CN115409430B - Logistics strength analysis method and system based on truck driving track and storage medium - Google Patents
Logistics strength analysis method and system based on truck driving track and storage medium Download PDFInfo
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Abstract
The invention provides a method, a system and a storage medium for analyzing logistics intensity based on a truck driving track, comprising the following steps of: acquiring track data sets of all freight trucks with preset loads in a target area and in a historical target time period; acquiring a trip chain starting point and a trip chain terminal point of each freight truck; the method comprises the steps of obtaining a research area in a target area, carrying out gridding processing on the research area to obtain a plurality of unit grids, and determining the actual logistics intensity of the unit grids according to the density of a trip chain starting point and a trip chain end point in the unit grids. The method and the device realize that the actual logistics intensity of the unit grid in the research area is determined after data processing is carried out according to the acquired track data set provided by the nationwide freight transport platform, can provide accurate data support for the planning management department, and solve the technical problems that the freight logistics intensity of the target city position is counted by adopting manual sampling survey and cannot provide accurate data support for the planning management department in the prior art.
Description
Technical Field
The invention relates to the technical field of logistics intensity analysis based on computer processing, in particular to a logistics intensity analysis method and system based on a truck driving track and a storage medium.
Background
The traditional freight strength survey usually adopts a manual sampling survey counting mode, so that a large amount of manpower and material resources are required to be input, and the problems of low sampling rate, large influence of weather on a survey day, incapability of obtaining continuous data in time and the like exist; and further, the quality of basic data is difficult to guarantee, so that the accuracy of survey results is low, accurate data support cannot be provided for a planning management department, and the method becomes a restriction factor influencing the decision of the planning management department.
In view of the above, there is a need to provide a method for analyzing logistics intensity based on a truck driving track, so as to solve or at least alleviate the above drawbacks.
Disclosure of Invention
The invention mainly aims to provide a freight logistics intensity analysis method based on a freight car running track, and aims to solve the technical problems that the existing freight logistics intensity of a target city position is calculated by adopting manual sampling survey statistics, the accuracy of survey results is low, accurate data support cannot be provided for a planning management department, and the existing freight logistics intensity analysis method becomes a restriction factor influencing the decision of the planning management department.
In order to achieve the purpose, the invention provides a logistics intensity analysis method based on a truck driving track, which comprises the following steps of: s10, acquiring a track data set of all freight trucks with preset loads in a target area and in a historical target time period; s20, carrying out data processing and data cleaning on the track data set to obtain a trip chain starting point and a trip chain end point of each freight truck, wherein the trip chain starting point and the trip chain end point are associated with a track data coordinate system; and S30, acquiring a research area in the target area, carrying out gridding processing on the research area to acquire a plurality of unit grids, wherein the unit grids are associated with a grid data coordinate system, are associated with a track data coordinate system and a grid data coordinate system, and determine the actual logistics intensity of the unit grids according to the density of a trip chain starting point and a trip chain ending point in the unit grids.
Further, S11, in the target area and in the historical target time period, reporting operation data information with freight track points of the freight wagon once every preset unit time, wherein the freight wagon does not report the operation data information when being stopped, and the operation data information comprises a unique license plate code corresponding to the freight wagon and a vehicle azimuth, a current longitude and a current latitude which are recorded by recording time and correspond to the unique license plate code; and S12, collecting all the operation data information to form a track data set.
Further, S21, carrying out data processing and data cleaning on the track data set to obtain the single-car segment track of each freight truck, wherein the single-car segment track is provided with track stop points, and the track stop points comprise a single-car segment starting point and a single-car segment ending point; s22, obtaining segment key indexes of each bicycle segment track, wherein the segment key indexes comprise bicycle segment mileage, bicycle segment average driving speed, bicycle segment space range and bicycle segment stay time length, the bicycle segment space range is a straight line distance between a bicycle segment starting point and a bicycle segment end point of the bicycle segment track, and the current bicycle segment stay time length is an interval time length between a bicycle segment starting point of the next bicycle segment track and a bicycle segment end point of the current bicycle segment track; and S23, performing data processing on the segment key indexes to obtain a complete trip chain of each truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point, and the trip chain starting point and the trip chain end point are associated with a trajectory data coordinate system.
Further, the data processing of the trajectory data set specifically includes: classifying the license plate unique codes in the track data set, wherein the same license plate unique code has a vehicle azimuth, a current longitude and a current latitude at corresponding recording time; sequencing according to the recording time to obtain the time interval, instantaneous speed, acceleration and azimuth angle change of the unique code of the same license plate between two adjacent recording times; the data cleaning of the trajectory data set specifically includes: if the information of the same license plate unique code is missing at the corresponding recording time, a group of data of the same license plate unique code at the corresponding recording time is removed; if the unique code of the same license plate has time overlap at the corresponding recording time, retaining a group of data of the unique code of the same license plate at the corresponding recording time; if the instantaneous speed is greater than the preset effective speed, a group of data corresponding to the instantaneous speed is removed; if the acceleration is larger than the preset effective acceleration, rejecting a group of data corresponding to the acceleration; if the azimuth angle change is larger than a preset angle change threshold value, a group of data corresponding to the azimuth angle change is removed; and determining the single-car segment track of each freight truck according to the time interval of the acquired data, and if the time interval of the acquired data is greater than a first time threshold, determining that the current track point is the single-car segment starting point of the current single-car segment track and the single-car segment end point of the next single-car segment track.
Further, freight related POI data of the target area are obtained, wherein the freight related POI data comprise POI large categories, and the POI large categories comprise company enterprises, automobile services, automobile maintenance, catering services, accommodation services, business residences, traffic facility services and road auxiliary facilities; setting POI priority for the large POI category according to the truck driving correlation; based on a geographic information system, associating POI large categories corresponding to the track stopping points according to POI priorities; if the single-vehicle segment mileage of the single-vehicle segment track is within a first mileage threshold range, the single-vehicle segment space range is within a first space threshold range, the average driving speed in the single-vehicle segment is within a first average speed threshold range, and the POI attribute space associated with the single-vehicle segment end point is of a company enterprise class and/or a transportation facility service class, determining the single-vehicle segment track as a cargo transportation track; if the single-vehicle segment mileage of the single-vehicle segment track is within a second mileage threshold range, the single-vehicle segment space range is within a second space threshold range, the average driving speed in the single-vehicle segment is within a second average speed threshold range, and POI attribute spaces related to the single-vehicle segment terminal point and the single-vehicle segment starting point are of company enterprises and traffic facility service classes, determining the single-vehicle segment track as a loading and unloading track; if the residence time is within the first residence threshold time range, determining the residence time as short residence time, and if the residence time is within the second residence threshold time range, determining the residence time as medium residence time; if the stay time is in the third stay threshold time range, determining the stay time is long-time stay; if the stopping time is short stopping or middle stopping, and the POI attribute space associated with the single car segment end point is related to rest of a truck driver, determining a track stopping point as a half-way rest stopping point; if the stay time is long stay, and the POI attribute space associated with the single car segment end point is related to long rest of a truck driver, determining a track stop point as a rest stop point to be waited for home; if the stopping time is short stopping time and the POI attribute space associated with the track stopping point of the single vehicle fragment is related to the vehicle driving service, determining the track stopping point as a transit behavior stopping point; if the POI attribute space associated with the track point of the single vehicle fragment is related to the track stop point related to the vehicle maintenance service, determining the POI attribute space as a transportation irrelevant stop point; and processing and combining the bicycle segment tracks of each freight truck to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point, and the trip chain starting point and the trip chain end point are associated with a track data coordinate system.
Further, the step of performing data processing on the segment key indexes to obtain a complete trip chain of each truck, wherein the complete trip chain has a trip chain starting point and a trip chain ending point specifically includes: reserving a starting point and an end point of a cargo transportation track; reserving the starting point and the end point of a loading and unloading track; merging the starting points and the end points of the adjacent stopover points and the behavior stopover points in the transportation process; filtering rest stop points to be kept and transport-irrelevant stop points; and sequentially connecting effective track stop points of each single-vehicle fragment track to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain terminal point.
Further, the step of "associating the trajectory data coordinate system with the grid data coordinate system" specifically includes: judging whether the track data coordinate system is consistent with the grid data coordinate system: if the track data coordinate system is consistent with the grid data coordinate system, connecting the trip chain starting point and the trip chain end point with the intersected unit grid space by using a geographic information system identification technology, and identifying the grid number of each trip chain starting point and trip chain end point; and if the track data coordinate system is inconsistent with the grid data coordinate system, projecting the grid data coordinate system to the track data coordinate system, connecting the trip chain starting point and the trip chain terminal point with the intersected unit grid space by using a geographic information system identification technology, and identifying the grid number of each trip chain starting point and each trip chain terminal point.
Furthermore, if the grid logistics intensity exceeds the preset intensity, prompt information of adding a truck driver service station, a gas station, a parking facility and a service facility at the corresponding unit grid position is sent out.
The invention also provides a logistics intensity analysis system based on the freight car running track, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein when the computer program is executed by the processor, the steps of the logistics intensity analysis method based on the freight car running track are realized.
The invention further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the method for analyzing logistics intensity based on the truck driving track are realized.
Compared with the prior art, the invention has the following advantages:
the invention provides a logistics intensity analysis method based on a truck traveling track, which comprises the steps of carrying out data processing and data cleaning on a track data set after obtaining the track data sets of all trucks with preset loads in a target area and in a historical target time period, and further obtaining a trip chain starting point and a trip chain end point of each truck, wherein the trip chain starting point and the trip chain end point are starting and ending points of a complete trip chain of the trucks, and the trip chain starting point and the trip chain end point are associated with a track data coordinate system; and finally, acquiring a research area in a target area, and carrying out gridding processing on the research area to acquire a plurality of unit grids, wherein the unit grids are associated with a grid data coordinate system, are associated with a track data coordinate system and a grid data coordinate system, and the actual logistics intensity of the unit grids is determined according to the density of a trip chain starting point and a trip chain end point in the unit grids, so that the actual logistics intensity of the unit grids in the research area is determined after data processing according to a collected track data set provided by a nationwide freight platform, accurate data support can be provided for a planning management department, and the technical problem that the existing freight logistics intensity of a target city position is calculated by adopting manual sampling survey statistics, the survey result accuracy is low, accurate data support cannot be provided for the planning management department, and the influence factors influencing the decision of the planning management department is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for analyzing logistics intensity based on a truck traveling track according to an embodiment of the present invention;
FIG. 2 is a detailed diagram of step S10 in FIG. 1;
fig. 3 is a detailed schematic diagram of step S20 in fig. 1.
The implementation, functional features and advantages of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive efforts based on the embodiments of the present invention, are within the scope of protection of the present invention.
Technical solutions between the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Referring to fig. 1, the invention provides a logistics intensity analysis method based on a truck traveling track, which comprises the following steps:
s10, acquiring a track data set of all freight trucks with preset loads in a target area and in a historical target time period; s20, carrying out data processing and data cleaning on the track data set, and obtaining a trip chain starting point and a trip chain terminal point of each truck, wherein the trip chain starting point and the trip chain terminal point are associated with a track data coordinate system; and S30, acquiring a research area in the target area, carrying out gridding processing on the research area to acquire a plurality of unit grids, wherein the unit grids are associated with a grid data coordinate system, are associated with a track data coordinate system and a grid data coordinate system, and determine the actual logistics intensity of the unit grids according to the density of a trip chain starting point and a trip chain ending point in the unit grids.
The invention provides a logistics intensity analysis method based on a truck travelling track, which comprises the steps of carrying out data processing and data cleaning on track data sets after obtaining the track data sets of all cargo trucks with preset loads in a target area and in a historical target time period, and further obtaining a trip chain starting point and a trip chain end point of each cargo truck, wherein the trip chain starting point and the trip chain end point are starting and ending points of a complete trip chain of the cargo trucks, and the trip chain starting point and the trip chain end point are associated with a track data coordinate system; and finally, acquiring a research area in a target area, and carrying out gridding processing on the research area to acquire a plurality of unit grids, wherein the unit grids are associated with a grid data coordinate system, are associated with a track data coordinate system and a grid data coordinate system, and the actual logistics intensity of the unit grids is determined according to the density of a trip chain starting point and a trip chain end point in the unit grids, so that the actual logistics intensity of the unit grids in the research area is determined after data processing according to a collected track data set provided by a nationwide freight platform, accurate data support can be provided for a planning management department, and the technical problem that the existing freight logistics intensity of a target city position is calculated by adopting manual sampling survey statistics, the survey result accuracy is low, accurate data support cannot be provided for the planning management department, and the influence factors influencing the decision of the planning management department is solved.
It can be understood that, in the present invention, the target area may be a certain country (e.g. china), or may be a certain province or autonomous region of a certain country (e.g. hunan province, chongqing, etc.); the historical target time period can be one year, one month or other time, and the historical target time period can be set according to actual requirements; the preset load can be 12 tons or more than 12 tons or 10 tons or more, and the preset load can be set according to actual requirements; the research area may be one of the cities (e.g., changsha, hunan province) or multiple cities of a certain province; the research area is subjected to gridding processing to obtain a plurality of unit grids, the research area can be divided into unit grids of 1 kilometer multiplied by 1 kilometer to be used as a minimum research unit for area logistics intensity analysis, or the research area can be divided into unit grids of 2 kilometers multiplied by 2 kilometers to be used as a minimum research unit for area logistics intensity analysis, and the minimum research unit is set according to actual conditions.
It will be appreciated that from the trajectory data for each truck, a complete trip chain for a single truck may be determined, the complete trip chain having a trip chain start point and a trip chain end point. In the invention, a track data coordinate system can be associated based on longitude and latitude of a trip chain starting point and a trip chain end point; the grid data coordinate system can be associated with the unit grid based on a geographic information system, and finally, after the track data coordinate system and the grid data coordinate system are associated, the actual logistics intensity of the unit grid is determined according to the density of the trip chain starting point and the trip chain end point in the unit grid. The higher the frequency of occurrence of the trip chain starting point and the trip chain terminal point in the unit grid is, the greater the actual logistics intensity of the unit grid is.
As can be appreciated, data processing and data cleansing of the trace dataset includes cleansing duplicate data in the trace dataset and cleansing invalid data in the trace dataset.
Referring to fig. 2, further, the step S10 specifically includes the steps of: s11, reporting operation data information with freight track points of freight trucks once every preset unit time in a target area and in a historical target time period, wherein the freight trucks do not report the operation data information when being in flameout parking, and the operation data information comprises license plate unique codes corresponding to the freight trucks and vehicle azimuth angles, current longitudes and current dimensions of recording time records corresponding to the license plate unique codes; and S12, collecting all the operation data information to form a track data set. It is understood that the preset unit time may be 15 seconds, and may be other time values such as 20 seconds, 30 seconds, 60 seconds, 120 seconds, etc.; the running data information is not reported when the freight wagon is in flameout parking, so that the freight wagon is determined to be in a flameout parking state after the duration of not reporting the running data information is longer than a certain duration (a first time threshold), and the single-wagon fragment track of the freight wagon is convenient to acquire, wherein the duration of the first time threshold is longer than the duration of a preset unit time. Optionally, the time interval when the freight truck is in flameout parking and the running data information is not reported is the stay time; or the stopping time of the current bicycle segment is the interval time between the bicycle segment starting point of the next bicycle segment track and the bicycle segment end point of the current bicycle segment track.
Referring to table 1 (track data set use field), the track data set includes a license plate unique code (license plate unique ID), a vehicle azimuth recorded by a recording time (GPS report time) corresponding to the license plate unique code, a current longitude, a current latitude, and a payload. According to the invention, all cargo trucks with preset loads can be determined according to the load capacity (ton) corresponding to the unique code of the license plate.
TABLE 1
| Unique code of license plate | Azimuth angle of vehicle | GPS time of reporting | Current latitude | Current longitude | Load capacity |
| 1104797915287110 | 289 | 2021/6/1 23:35 | 28.58573 | 111.07001 | 11.78 |
| 1972948023840940 | 326 | 2021/6/1 23:37 | 28.592786 | 111.092586 | 10.10 |
| 1567865372090860 | 11 | 2021/6/1 23:44 | 28.625226 | 111.177291 | 12.05 |
| 1757272118176660 | 94 | 2021/6/1 23:45 | 28.628721 | 111.189616 | 12.90 |
| 1688143499144770 | 337 | 2021/6/1 23:46 | 28.634161 | 111.201881 | 9.75 |
| 1501152476828150 | 156 | 2021/6/1 23:35 | 29.427751 | 113.214205 | 5.65 |
| 1222217908973130 | 27 | 2021/6/1 23:37 | 29.42129 | 113.195335 | 20.30 |
| 1874171863099970 | 82 | 2021/6/1 23:39 | 29.410195 | 113.181718 | 22.55 |
| 1944933784526310 | 124 | 2021/6/1 23:42 | 29.398385 | 113.180105 | 8.98 |
| 1376092011687900 | 353 | 2021/6/1 23:44 | 29.390326 | 113.179071 | 6.88 |
| 1161304462298880 | 118 | 2021/6/1 23:46 | 29.374283 | 113.177021 | 20.05 |
| 1575037507802430 | 157 | 2021/6/1 23:51 | 29.36286 | 113.17557 | 22.35 |
| 1559259011758730 | 148 | 2021/6/1 23:53 | 29.36286 | 113.17557 | 9.75 |
| 1623958910795730 | 123 | 2021/6/1 23:57 | 29.362848 | 113.175566 | 6.20 |
| 1270505318695890 | 318 | 2021/6/1 23:59 | 29.36284 | 113.175563 | 13.20 |
| 1491423422809110 | 17 | 2021/6/1 23:34 | 27.03214 | 110.663535 | 16.30 |
| 1735150964901490 | 122 | 2021/6/1 23:36 | 27.03214 | 110.663535 | 17.50 |
| 1861344092286190 | 141 | 2021/6/1 23:34 | 28.224595 | 113.201411 | 12.20 |
| 1789257302555510 | 306 | 2021/6/1 23:34 | 28.224575 | 113.20141 | 8.98 |
| 1590042016194950 | 183 | 2021/6/1 23:34 | 28.22456 | 113.201411 | 6.88 |
| 1128454073720930 | 39 | 2021/6/1 23:39 | 28.20598 | 113.199725 | 20.05 |
| 1684350192154350 | 330 | 2021/6/1 23:46 | 28.152741 | 113.197755 | 22.35 |
| 1061291341695560 | 63 | 2021/6/1 23:51 | 28.13931 | 113.22234 | 9.75 |
| 1304588545326850 | 151 | 2021/6/1 23:35 | 27.360065 | 109.345496 | 6.20 |
| 1568999531869330 | 32 | 2021/6/1 23:37 | 27.360121 | 109.362655 | 13.20 |
| 1195126996529610 | 259 | 2021/6/1 23:39 | 27.363506 | 109.395611 | 16.30 |
| 1481125722272210 | 195 | 2021/6/1 23:42 | 27.365891 | 109.424941 | 9.75 |
| 1144496023542430 | 128 | 2021/6/1 23:44 | 27.366275 | 109.427481 | 6.20 |
| 1590651501542760 | 217 | 2021/6/1 23:45 | 27.366275 | 109.427481 | 13.20 |
| 1965341778084910 | 76 | 2021/6/1 23:46 | 27.383661 | 109.455636 | 16.30 |
| 1714681817566900 | 252 | 2021/6/1 23:51 | 27.41929 | 109.51438 | 5.60 |
| 1951727526805060 | 224 | 2021/6/1 23:53 | 27.428346 | 109.533281 | 3.60 |
Referring to fig. 3, further, in order to accurately obtain the trip chain starting point and the trip chain ending point of each truck, the step S20 specifically includes the steps of: s21, carrying out data processing and data cleaning on the track data set to obtain a single-car segment track of each freight truck, wherein the single-car segment track is provided with track stop points, and the track stop points comprise a single-car segment starting point and a single-car segment ending point; s22, obtaining segment key indexes of each bicycle segment track, wherein the segment key indexes comprise bicycle segment mileage, bicycle segment average driving speed, bicycle segment space range and bicycle segment stay time, the bicycle segment space range is a straight line distance between a bicycle segment starting point and a bicycle segment end point of a bicycle segment track, and the current bicycle segment stay time is an interval time between a bicycle segment starting point of a next bicycle segment track and a bicycle segment end point of the current bicycle segment track; and S23, performing data processing on the segment key indexes to obtain a complete trip chain of each truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point, and the trip chain starting point and the trip chain end point are associated with a trajectory data coordinate system.
Further, step S21 specifically includes: the data processing of the trajectory data set specifically includes: classifying according to the unique license plate codes in the track data set, wherein the same unique license plate code has a vehicle azimuth, a current longitude and a current latitude at corresponding recording time; sequencing according to the recording time to obtain the time interval, instantaneous speed, acceleration and azimuth angle change of the unique code of the same license plate between two adjacent recording times (freight track points); the data cleaning of the trajectory data set specifically includes: if the information of the same license plate unique code is missing at the corresponding recording time, a group of data of the same license plate unique code at the corresponding recording time is removed; if the unique code of the same license plate has time overlap at the corresponding recording time, retaining a group of data of the unique code of the same license plate at the corresponding recording time; if the instantaneous speed is greater than the preset effective speed, rejecting a group of data corresponding to the instantaneous speed; if the acceleration is larger than the preset effective acceleration, rejecting a group of data corresponding to the acceleration; if the azimuth angle change is larger than a preset angle change threshold value, a group of data corresponding to the azimuth angle change is removed; and determining the single-car segment track of each freight truck according to the time interval of the acquired data, and if the time interval of the acquired data is greater than a first time threshold, determining that the current track point is the single-car segment starting point of the current single-car segment track and the single-car segment end point of the next single-car segment track. The duration of the first time threshold is greater than the duration of the preset unit time, the duration of the first time threshold may be 30 minutes, 40 minutes, 60 minutes, 90 minutes, or other durations, and the first time threshold is set according to an actual situation.
Through research, the phenomenon of abnormal values of the operation data information in the trajectory data set is found as follows: firstly, key attribute information is missing, and due to problems of network signals, equipment faults and the like, key attributes of a few parts of data, such as recording time, current longitude, current latitude and unique license plate codes, are missing; secondly, the time information is wrong, and the time information of a small part of data has the problems of repeated time and repeated report; thirdly, the running speed (instantaneous speed v) of the vehicle is abnormal, the distance between two points can be calculated according to longitude and latitude data in the running track of the vehicle, the instantaneous speed v between the two points can be estimated according to the running time difference of the vehicle, and if the instantaneous speed v is obviously greater than the normal running speed of the vehicle, the running track data information is judged to be abnormal; fourthly, the acceleration a of the vehicle is abnormal, and when the track point deviates, the acceleration a mostly shows large abnormality and exceeds the maximum acceleration allowed by a road section or vehicle hardware; fifth, vehicle azimuth Change
When the vehicle normally runs on the road, a relatively smooth track is generated, and the data abnormality is judged when the azimuth angle of the vehicle continuously changes greatly in a short time; sixthly, the vehicle position is abnormal, and the partially drifting data is separated from the actual road by a large distance, but at the instantaneous speed v, the acceleration a, the azimuth change->
And (4) the abnormal performance on the indexes is not obvious, and analysis needs to be carried out by combining with the space distribution of the actual road network.
Specifically, in the present invention, the following are preprocessed for the above six abnormal phenomena existing in the operation data information in the track data set: firstly, when the key attribute information is missing, deleting the group of data; secondly, when time information is wrong, the first piece of data is reserved in the moment data, and the rest of repeated data is deleted; thirdly, when the running speed (instantaneous speed v) of the vehicle is abnormal, considering that the maximum limit speed of the domestic vehicle is 120km/h, presetting a certain data error for the maximum limit speed of the truck is 100 km/h, increasing partial tolerance, setting the reasonable range of the vehicle speed to be 0-120 km/h, and deleting track points of which the instantaneous speed exceeds the reasonable range; fourth, please refer to table 2 (vehicle brake deceleration criteria reference), table 3 (vehicle brake deceleration criteria reference) and table 4 (road lane parameters of each level), wherein M 1 The number of the passenger cars is not more than 8; m is a group of 2 The passenger car is a passenger car with more than 8 seats and the total mass of not more than 5 tons; m is a group of 3 The passenger car is a passenger car with more than 8 seats and more than 5 tons of total mass; n is a radical of 1 The total mass of the truck or the tractor is not more than 3.5 tons; n is a radical of hydrogen 2 The truck with the total mass of 3.5 tons to 12 tons is referred to; n is a radical of hydrogen 3 The maximum braking deceleration of the truck is not more than 4.4M/s 2 Meanwhile, the change rate of the cargo speed generally does not exceed 2.5M/s under the typical driving working condition 2 Considering certain data error, presetting and increasing part of tolerance, and setting the reasonable range of the vehicle acceleration a to be-5.0 to 3.0M/s 2 Deleting track points with acceleration a out of a reasonable range; fifth, vehicle azimuth Change
Exception: calculating an azimuth for a first and a second freight track point in a single car section track>
And the second and third freight track points calculate the azimuth angle>
Normal data azimuth variation should be less than 90 °, i.e.:
by analogy, calculating the azimuth angle change rate of the vehicle for adjacent track points in the continuous track, and deleting freight track points of which the azimuth angle change rate exceeds the reasonable range; sixth, the vehicle position is abnormal: and (4) judging the position abnormity through a buffer area analysis method and an overlay analysis method in the geographic information system analysis method.
It can be understood that the buffer analysis is a method for forming a polygonal entity with a certain range around a group or a class of map elements according to a set distance condition, thereby implementing information analysis of data in two-dimensional space expansion. The overlay analysis is a method for overlaying various data layers representing different subjects to generate a new data layer for extracting spatial implicit information. The number of road lanes at each level should meet the specifications of table 3, and the lane widths at different design speeds should meet the specifications of table 4. Considering the width of other elements such as the center separator and the tolerance of trace acquisition tiny deviation, the maximum road surface width of the high-speed and first-level roads is set to be 50 meters, the maximum road surface width of the second-level highway is 30 meters, the maximum road surface width of the third-level highway is 20 meters, and the maximum road surface width of the fourth-level highway is 15 meters. And setting a road buffer zone by taking the position as a standard, and performing superposition analysis on the road buffer zone and the freight track points, wherein the freight track points falling out of the range of the buffer zone are judged to be abnormal in position and need to be repaired. And (3) accumulating the product of the sampling time and the instantaneous speed of each abnormal drifting point to obtain the track distance of the missing track, matching the track distance with the visual base map route to find an actual track route, calibrating track points along the track route by taking the starting point of the missing track as the starting point and according to the distance between the points, and finally completely supplementing the track of the single vehicle segment.
TABLE 2
TABLE 3
| Road grade | High-speed, first-class highway | Second-level road | Three-level road | Four-level road |
| Number of lanes | ≥4 | 2 | 2 | 2(1) |
TABLE 4
| Design speed (km/h) | 120 | 100 | 80 | 60 | 40 | 30 | 20 |
| Lane width (M) | 3.75 | 3.75 | 3.75 | 3.50 | 3.50 | 3.25 | 3.00 |
Further, step S23 specifically includes the steps of: the method comprises the steps of obtaining freight related POI data of a target area, wherein the freight related POI data comprise POI large categories, and the POI large categories comprise company enterprises, automobile services, automobile maintenance, catering services, accommodation services, business residences, traffic facility services and road auxiliary facilities; setting POI priority for the large POI category according to the truck driving relevance; based on a geographic information system, associating the POI large category corresponding to the track stay point according to the POI priority; if the single-vehicle fragment mileage of the single-vehicle fragment track is within a first mileage threshold range, the single-vehicle fragment space range is within a first space threshold range, the average driving speed in the single-vehicle fragment is within a first average speed threshold range, and the POI attribute space associated with the single-vehicle fragment end point is of a company enterprise class and/or a traffic facility service class, determining the single-vehicle fragment track as a cargo transportation track; if the single-vehicle segment mileage of the single-vehicle segment track is within a second mileage threshold range, the single-vehicle segment space range is within a second space threshold range, the average driving speed in the single-vehicle segment is within a second average speed threshold range, and POI attribute spaces associated with the single-vehicle segment terminal point and the single-vehicle segment starting point are of company enterprise class and traffic facility service class, determining the single-vehicle segment track as a loading track and an unloading track; if the residence time is within the first residence threshold time range, determining the residence time as short residence time, and if the residence time is within the second residence threshold time range, determining the residence time as medium residence time; if the stay time is within the third stay threshold time range, determining the stay time is long-time stay; if the stopping time is short stopping time or middle stopping time, and the POI attribute space associated with the single car segment terminal point is related to rest of a truck driver, determining the track stopping point as a half-way rest stopping point; if the stay time is long-term stay, and the POI attribute space associated with the single car segment terminal point is related to long-term rest of a truck driver, determining a track stop point as a rest stop point to be kept at home; if the stopping time is short stopping time and the POI attribute space associated with the track stopping point of the single vehicle fragment is related to the vehicle driving service, determining the track stopping point as a transit behavior stopping point; if the POI attribute space associated with the single vehicle fragment track point is related to a track stop point related to the vehicle maintenance service, the POI attribute space is determined to be a transportation irrelevant stop point; and processing and combining the bicycle segment tracks of each freight truck to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point, and the trip chain starting point and the trip chain end point are associated with a track data coordinate system. It will be appreciated that the duration of the first dwell threshold time is less than the duration of the second dwell threshold time, which is less than the duration of the third dwell threshold time. In the invention, the retention time threshold is set to be 3 minutes, namely, the freight track point with the data reporting time interval larger than 3 minutes is considered as the track starting and ending point, and the freight track point with the retention time smaller than 30 minutes is divided into short-time retention; the residence time is divided into medium residence times between 30 minutes and 120 minutes and long residence times exceeding 120 minutes.
In specific implementation, the first time threshold is 3 minutes, and if the time interval for acquiring the data is more than 3 minutes, the current track point is determined to be the bicycle segment starting point of the current bicycle segment track and the bicycle segment end point of the next bicycle segment track; if the mileage of the bicycle fragment track is more than 10km, the spatial range of the bicycle fragment is more than 3km, the average driving speed in the bicycle fragment is more than 30km/h, and the POI attribute space associated with the bicycle fragment end point is of a company enterprise class and/or a transportation facility service class, determining that the bicycle fragment track is a cargo transportation track; if the mileage of the bicycle segment track is more than 10km, the spatial range of the bicycle segment is less than 3km, the average driving speed in the bicycle segment is less than 30km/h, and the POI attribute space associated with the end point of the bicycle segment and the starting point of the bicycle segment is a company enterprise class and a traffic facility service class, determining the bicycle segment track as a loading and unloading track; if the residence time is less than 30 minutes, determining the residence time as short-time residence, and if the residence time is more than 30 minutes and less than 120 minutes, determining the residence time as medium-time residence; if the residence time is more than 120 minutes, confirming that the residence time is long; if the stopping time is short stopping or middle stopping, and the POI attribute space associated with the end point of the bicycle segment is related to rest of a truck driver, determining the track stopping point as a half-way rest stopping point; if the parking time is long, and the POI attribute space associated with the end point of the bicycle segment is related to long-term rest of a truck driver, determining the track stop point as a rest stop point to be waited for home; if the stopping time is short stopping time and the POI attribute space associated with the track stopping point of the single vehicle fragment is related to the vehicle driving service, determining the track stopping point as a transit behavior stopping point; and if the POI attribute space associated with the single car fragment track point is related to the track stop point related to the automobile maintenance service, determining the POI attribute space as a transportation irrelevant stop point.
It is understood that the network interface is utilized to obtain trucking-related POI data within the scope of Hunan province. In the invention, the POI is an abbreviation of "POInt of interest" and is translated into a "POInt of interest", and in a geographic information system, one POI can be a house, a shop, a mailbox, a bus station and the like. The POI data can obtain attribute data such as automobile service, automobile maintenance, catering service, accommodation service, business housing, transportation facility service, road auxiliary facilities and the like in a research area, and can be classified according to the data by using land functions; screening POI categories related to truck transportation, and collecting data by using a crawler technology; acquiring attributes such as longitude, latitude, name and the like of the freight transportation related POI, and vectorizing the POI; due to the fact that the number of the POIs is large, but the POIs are slightly related to the operation of a goods vehicle, such as catering services, the POIs easily have certain influence on the accuracy of the identification result. For example, when a truck track point is closer to both a food service POI and a corporate enterprise POI, the track point is preferentially connected to the corporate enterprise POI. Thus, the POI points are prioritized according to the truck driving relevance, and the relevant POI data and the weight table are shown in table 5 (truck transportation-related POI data).
TABLE 5
| POI numbering | Large class | Middle class | Priority level |
| 10100 | Automotive service | Gas station | Second priority |
| 10200 | Automobile service | Other energy stations | Second priority |
| 10300 | Automotive service | Gas station | Second priority |
| 10400 | Automotive service | Automobile maintenance/decoration | Second priority |
| 10500 | Automobile service | Car washing station | Second priority |
| 10700 | Automobile service | Automobile rescue | Second priority |
| 10800 | Automobile service | Sales of automobile parts | Second priority |
| 30000 | Automobile maintenance | Automobile maintenance | Second priority |
| 50100 | Catering service | Middle dining room | Second priority |
| 50200 | Catering service | Foreign restaurant | Second priority |
| 50300 | Catering service | Fast food restaurant | Second priority |
| 50400 | Catering service | Leisure catering place | Second priority |
| 50500 | Catering service | Coffee hall | Second priority |
| 50600 | Catering service | Tea art shop | Second priority |
| 50700 | Catering service | Cold drink shop | Second priority |
| 50800 | Catering service | Cake store | Second priority |
| 50900 | Catering service | Dessert shop | Second priority |
| 100100 | Accommodation service | Hotel | Second priority |
| 100200 | Accommodation service | Hotel reception post | Second priority |
| 120000 | Commercial residence | Commercial residential correlation | Second priority |
| 120100 | Commercial residence | Industrial park | Second priority |
| 120200 | Commercial residence | Building | Second priority |
| 120300 | Commercial residence | Residential area | Second priority |
| 150100 | Transportation facility service | Airport correlation | First priority level |
| 150200 | Transportation facility service | Railway station | First priority level |
| 150300 | Transportation facility service | Port wharf | First priority |
| 150400 | Transportation facility service | Long-distance bus stop | First priority level |
| 150900 | Transportation facility service | Parking lot | First priority level |
| 151000 | Transportation facility service | Crossing port | First priority |
| 151200 | Transportation facility service | Ferry station | First priority |
| 170100 | Company enterprise | Well-known enterprises | First priority |
| 170200 | Company enterprise | Company(s) | First priority |
| 170300 | Company enterprise | Plant | First priority level |
| 170400 | Company enterprise | Farming, forestry, animal husbandry and fishery base | First priority level |
| 180200 | Road accessory facility | Toll station | First priority level |
| 180300 | Road accessory facility | Service area | First priority level |
| 180400 | Road accessory | Traffic light | First priority |
In the invention, the priority of the road affiliated facilities, the company enterprises and the traffic facility services can be set to be higher than that of the automobile services, the automobile maintenance services, the catering services, the accommodation services and the commercial residences, thereby being convenient for accurately determining the current state of the freight wagon.
In specific implementation, the set priorities are as shown in table 5, and the POI attribute space is connected to the starting point and the ending point; and calculating a 100-meter buffer area by using a geographic information system and taking the POI point as a central point, connecting the attribute space of the POI to a starting point and an end point, and setting the POI attribute with higher priority connection weight.
Further, the step of performing data processing on the segment key indexes to obtain a complete trip chain of each truck, wherein the complete trip chain has a trip chain starting point and a trip chain ending point specifically includes: reserving a starting point and an end point of a cargo transportation track; reserving the starting point and the end point of a loading track and a unloading track; merging the starting points and the end points of the adjacent stopover points and the behavior stopover points in the transportation process; filtering rest staying points to be kept at home and transportation-unrelated staying points; and sequentially connecting effective track stop points of each bicycle segment track to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point.
During specific implementation, the track starting point and the track end point of each bicycle segment track are judged and combined again, and the trip chain starting point and the trip chain end point of the freight behaviors in the target area are obtained. Wherein, for the starting and ending points of the goods transportation, loading/unloading behaviors: (
) All remain; on the beginning and ending points (based on the status of the other actions during the rest and transportation process)>
) Deletion of
Point and next starting point>
I.e. that point pair of beginning and end (< >)>
) And the next starting and ending point pair (< >>
) Merge, consider (@ er)>
) It is a complete trip. Point pair of origin and termination ([ based on ] or [) for home-waiting, transport-independent behavior>
) And performing deletion processing.
Further, whether the trajectory data coordinate system is consistent with the grid data coordinate system is judged: if the track data coordinate system is consistent with the grid data coordinate system, connecting the trip chain starting point and the trip chain end point with the intersected unit grid space by using a geographic information system identification technology, and identifying the grid number of each trip chain starting point and trip chain end point; and if the track data coordinate system is inconsistent with the grid data coordinate system, projecting the grid data coordinate system to the track data coordinate system, connecting the trip chain starting point and the trip chain terminal point with the intersected unit grid space by using a geographic information system identification technology, and identifying the grid number of each trip chain starting point and each trip chain terminal point.
Furthermore, if the grid logistics intensity exceeds the preset intensity, prompt information of adding a truck driver service station, a gas station, a parking facility and a service facility at the position corresponding to the corresponding unit grid is sent.
According to the logistics intensity analysis method based on the freight car running track, on one hand, the distribution characteristics of the starting and ending points of the transportation of the freight car are analyzed, a basis can be provided for the site selection of infrastructure and service facilities, and in the area where the logistics intensity exceeds a certain threshold value, the arrangement of the current infrastructure is combined, and the additional arrangement of the service facilities such as a freight car driver service station, a gas station and a parking facility is considered; on the other hand, key logistics nodes and important channels are integrally identified from the provincial and urban level, theoretical support can be provided for relevant planning policy making, and the method is beneficial to getting through traffic 'aorta' and unblocked traffic 'microcirculation', so that a safe and efficient modern logistics system with inside and outside communication is constructed.
Taking Hunan province as an example, the invention provides a specific logistics intensity analysis method based on the driving track of a truck, which comprises the following steps:
reporting the running data information of the cargo trucks once every preset unit time, not reporting the running data information when the cargo trucks are in flameout parking, acquiring track data sets of all the cargo trucks of 12 tons or more in the period from 3 months 1 days in 2021 to 3 months 31 days in 2022 in Hunan province, wherein the track data sets are collections of the running data information of all the cargo trains, and the running data information comprises unique license plate codes corresponding to the cargo trucks and vehicle azimuth angles, current longitudes and current dimensions of recording time records corresponding to the unique license plate codes;
and (3) carrying out data processing on the track data set: uniquely encoded in the trajectory dataset according to the license plateLine classification, wherein the unique code of the same license plate has a vehicle azimuth, a current longitude and a current latitude at corresponding recording time; sequencing according to the recording time to obtain the time interval of the unique code of the same license plate between two adjacent recording times
Instant speed V, acceleration a, azimuth change>
(ii) a And (3) carrying out data cleaning on the track data set: if the information of the same license plate unique code is missing at the corresponding recording time, a group of data of the same license plate unique code at the corresponding recording time is removed; if the unique code of the same license plate has time overlap at the corresponding recording time, only one group of data of the unique code of the same license plate at the corresponding recording time is reserved; if the instantaneous speed V is greater than the preset effective speed, rejecting a group of data corresponding to the instantaneous speed; if the azimuth changes
If the angle change is larger than a preset angle change threshold value, a group of data corresponding to the azimuth angle change is removed; determining the single-car segment track of each freight truck according to the time interval dt of the acquired data, and if the time interval dt of the acquired data is greater than a first time threshold, determining that the current track point is the single-car segment starting point of the current single-car segment track and the single-car segment end point of the next single-car segment track;
acquiring segment key indexes of each bicycle segment track, wherein the segment key indexes comprise bicycle segment mileage, bicycle segment average driving speed, bicycle segment space range and bicycle segment stay time length, the bicycle segment space range is a straight-line distance between a bicycle segment starting point and a bicycle segment end point of a bicycle segment track, and the current bicycle segment stay time length is an interval time length between a bicycle segment starting point of a next bicycle segment track and a bicycle segment end point of the current bicycle segment track; the method comprises the steps of obtaining freight related POI data of a target area, wherein the freight related POI data comprise large POI categories, and the large POI categories comprise company enterprises, automobile services, automobile maintenance, catering services, accommodation services, business residences, traffic facility services and road auxiliary facilities; setting POI priority for the large POI category according to the truck driving relevance; based on a geographic information system, associating a POI large category corresponding to a track stop point (a starting point and an ending point of a single vehicle segment track) according to the POI priority; if the single-vehicle segment mileage of the single-vehicle segment track is within a first mileage threshold range, the single-vehicle segment space range is within a first space threshold range, the average driving speed in the single-vehicle segment is within a first average speed threshold range, and the POI attribute space associated with the single-vehicle segment end point is of a company enterprise class and/or a transportation facility service class, determining the single-vehicle segment track as a cargo transportation track; if the single-vehicle segment mileage of the single-vehicle segment track is within a second mileage threshold range, the single-vehicle segment space range is within a second space threshold range, the average driving speed in the single-vehicle segment is within a second average speed threshold range, and POI attribute spaces associated with the single-vehicle segment terminal point and the single-vehicle segment starting point are of company enterprise class and traffic facility service class, determining the single-vehicle segment track as a loading track and an unloading track; when the freight truck is in flameout parking, the time interval for not reporting the operation data information is the stop time, if the stop time is in the first stop threshold time range, the stop time is determined as short-time stop, and if the stop time is in the second stop threshold time range, the stop time is determined as middle-time stop; if the stay time is within the third stay threshold time range, determining the stay time is long-time stay; if the stopping time is short stopping time or middle stopping time, and the POI attribute space associated with the single car segment terminal point is related to rest of a truck driver, determining the track stopping point as a half-way rest stopping point; if the stay time is long stay, and the POI attribute space associated with the single car segment end point is related to long rest of a truck driver, determining a track stop point as a rest stop point to be waited for home; if the stopping time is short stopping time and the POI attribute space associated with the track stopping point of the single vehicle fragment is related to the vehicle driving service, determining the track stopping point as a behavior stopping point in the transportation process; if the POI attribute space associated with the single vehicle fragment track point is related to a track stop point related to the vehicle maintenance service, the POI attribute space is determined to be a transportation irrelevant stop point; reserving a starting point and an end point of a cargo transportation track; reserving the starting point and the end point of a loading track and a unloading track; merging the starting point and the end point of the adjacent stopover points and the behavior stopover points in the transportation process; filtering rest staying points to be kept at home and transportation-unrelated staying points; connecting effective track stop points of each bicycle segment track to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point, and the trip chain starting point and the trip chain end point are associated with a track data coordinate system;
dividing the Changsha city into a plurality of unit grids of 1 kilometer multiplied by 1 kilometer to serve as a minimum research unit for analyzing the logistics intensity of the Changsha city, and counting the occurrence frequency of a goods transportation starting point and a goods transportation end point in each unit grid to represent the highway logistics intensity in each grid in a research time period so as to obtain a logistics intensity distribution map of the Changsha city.
The invention also provides a logistics intensity analysis system based on the truck driving track, which is characterized by comprising a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein when the computer program is executed by the processor, the steps of the logistics intensity analysis method based on the truck driving track are realized.
The invention also provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the steps of the logistics intensity analysis method based on the freight train traveling track.
In the above technical solutions of the present invention, the above are only preferred embodiments of the present invention, and the technical scope of the present invention is not limited thereby, and all the technical concepts of the present invention, equivalent structural changes made by using the contents of the description and the drawings of the present invention, or direct/indirect applications in other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A logistics intensity analysis method based on a truck driving track is characterized by comprising the following steps:
s10, acquiring track data sets of all goods trucks with preset loads in a target area and in a historical target time period;
s20, performing data processing and data cleaning on the track data set to obtain a trip chain starting point and a trip chain end point of each freight truck, wherein the trip chain starting point and the trip chain end point are associated with a track data coordinate system;
the step S20 specifically includes the steps of:
s21, carrying out data processing and data cleaning on the track data set to obtain a single-car segment track of each freight truck, wherein the single-car segment track is provided with track stop points, and the track stop points comprise a single-car segment starting point and a single-car segment end point; s22, obtaining segment key indexes of each bicycle segment track, wherein the segment key indexes comprise bicycle segment mileage, bicycle segment average driving speed, bicycle segment space range and bicycle segment stopping time length, the bicycle segment space range is a straight line distance between a bicycle segment starting point and a bicycle segment end point of the bicycle segment track, and the current bicycle segment stopping time length is an interval time length between a bicycle segment starting point of the next bicycle segment track and the bicycle segment end point of the current bicycle segment track; s23, performing data processing on the segment key indexes to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point, and the trip chain starting point and the trip chain end point are associated with the trajectory data coordinate system;
and S30, acquiring a research area in the target area, and gridding the research area to acquire a plurality of unit grids, wherein the unit grids are associated with a grid data coordinate system, are associated with the trajectory data coordinate system and the grid data coordinate system, and determine the actual logistics intensity of the unit grids according to the densities of the trip chain starting point and the trip chain end point in the unit grids.
2. The logistics intensity analysis method based on the freight car driving track according to claim 1, wherein the step S10 specifically comprises the steps of:
s11, reporting operation data information with freight track points of the freight wagon once every preset unit time in the target area and in the historical target time period, wherein the freight wagon is not reported when being parked in a flameout state, and the operation data information comprises a license plate unique code corresponding to the freight wagon and a vehicle azimuth, a current longitude and a current latitude, corresponding to the license plate unique code, of a recording time record;
and S12, forming the track data set by the set of all the running data information.
3. The logistics intensity analysis method based on the freight car driving track according to claim 2, wherein the step S21 specifically comprises:
the data processing of the trajectory data set specifically includes: classifying the license plate unique codes in the track data set, wherein the same license plate unique code has a vehicle azimuth, a current longitude and a current latitude at corresponding recording time; sequencing according to the recording time to obtain the time interval, instantaneous speed, acceleration and azimuth angle change of the same license plate unique code between two adjacent recording times;
the data cleaning of the trajectory data set specifically includes: if the same license plate unique code has information loss at the corresponding recording time, a group of data of the same license plate unique code at the corresponding recording time is removed; if the same license plate unique code is overlapped at the corresponding recording time, retaining a group of data of the same license plate unique code at the corresponding recording time; if the instantaneous speed is larger than a preset effective speed, rejecting a group of data corresponding to the instantaneous speed; if the acceleration is larger than a preset effective acceleration, rejecting a group of data corresponding to the acceleration; if the azimuth angle change is larger than a preset angle change threshold value, a group of data corresponding to the azimuth angle change is removed;
and determining the single-car segment track of each freight truck according to the time interval of the acquired data, and if the time interval of the acquired data is greater than a first time threshold, determining that the current track point is the single-car segment starting point of the current single-car segment track and the single-car segment end point of the next single-car segment track.
4. The logistics intensity analysis method based on the freight car driving track according to claim 1, wherein the step S23 specifically comprises the steps of:
acquiring freight related POI data of the target area, wherein the freight related POI data comprises a large POI category, and the large POI category comprises a company enterprise category, an automobile service category, an automobile maintenance category, a catering service category, an accommodation service category, a business residence category, a traffic facility service category and a road auxiliary facility category;
setting POI priority for the POI large category according to the truck driving correlation;
based on a geographic information system, associating the POI large category corresponding to the track stay point according to the POI priority;
if the single-vehicle segment mileage of the single-vehicle segment track is within a first mileage threshold range, the single-vehicle segment space range is within a first space threshold range, the average driving speed in the single-vehicle segment is within a first average speed threshold range, and the POI attribute space associated with the single-vehicle segment terminal point is of a company enterprise class and/or a transportation facility service class, determining that the single-vehicle segment track is a cargo transportation track;
if the single-vehicle segment mileage of the single-vehicle segment track is within a second mileage threshold range, the single-vehicle segment spatial range is within a second spatial threshold range, the average driving speed within the single-vehicle segment is within a second average speed threshold range, and the single-vehicle segment track is determined to be a loading and unloading track when the POI attribute space associated with the single-vehicle segment terminal point and the single-vehicle segment starting point is of a company enterprise class and a traffic facility service class;
if the residence time is within the first residence threshold time range, determining the residence time as short-time residence, and if the residence time is within the second residence threshold time range, determining the residence time as medium-time residence; if the stay time is within a third stay threshold time range, determining that the stay time is long-time stay; if the stopping time is short stopping time or middle stopping time, and the POI attribute space associated with the single car segment terminal point is related to rest of a truck driver, determining the track stopping point as a half-way rest stopping point; if the stay time is long-term stay, and the POI attribute space associated with the single-car segment terminal point is related to long-term rest of a truck driver, determining the track stop point as a rest stop point to be kept at home; if the stopping time is short stopping time and the POI attribute space associated with the track stopping point of the single vehicle fragment is related to the vehicle driving service, determining the track stopping point as a transit behavior stopping point;
if the POI attribute space associated with the single vehicle fragment track point is related to a track stop point related to the vehicle maintenance service, the POI attribute space is determined to be a transportation irrelevant stop point;
and processing and combining the bicycle segment tracks of each freight truck to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point, and the trip chain starting point and the trip chain end point are associated with the track data coordinate system.
5. The method according to claim 4, wherein the step of performing data processing on the segment key indicators to obtain a complete trip chain of each truck, the complete trip chain having a trip chain start point and a trip chain end point specifically comprises:
reserving a starting point and an end point of the cargo transportation track;
reserving the starting point and the end point of the loading and unloading track;
merging the starting points and the end points of the adjacent half-way rest stopping points and the on-the-way transportation behavior stopping points;
filtering the rest stopping points to be kept at home and the transportation-unrelated stopping points;
and sequentially connecting the effective track stop points of each bicycle segment track to obtain a complete trip chain of each freight truck, wherein the complete trip chain is provided with a trip chain starting point and a trip chain end point.
6. The logistics intensity analysis method based on the truck driving track as claimed in claim 1, wherein the step of "associating the track data coordinate system with the grid data coordinate system" specifically comprises:
judging whether the track data coordinate system is consistent with the grid data coordinate system:
if the trajectory data coordinate system is consistent with the grid data coordinate system, connecting the trip chain starting point and the trip chain end point with the unit grid space intersected with the trip chain starting point and the trip chain end point by using a geographic information system identification technology, and identifying the grid number of each trip chain starting point and each trip chain end point;
if the track data coordinate system is inconsistent with the grid data coordinate system, projecting the grid data coordinate system to the track data coordinate system, connecting the trip chain starting point and the trip chain end point with a unit grid space intersected with the trip chain starting point and the trip chain end point by using a geographic information system identification technology, and identifying the grid number of each trip chain starting point and each trip chain end point.
7. The logistics intensity analysis method based on the truck driving track as claimed in claim 1,
if the grid logistics intensity exceeds the preset intensity, sending out prompt information for adding a truck driver service station, a gas station, a parking facility and a service facility at the corresponding unit grid position.
8. A logistics intensity analysis system based on a truck traveling track, which is characterized by comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the logistics intensity analysis method based on the truck traveling track according to any one of claims 1 to 7 are realized.
9. A computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method for analyzing logistics intensity based on a freight car driving track of any one of claims 1 to 7.
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| CN107622085A (en) * | 2017-08-11 | 2018-01-23 | 河海大学 | A kind of adaptive method of trajectory clustering based on grid and density |
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| CN107622085A (en) * | 2017-08-11 | 2018-01-23 | 河海大学 | A kind of adaptive method of trajectory clustering based on grid and density |
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