CN116434529B - Inter-city highway freight characteristic analysis method and device and electronic equipment - Google Patents
Inter-city highway freight characteristic analysis method and device and electronic equipment Download PDFInfo
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/52—Determining velocity
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/53—Determining attitude
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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Abstract
The invention provides an intercity highway freight characteristic analysis method, an intercity highway freight characteristic analysis device and electronic equipment, which relate to the technical field of highway OD calculation and comprise the following steps: acquiring GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area; determining a travel speed set of the target automobile based on the GPS track data of the target automobile; determining a speed threshold for determining vehicle stopping based on a travel speed set of a plurality of target provincial heavy-duty cargo vehicles; determining a vehicle stay time threshold for determining an end of travel based on the speed threshold and the plurality of GPS trajectory data; and determining traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area based on the vehicle stay time threshold and the plurality of GPS track data. The method is used for determining the OD data of a plurality of target provincial heavy-duty cargo vehicles in a target area in a data driving mode, providing data support for highway freight analysis and the like, and having important significance for promoting the high-quality development of the area.
Description
Technical Field
The invention relates to the technical field of road OD calculation, in particular to an intercity road freight characteristic analysis method, an intercity road freight characteristic analysis device and electronic equipment.
Background
In recent years, in order to construct a new development pattern, two markets at home and abroad are better communicated, and the demand for trans-provincial logistics transportation is gradually increased. The good regional logistics transportation structure has important significance for accelerating product circulation, enhancing industrial advantages, adjusting market supply and demand and the like. Wherein the highway cargo volume is at its maximum, about 73.9%. Therefore, the research on the travel characteristics of the trans-provincial heavy truck has important significance for promoting the economic high-quality development of the area.
Because of the lack of a conventional statistical mode, local governments can only know the road transportation structure and the communication condition of each local city and other provinces through the modes of spot check and investigation, key enterprise reporting and the like. However, these methods generally have the disadvantages of low accuracy, untimely information acquisition, and insufficient data representation. In the big data age, with the increasing data acquisition mode, students begin to try to analyze the truck operation characteristics of the expressway by using the expressway access data. However, since the truck data of the general highway is lacking and there is a great difference between the highway and the general highway, the analysis result cannot be applied to the highway of the entire area.
Disclosure of Invention
The invention aims to provide an inter-city highway freight characteristic analysis method, an inter-city highway freight characteristic analysis device and electronic equipment, so as to provide data support for policy establishment, traffic planning, highway freight analysis, road network research judgment early warning and the like, and has important significance for analyzing the running structure of a trans-provincial highway truck and promoting the high-quality development of an area.
In a first aspect, the present invention provides a method for analyzing freight characteristics of an intercity highway, comprising: acquiring GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area; wherein the GPS track data comprises positioning information of a plurality of GPS track points, and the positioning information comprises: time data, longitude and latitude high data, speed data and direction data; determining a travel speed set of a target automobile based on GPS track data of the target automobile; wherein the target car represents any car of the plurality of target provincial heavy-duty cargo cars; the travel speed set is a set of travel speeds between adjacent GPS track points; determining a speed threshold value for judging vehicle stay based on a plurality of travel speed sets of the target provincial heavy-duty cargo vehicles; determining a vehicle stay time threshold for determining the end of travel based on the speed threshold and a plurality of the GPS track data; and determining traffic volume OD data of a plurality of target provincial heavy-duty cargo vehicles in the target area based on the vehicle stay time threshold and the GPS track data.
In an alternative embodiment, determining a set of travel speeds of a target car based on GPS trajectory data of the target car includes: data cleaning is carried out on the GPS track data of the target automobile, and cleaned track data are obtained; correcting the cleaned track data based on preset road network data in the target area to obtain corrected track data; and calculating the travel speed between adjacent GPS track points in the corrected track data to obtain a travel speed set of the target automobile.
In an alternative embodiment, determining a speed threshold for determining vehicle stay based on a plurality of the set of travel speeds of the target provincial heavy-duty cargo vehicle includes: calculating the distribution probability of each travel speed based on a plurality of travel speed sets of the target provincial heavy-duty cargo vehicle; constructing a Gaussian mixture distribution model of the vehicle speed based on all the travel speeds and the corresponding distribution probabilities; fitting speed distribution curves of all automobiles based on a maximum likelihood estimation algorithm and the Gaussian mixture distribution model; the speed threshold used to determine vehicle stopping is determined based on the speed profile.
In an alternative embodiment, determining a vehicle dwell time threshold for determining an end of travel based on the speed threshold and a plurality of the GPS trajectory data includes: determining all stopping points of the target automobile and the stopping time of each stopping point based on the speed threshold value for judging the stopping of the automobile and the GPS track data of the target automobile to obtain a stopping time set of the target automobile; calculating the distribution probability of each stay time based on a plurality of stay time sets of the target provincial heavy-duty cargo vehicles; fitting a residence time distribution curve based on all residence times and corresponding distribution probabilities; the vehicle dwell time threshold value used for judging the end of the journey is determined based on the dwell time distribution curve.
In an alternative embodiment, fitting the residence time distribution curve based on all residence times and corresponding distribution probabilities includes: constructing three power law functions based on all residence time and corresponding distribution probability; fitting the residence time distribution curve based on the three power law functions.
In an alternative embodiment, determining the vehicle dwell time threshold to determine end of travel based on the dwell time profile includes: and taking a first breakpoint of the residence time distribution curve fitted based on the three-section power law function as the vehicle residence time duration threshold value.
In an alternative embodiment, determining traffic volume OD data of a plurality of the target provincial heavy-duty cargo vehicles in the target area based on the vehicle stay time threshold and the plurality of GPS track data includes: determining at least one set of trip starting and stopping points of the target automobile based on the vehicle stay time threshold and GPS track data of the target automobile; determining sub-OD data of the target automobile in the target area based on the at least one set of travel starting points and administrative division information of the target area; and determining traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area based on sub OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area.
In a second aspect, the present invention provides an intercity highway freight characteristics analysis device, comprising: the acquisition module is used for acquiring GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area; wherein the GPS track data comprises positioning information of a plurality of GPS track points, and the positioning information comprises: time data, longitude and latitude high data, speed data and direction data; the first determining module is used for determining a travel speed set of the target automobile based on GPS track data of the target automobile; wherein the target car represents any car of the plurality of target provincial heavy-duty cargo cars; the travel speed set is a set of travel speeds between adjacent GPS track points; the second determining module is used for determining a speed threshold value for judging the vehicle to stay on the basis of the travel speed sets of the target provincial heavy-duty cargo vehicles; a third determining module for determining a vehicle stay time threshold for determining an end of travel based on the speed threshold and a plurality of the GPS trajectory data; and the fourth determining module is used for determining traffic volume OD data of the plurality of target power-saving heavy-duty cargo vehicles in the target area based on the vehicle stay time threshold and the plurality of GPS track data.
In a third aspect, the present invention provides an electronic device comprising a memory, a processor, the memory having stored thereon a computer program executable on the processor, when executing the computer program, implementing the steps of the inter-urban highway freight feature analysis method according to any of the preceding embodiments.
In a fourth aspect, the present invention provides a computer readable storage medium storing computer instructions which, when executed by a processor, implement the method of interurban highway freight characteristics analysis according to any one of the preceding embodiments.
The invention provides an inter-city road freight characteristic analysis method in a data driving mode, which can determine a speed threshold value for determining vehicle stay and a vehicle stay time threshold value for determining travel end by analyzing GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area, can further determine traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area by combining the GPS track data, can provide data support for policy formulation, traffic planning, road freight analysis, road network research and judgment early warning and the like, and has important significance for analyzing the running structure of the trans-provincial road freight vehicles and the high-quality development of a pushing area.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for analyzing the freight characteristics of an intercity highway according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a driving track of a vehicle according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a speed profile and a speed profile according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of a residence time distribution and residence time distribution curve provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of an OD matrix of a Jiangsu nationality vehicle in Shandong province according to an embodiment of the present invention;
FIG. 6 is a functional block diagram of an intercity highway freight characteristic analysis device according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the present invention will be combined with
The accompanying drawings, which are included to provide a further understanding of the invention, illustrate and together with the description serve to explain the principles of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Some embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Example 1
Fig. 1 is a flowchart of a method for analyzing freight characteristics of an intercity highway according to an embodiment of the present invention, as shown in fig. 1, the method specifically includes the following steps:
Step S102, GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area is obtained.
The method provided by the embodiment of the invention focuses on the travel characteristics of the trans-provincial heavy truck, so that the acquired data are GPS track data of a plurality of target provincial heavy trucks in a target area, namely, the target area and the target provincial representative are different provincials, for example, the step S102 is to acquire GPS track data of the Jiangsu provincial heavy trucks in Shandong provincial. Wherein the GPS track data comprises positioning information of a plurality of GPS track points, and the positioning information comprises: time data, longitude and latitude height data (longitude data, latitude data, and elevation data), speed data, and direction data.
The GPS track data can be obtained through a national public road freight vehicle supervision and service platform, and according to requirements, each vehicle-mounted terminal uploads positioning information according to the time interval set by a monitoring center even if the vehicle is in a dormant state, and the time interval for vehicle-mounted terminal data acquisition and reporting can be 30s.
Step S104, determining a travel speed set of the target automobile based on the GPS track data of the target automobile.
When GPS track data is analyzed, the travel speed of the vehicle is one of important intermediate parameters, after the GPS track data of the target automobile is acquired, the positioning information of each GPS track point is known to comprise longitude and latitude height data and time data, so that the travel speed set of the target automobile, namely the interval speed between two track points, can be obtained by carrying out displacement speed calculation processing on the data. Wherein the target car represents any car of a plurality of target provincial heavy-duty cargo cars; the set of travel speeds is a set of travel speeds between adjacent GPS track points. And processing the GPS track data of each automobile according to the flow, so as to obtain a travel speed set of the plurality of target provincial heavy-duty cargo automobiles.
Step S106, determining a speed threshold for judging the vehicle to stay based on the travel speed sets of the plurality of target provincial heavy-duty cargo vehicles.
In general, it is generally determined whether the vehicle is stopped by using the speed, and if the speed of the vehicle is 0 for a while, it can be determined that the vehicle is stopped. However, in practical studies, as shown in fig. 2, the vehicle may not have a speed of 0 even if it is parked due to the problem of the offset of the GPS track of a part of the vehicle (because of the displacement between adjacent GPS track points). But the speed in this case will generally be very small, much less than during normal travel. Therefore, a speed threshold needs to be defined to determine whether the vehicle is stationary.
After the travel speed sets of all the automobiles are obtained, a speed distribution curve can be constructed based on the travel speed sets, a speed threshold value which can be used for judging the stopping of the automobiles is further determined through the curve, and if the travel speed of the automobiles is greater than or equal to the speed threshold value, the automobiles are determined to be in a running state; if the travel speed of the vehicle is less than the speed threshold, it is determined that the vehicle is in a parked state.
Step S108, determining a vehicle stay time threshold for judging the end of the journey based on the speed threshold and the GPS track data.
After determining a speed threshold value capable of judging whether the vehicle is in a stop state, determining which GPS track points are in the stop state in the GPS track data of each automobile by using the speed threshold value, and further counting the stop time of each vehicle at each stop point according to the time data in the GPS track data. Next, a residence time distribution curve is constructed from the residence times, from which a vehicle residence time threshold value for determining the end of the journey can be further determined. That is, if the stay time length of the vehicle is greater than or equal to the above-described vehicle stay time length threshold value, it is determined that the current trip is ended; if the vehicle stay time is smaller than the vehicle stay time threshold, determining that the vehicle is still in a journey state, and not reaching the preset end point of the current journey, and stopping temporarily only once.
Step S110, determining traffic volume OD data of a plurality of target provincial heavy-duty cargo vehicles in a target area based on the vehicle stay time threshold and the plurality of GPS track data.
As can be seen from the above description, after determining the vehicle stay time threshold, it is determined which GPS track points in the GPS track data of each vehicle are in the stop-of-travel state, and further at least one stop-of-travel point in each track data is determined, that is, one GPS track data may be a track set of multiple-travel tracks, for example, the target vehicle is loaded from the location a, then transported to the location B, then loaded from the location B, and then transported to the location C for unloading … …. After all travel starting and stopping points of the plurality of target provincial heavy-duty cargo vehicles are obtained, the administrative division information of the target area is combined, so that the traffic volume OD (Origin of departure) data of the plurality of target provincial heavy-duty cargo vehicles in the target area can be determined.
The invention provides an inter-city road freight characteristic analysis method in a data driving mode, which can determine a speed threshold value for determining vehicle stay and a vehicle stay time threshold value for determining travel end by analyzing GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area, can further determine traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area by combining the GPS track data, can provide data support for policy formulation, traffic planning, road freight analysis, road network research and judgment early warning and the like, and has important significance for analyzing the running structure of the trans-provincial road freight vehicles and the high-quality development of a pushing area.
In an optional embodiment, the step S104 determines the travel speed set of the target automobile based on the GPS track data of the target automobile, and specifically includes the following steps:
step S1041, performing data cleaning on the GPS track data of the target automobile, to obtain cleaned track data.
Specifically, in the embodiment of the present invention, the purpose of data cleaning is to screen and reject abnormal data, where the abnormal data includes: GPS track points with longitude and latitude positions not belonging to a target area, the number of continuous GPS track points is smaller than GPS track data of a first threshold (insufficient data quantity), and the interval speed is larger than GPS track points of a second threshold (abnormal interval speed). For example, if the target area is a Shandong province, the data with the longitude and latitude position not in the geographical area space of the Shandong province is abnormal data.
Step S1042, the cleaned track data is corrected based on the preset road network data in the target area, and the corrected track data is obtained.
The accuracy of GPS positioning is known to be limited, so that there may be some error in the positioning data of the GPS track points, but the vehicle must travel on the lane, i.e. all track points should be located on the road network. Therefore, after the cleaned track data is obtained, the embodiment of the invention corrects the cleaned track data by using the preset road network data in the target area, namely, the longitude and latitude data is matched to the road network to correct the longitude and latitude data, so as to obtain corrected track data.
Step S1043, calculating the travel speed between adjacent GPS track points in the corrected track data to obtain a travel speed set of the target automobile.
After the corrected track data is obtained, the displacement between the adjacent GPS track points can be calculated according to the longitude and latitude data of the adjacent GPS track points, the time difference between the adjacent GPS track points is calculated according to the reporting time of the adjacent GPS track points, and then the travel speed between the adjacent GPS track points is calculated according to the displacement and the time difference, so that the travel speed set of the target automobile can be obtained through pushing.
Furthermore, under the condition of known road network data, the embodiment of the invention can also select the actual distance before the two adjacent GPS track points on the highway by utilizing the longitude and latitude data of the adjacent GPS track points and the road network data, and then replace the straight line distance calculated by directly using the longitude and latitude between the two points by using the actual distance, namely, the displacement calculation method can obtain more accurate displacement data, thereby obtaining more accurate travel speed.
In an alternative embodiment, the step S106, based on the travel speed sets of the plurality of target provincial heavy-duty cargo vehicles, determines a speed threshold for determining the vehicle stop, specifically includes the following steps:
Step S1061, calculating a distribution probability of each trip speed based on the trip speed sets of the plurality of target provincial heavy-duty vehicles.
Step S1062, a gaussian mixture distribution model of the vehicle speed is constructed based on all the journey speeds and the corresponding distribution probabilities.
Specifically, after calculating the travel speed sets of the plurality of target provincial heavy-duty cargo vehicles, the distribution probability corresponding to each travel speed can be calculated respectively, further, the speed distribution diagram shown in fig. 3 can be obtained by marking all the real data points (speed and distribution probability) in the coordinate system, and as can be seen from fig. 3, the speed distribution curve is in a bimodal distribution, and as can be seen from the study, the peak value on the right side is the speed of the vehicle when the vehicle normally runs on a road, and the peak value on the left side is mainly caused by the deviation of the GPS track points. Therefore, it is necessary to construct a gaussian mixture distribution model of the vehicle speed with the track speed of the offset portion and the track speed of the normal running as two distribution references.
Wherein, the Gaussian mixture distribution model is expressed as:f (x) represents the probability density function of the hybrid distribution model, x represents the velocity, +.>Weights of the track velocity probability distribution sub-function representing the offset part +. >Weights, delta, representing the trajectory speed probability distribution subfunctions of normal travel 1 ，μ 1 Parameters, δ, representing the trajectory velocity probability distribution subfunction of the offset portion 2 ，μ 2 Parameters representing a trajectory speed probability distribution sub-function of normal travel.
Step S1063, fitting the speed distribution curves of all automobiles based on the maximum likelihood estimation algorithm and the Gaussian mixture distribution model.
In step S1064, a speed threshold for determining vehicle stop is determined based on the speed distribution curve.
After a Gaussian mixture distribution model is constructed, the embodiment of the invention utilizes a maximum likelihood estimation algorithm to estimate the unknown parameters in the model so as to obtain a speed distribution curve. The lowest point between the two peaks in the speed distribution curve is the saddle point of the curve, namely, the point is judged to be the position of the vehicle stop point, and the speed corresponding to the position is the speed threshold value for judging the vehicle stop. In FIG. 3, the speed value of the saddle point is 1.01km/h, so when the vehicle speed is lower than 1.01km/h, it is determined that the vehicle is in a stopped state.
In an optional embodiment, the step S108 determines a vehicle stay time threshold for determining the end of the journey based on the speed threshold and the plurality of GPS track data, and specifically includes the steps of:
In step S1081, all the stop points of the target vehicle and the stop time of each stop point are determined based on the speed threshold value for determining the stop of the vehicle and the GPS track data of the target vehicle, so as to obtain a stop time set of the target vehicle.
Optionally, in the embodiment of the present invention, the travel speed between adjacent GPS track points is assigned to the track point with relatively late time, that is, the travel speed between a→b is assigned to B, and after the speed threshold is obtained, it can be screened out which track points of the target automobile are in a stop state according to the speed threshold and the calculated travel speed set. Alternatively, the speed threshold may be compared with speed data (uploaded by the vehicle terminal) corresponding to each GPS track point, so as to determine which track points are in a stopped state. And finally, taking the track point in the stop state as the stop point of the target automobile. If there are consecutive dwell points, it is naturally necessary to superimpose the dwell times of the individual consecutive dwell points to obtain a total dwell time. And counting the residence time corresponding to each residence point of the target automobile according to the scheme to obtain a residence time set of the target automobile. If it is counted that the target automobile coexist at 8 stopping points in the journey, the stopping time set is represented as { T1, T2, T3, T4, T5, T6, T7, T8}.
Step S1082, calculating a distribution probability of each residence time based on the residence time sets of the plurality of target provincial heavy-duty vehicles.
Referring to the method described in the above step S1081, the residence time sets of the target provincial heavy-duty cargo vehicles are respectively counted, so as to calculate the distribution probability of each residence time. For example, if there are 1000 residence times in total in all residence time sets, and 300 for 500 minutes of residence time data, then the probability of a distribution for 500 minutes of residence time is 30%.
Step S1083, fitting a dwell time distribution curve based on all dwell times and corresponding distribution probabilities.
In step S1084, a vehicle stay length threshold value for determining the end of the journey is determined based on the stay time distribution curve.
After the distribution probability corresponding to each residence time is obtained, all the real data points (residence time and distribution probability) are marked in a coordinate system, so that the residence time distribution and the residence time distribution curve shown in fig. 4 can be obtained, and further, the vehicle residence time threshold value for judging the end of the journey can be determined according to the residence time distribution curve.
In the embodiment of the invention, the vehicle stay time threshold is used for judging whether the stay point is the end point of the travel of the vehicle. And when the stay time of a certain vehicle is lower than the stay time threshold of the vehicle, neglecting the stay point, and judging whether the stay time of the next stay point of the vehicle exceeds the stay time threshold of the vehicle. And until a point that the residence time exceeds the vehicle residence time threshold value is found, and when the residence time exceeds the vehicle residence time threshold value, the residence point is the end point of the travel.
In an alternative embodiment, step S1083 fits a residence time distribution curve based on all residence times and corresponding distribution probabilities, and specifically includes the following: firstly, constructing three power law functions based on all residence time and corresponding distribution probability; a residence time distribution curve is then fitted based on a three-segment power law function.
In studies in the human behavioral and hydrologic fields, it has been shown that breakpoints of power law distribution can generally reveal different types of dominant mechanisms or group behaviors. There are generally several behaviors during the running of a vehicle: normal driving, temporary stopping, loading and unloading and stroke end rest. In order of the vehicle residence time, the vehicle is temporarily stopped, loaded and unloaded, and the vehicle is at rest at the end of the journey. Therefore, in the embodiment of the invention, three power law function fitting residence time distribution curves are constructed through (residence time and distribution probability), and then the break points of the power law function are used as the determination basis of the residence time threshold. As shown in FIG. 4, the residence time distribution curve is in three steps, f 1 (x) Representing the stay state of temporary parking, f 2 (x) Representing the resting state of loading and unloading, f 3 (x) Representing the resting state of the end of travel rest.
In embodiments of the present invention, both the loading and unloading of the wagon and the end of travel rest may be determined as the end or beginning of the wagon's travel. Therefore, in the above step S1084, the vehicle stay length threshold value for determining the end of the journey is determined based on the stay time distribution curve, specifically including the following: the first breakpoint of the residence time distribution curve fitted based on the three-section power law function is used as a vehicle residence time threshold value.
In fig. 4, the first break point is 153min, that is, if the residence time of a certain position is less than 153min, the position is indicated as a temporary stop; if the time is greater than 153 minutes, the wagon is at the position and the travel is finished.
In an optional embodiment, the step S110, based on the vehicle stay time threshold and the plurality of GPS track data, determines traffic OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area, and specifically includes the following steps:
step S1101, determining at least one set of travel starting points of the target automobile based on the vehicle stay time threshold and the GPS track data of the target automobile.
In step S1102, sub-OD data of the target automobile in the target area is determined based on at least one set of travel starting and stopping points and administrative division information of the target area.
Based on the analysis, all travel end points in the GPS track data of each automobile can be identified by utilizing the vehicle stay time threshold, each travel corresponds to a group of travel start points, one GPS track data can be a track set of a plurality of travel sections, and the administrative division positions of the travel start points in the target area can be determined according to the administrative division information of the target area and the longitude and latitude data of the travel start points, so that the sub OD data of the target automobile in the target area can be obtained.
In step S1103, traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area is determined based on the sub OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area.
After calculating the sub OD data of each target provincial heavy-duty cargo vehicle in the target area according to the method, synthesizing the sub OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area, and constructing the traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area.
The inventor applies the method of the invention, and the GPS track data of 3 ten thousand Jiangsu heavy-duty trucks running in Shandong province are collected based on 2021, 8, 1, 8, 7 and 3 days. And obtaining the running OD of the vehicle through analysis of each vehicle, and matching the running OD to the corresponding administrative division according to the longitude and latitude of the starting point and the ending point of the vehicle. Through statistical analysis, the total number of vehicles is 2.1 ten thousand, and the extraction stroke is 18.2 ten thousand times. By matching the administrative division where the travel start point and the travel end point are located, an OD matrix of the Jiangsu nationality vehicle in Shandong province is obtained as shown in fig. 5.
In summary, the embodiment of the invention provides a set of inter-city highway freight characteristic analysis method based on GPS track data in a data driving mode for solving the problem that the operation characteristics and distribution of heavy trucks in inter-city highway freight travel cannot be accurately judged, the travel starting point of each truck can be determined, the operation characteristics of inter-region highway trucks can be statistically analyzed, a great deal of funds can be saved for statistical work of traffic industry, data support can be provided for policy formulation, traffic planning, highway freight analysis, road network judgment early warning and the like, and the method has important significance for reinforcing regional heavy truck supervision, analyzing inter-region highway freight operation structures, promoting regional high-quality development, accelerating product circulation, enhancing industrial advantages, regulating market supply and demand and the like.
Example two
The embodiment of the invention also provides an inter-city highway freight characteristic analysis device which is mainly used for executing the inter-city highway freight characteristic analysis method provided by the first embodiment, and the inter-city highway freight characteristic analysis device provided by the embodiment of the invention is specifically introduced below.
Fig. 6 is a functional block diagram of an inter-city highway freight characteristic analysis device according to an embodiment of the present invention, and as shown in fig. 6, the device mainly includes: an acquisition module 10, a first determination module 20, a second determination module 30, a third determination module 40, a fourth determination module 50, wherein:
An acquisition module 10, configured to acquire GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area; wherein the GPS track data comprises positioning information of a plurality of GPS track points, and the positioning information comprises: time data, longitude and latitude high data, speed data and direction data.
A first determining module 20, configured to determine a set of travel speeds of the target automobile based on the GPS track data of the target automobile; wherein the target car represents any car of a plurality of target provincial heavy-duty cargo cars; the set of travel speeds is a set of travel speeds between adjacent GPS track points.
A second determination module 30 for determining a speed threshold for determining vehicle stopping based on a set of travel speeds of a plurality of target provincial heavy-duty cargo vehicles.
A third determination module 40 for determining a vehicle dwell time threshold for determining an end of travel based on the speed threshold and the plurality of GPS trajectory data.
A fourth determining module 50 is configured to determine traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area based on the vehicle stay time threshold and the plurality of GPS track data.
The invention provides an inter-city road freight characteristic analysis device in a data driving mode, which can determine a speed threshold value for determining vehicle stay and a vehicle stay time threshold value for determining travel end by analyzing GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area, can further determine traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area by combining the GPS track data, can provide data support for policy formulation, traffic planning, road freight analysis, road network research and judgment early warning and the like, and has important significance for analyzing the running structure of the trans-provincial road freight vehicles and the high-quality development of a pushing area.
Optionally, the first determining module 20 is specifically configured to:
and cleaning the GPS track data of the target automobile to obtain cleaned track data.
And correcting the cleaned track data based on preset road network data in the target area to obtain corrected track data.
And calculating the travel speed between adjacent GPS track points in the corrected track data to obtain a travel speed set of the target automobile.
Optionally, the second determining module 30 is specifically configured to:
and calculating the distribution probability of each travel speed based on the travel speed sets of the plurality of target provincial heavy-duty cargo vehicles.
And constructing a Gaussian mixture distribution model of the vehicle speed based on all the travel speeds and the corresponding distribution probabilities.
The speed distribution curves of all automobiles are fitted based on a maximum likelihood estimation algorithm and a Gaussian mixture distribution model.
A speed threshold for determining vehicle stopping is determined based on the speed profile.
Optionally, the third determining module 40 includes:
and the first determining unit is used for determining all stopping points of the target automobile and the stopping time of each stopping point based on the speed threshold value used for judging the stopping of the automobile and the GPS track data of the target automobile, so as to obtain a stopping time set of the target automobile.
And the calculating unit is used for calculating the distribution probability of each stay time based on the stay time sets of the plurality of target provincial heavy-duty cargo vehicles.
And the fitting unit is used for fitting the residence time distribution curve based on all residence time and the corresponding distribution probability.
And a second determining unit for determining a vehicle stay length threshold value for determining the end of the journey based on the stay time distribution curve.
Optionally, the fitting unit is specifically configured to:
and constructing a three-segment power law function based on all residence time and corresponding distribution probability.
A residence time distribution curve is fitted based on a three-segment power law function.
Optionally, the second determining unit is specifically configured to:
the first breakpoint of the residence time distribution curve fitted based on the three-section power law function is used as a vehicle residence time threshold value.
Optionally, the fourth determining module is specifically configured to:
at least one set of trip start-stop points for the target vehicle is determined based on the vehicle dwell time threshold and the GPS trajectory data for the target vehicle.
And determining sub OD data of the target automobile in the target area based on at least one set of travel starting and stopping points and administrative division information of the target area.
And determining traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area based on the sub OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area.
Example III
Referring to fig. 7, an embodiment of the present invention provides an electronic device, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, the processor 60, the communication interface 63 and the memory 61 being connected by the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The memory 61 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is achieved via at least one communication interface 63 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 62 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 7, but not only one bus or type of bus.
The memory 61 is configured to store a program, and the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus for defining a process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60 or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in the processor 60. The processor 60 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 61 and the processor 60 reads the information in the memory 61 and in combination with its hardware performs the steps of the method described above.
The computer program product of the inter-city highway freight feature analysis method, the inter-city highway freight feature analysis device and the electronic device provided by the embodiment of the invention comprise a computer readable storage medium storing non-volatile program codes executable by a processor, wherein the instructions included in the program codes can be used for executing the method described in the method embodiment, and specific implementation can be seen from the method embodiment and will not be repeated here.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be noted that, directions or positional relationships indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or are directions or positional relationships conventionally put in use of the inventive product, are merely for convenience of describing the present invention and simplifying the description, and are not indicative or implying that the apparatus or element to be referred to must have a specific direction, be constructed and operated in a specific direction, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the terms "horizontal," "vertical," "overhang," and the like do not denote a requirement that the component be absolutely horizontal or overhang, but rather may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (9)
1. An intercity highway freight characteristic analysis method is characterized by comprising the following steps:
Acquiring GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area; wherein the GPS track data comprises positioning information of a plurality of GPS track points, and the positioning information comprises: time data, longitude and latitude high data, speed data and direction data;
determining a travel speed set of a target automobile based on GPS track data of the target automobile; wherein the target car represents any car of the plurality of target provincial heavy-duty cargo cars; the travel speed set is a set of travel speeds between adjacent GPS track points;
determining a speed threshold value for judging vehicle stay based on a plurality of travel speed sets of the target provincial heavy-duty cargo vehicles;
determining a vehicle stay time threshold for determining the end of travel based on the speed threshold and a plurality of the GPS track data;
determining traffic volume OD data of a plurality of target provincial heavy-duty cargo vehicles in the target area based on the vehicle stay time threshold and the GPS track data;
wherein determining a vehicle stay length threshold for determining an end of travel based on the speed threshold and the plurality of GPS trajectory data, comprises:
Determining all stopping points of the target automobile and the stopping time of each stopping point based on the speed threshold value for judging the stopping of the automobile and the GPS track data of the target automobile to obtain a stopping time set of the target automobile;
calculating the distribution probability of each stay time based on a plurality of stay time sets of the target provincial heavy-duty cargo vehicles;
fitting a residence time distribution curve based on all residence times and corresponding distribution probabilities;
the vehicle dwell time threshold value used for judging the end of the journey is determined based on the dwell time distribution curve.
2. The method of intercity highway freight transportation characteristics analysis according to claim 1, wherein determining the set of journey speeds for the target car based on the GPS track data for the target car comprises:
data cleaning is carried out on the GPS track data of the target automobile, and cleaned track data are obtained;
correcting the cleaned track data based on preset road network data in the target area to obtain corrected track data;
and calculating the travel speed between adjacent GPS track points in the corrected track data to obtain a travel speed set of the target automobile.
3. The method of intercity highway freight characteristics analysis according to claim 1, wherein determining a speed threshold for determining vehicle stopping based on a plurality of said target provincial heavy-duty cargo vehicle travel speed sets comprises:
calculating the distribution probability of each travel speed based on a plurality of travel speed sets of the target provincial heavy-duty cargo vehicle;
constructing a Gaussian mixture distribution model of the vehicle speed based on all the travel speeds and the corresponding distribution probabilities;
fitting speed distribution curves of all automobiles based on a maximum likelihood estimation algorithm and the Gaussian mixture distribution model;
the speed threshold used to determine vehicle stopping is determined based on the speed profile.
4. The method of intercity highway freight characteristics analysis according to claim 1, wherein fitting a residence time distribution curve based on all residence times and corresponding distribution probabilities comprises:
constructing three power law functions based on all residence time and corresponding distribution probability;
fitting the residence time distribution curve based on the three power law functions.
5. The method of intercity highway freight transportation characteristics analysis according to claim 4, wherein determining the vehicle stay length threshold for determining the end of travel based on the stay time profile comprises:
And taking a first breakpoint of the residence time distribution curve fitted based on the three-section power law function as the vehicle residence time duration threshold value.
6. The method of claim 1, wherein determining traffic OD data for a plurality of said target provincial heavy-duty cargo vehicles in said target area based on said vehicle residence time threshold and a plurality of said GPS trajectory data, comprises:
determining at least one set of trip starting and stopping points of the target automobile based on the vehicle stay time threshold and GPS track data of the target automobile;
determining sub-OD data of the target automobile in the target area based on the at least one set of travel starting points and administrative division information of the target area;
and determining traffic volume OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area based on sub OD data of the plurality of target provincial heavy-duty cargo vehicles in the target area.
7. An intercity highway freight characteristics analysis device, comprising:
the acquisition module is used for acquiring GPS track data of a plurality of target provincial heavy-duty cargo vehicles in a target area; wherein the GPS track data comprises positioning information of a plurality of GPS track points, and the positioning information comprises: time data, longitude and latitude high data, speed data and direction data;
The first determining module is used for determining a travel speed set of the target automobile based on GPS track data of the target automobile; wherein the target car represents any car of the plurality of target provincial heavy-duty cargo cars; the travel speed set is a set of travel speeds between adjacent GPS track points;
the second determining module is used for determining a speed threshold value for judging the vehicle to stay on the basis of the travel speed sets of the target provincial heavy-duty cargo vehicles;
a third determining module for determining a vehicle stay time threshold for determining an end of travel based on the speed threshold and a plurality of the GPS trajectory data;
a fourth determining module, configured to determine traffic volume OD data of the plurality of target power-saving heavy-duty cargo vehicles in the target area based on the vehicle stay time threshold and the plurality of GPS track data;
wherein the third determining module includes:
the first determining unit is used for determining all stopping points of the target automobile and the stopping time of each stopping point based on the speed threshold value for judging the stopping of the automobile and the GPS track data of the target automobile, so as to obtain a stopping time set of the target automobile;
A calculating unit, configured to calculate a distribution probability of each residence time based on a plurality of residence time sets of the target provincial heavy-duty cargo vehicles;
a fitting unit for fitting a residence time distribution curve based on all residence times and corresponding distribution probabilities;
and a second determining unit for determining the vehicle stay time threshold value for determining the end of the journey based on the stay time distribution curve.
8. An electronic device comprising a memory, a processor, the memory having stored thereon a computer program executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the inter-urban highway freight feature analysis method according to any one of claims 1 to 6.
9. A computer readable storage medium storing computer instructions which when executed by a processor implement the inter-urban highway freight feature analysis method according to any one of claims 1 to 6.
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