CN112017429B - Overload control monitoring stationing method based on truck GPS data - Google Patents
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Abstract
The invention provides a truck GPS data-based overload control monitoring stationing method, which screens and cleans vehicle GPS data to obtain the GPS data of freight vehicles; then, dividing the vehicle travel state based on the travel angle and the truck speed, identifying a truck travel stop point, and marking a truck travel chain; then, matching moving points in the travel track of the truck to the urban road network based on a direction matching method and a shortest distance method; then, two evaluation indexes of the daily average vehicle passing frequency and the daily average vehicle passing number are constructed, and the daily average vehicle passing frequency and the daily average vehicle passing number of each road truck are calculated; then, constructing a road comprehensive passing index according to the daily average passing frequency and the daily average passing number, sequencing the stationing importance of the road sections, and selecting the road section with the front comprehensive passing index value to form a monitoring stationing scheme; and finally, two evaluation indexes of daily average detected vehicle ratio and truck trip coverage rate are constructed, and the overload control monitoring effect after the monitoring point positions are newly added is evaluated.
Description
Technical Field
The invention relates to the technical field of science and technology overload control monitoring, in particular to an overload control monitoring stationing method based on truck GPS data.
Background
Over the years, due to the facts that the goods transportation market is not standardized, the freight price is low, the goods transportation quantity is huge, and the law enforcement department is lagged behind and single in over-limit overload means, insufficient allocation of law enforcement personnel and the like, enterprises and drivers have a lucky psychology, and the over-limit overload phenomenon is serious under the driving of economic benefits. The overload of freight vehicles often causes frequent traffic accidents and endangers the service life of roads and bridges. Therefore, the urban science and technology super-job is still a problem to be solved urgently.
The reasonable selection of the control over monitoring distribution points is an important basic work of science and technology control over. However, the existing research is mainly embodied in the aspects of the design of the overload control strategy for the over-limit of the highway trucks and the over-management system for the scientific and technological control. However, the travel path of the freight vehicle is an intuitive and important factor in setting up the layout of the monitoring points, and the trucks are used as important carriers of the overload behavior, and the concentrated sections of the travel path of the freight vehicle are usually high-rise places of the overload behavior. Currently, in the research on the utilization of truck GPS data, the research is limited to the research on the source and the highway. With the development of urban roads, weighing devices are arranged at each entrance and exit of expressways and urban expressways, overloaded vehicles cannot enter the expressways, and trucks are shunted to national roads, provincial roads and urban roads in each region. Therefore, a super-monitoring distribution point method based on truck GPS data analysis is needed, monitoring points under a large-area road network are quickly selected, the problem of insufficient monitoring of the existing monitoring distribution points is solved, the problem of large manpower and material resources of the traditional field survey method is solved, and powerful data support is provided for city super-monitoring distribution points.
The patent specification with application number 201510645296.7 discloses a method for urban road traffic detector stationing, which uses an undirected graph G (V, E) to model urban road networks and assigns three parameters to each road: whether a traffic detector's boolean variable is installed, the boolean variable detected or inferred, and the importance in the entire road network; optimizing the whole model and determining a maximized detection important road section; however, the patent cannot realize quick selection of monitoring points under a large-area road network, so that the problem of insufficient monitoring of the existing monitoring distribution points is solved, the problem of large manpower and material resources of the traditional field survey method is solved, and powerful data support is provided for urban treatment and super distribution points.
Disclosure of Invention
The invention provides a truck GPS data-based overload monitoring and stationing method, which can realize quick selection of monitoring points in a large-area road network so as to make up for the problem of insufficient monitoring of the conventional monitoring and stationing method.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
a truck GPS data-based overload control monitoring stationing method comprises the following steps:
s1: screening and cleaning vehicle GPS data to obtain GPS data of freight vehicles;
s2: dividing the vehicle travel state based on the travel angle and the truck speed, identifying a truck travel stop point, and marking a truck travel chain;
s3: matching moving points in the travel track of the truck to the urban road network based on a direction matching method and a shortest distance method;
s4: two evaluation indexes of the daily average vehicle-passing frequency and the daily average vehicle-passing number are established, the daily average vehicle-passing frequency and the daily average vehicle-passing number of each road truck are calculated, and analysis is carried out;
s5: constructing a road comprehensive passing index according to the daily average passing frequency and the daily average passing number, sequencing the stationing importance of the road sections, and selecting the road section with the front comprehensive passing index value to form a monitoring stationing scheme;
s6: and two evaluation indexes of daily average detected vehicle ratio and freight car trip coverage rate are established, and the overload control monitoring effect after the newly added monitoring point positions is evaluated.
Further, the data cleansing process in step S1 is:
GPS data of urban freight vehicles are selected, and the GPS data which exceeds the latitude and longitude range of a road network and is repeated by the same vehicle in time is eliminated.
Further, in the step S2, the truck stop point identification and trip chain marking method is as follows:
the data with the speed of 0 is not necessarily the data of the stay state due to the condition of waiting for a traffic light or temporary parking; dynamically adjusting and judging the threshold value of the stopping point, and marking the GPS data with the speed of 0 for x seconds or more continuously of the same vehicle as the stopping point data; the GPS data of the vehicle is sorted according to time, the GPS data which are continuously marked as the stop points are regarded as a stop point cluster, the GPS data between the adjacent stop point clusters are a trip chain, the moving point in the first trip chain is marked as 1 according to the number and the time sequence of the trip chains of the user, and the like, and the moving point in the Nth trip chain is marked as N.
Further, the road network matching process in step S3 is:
s31: unifying a space coordinate system;
s32: the positioning accuracy of the GPS data is high, and each data point is provided with a direction angle field, so that the GPS data is matched into a road network based on a direction matching method and a shortest distance method.
Further, the specific process of step S31 is:
the GPS data of the floating car takes longitude and latitude as coordinates, and adopts a WGS84 space coordinate system; in order to calculate the distance between the GPS point and the road, before performing road network matching, the coordinates need to be converted into projection coordinates, and the GPS coordinates and the coordinates of the road network map are both converted into the same projection coordinates for calculation, where the projection coordinates are: xian _1980_3_ hierarchy _ GK _ Zone _38, WKID 2362.
Further, the specific process of step S32 is:
1) and road network interruption: breaking all continuous broken line sections in a road network into small line sections, and calculating clockwise included angles between driving directions and true north directions of all the line sections as direction angles;
2) and road section searching: for each GPS point, searching all road sections which take the GPS point as a center and have the side length of w meters in a square, and marking the road sections as alternative road sections;
3) calculating the direction and the distance: calculating the distance L between the GPS point and all the alternative road sections and the difference between the vehicle angle of the GPS point and the direction angle of all the alternative road sections
The angle difference of the GPS points is equal to that the GPS points are 1 meter away from the road, so the distance G value of all the alternative road sections and the GPS points is calculated according to the following formula:
4) and selecting road sections: selecting the road section with the minimum G value, judging whether the distance L is greater than 25m, if the distance L is less than 25m, the road section is the road section matched with the GPS point, otherwise, the GPS point cannot be matched with the road network; wherein, the 25 meters is selected as the threshold value because the error range of the civil GPS is 25 meters at most.
Further, the average daily passing frequency and the average daily passing number in step S4 mean the number of vehicles passing through the road segment per day and the number of vehicles, and the statistical method is as follows: traversing each trip of each vehicle every day, searching the road section passed by each trip of the truck, then carrying out road section de-weighting, superposing the corresponding vehicle passing frequency or vehicle passing number of each road section, and finally averaging to obtain the daily average vehicle passing frequency and the daily average vehicle passing number.
Further, the integrated vehicle passing index in step S5 is obtained by performing maximum and minimum normalization processing on the two indexes of the average daily vehicle passing frequency and the average daily vehicle passing number of all road segments, and performing weighted average to obtain the integrated vehicle passing index Q of each road segmentiAfter the built or planned point is eliminated, Q is selectediTaking the road section with M positions before the value as a road section to be selected; then, aggregating part of monitoring point locations of continuous or similar road sections, transferring part of monitoring point locations of internal roads or villages to roads connected with the monitoring point locations, and finally summarizing to obtain a super-control monitoring point distribution scheme, wherein the comprehensive vehicle passing index calculation formula is as follows:
wherein Q isiIs a comprehensive passing index of the ith road section, FiThe average daily passing frequency of the ith road section, NiThe coefficients alpha and beta are default to 0.5, which is the average number of vehicles passing through the ith road section on day.
Further, the calculation formula of the average-day detected vehicle ratio in step S6 is as follows:
the average daily ratio of the detected vehicles refers to the percentage of the number of the vehicles passing through the point of controlling the over-point in the day, and the calculation formula is as follows:
in the above formula RvMean average daily vehicle ratio, t is the number of days counted, ViThe number of vehicles passing through the point of controlling the passing point on the ith day, AiThe number of vehicles on the ith day.
Further, the calculation formula of the truck trip coverage rate is as follows:
the truck trip coverage rate refers to the percentage of trip times passing through the point of treatment exceeding the point, and the calculation formula is as follows:
assuming n floating vehicle data, each floating vehicle can be divided into m trips, R in the above formuladFinger grip excess row coverage, eikIs used to identify whether the kth trip of the ith vehicle passes through the road segment where the constructed or planned override point is located.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the method fully utilizes the advantages of large sample size, high positioning precision, high uploading frequency and the like of the truck GPS data, controls the super work by data-based driving, can scientifically plan and build the scientific and technological control super monitoring distribution point, overcomes the defect that the traditional control super monitoring distribution point is mainly based on experience judgment and lacks data support, and is favorable for forming a perfect scientific and technological control super monitoring network; meanwhile, the rapid screening and monitoring of the large-area urban road network is performed, so that the difficulties brought by the traditional field survey method in the aspects of manpower, material resources and the like can be overcome, and the point distribution efficiency is improved.
Drawings
FIG. 1 is a flow chart of a freight vehicle travel track analysis;
FIG. 2 is a schematic diagram of a road network map layer and an established or planned super-monitoring point;
fig. 3 is a schematic diagram of a point distribution scheme of newly added super-control monitoring points.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, a truck GPS data-based overload monitoring and stationing method includes the following steps:
s1: screening and cleaning vehicle GPS data to obtain GPS data of freight vehicles;
s2: dividing the vehicle travel state based on the travel angle and the truck speed, identifying a truck travel stop point, and marking a truck travel chain;
s3: matching moving points in the travel track of the truck to the urban road network based on a direction matching method and a shortest distance method;
s4: two evaluation indexes of the daily average vehicle-passing frequency and the daily average vehicle-passing number are established, the daily average vehicle-passing frequency and the daily average vehicle-passing number of each road truck are calculated, and analysis is carried out;
s5: constructing a road comprehensive passing index according to the daily average passing frequency and the daily average passing number, sequencing the stationing importance of the road sections, and selecting the road section with the front comprehensive passing index value to form a monitoring stationing scheme;
s6: and two evaluation indexes of daily average detected vehicle ratio and freight car trip coverage rate are established, and the overload control monitoring effect after the newly added monitoring point positions is evaluated.
The data cleansing process in step S1 is:
GPS data of urban freight vehicles are selected, and the GPS data which exceeds the latitude and longitude range of a road network and is repeated by the same vehicle in time is eliminated.
In step S2, the truck stop point identification and trip chain marking method is as follows:
the data with the speed of 0 is not necessarily the data of the stay state due to the condition of waiting for a traffic light or temporary parking; dynamically adjusting and judging the threshold value of the stopping point, and marking the GPS data with the speed of 0 for x seconds or more continuously of the same vehicle as the stopping point data; the GPS data of the vehicle is sorted according to time, the GPS data which are continuously marked as the stop points are regarded as a stop point cluster, the GPS data between the adjacent stop point clusters are a trip chain, the moving point in the first trip chain is marked as 1 according to the number and the time sequence of the trip chains of the user, and the like, and the moving point in the Nth trip chain is marked as N.
The road network matching process in step S3 is:
s31: unifying a space coordinate system;
s32: the positioning accuracy of the GPS data is high, and each data point is provided with a direction angle field, so that the GPS data is matched into a road network based on a direction matching method and a shortest distance method.
The specific process of step S31 is:
the GPS data of the floating car takes longitude and latitude as coordinates, and adopts a WGS84 space coordinate system; in order to calculate the distance between the GPS point and the road, before performing road network matching, the coordinates need to be converted into projection coordinates, and the GPS coordinates and the coordinates of the road network map are both converted into the same projection coordinates for calculation, where the projection coordinates are: xian _1980_3_ hierarchy _ GK _ Zone _38, WKID 2362.
The specific process of step S32 is:
1) and road network interruption: breaking all continuous broken line sections in a road network into small line sections, and calculating clockwise included angles between driving directions and true north directions of all the line sections as direction angles;
2) and road section searching: for each GPS point, searching all road sections which take the GPS point as a center and have the side length of w meters in a square, and marking the road sections as alternative road sections;
3) calculating the direction and the distance: calculating the distance L between the GPS point and all the alternative road sections and the difference between the vehicle angle of the GPS point and the direction angle of all the alternative road sections
The angle difference of the GPS points is equal to that the GPS points are 1 meter away from the road, so the distance G value of all the alternative road sections and the GPS points is calculated according to the following formula:
4) and selecting road sections: selecting the road section with the minimum G value, judging whether the distance L is greater than 25m, if the distance L is less than 25m, the road section is the road section matched with the GPS point, otherwise, the GPS point cannot be matched with the road network; wherein, the 25 meters is selected as the threshold value because the error range of the civil GPS is 25 meters at most.
The average daily vehicle passing frequency and the average daily vehicle passing number in step S4 mean the number of vehicles passing through the road segment per day and the number of vehicles, and the statistical method is as follows: traversing each trip of each vehicle every day, searching the road section passed by each trip of the truck, then carrying out road section de-weighting, superposing the corresponding vehicle passing frequency or vehicle passing number of each road section, and finally averaging to obtain the daily average vehicle passing frequency and the daily average vehicle passing number.
The comprehensive vehicle passing index in step S5 is obtained by respectively performing maximum and minimum normalization processing on the daily average vehicle passing frequency and the daily average vehicle passing number of all road sections, and performing weighted average to obtain a comprehensive vehicle passing index Q of each road sectioniAfter the built or planned point is eliminated, Q is selectediTaking the road section with M positions before the value as a road section to be selected; then, aggregating part of monitoring point locations of continuous or similar road sections, transferring part of monitoring point locations of internal roads or villages to roads connected with the monitoring point locations, and finally summarizing to obtain a super-control monitoring point distribution scheme, wherein the comprehensive vehicle passing index calculation formula is as follows:
wherein Q isiIs a comprehensive passing index of the ith road section, FiThe average daily passing frequency of the ith road section, NiThe coefficients alpha and beta are default to 0.5, which is the average number of vehicles passing through the ith road section on day.
The calculation formula of the average-day detected vehicle ratio in step S6 is as follows:
the average daily ratio of the detected vehicles refers to the percentage of the number of the vehicles passing through the point of controlling the over-point in the day, and the calculation formula is as follows:
in the above formula RvThe average daily vehicle ratio is detected, t is the number of statistical days,ithe number of vehicles passing through the point of controlling the passing point on the ith day, AiThe number of vehicles on the ith day.
The calculation formula of the truck trip coverage rate is as follows:
the truck trip coverage rate refers to the percentage of trip times passing through the point of treatment exceeding the point, and the calculation formula is as follows:
assuming n floating vehicle data, each floating vehicle can be divided into m trips, R in the above formuladFinger grip excess row coverage, eikIs used to identify whether the kth trip of the ith vehicle passes through the road segment where the constructed or planned override point is located.
The above steps are explained in detail by taking floating car GPS data in a certain city as an example.
Firstly, carrying out data screening on floating car GPS data of 5 months in 2020 of a certain city, eliminating GPS data outside an administrative area of the certain city and repeated data of the same car in terms of time, and obtaining 823 general freight cars, 24877 heavy goods cars, 1956 dangerous goods cars and 27656 freight cars in total; selecting a certain city road network map layer, wherein the city road network map layer comprises 9824 roads, 507 expressways, 296 national roads, 145 expressways, 2835 main roads, 3836 secondary roads, 946 provincial roads, 956 county and country roads and 303 roads inside county and country roads; marking the constructed or planned beyond monitoring point, as shown in fig. 2.
And secondly, carrying out travel chain extraction on the cleaned GPS data of the freight vehicle in a certain city according to a truck stopping point identification method and a travel chain extraction principle. In the invention, the GPS data of the same vehicle with the speed of 0 for 90 seconds or more is marked as the stop point data.
And then converting the GPS coordinates and the road network map coordinates into the same projection coordinates. Breaking a certain city road network into small line segments, calculating the degree of clockwise included angles between the driving direction and the due north direction of all the line segments, and recording the degree as a direction angle; searching all road sections which take each GPS point as a center and have the side length of 150 meters in a square range, and marking the road sections as alternative road sections; then calculating the difference between the vehicle angle of the GPS point and the direction angles of all the alternative road sections and the distance between the GPS point and all the alternative road sections; and finally, selecting the road section with the minimum distance as a matching road section to complete road network matching of the truck GPS points.
And then, on the premise of eliminating the constructed and planned monitoring point road sections, traversing every day trip of each vehicle, searching the road sections passed by each trip of the truck, and carrying out road section duplication elimination. Superposing the corresponding vehicle passing frequency and the vehicle passing number of each road section, and finally averaging the vehicle passing frequency and the vehicle passing number to obtain the average daily vehicle passing frequency and the average daily vehicle passing number, wherein the statistical data are shown in tables 1 and 2:
TABLE 1 road number distribution excluding average daily vehicle-passing frequency after constructed or planned point location
TABLE 2 road number distribution excluding the average number of cars passing by day after the point location has been built or planned
And then, calculating and analyzing road comprehensive vehicle passing indexes, wherein the expressway and the urban expressway are closed roads, weighing devices are arranged at all entrances and exits, and overloaded vehicles cannot pass through, so that the expressway and the urban expressway are excluded from analyzing the running track of the freight vehicle, and monitoring point distribution analysis is performed on non-closed roads such as provincial, county and rural roads, urban main roads, secondary roads, branch roads and the like. For the layout of the science and technology control over monitoring points, if a certain trip of the freight vehicle passes through one or more established or planned control over monitoring points, the trip is indicated to be in the monitoring of the science and technology control over monitoring system in a certain city. Therefore, when a new technology beyond monitoring point is planned, the new technology beyond monitoring point is selected in a sequencing mode based on the comprehensive vehicle passing indexes after the built or planned point location is eliminated. Through analyzing and sequencing the comprehensive vehicle passing indexes of all roads, the road sections 60 at the front of the comprehensive vehicle passing indexes are finally screened as the road sections to be selected of the monitoring point distribution points, and part of data is shown in table 3.
TABLE 3 partial section 60 ahead of the Integrated vehicle passing index
Because the road section to be selected has partial continuous or similar monitoring point positions, and partial monitoring point positions are positioned on the internal road or village road. Therefore, it is necessary to aggregate consecutive or nearby points and transfer points with lower road grades to the road to which they are connected. Finally, by combining the industrial structures near the road section, 21 overload monitoring points to be set are obtained in a summary mode, and a point distribution schematic diagram is shown in fig. 3.
And finally, calculating the daily average detected vehicle ratio and the truck trip coverage rate before and after the newly-added super-monitoring point location. The statistical result shows that the total number of the vehicles in the average daily trip is 20497, and the total number of the vehicles in the average daily trip is 229083. Before the new monitoring points are added, the number of vehicles passing through the existing built overtaking point is 13120, and the number of daily average trips is 53657; after 21 new overtemperature treatment points are added, the number of vehicles passing the overtemperature treatment day-average detection is 15758, the daily-average detection vehicle ratio is 76.9%, and the improvement is 12.9%; the average daily trip times passing the overtime control are 81998, and the truck trip coverage rate is improved by 12.4%.
In summary, the existing constructed or planned overtaking points in a certain city cover part of the truck driving route, and if 21 overtaking points selected based on the analysis of the GPS data are added, more truck driving road sections can be further covered, and the overtaking effect of the city road network is improved.
The same or similar reference numerals correspond to the same or similar parts;
the positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (3)
1. A truck GPS data-based overload control monitoring stationing method is characterized by comprising the following steps:
s1: screening and cleaning vehicle GPS data to obtain GPS data of freight vehicles;
s2: dividing the vehicle travel state based on the travel angle and the truck speed, identifying a truck travel stop point, and marking a truck travel chain;
s3: matching moving points in the travel track of the truck to the urban road network based on a direction matching method and a shortest distance method;
s4: two evaluation indexes of the daily average vehicle-passing frequency and the daily average vehicle-passing number are established, the daily average vehicle-passing frequency and the daily average vehicle-passing number of each road truck are calculated, and analysis is carried out;
s5: constructing a road comprehensive passing index according to the daily average passing frequency and the daily average passing number, sequencing the stationing importance of the road sections, and selecting the road section with the front comprehensive passing index value to form a monitoring stationing scheme;
s6: two evaluation indexes of daily average detected vehicle ratio and truck trip coverage rate are constructed, and the overload control monitoring effect after the monitoring point positions are newly added is evaluated;
the data cleansing process in step S1 is:
selecting GPS data of urban freight vehicles, and eliminating the repeated GPS data of the same vehicle in time, wherein the GPS data exceeds the longitude and latitude range of a road network;
in the step S2, the truck stop point identification and trip chain marking method is as follows:
the data with the speed of 0 is not necessarily the data of the stay state due to the condition of waiting for a traffic light or temporary parking; dynamically adjusting and judging the threshold value of the stopping point, and marking the GPS data with the speed of 0 for x seconds or more continuously of the same vehicle as the stopping point data; sequencing the GPS data of the vehicle according to time, regarding the GPS data continuously marked as a stop point cluster, wherein the GPS data between adjacent stop point clusters is a trip chain, and according to the number and time sequence of the trip chains of the user, a moving point in the first trip chain is marked as 1, and by analogy, a moving point in the Nth trip chain is marked as N;
the road network matching process in step S3 is:
s31: unifying a space coordinate system;
s32: the positioning accuracy of the GPS data is high, and each data point is provided with a direction angle field, so that the GPS data is matched into a road network based on a direction matching method and a shortest distance method;
the specific process of step S31 is:
the GPS data of the floating car takes longitude and latitude as coordinates, and adopts a WGS84 space coordinate system; in order to calculate the distance between the GPS point and the road, before performing road network matching, the coordinates need to be converted into projection coordinates, and the GPS coordinates and the coordinates of the road network map are both converted into the same projection coordinates for calculation, where the projection coordinates are: xian _1980_3_ Degree _ GK _ Zone _38, WKID 2362;
the specific process of step S32 is:
1) and road network interruption: breaking all continuous broken line sections in a road network into small line sections, and calculating clockwise included angles between driving directions and true north directions of all the line sections as direction angles;
2) and road section searching: for each GPS point, searching all road sections which take the GPS point as a center and have the side length of w meters in a square, and marking the road sections as alternative road sections;
3) calculating the direction and the distance: calculating the distance L between the GPS point and all the alternative road sections and the difference between the vehicle angle of the GPS point and the direction angle of all the alternative road sections
The angle difference of the GPS points is equal to that the GPS points are 1 meter away from the road, so the distance G value of all the alternative road sections and the GPS points is calculated according to the following formula:
4) and selecting road sections: selecting the road section with the minimum G value, judging whether the distance L is greater than 25m, if the distance L is less than 25m, the road section is the road section matched with the GPS point, otherwise, the GPS point cannot be matched with the road network; wherein, the 25 meters are selected as the threshold value because the error range of the civil GPS is 25 meters at most;
the average daily passing frequency and the average daily passing number in step S4 mean the number of vehicles passing through the road segment per day and the number of vehicles, and the statistical method is as follows: traversing each trip of each vehicle every day, searching a road section passed by each trip of the truck, then carrying out road section de-weighting, superposing corresponding vehicle passing frequency or vehicle passing number of each road section, and finally averaging to obtain the daily average vehicle passing frequency and the daily average vehicle passing number;
the comprehensive vehicle passing index in the step S5 is obtained by respectively performing maximum and minimum normalization processing on the daily average vehicle passing frequency and the daily average vehicle passing number of all road sections, and performing weighted average to obtain a comprehensive vehicle passing index Q of each road sectioniAfter the built or planned point is eliminated, Q is selectediTaking the road section with M positions before the value as a road section to be selected; then, aggregating part of monitoring point locations of continuous or similar road sections, transferring part of monitoring point locations of internal roads or villages to roads connected with the monitoring point locations, and finally summarizing to obtain a super-control monitoring point distribution scheme, wherein the comprehensive vehicle passing index calculation formula is as follows:
wherein Q isiIs a comprehensive passing index of the ith road section, FiThe average daily passing frequency of the ith road section, NiThe coefficients alpha and beta are default to 0.5, which is the average number of vehicles passing through the ith road section on day.
2. The truck GPS data-based overload monitoring and stationing method according to claim 1, wherein the average daily ratio is calculated in step S6 as follows:
the average daily ratio of the detected vehicles refers to the percentage of the number of the vehicles passing through the point of controlling the over-point in the day, and the calculation formula is as follows:
in the above formula RvMean average daily vehicle ratio, t is the number of days counted, ViThe number of vehicles passing through the point of controlling the passing point on the ith day, AiThe number of vehicles on the ith day.
3. The truck GPS data-based overload monitoring and stationing method according to claim 2, wherein the truck trip coverage rate is calculated by the following formula:
the truck trip coverage rate refers to the percentage of trip times passing through the point of treatment exceeding the point, and the calculation formula is as follows:
assuming n floating vehicles as data, each floating vehicle is divided into m trips, R in the above formuladFinger grip excess row coverage, eikIs used to identify whether the kth trip of the ith vehicle passes through the road segment where the constructed or planned override point is located.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104282151A (en) * | 2014-09-30 | 2015-01-14 | 北京交通发展研究中心 | Real-time floating vehicle path matching method based on high-frequency satellite positioning data |
| CN205405893U (en) * | 2016-03-08 | 2016-07-27 | 山东交通学院 | Freight train overload real -time monitoring system based on networking of big dipper car |
| CN106652483A (en) * | 2017-03-06 | 2017-05-10 | 同济大学 | Method for arranging traffic information detection points in local highway network by utilizing detection device |
| CN106781494A (en) * | 2016-12-31 | 2017-05-31 | 中国科学技术大学 | A kind of telemetering motor vehicle tail equipment points distributing method based on track of vehicle Yu flow |
| CN110796858A (en) * | 2019-10-24 | 2020-02-14 | 山东科技大学 | Vehicle track prediction method and system based on video vehicle passing data |
| CN111126713A (en) * | 2019-12-31 | 2020-05-08 | 方正国际软件(北京)有限公司 | Space-time hot spot prediction method and device based on bayonet data and controller |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101617543B1 (en) * | 2014-10-06 | 2016-05-02 | 주식회사 만도 | Detecting system for vehicle emergency |
-
2020
- 2020-07-10 CN CN202010660866.0A patent/CN112017429B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104282151A (en) * | 2014-09-30 | 2015-01-14 | 北京交通发展研究中心 | Real-time floating vehicle path matching method based on high-frequency satellite positioning data |
| CN205405893U (en) * | 2016-03-08 | 2016-07-27 | 山东交通学院 | Freight train overload real -time monitoring system based on networking of big dipper car |
| CN106781494A (en) * | 2016-12-31 | 2017-05-31 | 中国科学技术大学 | A kind of telemetering motor vehicle tail equipment points distributing method based on track of vehicle Yu flow |
| CN106652483A (en) * | 2017-03-06 | 2017-05-10 | 同济大学 | Method for arranging traffic information detection points in local highway network by utilizing detection device |
| CN110796858A (en) * | 2019-10-24 | 2020-02-14 | 山东科技大学 | Vehicle track prediction method and system based on video vehicle passing data |
| CN111126713A (en) * | 2019-12-31 | 2020-05-08 | 方正国际软件(北京)有限公司 | Space-time hot spot prediction method and device based on bayonet data and controller |
Non-Patent Citations (2)
| Title |
|---|
| 基于GPS数据的货运特征分析_以深圳市为例;张天怡 等;《品质交通与协同共治——2019年中国城市交通规划年会论文集》;20191016;全文 * |
| 基于货运卡车轨迹大数据的货运OD信息提取方法研究;李江涛;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20200115;正文第2、5、9、13-18、24-26、34-35、46-47页 * |
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