in claim 6 or 7, is characterized in that, described screening module is specifically for getting rid of planning carrying point; Get rid of and wait traffic lights the parking aggregation zone causing; Get rid of the jogging region that blocks up; And get rid of other reasonable parking areas, described other reasonable parking areas comprise charge station, inspection post, joint inspection station, motor vehicle inspection station, maintenance factory, automobile fitting, car detailing shop.
10. system as claimed in claim 9, is characterized in that, described suspicious intensity grade comprise high suspicious, in suspicious, low suspicious.
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105206083A (en) * | 2015-09-24 | 2015-12-30 | 上海车音网络科技有限公司 | Designated driving monitoring early warning device, system and method |
| CN105206082A (en) * | 2015-09-24 | 2015-12-30 | 上海车音网络科技有限公司 | Designated driving management device, system and method |
| CN105321226A (en) * | 2014-07-31 | 2016-02-10 | 福特全球技术公司 | Method and system for vehicle data gathering and analysis |
| CN105702043A (en) * | 2016-04-22 | 2016-06-22 | 北京国交信通科技发展有限公司 | Early warning method for illegal parking on highway of key operating vehicle |
| CN105810006A (en) * | 2016-03-29 | 2016-07-27 | 福建工程学院 | Method and system for recognizing roadside parking place |
| CN108021625A (en) * | 2017-11-21 | 2018-05-11 | 深圳广联赛讯有限公司 | Vehicle abnormality aggregation ground monitoring method and system, computer-readable recording medium |
| CN108122414A (en) * | 2016-11-30 | 2018-06-05 | 杭州海康威视数字技术股份有限公司 | The detection method and device of car on-board and off-board on highway |
| CN109656245A (en) * | 2018-10-31 | 2019-04-19 | 百度在线网络技术（北京）有限公司 | The determination method and apparatus of normal danger |
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| CN111788617A (en) * | 2018-03-06 | 2020-10-16 | 斯堪尼亚商用车有限公司 | Method and control device for identifying parking areas |
| CN111951554A (en) * | 2020-08-20 | 2020-11-17 | 北京嘀嘀无限科技发展有限公司 | Illegal parking road information acquisition method and system |
| CN112185103A (en) * | 2019-09-24 | 2021-01-05 | 成都通甲优博科技有限责任公司 | Traffic monitoring method and device and electronic equipment |
| CN112712696A (en) * | 2020-12-30 | 2021-04-27 | 北京嘀嘀无限科技发展有限公司 | Method and device for determining road section with illegal parking |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060100921A1 (en) * | 2002-12-17 | 2006-05-11 | Gianfranco Zanotti | Automatic system for monitoring and managing the admittance to parking places |
| CN101241643A (en) * | 2008-03-05 | 2008-08-13 | 中科院嘉兴中心微系统所分中心 | Passenger car monitoring system based on sensor network technology |
| CN101425225A (en) * | 2007-10-30 | 2009-05-06 | 厦门雅迅网络股份有限公司 | Method for managing automobile line by using GPS technology |
| CN101800772A (en) * | 2010-02-02 | 2010-08-11 | 大连海创高科信息技术有限公司 | 3G vehicle-mounted intelligent monitoring management system |
| CN102231822A (en) * | 2011-06-24 | 2011-11-02 | 广州亿程交通信息有限公司 | Monitoring system |
| KR20120109163A (en) * | 2011-03-28 | 2012-10-08 | 제주대학교 산학협력단 | Parking and stopping control system, mobile for official and server used in there, and method therefor |
| CN103150920A (en) * | 2013-01-29 | 2013-06-12 | 北京瑞拓电子技术发展有限公司 | Vehicle illegal parking remote monitoring system |
| CN103236182A (en) * | 2013-03-29 | 2013-08-07 | 安科智慧城市技术（中国）有限公司 | Method and system for monitoring traffic safety and monitoring device |
| CN103440767A (en) * | 2013-09-04 | 2013-12-11 | 彭博 | Highway-driving-information vehicle-mounted terminal, monitoring system and method |
-
2014
- 2014-03-14 CN CN201410095229.8A patent/CN103903438B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060100921A1 (en) * | 2002-12-17 | 2006-05-11 | Gianfranco Zanotti | Automatic system for monitoring and managing the admittance to parking places |
| CN101425225A (en) * | 2007-10-30 | 2009-05-06 | 厦门雅迅网络股份有限公司 | Method for managing automobile line by using GPS technology |
| CN101241643A (en) * | 2008-03-05 | 2008-08-13 | 中科院嘉兴中心微系统所分中心 | Passenger car monitoring system based on sensor network technology |
| CN101800772A (en) * | 2010-02-02 | 2010-08-11 | 大连海创高科信息技术有限公司 | 3G vehicle-mounted intelligent monitoring management system |
| KR20120109163A (en) * | 2011-03-28 | 2012-10-08 | 제주대학교 산학협력단 | Parking and stopping control system, mobile for official and server used in there, and method therefor |
| CN102231822A (en) * | 2011-06-24 | 2011-11-02 | 广州亿程交通信息有限公司 | Monitoring system |
| CN103150920A (en) * | 2013-01-29 | 2013-06-12 | 北京瑞拓电子技术发展有限公司 | Vehicle illegal parking remote monitoring system |
| CN103236182A (en) * | 2013-03-29 | 2013-08-07 | 安科智慧城市技术（中国）有限公司 | Method and system for monitoring traffic safety and monitoring device |
| CN103440767A (en) * | 2013-09-04 | 2013-12-11 | 彭博 | Highway-driving-information vehicle-mounted terminal, monitoring system and method |
Non-Patent Citations (1)
| Title |
|---|
| 李玲 等: "基于GPS定位及3G通信客运车辆监控系统设计", 《现代电子技术》 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105321226A (en) * | 2014-07-31 | 2016-02-10 | 福特全球技术公司 | Method and system for vehicle data gathering and analysis |
| CN105206083A (en) * | 2015-09-24 | 2015-12-30 | 上海车音网络科技有限公司 | Designated driving monitoring early warning device, system and method |
| CN105206082A (en) * | 2015-09-24 | 2015-12-30 | 上海车音网络科技有限公司 | Designated driving management device, system and method |
| CN105810006B (en) * | 2016-03-29 | 2018-08-21 | 福建工程学院 | The recognition methods of parking position and system |
| CN105810006A (en) * | 2016-03-29 | 2016-07-27 | 福建工程学院 | Method and system for recognizing roadside parking place |
| CN105702043B (en) * | 2016-04-22 | 2018-07-06 | 北京国交信通科技发展有限公司 | To the method for early warning of emphasis commerial vehicle parking offense on a highway |
| CN105702043A (en) * | 2016-04-22 | 2016-06-22 | 北京国交信通科技发展有限公司 | Early warning method for illegal parking on highway of key operating vehicle |
| CN108122414A (en) * | 2016-11-30 | 2018-06-05 | 杭州海康威视数字技术股份有限公司 | The detection method and device of car on-board and off-board on highway |
| CN108021625A (en) * | 2017-11-21 | 2018-05-11 | 深圳广联赛讯有限公司 | Vehicle abnormality aggregation ground monitoring method and system, computer-readable recording medium |
| CN111788617A (en) * | 2018-03-06 | 2020-10-16 | 斯堪尼亚商用车有限公司 | Method and control device for identifying parking areas |
| US11410554B2 (en) | 2018-03-06 | 2022-08-09 | Scania Cv Ab | Method and control arrangement for identification of parking areas |
| CN109656245B (en) * | 2018-10-31 | 2020-10-27 | 百度在线网络技术（北京）有限公司 | Method and device for determining brake position |
| CN109656245A (en) * | 2018-10-31 | 2019-04-19 | 百度在线网络技术（北京）有限公司 | The determination method and apparatus of normal danger |
| CN111179578A (en) * | 2018-11-09 | 2020-05-19 | 北京嘀嘀无限科技发展有限公司 | Method and system for determining parking place limitation |
| CN110428604A (en) * | 2019-07-30 | 2019-11-08 | 山东交通学院 | It is a kind of based on the taxi illegal parking of GPS track data and map datum monitoring and method for early warning |
| CN112185103A (en) * | 2019-09-24 | 2021-01-05 | 成都通甲优博科技有限责任公司 | Traffic monitoring method and device and electronic equipment |
| CN111951554A (en) * | 2020-08-20 | 2020-11-17 | 北京嘀嘀无限科技发展有限公司 | Illegal parking road information acquisition method and system |
| CN112712696A (en) * | 2020-12-30 | 2021-04-27 | 北京嘀嘀无限科技发展有限公司 | Method and device for determining road section with illegal parking |
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