CN113138979A - High-speed service area truck service level evaluation method based on trajectory data - Google Patents

High-speed service area truck service level evaluation method based on trajectory data Download PDF

Info

Publication number
CN113138979A
CN113138979A CN202110468278.1A CN202110468278A CN113138979A CN 113138979 A CN113138979 A CN 113138979A CN 202110468278 A CN202110468278 A CN 202110468278A CN 113138979 A CN113138979 A CN 113138979A
Authority
CN
China
Prior art keywords
truck
service area
points
data
speed
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110468278.1A
Other languages
Chinese (zh)
Inventor
于世军
周鹏
邓社军
卞张蕾
彭浪
刘根基
杨孝清
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yangzhou University
Original Assignee
Yangzhou University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Yangzhou University filed Critical Yangzhou University
Priority to CN202110468278.1A priority Critical patent/CN113138979A/en
Publication of CN113138979A publication Critical patent/CN113138979A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Databases & Information Systems (AREA)
  • Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Quality & Reliability (AREA)
  • General Engineering & Computer Science (AREA)
  • Operations Research (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Marketing (AREA)
  • Mathematical Analysis (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Remote Sensing (AREA)
  • Software Systems (AREA)
  • Algebra (AREA)
  • Game Theory and Decision Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a method for evaluating the freight service level of a high-speed service area based on track data, which comprises the steps of utilizing a wireless transmission system on a freight passenger car to acquire the GPS track data of the freight car in real time; preliminarily cleaning the GPS track data of the truck, and extracting initial parking points of the truck, parking points in the range of a high-speed service area and the traffic volume of the truck on the high-speed road; and establishing the capacity index of the service truck in the high-speed service area, and setting the comprehensive dynamic weight of the index in the high-speed service area to finish capacity identification of the service truck in the high-speed service area. The invention defines the running direction route and the stopping position of the truck by identifying the truck stopping point and the truck corridor, further improves the management quality of the truck, simplifies the energy and time of manual monitoring and promotes the development of a high-speed service area.

Description

High-speed service area truck service level evaluation method based on trajectory data
Technical Field
The invention relates to the technical field of truck track identification and judgment and high-speed service area truck management, in particular to a track data-based high-speed service area truck service level evaluation method.
Background
The expressway service area is used as an expressway facility and plays an important role in guaranteeing driving safety, improving transportation efficiency, relieving driving fatigue, overhauling vehicles, refueling, dining and the like, along with the continuous development of social economy, the expressway driving mileage is increased year by year, road networks are continuously perfect, cross-border long-distance transportation vehicles are increased, the service area receiving capacity is continuously increased, the characteristics of large personnel mobility, numerous personnel number, high risk potential hazard prevention and control difficulty, high social attention and the like are presented, and how to guarantee the life and property safety of drivers and conductors becomes the important part of the management work of the service area.
With the rapid development of the logistics freight industry, most goods vehicles in the country carry out access network service, certain independent use permission is provided for the trajectory data of the goods vehicles, with the rapid increase of the number of the accessed vehicles, mass data come along with the access network service, the conventional management technology cannot well support the frequent movement routes of the goods vehicles, goods matching and user information recommendation, and the data management service detailed and detailed for the goods vehicles comes out from moving to and fro, so that unavoidable and small economic loss is caused.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a track data-based method for evaluating the freight car service level in the high-speed service area, which can solve the problem of careless omission caused by improper management of the existing freight cars.
In order to solve the technical problems, the invention provides the following technical scheme: the method comprises the steps of utilizing a wireless transmission system on a freight passenger car to collect truck GPS track data in real time; preliminarily cleaning the GPS track data of the truck, and extracting initial parking points of the truck, parking points in the range of a high-speed service area and the traffic volume of the truck on the high-speed road; and establishing the capacity index of the service truck in the high-speed service area, and setting the comprehensive dynamic weight of the index in the high-speed service area to finish capacity identification of the service truck in the high-speed service area.
As a preferred scheme of the track data-based method for evaluating the service level of the truck in the high-speed service area, the method comprises the following steps: the truck GPS track data comprises GPS response time and real-time coordinate longitude and latitude of GPS response; the GPS response time comprises a time type field; the real-time coordinate longitude and latitude responded by the GPS comprises a floating point number type field; when the truck GPS track data is returned, returning operation is carried out by taking the corresponding truck license plate number as a file name, yellow and blue represent the color of the license plate, the size of the truck type is reflected, and the truck GPS track data is formed by combining the letter codes and the license plate numbers of provinces and cities.
As a preferred scheme of the track data-based method for evaluating the service level of the truck in the high-speed service area, the method comprises the following steps: the preliminary cleaning comprises deleting error data, including data with invalid positioning longitude and latitude and data with invalid positioning time; deleting coordinates of the GPS coordinates which are not in the research range; and according to the field type format, carrying out data conversion on each field in the truck GPS track data.
As a preferred scheme of the track data-based method for evaluating the service level of the truck in the high-speed service area, the method comprises the following steps: extracting the initial stopping point of the truck includes passing the stopping point featureFinding that the positions of the stop points of the truck transportation are basically the same in a period T, defining the stop points as a point set S, wherein the point set S comprises n points Sn(n ═ 1,2,3 … n); set of computation points SnThe distance between two points is calculated as follows,
Figure BDA0003044897270000021
wherein, α is lat1-lat2, β is lon1-lon2, lon1 and lat1 represent longitude and latitude coordinates of one of the two points, and lon2 and lat2 represent longitude and latitude coordinates of the other point.
As a preferred scheme of the track data-based method for evaluating the service level of the truck in the high-speed service area, the method comprises the following steps: extracting the dwell points of the high-speed service area range comprises the steps of grabbing POI points marked as service area fields in the determined range through a related map platform by using a web crawler technology, wherein main fields returned by grabbing data comprise the longitude and latitude of point coordinates and the name of the service area; adding the POI points into geographic space map editing software through related gis software, manually checking, screening whether the POI points are positioned near expressways, and deleting the POI points which do not belong to the vicinity of the expressways; marking the POI points belonging to the high-speed service area after screening as phsFor each phsThe point utilizes gis buffer area technology to generate a circular buffer area with the radius of 500 meters, and the circular buffer area is marked as phsb(ii) a Extracting each circular buffer p by gis space intersection techniquehsbThe stop point p contained in (1)real。
As a preferred scheme of the track data-based method for evaluating the service level of the truck in the high-speed service area, the method comprises the following steps: extracting the traffic volume of the expressway freight car, namely identifying the running direction of the track points on the road according to the track points; processing the phenomenon that the track points are crossed by two roads and are counted at the intersection at the same time; and processing the road traffic and extracting the bidirectional road traffic.
As a preferred scheme of the track data-based method for evaluating the service level of the truck in the high-speed service area, the method comprises the following steps: and establishing the capacity indexes of the service trucks in the high-speed service area, including the daily average driving rate of the trucks in the service area, the peak rate of the trucks in the service area, the turnover rate of the trucks in the service area and the vehicle type structure ratio of the service area.
As a preferred scheme of the track data-based method for evaluating the service level of the truck in the high-speed service area, the method comprises the following steps: setting the comprehensive dynamic index weight of the high-speed service area comprises determining a static index weight and a dynamic index weight; static weight omega of each indexiDynamic weight s with each indexiThe combination is carried out according to the following formula,
αi＝λωi+(1-λ)si
wherein alpha isiIn the interval [0,1]And Σ αiλ is a weight, and may be 0.5.
As a preferred scheme of the track data-based method for evaluating the service level of the truck in the high-speed service area, the method comprises the following steps: comprises normalizing according to the obtained index value, wherein the normalization formula is as follows,
Figure BDA0003044897270000031
each normalized value is multiplied by a corresponding weight and summed,
Score＝∑αi*xi'
wherein the Score value reflects the capacity of the high-speed service area service truck.
The invention has the beneficial effects that: the invention defines the running direction route and the stopping position of the truck by identifying the truck stopping point and the truck corridor, further improves the management quality of the truck, simplifies the energy and time of manual monitoring and promotes the development of a high-speed service area.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flowchart of a method for evaluating a truck service level in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a part of operation codes of a method for evaluating a service level of a truck in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 3 is a schematic operation code diagram of another part of the method for evaluating the service level of a truck in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a part of operation codes of a method for evaluating a service level of a truck in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 5 is a schematic diagram of initial data of a method for evaluating a truck service level in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an initial data spatial distribution of a method for evaluating a truck service level in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 7 is a schematic diagram of preliminary data of a method for evaluating the service level of a truck in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 8 is a schematic diagram illustrating track point distance calculation in the method for evaluating the service level of a truck in a high-speed service area based on track data according to an embodiment of the present invention;
fig. 9 is a schematic diagram illustrating a track point discrimination method for evaluating the truck service level in a high-speed service area based on track data according to an embodiment of the present invention;
fig. 10 is a schematic diagram illustrating truck stopping point characteristics of a method for evaluating truck service level in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 11 is a schematic diagram illustrating preliminary extraction of alternative truck stopping points of the trajectory data-based method for evaluating the service level of a truck in a high-speed service area according to an embodiment of the present invention;
fig. 12 is a schematic view of the truck actual parking spot global data of the method for evaluating the truck service level in the high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 13 is a schematic diagram of a high-speed service and a stopping point of a method for evaluating truck service level in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 14 is a schematic diagram of extracting a track point direction in the method for evaluating the freight car service level in the high-speed service area based on the track data according to an embodiment of the present invention;
fig. 15 is a schematic diagram of road traffic volume extraction of the method for evaluating the service level of trucks in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 16 is a schematic diagram of road section bidirectional traffic volume extraction of the method for evaluating the service level of a truck in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 17 is a schematic view of highway traffic volume extraction of the method for evaluating truck service level in a high-speed service area based on trajectory data according to an embodiment of the present invention;
fig. 18 is a schematic static weight operation diagram of a trajectory data-based method for evaluating the service level of a truck in a high-speed service area according to an embodiment of the present invention;
fig. 19 is a schematic diagram illustrating operation of index clustering results of the trajectory data-based high-speed service area truck service level evaluation method according to an embodiment of the present invention;
fig. 20 is a schematic diagram of importance ranking of a random forest model in the method for evaluating the truck service level in a high-speed service area based on trajectory data according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 6, a first embodiment of the present invention provides a method for evaluating service level of a truck in a high speed service area based on trajectory data, which is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
s1: and a wireless transmission system on the freight passenger car is utilized to acquire the GPS track data of the freight car in real time. It should be noted that the truck GPS track data includes:
GPS response time and real-time coordinate longitude and latitude of GPS response;
the GPS response time comprises a time type field;
the real-time coordinate longitude and latitude of the GPS response comprises a floating point number type field;
when the GPS track data of the truck returns, the return operation is carried out by taking the corresponding truck license plate number as the file name, yellow and blue represent the color of the license plate, the size of the truck type is reflected, and the GPS track data of the truck is formed by combining the letter code and the license plate number of each grade city, which are short for representing provinces.
S2: and preliminarily cleaning the GPS track data of the truck, and extracting the initial stop point of the truck, the stop point in the range of the high-speed service area and the traffic volume of the truck on the high-speed road. It should be noted that in this step, the preliminary cleaning includes:
deleting error data, including data with invalid positioning longitude and latitude and data with invalid positioning time;
deleting coordinates of the GPS coordinates which are not in the research range;
and according to the field type format, carrying out data conversion on each field in the truck GPS track data.
Specifically, the step of extracting the initial station of the truck comprises the following steps:
considering that the GPS track data of different trucks are different in reaction time interval and reaction distance, the stop points can be extracted by combining a distance threshold value between a point and a certain regional range in a certain city with the number of the stop points and a density clustering algorithm;
the positions of the stop points transported by the truck are basically the same in a period T through the stop point characteristics, the stop points are defined as a point set S, and the point set S comprises n points Sn(n＝1,2,3…n)；
Set of computation points SnThe distance between two points is calculated as follows,
Figure BDA0003044897270000061
wherein, α is lat1-lat2, β is lon1-lon2, lon1 and lat1 represent longitude and latitude coordinates of one of the two points, and lon2 and lat2 represent longitude and latitude coordinates of the other point;
setting a distance threshold range of the stop point according to the calculated distance value, wherein the threshold setting method comprises the following steps:
calculating the minimum value of the distance between two adjacent points of the truck in the road as d1；
Calculating the minimum value of the distance between two adjacent points of the freight car track points distributed outside the road as d2Distance threshold
Figure BDA0003044897270000071
Can be set within (d)1,d2]Among the intervals, the interval is an interval with left opening and right closing;
for judging whether the track point is on the road, the specific method is as follows:
capturing a road network in a research range by using a web crawler technology, wherein the type returned by the road network is a geographic file line type, and fields contained in the road network mainly comprise a road section id, a road section grade and a road section name;
the technology of generating a buffer area by using a GIS generates a distance d to a road networkbOf the type of face type, dbCan be selected according to actual needsDifferent adjustments are made;
the track point and the generated road buffer area are screened out by utilizing the technology of spatial connection in the GIS (geographic information System)rThe other points are points outside the road and are ps；
For another characteristic of the stop point and the point set S, the minimum number sam of the points needs to be judged in the point set S, namely, the truck should stop in the area for multiple times within a period of time T;
the value of sam can be divided according to different time periods T, for example, the value of sam should be greater than or equal to 10 in one month;
determining a good distance threshold
Figure BDA0003044897270000072
After the sum of the minimum point set number sam is reached, all track points are identified and extracted by using a density clustering algorithm in unsupervised learning, and the identified characteristics are as follows:
for point sets p not belonging to alternative stop pointselseDenoted tag-1, for point p belonging to an alternative stop pointssMarking as labels 1,2,3 … n, wherein n represents the total number of the alternative stop points;
for alternative stop points pssThe pick-up is carried out independently, and the stop point p is corresponding to different days and different periods of each day because the truck can have a plurality of similar points in adjacent time periods in an area when loading, unloading or short stop behaviors occur at the stop pointssGrouping division is carried out to obtain a grouping point set pssd；
Screening out each pssdThe recording time still entering the service area for the first time and the recording time etime exiting the service area for the last time in the point set are calculated, and the time difference value delta t is calculated according to the following formula:
obtaining the time difference value delta t of the truck at a certain alternative stopping point in a certain time of a certain day;
and screening out the point with the stopping time exceeding 5 minutes in the range according to the delta t as the actual stopping point p of the truckreal；
Since the actual dwell point should be a single point, not a set of points, for prealSelecting the longitude and latitude coordinate of any point in the point set as the longitude and latitude coordinate of the single actual stop point;
single actual parking point p of truckrealI.e. the stopping point of the truck in a certain area within a period of time T;
actual dwell point prealThe contained fields comprise time stime of the first time of driving in of the truck, time etime of driving out of the truck, time difference value delta t, license plate number of the truck, longitude coordinate of the point and latitude coordinate of the point.
Extracting the stop point of the high-speed service area range comprises the following steps:
capturing POI points marked as service area fields in a determined range by using a web crawler technology through a related map platform, wherein the main fields returned by captured data comprise the longitude and latitude of point coordinates and the name of a service area;
adding the POI points into geographic space map editing software through related gis software, manually checking, screening whether the POI points are positioned near expressways, and deleting the POI points which do not belong to the vicinity of the expressways;
marking the POI points belonging to the high-speed service area after screening as phsFor each phsThe point utilizes gis buffer area technology to generate a circular buffer area with the radius of 500 meters, and the circular buffer area is marked as phsb；
Extracting each circular buffer p by gis space intersection techniquehsbThe stop point p contained in (1)real。
The method for extracting the traffic volume of the expressway truck comprises the following steps:
identifying the running direction of the track points on the road according to the track points;
processing the phenomenon that two paths of track points can be crossed and counted at the intersection at the same time;
processing road traffic and extracting bidirectional road traffic;
sequencing the track points p according to the time as a reference, obtaining the sequenced track points as psort, and establishing a two-dimensional Cartesian coordinate system by taking the previous track point as a coordinate origin each time;
the latter track point is subjected to angle identification on the basis of the former track point;
the calculation formula of the angle identification is as follows:
converting longitude and latitude coordinates lon1, lat1, lon2 and lat2 of front and rear track points into radian system radlon1radlat1 and radlon2 radlat 2;
the values are obtained by an arctan function arctan2(y, x) and then converted into degrees, wherein y and x respectively represent: y is sin (Δ lon) × cos (radlat2), x is cos (radlat1) × sin (radlat2) -sin (radlat1) × cos (radlat2) × cos (Δ lon), where Δ lon is radolon 2-radlon1, and an angle degree of each trace point based on the previous trace point is obtained, which ranges from 0 degree to 360 degrees;
for eight ranges of the angle degree divided into a Chinese character 'mi', the north orientation is marked as 1, and the eight ranges are sequentially 2,3,4,5,6,7 and 8 in the clockwise direction, and the division principle is as follows:
Figure BDA0003044897270000091
extracting the 360-degree directions into 8 directions;
space connection is performed again between the track point and the road buffer area, and the road link containing the track point in the road section is extractednN represents n extracted roads, and for a certain i-section linkiThe main fields contained in the road section link include road section ID, road section name, road section grade, track point longitude and latitude, track point direction, track point time and track point direction, and for each road section linknGrouping, wherein the grouping is carried out according to three levels of date, road section ID and track point direction to obtain link through grouping statisticsnk；
In the first case: the phenomenon that intersections are repeatedly recorded appears in a section of continuous road section ID, and links are counted by the scoring groupnkIf the middle link ID is r in one row, the continuous link ID is riIn the middle, a row not belonging to the continuous section riIs marked as rjI ≠ j, handles road section rjThe method comprises the following steps:
moving the whole row of the segment data r forward to obtain a new row rfAnd then go backward to get rbIf i of a certain row occurs rfi＝rbiAnd r isfi≠riThen the road section which does not belong to the front and back continuous road section can be screened out;
deleting the screened road sections;
in the second case: for the phenomenon that when the vehicle passes through two different roads before and after the intersection, the vehicle is repeatedly recorded by the road in the other direction at the same time, the previous continuous road section is recorded as riThe latter continuous section being rjRepeatedly recorded road section rw；
Moving the whole row of the segment data r forward to obtain a new row rfAnd then go backward to get rbIf i of a certain row occurs rfi≠rbiAnd r isfi≠riThen the road sections with repeated records can be screened out;
deleting the road sections screened out by the centering;
all times of the vehicles appearing on a certain road section at a certain moment are modified into 1, and the number of times of the vehicles appearing on the certain road section at the certain moment is recorded as one time;
considering that a road segment may be passed by a vehicle for multiple times on the same day, or for multiple times on multiple days, but simply grouping with the segment ID ignores this phenomenon, and thus a specific processing method for solving this problem is as follows:
performing first-order difference processing on the road section ID to obtain a new row K, namely subtracting the previous row from the next row every time to obtain a difference value, directly marking the first row as 1, wherein the first row is a number i when a vehicle runs on a continuous road, i is an arbitrary real number value, the last n-1 rows of the continuous road are all numbers 0, when the vehicle changes the road, 0 is changed into the number i, and further processing is performed on the numbers i and is all changed into 1, namely, any row in the K row is subjected to the first-order difference processing
Figure BDA0003044897270000101
According to the ID of the road section and the ID of the direction, carrying out hierarchical grouping statistics on the sum of K columns, wherein the sum value is the statistical times of the traffic of the road section in different directions;
further processing is carried out on the total traffic volume of each road section ID and each direction ID, and eight directions are converted into two directions of the road;
arranging eight directions of each road section ID from small to large, then judging a first digit in each road section ID, and when the first digit of a certain road section ID is 1, determining that the whole trend of the road section tends to a north-south trend, so that the total traffic volume with the direction IDs of 1,2,3 and 8 is regarded as the traffic volume of one direction of the road, and the total traffic volume with the direction IDs of 4,5,6 and 7 is regarded as the traffic volume of the road in the other direction of the road;
similarly, when the first digit of the ID of a certain road section is 2, the whole road section trend is determined to tend to be the south-north-east trend or the west-west trend, so that the total traffic volume with the direction IDs of 2,3,4 and 5 is determined as the traffic volume of one road in one direction, and the total traffic volume with the direction IDs of 6,7 and 8 is determined as the traffic volume of the road in the other direction;
when the first digit of the ID of a certain road section is 3, the whole direction of the road section is determined to tend to be an east-west direction, so that the total traffic volume with the direction IDs of 4 and 5 is determined as the traffic volume of one road in one direction, and the total traffic volume with the direction IDs of 6,7 and 8 is determined as the traffic volume of the road in the other direction;
when the first digit of the ID of a certain road section is 4, the whole trend of the road section is determined to tend to be the south-east or north-south trend, so that the total traffic volume with the direction IDs of 5 and 6 is determined as the traffic volume of one road in one direction, the direction ID of 7 and the total traffic volume of 8 is determined as the traffic volume of the other road in the other direction;
acquiring the traffic volume of the road truck with each road section ID in two directions, wherein the fields comprise the traffic volume of the road section ID, the road section grade, the road section name, the direction ID (in two directions) and the direction ID;
and the road section with the first-level road grade of the expressway is extracted, and the extraction of the (bidirectional) traffic volume of the expressway truck is obtained in conclusion.
S3: and establishing the capacity index of the service truck in the high-speed service area, and setting the comprehensive dynamic weight of the index in the high-speed service area to finish capacity identification of the service truck in the high-speed service area. It should be further noted that the establishing of the capacity index of the service truck in the high-speed service area includes: the daily average driving rate of trucks in the service area, the peak rate of trucks in the service area, the turnover rate of trucks in the service area and the structural ratio of the trucks in the service area;
service area truck entrance rate index representing daily average road truck traffic volume volroadDrives into service area vol with the average dayinRatio of traffic volume of
Figure BDA0003044897270000111
The road truck traffic volume is obtained according to the extracted highway truck traffic volume, i.e. each high-speed service area buffer phsbAverage daily traffic volume of nearby expressways;
daily entering service area volinThe traffic volume is extracted according to the extraction stop point, namely, the first time of driving-in time in the field attribute of the stop point is judged, the average value of the stop point in the service area every day is calculated, and the average value is the daily average driving-in service area volinThe amount of traffic of (2);
service area freight car peak rate this index represents the number cn of freight cars parked during peak hourshAnd the number of parked truck vehicles cn per daydRatio of
Figure BDA0003044897270000112
Number cn of truck vehicles staying at peak hourshThe method comprises the steps of (1) obtaining, judging according to the extracted stopping point of the high-speed service area range, judging whether the vehicle stops in a peak time period and counting the quantity of the vehicle according to the value of a field, wherein the obtained stopping point data comprises the time when the vehicle firstly enters the area and the time when the vehicle leaves the area, and the peak time period is generally expressed as an early peak time period (6:00-8:00) and a late peak time period (5:00-7: 00);
number of parked truck vehicles cn per daydThe total number of the freight vehicles staying in a certain high-speed service area every day is judged according to two time fields in the staying points;
service area truck turnover rate the index represents the number of times of use in 1h of the service area, namely the number of times n of stop of the truck in 1hhour；
Number n of times that the truck stops within 1hhourIs determined by the time difference Δ t contained in the extracted dwell point of the high speed service area range, i.e. when Δ t is reached<When the time is 60min, the vehicle can be determined to stop for less than 1h in the service area;
counting all vehicles meeting the conditions, and calculating the ratio of the total amount to the vehicles, wherein the ratio is the turnover rate;
service area vehicle type structure ratio index represents the ratio of large truck and small truck entering service area
Figure BDA0003044897270000113
And judging the large truck and the small truck according to the extracted license plate number contained in the high-speed stop point, namely when the license plate number is yellow, the vehicle represented by the stop point can be determined as a large truck, and when the license plate number is blue, the vehicle represented by the stop point can be determined as a small truck.
The static weight represents the respective importance degree of different indexes in an index system, and the determination method comprises a subjective weighting method and an objective weighting method, wherein the subjective weighting method can be an Analytic Hierarchy Process (AHP) method, an efficacy coefficient method, an integrated value statistical method, the objective weighting method can be an entropy weight method and the like, and the static index weight omega of each index can be obtained by the methodi，∑ωi＝1；
Calculating an index value of each high-speed service area, dividing the calculated index value by utilizing a clustering algorithm based on division in an unsupervised model and a k-means clustering algorithm and combining indexes to divide each high-speed service area, wherein the divided parameters can be manually adjusted through the effect of an actual algorithm, and generally can be set to be 2,3,4 and 5 … n to obtain high-speed service areas of different categories, the number of the categories is determined by the set parameters, and the different categories are labels of each high-speed service area, so that the data of the high-speed service areas are changed from unsupervised to supervised;
index dynamic weight extraction is carried out by utilizing a random forest model in a supervised model, a tree model mainly utilizes a classification tree in the random forest model, a dependent variable of the model is a class label obtained by a partition clustering algorithm, and an independent variable is an established index;
the branching algorithm for each tree may use an information gain or a kini coefficient, where the formula of the information gain and the kini coefficient is as follows:
information entropy:
Figure BDA0003044897270000121
the Kiny index:
Figure BDA0003044897270000122
performing model training on the obtained supervised data by using a 10-fold cross validation method, namely dividing the data into 10 parts, alternately using 9 parts of the data for training and 1 part of the data for validation, circulating for 10 times, obtaining the most appropriate parameter setting in the random forest model by using a grid search algorithm, and adopting a strategy of not pruning;
for the trained model, extracting the important contribution degree s of each index fed back in the modeli，∑si1, the important contribution degree siAnd recording as the dynamic index weight.
The setting of the high-speed service area index comprehensive dynamic weight comprises the following steps:
determining a static index weight and a dynamic index weight;
static weight omega of each indexiDynamic weight s with each indexiThe combination is carried out according to the following formula,
αi＝λωi+(1-λ)si
wherein alpha isiIn the interval [0,1]And Σ αiλ is a weight, and may be 0.5.
Normalizing according to the obtained index value, wherein the normalization formula is as follows,
Figure BDA0003044897270000131
each normalized value is multiplied by a corresponding weight and summed,
Score＝∑αi*xi'
wherein the Score value reflects the capacity of the high-speed service area service truck.
Example 2
Referring to fig. 5 to 20, a second embodiment of the present invention provides a test example of a method for evaluating a service level of a truck in a high speed service area based on trajectory data, which specifically includes:
in this embodiment, the truck track data passing through a certain province in 2021 is taken as an example to perform example analysis, and the mainly used software is python programming software and qgis open source geographic software.
(1) Initial data introduction
Referring to fig. 5 and 6, the initial data and the spatial distribution of the portion of the initial data include three columns, the first column is time, the second column is a latitude coordinate of the truck's instantaneous coordinate, and the third column is a longitude coordinate of the truck's instantaneous coordinate.
(2) Preliminary cleaning of data
Referring to fig. 7, according to the data cleansing method, data is preliminarily cleansed using python software.
(3) Calculating the distance between the tracing points
The distance between the longitude and latitude coordinates of the vehicle is calculated, and the result is shown in fig. 8, wherein two columns of lon2 and lat2 represent track points at the next moment of the vehicle, and one column of dis represents the space distance between the track points, and the unit is m.
(4) Judging whether the track point is on the road
Referring to fig. 9, road network data is obtained, a road buffer area is generated by using qgis open source software, and points in the road buffer area are obtained by combining a spatial connection technology.
(5) Extracting alternative parking points of truck
The spatial characteristics of the truck stopping points are shown in fig. 10, it can be seen that the stopping points are point sets, a DBSCAN distance algorithm in an unsupervised clustering algorithm is used for preliminary extraction, a distance threshold value is 25m, the number of minimum sample points is 10, data is obtained to obtain a new column of 'stay _ points', the characteristics are labels of the stopping points at each position, and referring to fig. 11, the data 'dis' is found to have a value of 0 in the distance column, and the stopping behavior of the truck is indirectly verified.
(6) Extracting actual parking points of trucks
The actual stopping point of the truck is further extracted, as shown in fig. 12 in particular, where "ptime" represents the time difference Δ t.
(7) Extracting high speed service area range stopover points
Referring to fig. 13, a trace point located in a high speed service area is obtained according to the method.
(8) Extraction of highway traffic volume
The direction of the trace point is further extracted, as shown in fig. 14, where "degree" is a row of angle values, "dirID" is a row of range values in the shape of "m", and "direction" is a row of specific trace point orientations.
Referring to fig. 15, to extract the truck traffic volume of each road segment in each direction, where "ID" is a road segment ID number, "dirID" is a simplified direction ID, and "counts" represents the road traffic volume of the road segment in a certain direction.
Referring to fig. 16, the extracted bidirectional road traffic is shown, where the first column is the link ID, "AB _ flow" and "BA _ flow" columns are bidirectional road traffic.
Referring to fig. 17, the traffic volume of a truck on a highway is further extracted.
(9) Quantitative extraction of index of service truck in expressway service area
Indexes of 10 high-speed service areas are quantitatively extracted. Specifically, as shown in table 1 below:
table 1: service area indicators.
Figure BDA0003044897270000141
Figure BDA0003044897270000151
(10) Integrated dynamic weight acquisition and service area capability identification
Referring to fig. 18, a static weight of each index is obtained by using an analytic hierarchy process, and the analytic process is as follows: the consistency index and the random consistency index are both less than 0.1, and the weight setting is reasonable.
The weight of each index is shown in Table 2 below
Table 2: the index static weight.
Average rate of daily entry Peak rate of truck Turnover rate of truck Vehicle type structure ratio
Weight of 0.38 0.18 0.12 0.31
The dynamic index weight is calculated, the label of each service area is obtained by using a K-means clustering algorithm, and the result is shown in fig. 19, wherein one column of labels is a clustering result, and the service areas are divided into two types.
Referring to fig. 20 and table 3, the index weights are further solved using a random forest model.
Table 3: the index dynamic weight.
Average rate of daily entry Peak rate of truck Turnover rate of truck Vehicle type structure ratio
Weight of 0.16 0.12 0.12 0.59
And (3) solving the most comprehensive index weight by utilizing a comprehensive index calculation formula, wherein lambda in the formula is 0.5, and the result is shown in a table 4.
Table 4: and (4) index comprehensive weight.
Average rate of daily entry Peak rate of truck Turnover rate of truck Vehicle type structure ratio
Weight of 0.27. 0.15 0.12 0.45
The number of indices obtained using the normalization formula is shown in table 5.
Table 5: and normalizing the service area indexes.
Figure BDA0003044897270000152
Figure BDA0003044897270000161
Finally, according to the score formula, the score value of each service area is calculated, and the result is shown in table 6.
Table 6: a service differentiation number.
Figure BDA0003044897270000162
According to the score value, the score of the service area 5 is the best, and the score of the service area 4 is the lowest, so that the method can effectively distinguish the level of the service truck in the high-speed service area.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (9)

1. A high-speed service area truck service level evaluation method based on track data is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring GPS track data of the freight car in real time by using a wireless transmission system on the freight passenger car;
preliminarily cleaning the GPS track data of the truck, and extracting initial parking points of the truck, parking points in the range of a high-speed service area and the traffic volume of the truck on the high-speed road;
and establishing the capacity index of the service truck in the high-speed service area, and setting the comprehensive dynamic weight of the index in the high-speed service area to finish capacity identification of the service truck in the high-speed service area.
2. The trajectory data-based high-speed service area truck service level evaluation method as claimed in claim 1, wherein: the truck GPS track data comprises GPS response time and real-time coordinate longitude and latitude of GPS response;
the GPS response time comprises a time type field;
the real-time coordinate longitude and latitude responded by the GPS comprises a floating point number type field;
when the truck GPS track data is returned, returning operation is carried out by taking the corresponding truck license plate number as a file name, yellow and blue represent the color of the license plate, the size of the truck type is reflected, and the truck GPS track data is formed by combining the letter codes and the license plate numbers of provinces and cities.
3. The trajectory-data-based high-speed service area truck service level evaluation method according to claim 1 or 2, characterized in that: the preliminary cleaning comprises the steps of,
deleting error data, including data with invalid positioning longitude and latitude and data with invalid positioning time;
deleting coordinates of the GPS coordinates which are not in the research range;
and according to the field type format, carrying out data conversion on each field in the truck GPS track data.
4. The trajectory data-based high-speed service area truck service level evaluation method as claimed in claim 3, wherein: extracting the initial stopping point of the truck includes,
finding that the positions of the stop points transported by the truck are basically the same in a period T through the stop point characteristics, defining the stop points as a point set S, wherein the point set S comprises n points Sn(n＝1,2,3…n)；
Set of computation points SnThe distance between two points is calculated as follows,
Figure FDA0003044897260000011
wherein, α is lat1-lat2, β is lon1-lon2, lon1 and lat1 represent longitude and latitude coordinates of one of the two points, and lon2 and lat2 represent longitude and latitude coordinates of the other point.
5. The trajectory data-based high-speed service area truck service level evaluation method as claimed in claim 4, wherein: extracting the stop point of the high speed service area range includes,
capturing POI points marked as service area fields in a determined range by using a web crawler technology through a related map platform, wherein the main fields returned by captured data comprise the longitude and latitude of point coordinates and the name of a service area;
adding the POI points into geographic space map editing software through related gis software, manually checking, screening whether the POI points are positioned near expressways, and deleting the POI points which do not belong to the vicinity of the expressways;
marking the POI points belonging to the high-speed service area after screening as phsFor each phsThe point utilizes gis buffer area technology to generate a circular buffer area with the radius of 500 meters, and the circular buffer area is marked as phsb；
Extracting each circular buffer p by gis space intersection techniquehsbThe stop point p contained in (1)real。
6. The trajectory data-based high-speed service area truck service level evaluation method as claimed in claim 5, wherein: extracting the highway truck traffic volume includes,
identifying the running direction of the track points on the road according to the track points;
processing the phenomenon that the track points are crossed by two roads and are counted at the intersection at the same time;
and processing the road traffic and extracting the bidirectional road traffic.
7. The trajectory data-based high-speed service area truck service level evaluation method as recited in claim 6, wherein: and establishing the capacity indexes of the service trucks in the high-speed service area, including the daily average driving rate of the trucks in the service area, the peak rate of the trucks in the service area, the turnover rate of the trucks in the service area and the vehicle type structure ratio of the service area.
8. The trajectory-data-based high-speed service area truck service level evaluation method as recited in claim 7, wherein: setting the high speed service area indicator integrated dynamic weight comprises,
determining a static index weight and a dynamic index weight;
static weight omega of each indexiDynamic weight s with each indexiThe combination is carried out according to the following formula,
αi＝λωi+(1-λ)si
wherein alpha isiIn the interval [0,1]And Σ αiλ is a weight, and may be 0.5.
9. The trajectory-data-based high-speed service area truck service level evaluation method as recited in claim 8, wherein: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
normalizing according to the obtained index value, wherein the normalization formula is as follows,
Figure FDA0003044897260000021
each normalized value is multiplied by a corresponding weight and summed,
Score＝∑αi*xi'
wherein the Score value reflects the capacity of the high-speed service area service truck.
CN202110468278.1A 2021-04-28 2021-04-28 High-speed service area truck service level evaluation method based on trajectory data Pending CN113138979A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110468278.1A CN113138979A (en) 2021-04-28 2021-04-28 High-speed service area truck service level evaluation method based on trajectory data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110468278.1A CN113138979A (en) 2021-04-28 2021-04-28 High-speed service area truck service level evaluation method based on trajectory data

Publications (1)

Publication Number Publication Date
CN113138979A true CN113138979A (en) 2021-07-20

Family

ID=76816286

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110468278.1A Pending CN113138979A (en) 2021-04-28 2021-04-28 High-speed service area truck service level evaluation method based on trajectory data

Country Status (1)

Country Link
CN (1) CN113138979A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113611130A (en) * 2021-08-03 2021-11-05 中国环境科学研究院 Method, system and storage medium for acquiring traffic flow of local and transit trucks
CN115830901A (en) * 2023-02-17 2023-03-21 交通运输部规划研究院 Method and device for determining truck stopping point

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103942410A (en) * 2014-03-12 2014-07-23 华南理工大学 Expressway operation comprehensive service quality real-time assessment method
CN108168562A (en) * 2018-01-31 2018-06-15 上海势航网络科技有限公司 A kind of positioning track stops point extracting method
CN109816271A (en) * 2019-02-25 2019-05-28 东南大学 Cycle track service level evaluation method based on shared bicycle track data
CN110070718A (en) * 2019-04-30 2019-07-30 山东省交通规划设计院 Expressway Service service quality dynamic assessment method, system and equipment

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103942410A (en) * 2014-03-12 2014-07-23 华南理工大学 Expressway operation comprehensive service quality real-time assessment method
CN108168562A (en) * 2018-01-31 2018-06-15 上海势航网络科技有限公司 A kind of positioning track stops point extracting method
CN109816271A (en) * 2019-02-25 2019-05-28 东南大学 Cycle track service level evaluation method based on shared bicycle track data
CN110070718A (en) * 2019-04-30 2019-07-30 山东省交通规划设计院 Expressway Service service quality dynamic assessment method, system and equipment

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
潘嘉杰等: "基于GPS数据的货运特征分析——以深圳市为例", 《品质交通与协同共治——2019年中国城市交通规划年会论文集》, pages 1 - 10 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113611130A (en) * 2021-08-03 2021-11-05 中国环境科学研究院 Method, system and storage medium for acquiring traffic flow of local and transit trucks
CN113611130B (en) * 2021-08-03 2023-08-25 中国环境科学研究院 Method, system and storage medium for acquiring traffic flow of local and transit trucks
CN115830901A (en) * 2023-02-17 2023-03-21 交通运输部规划研究院 Method and device for determining truck stopping point

Similar Documents

Publication Publication Date Title
Saplıoğlu et al. Choosing safe and suitable bicycle routes to integrate cycling and public transport systems
Zhao et al. Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel
CN109686090B (en) Virtual traffic flow calculation method based on multi-source data fusion
CN113138979A (en) High-speed service area truck service level evaluation method based on trajectory data
CN112447041B (en) Method and device for identifying operation behavior of vehicle and computing equipment
Jimenez et al. Bus line classification using neural networks
CN114969007A (en) Urban functional area identification method based on function mixing degree and integrated learning
CN110413855A (en) A kind of region entrance Dynamic Extraction method based on taxi drop-off point
CN108182196B (en) Urban traffic demand prediction method based on POI
Murugan et al. Mode shift behaviour and user willingness to adopt the electric two-wheeler: A study based on Indian road user preferences
CN109979198B (en) Urban expressway vehicle speed discrete identification method based on large-scale floating vehicle data
CN113095387B (en) Road risk identification method based on networking vehicle-mounted ADAS
CN112767686B (en) Road network automobile emission estimation method based on multi-source data fusion
CN116092037B (en) Vehicle type identification method integrating track space-semantic features
Chen et al. Detecting and analyzing unlicensed taxis: A case study of Chongqing City
Lee Development of Traffic safety zones and integrating macroscopic and microscopic safety data analytics for novel hot zone identification
Olawole et al. Mode choice of undergraduates: a case study of lecture trips in Nigeria
CN116061953A (en) Truck dangerous driving behavior discrimination evaluation method based on driving track data
CN115936541A (en) Safe road recommendation method and device
Van Houten et al. 17-05 Effects of Safe Bicycle Passing Laws on Drivers' Behavior and Bicyclists' Safety
Larsson et al. Potentials and limitations for the use of accessibility measures for national transport policy goals in freight transport and logistics: Evidence from Västra Götaland County, Sweden
Hu et al. A data-fusion based method for identifying individual passenger travel dependence on public transportation
Laviolette et al. A kilometer or a mile? Does buffer size matter when it comes to car ownership?
Boitor et al. A new tool for the evaluation of co2 emissions from road traffic: a case study in cluj-napoca, romania
Gunathilaka et al. Household travel survey method for vehicle kilometers travel estimations: A case study in a developing country.

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination