CN116187887B - Method for constructing and tracking traffic subsection door goods communication data - Google Patents
Method for constructing and tracking traffic subsection door goods communication data Download PDFInfo
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Abstract
The invention discloses a method, a device and electronic equipment for constructing and tracking goods communication data of a traffic subsection gate, wherein the method comprises the following steps: 1) Based on input-output relations of the division doors of each region in the input-output table, carrying out division downscaling, and downscaling the OD data between the regions to the OD data of the division doors between the regions; 2) Based on the socioeconomic data, carrying out space downscaling, and downscaling the OD data of the regional division into the OD data of the refined space scale division; and 3) establishing a traffic and freight flow network by utilizing a shortest path algorithm based on the refined spatial scale division OD data, and simultaneously realizing path tracking of the division OD data. The traffic division gate traffic data can be easily and effectively constructed based on the easily-obtained public data, so that scientific and accurate path tracking of the traffic division gate traffic data is realized.
Description
Technical Field
The invention belongs to the technical field of traffic information, and particularly relates to a method for constructing and tracking traffic data of goods of a traffic subsection gate.
Background
The regional division gate traffic goods communication data (OD data) is the basis for researching regional economic communication; and the cargo path tracking is of great significance to traffic infrastructure construction, traffic planning and the like. At present, statistics and tracking of the OD data of the sub-departments among the areas are generally carried out by using a statistics general investigation means and a positioning monitoring instrument at home and abroad. For example, rail transportation is classified by registered freight, postal transportation is classified by registered delivery varieties, and statistical aggregation of the OD data of the regional division gate freight can be achieved. The highway truck is provided with the GPS instrument, the water carrier ship is provided with the AIS instrument, the running track data of the truck and the ship are obtained in real time, and the flow tracking can be realized based on the running track data of the vehicle and the ship obtained by the monitoring instrument. In addition, the statistics of the traffic goods OD data of the regional division doors can be realized by summarizing the monitoring data and the goods information.
However, the method is limited by industry protectiveness and high monitoring statistical cost, so that the traffic OD data of the division gate among areas cannot be popularized between the industry and scientific research, and the development of related industry and academic research is limited to a certain extent.
Accordingly, there is a need for new methods and techniques to at least partially overcome the above-described problems in the prior art.
Disclosure of Invention
Aiming at high monitoring and statistics cost and based on easy-to-obtain public data, the invention provides a method and a system for easily and quickly constructing traffic subsection gate flow data and realizing path tracking of the flow data by constructing a department downscaling, space downscaling and path optimizing module.
According to one aspect of the present invention, there is provided a method for constructing and tracking traffic data of goods in a traffic division gate, comprising:
1) Based on input-output relations of the division doors of each region in the input-output table, carrying out division downscaling, and downscaling the OD data between the regions to the OD data of the division doors between the regions;
2) Based on the socioeconomic data, carrying out space downscaling, and downscaling the OD data of the regional division into the OD data of the refined space scale division; and
3) Based on the OD data of the sub-division of the refined space scale, a shortest path algorithm is utilized to establish a traffic and freight flow network, and meanwhile, path tracking of the OD data of the sub-division is realized.
According to an embodiment of the present invention, the inter-zone OD data includes inter-zone OD data for a plurality of transportation modes including rail transportation, road transportation, water transportation, and air transportation.
According to the embodiment of the invention, the input-output table is a multi-region input-output table and comprises four quadrants, wherein the first quadrant is the industrial association of departments among regions; the second quadrant represents the final use of the products and services provided by the departments of each area; the third quadrant shows the initial input amount of each department in each region; the fourth quadrant is empty.
According to the embodiment of the invention, the step 1) comprises the steps of carrying out division downscaling on OD data between the goods areas based on the total output of goods of the division doors and the input-output relation of different departments between the areas, and constructing the goods transportation quantity of the division between the areas in a certain transportation mode, wherein the goods transportation quantity is shown in a formula (1):
Wherein, Is the certain traffic of departments s between the areas i and j, T ij is the certain traffic of all cargoes between the areas i and j,/>The ratio of the transportation quantity of the certain transportation mode of the departments s from the region i to the region j in the output table to the transportation quantity of the certain transportation mode of all departments is calculated;
Wherein, Calculated according to the formula (2):
Wherein, The weight of the certain transportation mode transportation input from the region i to the region j of the production table is input;
Wherein, Calculated according to the formula (3):
Wherein, The economic input of departments s between the region i and the region j in the output table is input, and IO s is the total economic output of the departments s of the input-output table; f s is the total capacity of all departments, C s is the delivery coefficient of department s for the certain mode of transportation;
Wherein the yield coefficient C s is calculated according to equation (4) when the certain transportation means traffic and the cargo yield are known, and is calculated according to equation (5) when the certain transportation means traffic and the cargo yield are unknown:
Where T s is the transport capacity of a certain transportation means described by section s, F s is the total yield of section s; the total volume of cargo for a certain transportation mode described in T total, F total is the corresponding total volume of cargo.
According to an embodiment of the present invention, step 2) includes downscaling the administrative section division OD data to the site scale division OD data using an attraction model and a simulation technique based on social elements, wherein the social elements include a site daily load number, a site daily unload number, a site radiated population number, and a site radiated GDP.
According to an embodiment of the invention, wherein step 2) comprises:
calculating a transmission probability P Oo of the departure station and the station o by using formulas (6) and (7), and a cumulative transmission probability CP Oo:
wherein car o is the average daily load number at departure station o, The average daily loading number of all stations in the administrative area where the station o is located is the sum of the average daily loading numbers of all stations; /(I)Is the sum of the daily average loading numbers of the 1 st to the o-th stations (1, 2, …, o);
Calculating an arrival probability P Dd of the station d-th station and an accumulated arrival probability CP Dd using (8) and (9):
where POP d is the population of station d buffer b, The population total number of b is the buffer area of all sites in the administrative area where the site d is located, and the radiation affecting range of the site d in the buffer area b; and
Obtaining minute gate OD data of fine scale, i.e. a certain traffic-way traffic of o site of administrative district i to d site department s of administrative district j, using formulas (10) - (12)
Wherein,Is the traffic of the section s between the region i and the region j,/>Is a single delivery, N is the number of simulations, F o is the departure site selection parameter, F d is the arrival site selection parameter, where
Wherein, CP Oo- is the cumulative transmission probability of the (o-1) th site, [ CP Oo-1,CPOo ] is the interval distribution of site o in the social cumulative element function, R o is a uniformly distributed random number between 0 and 1, when R o∈[CPOo-1,CPOo ], the goods departure site is o site;
wherein, CP Dd-1 is the cumulative arrival probability of the d-1 site, [ CP Dd-1,CPDd ] is the interval distribution of site d in the social cumulative element function, R d is the uniformly distributed random number between 0 and 1, when R d∈[CPDd-1,CPDd ], the goods reach site d.
According to an embodiment of the invention, wherein step 3) comprises: based on freight road line network and line operation energy limit, using Di Jie Style algorithm to search transportation path for reduced-scale sub gate station OD data, and comparingOptimizing to obtain a transportation path between the station o and the station d, wherein the transportation path comprises a road section e od1,eod2,…,eodn; wherein n is a natural number greater than 1;
Wherein the flow f odn of the segment e odn is calculated as follows formulas (13) and (14):
fodn≤Codn (14)
Where f odn is the flow through segment e odn, Is the traffic of the o site of administrative district i to the d site department s of administrative district j, C odn is the performance limit of road segment e odn, and when the flow through road segment e odn is greater than the performance limit of road segment e odn, the road segment e odn is deleted from the freight road route network and the traffic is re-paired/>Optimizing until the operation energy limit is met; when the station o to the station d pass through the section e odn, t odn =1, otherwise t odn =0.
According to the embodiment of the invention, the method for constructing and tracking the traffic subsection door goods communication data further comprises the step of comparing and verifying by using the statistical values after the steps 1) and 3) to verify the reliability of the regional subsection door OD data obtained in the step 1) and the freight flow network obtained in the step 3).
According to another aspect of the present invention, there is provided an apparatus for constructing and tracking traffic division gate cargo communication data: comprising the following steps:
the division downscaling module is used for performing division downscaling based on the input-output relation of the division doors of each region in the input-output table and downscaling the OD data between the regions to the OD data of the division doors between the regions;
The spatial downscaling module is used for performing spatial downscaling based on the socioeconomic data and downscaling the inter-regional division OD data to the refined spatial scale division OD data; and
The path optimizing module is used for establishing a traffic freight flow network by utilizing a shortest path algorithm based on the fine space scale division OD data and simultaneously realizing path tracking of the division OD data.
According to still another aspect of the present invention, there is provided an electronic apparatus including: a memory and one or more processors;
the memory is used for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the methods described herein.
The traffic subsection gate flow data can be easily and quickly constructed based on the easily-obtained public data, the tracking of the flow data is realized, the cost is low, the method is scientific, and the result is accurate and reliable.
Drawings
FIG. 1 is a flow chart of a method for constructing and tracking traffic segment gate cargo communication data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a cumulative probability distribution function of social elements of a site for a method of constructing and tracking traffic segment gate cargo communication data according to an embodiment of the present invention;
FIG. 3 is a shortest path optimization flow chart of a method of traffic segment gate cargo ac data construction and tracking in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram of a traffic segment gate cargo communication data construction and tracking device according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an electronic device for implementing a method of traffic segment gate cargo communication data construction and tracking according to an embodiment of the present invention;
FIG. 6 is a block diagram of an administrative section rail cargo transportation according to an embodiment of the present invention;
FIG. 7 is a diagram showing the results of verification of the comparison of estimated cargo traffic values and statistics for 11 sub-units obtained by the method for constructing and tracking traffic data for a traffic sub-door according to an embodiment of the present invention;
FIG. 8 is a graph of the result of comparing and verifying the estimated and statistical values of the coal traffic volume in the administrative section obtained by the method for constructing and tracking the traffic data of the traffic division gate according to the embodiment of the invention;
FIG. 9 is a path of metal ore from site a of administrative district A to site B of administrative district B, obtained by a method of traffic division gate cargo communication data construction and tracking, according to an embodiment of the present invention; and
Fig. 10 is a graph of the estimated value and the statistical value of the line cargo turnover versus the verification result obtained by the method for constructing and tracking the traffic data of the traffic division gate according to the embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and specific examples, but the examples or descriptions are not intended to limit the scope of the present invention.
In order that those of ordinary skill in the art will better understand the present invention, some terms to which the present invention pertains will be described below. Inter-zone cargo communication data (inter-zone OD data): cargo data, such as cargo weight, transferred between different areas; inter-zone gate goods communication data (inter-zone gate OD data): different industry departments, goods data transmitted between different areas; traffic division OD data: the OD data of the regional division of the transportation by a certain transportation mode (railway, highway, aviation, water transport, etc.); refining the OD data of the spatial scale division department: the regional division gate OD data or the traffic division gate OD data is spatially downscaled, for example, spatially refined from administrative region scale to site scale. It should further be appreciated that some of the model algorithms to which the present invention relates, such as gravitational models, simulation techniques, dijiestra algorithms, etc., are known per se, and that the present invention therefore focuses on how to combine and apply these model algorithms.
FIG. 1 is a flow chart of a method for constructing and tracking traffic segment gate cargo communication data according to an embodiment of the present invention; referring to fig. 1, a model of traffic segment gate cargo communication data construction and tracking of this embodiment may include:
In step S1, a division downscaling module is constructed to downscale all division OD data (total OD data) to division gate OD data based on the input-output relationship of the division gate in each region of the input-output table (IO table).
More specifically, based on the total output of the goods of the division doors and the total output data of the division doors of the input-output table, the input-output relation of different departments among the areas is comprehensively considered, the OD data of all the goods departments are subjected to department scale reduction, and the goods transportation quantity (ton) of the division departments among administrative areas of a certain transportation mode is constructed.
The input-output table (IO table) can be a multi-zone input-output table comprising four quadrants, the first quadrant (I) being the industry association of departments between zones, wherein the diagonal matrix reflects the internal association of the respective zones, looking transversely at supplies between expressed zones, such asThe supply quantity of the r region department m to the s region n department is shown; the requirement between the expression areas is seen longitudinally, so that the requirement quantity of the s-area department n to the r-area m department can be understood, and the ratio of the requirement quantity to the total output of the s-area department n to the r-area m department can be understood as a direct consumption matrix of each department between the expression areas. The second quadrant (II) represents the end use of the products and services offered by the departments of the areas, where/>Indicating the total yield of r-zone division m. The third quadrant (III) shows the initial input amount of each division of each area. The fourth quadrant is empty. In addition, the departments described herein may be, for example, corresponding departments in an IO table, including, for example, metal mining, agriculture, forestry, animal husbandry, coal mining, construction, and the like. The transportation means may include, for example, railway, highway, aviation, water transportation, etc.
The cargo traffic of a certain traffic pattern division between regions can be expressed as shown in formula (1):
Wherein, Is the certain traffic of departments s between the areas i and j, T ij is the certain traffic of all cargoes between the areas i and j,/>The ratio of the transportation quantity of the certain transportation mode of the departments s from the region i to the region j in the output table to the transportation quantity of the certain transportation mode of all departments is calculated;
Wherein, Calculated according to the formula (2):
Wherein, The weight of the certain transportation mode transportation input from the region i to the region j of the production table is input;
Wherein, Calculated according to the formula (3):
Wherein, The economic quantity (ten thousand yuan) is input from the region i to the region j in the output table by the department s, and IO s is the total economic output of the input-output table department s; f s is the total throughput of all departments, C s is the transport coefficient of department s for the transport mode (the ratio of transport mode to total throughput of the goods); the formula can convert the input economic quantity of cargo varieties s from administrative district i to administrative district j into the input weight for transportation in a certain transportation mode.
Wherein the yield coefficient C s is calculated according to equation (4) when the certain transportation means traffic and the cargo yield are known, and is calculated according to equation (5) when the certain transportation means traffic and the cargo yield are unknown:
Where T s is the transport capacity of a certain transportation means described by section s, F s is the total yield of section s; the total volume of cargo for a certain transportation mode described in T total, F total is the corresponding total volume of cargo.
After the division downscaling is completed, division OD data is obtained, where the division OD data is of a coarse spatial scale (e.g., inter-region scale).
Thereafter, based on the obtained inter-region division OD data, a spatial downscaling process is performed, that is, step S2. The region downscaling mainly includes downscaling the coarse space scale division OD data to the fine space scale division gate OD data based on the socioeconomic class data, for example, by using an gravitation model and a simulation technology, for example, the region space scale is reduced to a site space scale, and the region can be an administrative region, for example.
Based on social elements (such as indexes of daily loading number of stations, daily unloading number of stations, population number of stations radiating, GDP of stations radiating and the like), the administrative section OD data is downscaled to the station section OD data by utilizing an attraction model and a simulation technology, wherein the station transmission quantity and the arrival quantity can be distributed and calculated by using the social elements as parameters of the attraction model.
More specifically, the spatial downscaling process may first include calculating the transmission probability P Oo of the departure station and the cumulative transmission probability CP Oo of the departure station's o-th station using equations (6) and (7):
wherein car o is the average daily load number at departure station o, The average daily loading number of all stations in the administrative area where the station o is located is the sum of the average daily loading numbers of all stations; /(I)Is the sum of the daily average loading numbers of the 1 st to the o-th stations (1, 2, …, o); all sites in the administrative area are marked as n, natural numbers are taken, o is one of 1 to n, and the sites are marked as o-th sites.
Then, the arrival probability P Dd of the station d-th station and the cumulative arrival probability CP Dd are calculated using (8) and (9):
where POP d is the population of station d buffer b, Is the population total of b of all site buffer areas in the administrative area where the site d is located, and the radiation affecting range of the site d in the buffer area b can be 40-80km, for example, 60km. All sites in the administrative area are marked as n, natural numbers are taken, and d is one of 1 to d, and the site is marked as the d site. Population numbers for each site may be obtained based on Worldpops 2017 population density data (https:// data. Humdata. Org/dataset /).
Obtaining minute-scale division gate OD data, namely, a certain traffic mode delivery amount from o site of administrative district i to d site department s of administrative district j by using formulas (10) - (12)
Wherein,Is the traffic of the section s between the region i and the region j,/>Is single delivery, N is simulation times, F o is a departure site selection parameter, a site for determining departure of goods in the simulation process, F d is a arrival site selection parameter, a site for determining arrival of goods in the simulation process, wherein
Wherein, CP Oo-1 is the cumulative transmission probability of the (o-1) th site, [ CP Oo-1,CPOo ] is the interval distribution of site o in the social cumulative element function, R o is a uniformly distributed random number between 0 and 1, when R o∈[CPOo-1,CPOo ], the goods departure site is o site;
Wherein, CP dd-1 is the cumulative arrival probability of the d-1 site, [ CP Dd-1,CPDd ] is the interval distribution of site d in the social cumulative element function, R d is the uniformly distributed random number between 0 and 1, when R d∈[CPDd-1,CPDd ], the goods reach site d.
Fig. 2 is a schematic diagram of a cumulative probability distribution function of social elements of a site according to a method for constructing and tracking traffic distribution gate cargo communication data according to an embodiment of the present invention, in which the cumulative probability and interval distribution are shown.
And then, performing step S3, constructing a path optimizing module, namely, comprehensively considering traffic energy limitation by utilizing a shortest path algorithm, realizing path tracking of the OD data of the sub-departments in the traffic and transportation network, and simultaneously obtaining the traffic and transportation flow network.
More specifically, the haul road link network and link performance limits may be obtained from existing traffic information. FIG. 3 is a schematic diagram of shortest path optimization in accordance with an embodiment of the present invention, showing searching for transport paths for reduced-scale portal OD data using Dijiestra's algorithm, for pairs of pointsOptimizing to obtain a transportation path between the stations o and d, wherein the transportation path comprises n road sections e od1,eod,…,eodn; wherein n is a natural number greater than 1; the flow f odn of each segment e odn can be calculated using the following equations (13) and (14):
fodn≤Codn (14)
Where f odn is the flow through segment e odn, Is the traffic of the o site of administrative district i to the d site department s of administrative district j, C odn is the performance limit of road segment e odn, and when the flow through road segment e odn is greater than the performance limit of road segment e odn, the road segment e odn is deleted from the freight road route network and the traffic is re-paired/>Optimizing until the operation energy limit is met; when the station o to the station d pass through the section e odn, t odn =1, otherwise t odn =0.
FIG. 4 is a schematic diagram of a traffic segment gate cargo communication data construction and tracking device according to an embodiment of the present invention; fig. 5 is a schematic diagram of an electronic device for implementing the method for constructing and tracking traffic data of a traffic division gate according to an embodiment of the present invention. As shown in fig. 5, the electronic device 800 includes a processor 810, a memory 820, an input device 830, and an output device 840; the number of processors 810 in the electronic device may be one or more, one processor 810 being illustrated in fig. 5. The processor 810, memory 820, input device 830, and output device 840 in the electronic device may be connected by a bus or other means, for example in fig. 5.
The memory 820 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to the traffic segment door cargo data construction and tracking method according to the embodiments of the present invention (e.g., the department downscaling module 710, the space downscaling module 720, and the path optimizing module 730 in the traffic segment door cargo data construction and tracking device 700 in fig. 4). The processor 810 executes various functional applications of the electronic device and data processing by running software programs, instructions and modules stored in the memory 820, i.e., implementing the traffic segment gate cargo communication data construction and tracking method described above.
Memory 820 may include primarily a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 820 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 820 may further include memory remotely located relative to processor 810, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 830 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the electronic device. The output device 840 may include a display device such as a display screen.
Examples
The following further illustrates the implementation of the method of the present invention by taking 2017 rail freight transportation between site a of northern administrative district a to site B of northern administrative district B as an example.
The following related data materials are obtained from the national statistical office website or related websites: (1) Cargo communication data between 2017 national railway administrative areas and coal communication data between 2017 national railway administrative areas (https:// navi. Cnki. Net/knavi/yearbooks/YZGTD/detailuniplatform = NZKPT & land=chs); (2) National railway freight traffic in 2017 and various kinds of duty ratio (https:// data. Stats. Gov. Cn/easy request. Htmcn=c01 & zb=a0g0d01 & sj=2020); (3) 2017, the total yield data of each variety of the national railway and the total yield data of each variety and corresponding department data (https:// data. Stats. Gov. Cn/easy. Htmcn=c01 & zb=a0e0h & sj=2020); (4) Cargo volume data of the national rail main line 2012 (https:// navi. Cnki. Net/knavi/yearbooks/YZGTD/detailuniplatform = NZKPT & land=chs).
In addition, the loading number of freight sites of each railway office in 2020/9/27-2020/10/4 eight days is obtained from a railway freight shipment public website (http:// hyfw.95306. Cn), the loading number can reflect the freight transmission quantity of the site, and the average value of the loading number in eight days is taken as the daily loading number of the site.
Based on step S1, using formulas (1) - (5), zone OD data of 11 railway transportation divisions (table 1) are obtained, and OD data of administrative sections are plotted as a box-type diagram according to the transportation cargo varieties, as shown in fig. 6. The box-shaped diagram is ordered from left to right according to the total quantity of railway goods transportation from small to large (the box body represents a quartile range, the horizontal line is a middle value, the positions of two end edges respectively correspond to the upper quartile and the lower quartile of data, and the upper edge and the lower edge represent 90% and 10%). As can be seen from the box fig. 6, the coal transportation railway has the greatest freight traffic, the other freight ranks second, followed by the metal ore. The median of the OD traffic of the inter-zone cargo varieties is close to 0 ton, except for steel and nonferrous metals and other cargoes. In addition, the OD transport amount difference among railway areas of goods varieties including cement, wood, mineral materials, grains, metal ores, petroleum and the like is small, and the OD value among more than 90% of administrative areas is less than 5 ten thousand tons; the difference in transport OD between other cargo varieties is large, substantially between 0 and 30 tons.
TABLE 1 OD data for each zone division
Note that: administrative district C is located between administrative district A and administrative district B
In order to evaluate the reliability of the obtained OD data between the cargo variety areas, the invention compares and verifies the estimated value and the statistical value of the railway traffic of 11 cargos (the division gates), and as shown in the result of FIG. 7, R 2 can reach more than 0.99, and the correlation between the estimated value and the statistical value of the freight traffic of the division gates is very high. Wherein, the coal transportation amount is maximum, the estimated value is 140,000 ten thousand tons, the statistical value is 150,000 ten thousand tons, and the deviation is only 6.9%; the other goods and metal ores were transported in secondary amounts with deviations of 6.8% and 12.9%, respectively. In addition, we compare the estimated value and the statistical value of the administrative section coal traffic OD, and the result is shown in 7, R 2 can reach 0.94, and the generated inter-regional division gate OD data can be proved to be very reliable. This shows that the department of the invention has accurate downscaling result and the downscaling model has good practical applicability.
Then, based on step S2, using formulas (6) - (12), the minute gate OD data of the fine scale after the spatial downscaling is obtained, and table 2 is freight site scale transportation results of the metal ore freight volume from site a of administrative district a to site B of administrative district B:
Table 2: metal ore freight volume between site a to site b on freight site scale transportation results
Finally, based on step S3, a route for transporting the metal ore from Fengtai west (station a) to the Tianjin harbor (station b) is obtained by using formulas (13) and (14), and as a result, as shown in fig. 9, the goods start from Fengtai west, reach Li Ying stations first, then pass through the goods stations in administrative area C, and finally reach the Tianjin harbor through Yang Cun stations and the like.
Based on the turnover data of the 2012 national railway main trunk, the 2017 national railway main trunk turnover is obtained according to the ratio of the 2012 national railway freight total turnover to the 2017 national railway freight total turnover. In order to evaluate the reliability of the obtained freight flow network, the invention compares the estimated value and the statistical value of the freight turnover of the main line in China, and the result is shown in figure 10, and the R side of the statistical and estimated result can reach 0.76; the turnover of the large Qin line of the special coal transportation line is maximum, the estimated value is 2100 hundred million tons kilometers, the statistical value is 2300 hundred million tons kilometers, and the deviation is 10.2%. The result shows that on the basis of considering the line traffic capacity, the accuracy of the freight traffic network constructed by using the Di Jie Style algorithm is higher, and the freight traffic tracking can be realized by the method.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the above examples being provided only to assist in understanding the device and its core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (8)
1. A method for constructing and tracking traffic division gate cargo communication data, comprising:
1) Based on input-output relations of the division doors of each region in the input-output table, carrying out division downscaling, and downscaling the OD data between the regions to the OD data of the division doors between the regions;
2) Based on the socioeconomic data, carrying out space downscaling, and downscaling the OD data of the regional division into the OD data of the refined space scale division; and
3) Based on the OD data of the sub-division of the refined space scale, a shortest path algorithm is utilized to establish a traffic and freight flow network, and meanwhile, path tracking of the OD data of the sub-division is realized;
Step 2) comprises the steps of utilizing an attraction model and a simulation technology to scale down administrative section division OD data to site scale division OD data based on social elements, wherein the social elements comprise site daily loading number, site daily unloading number, site radiation population number and site radiation GDP;
Step 2) further comprises:
calculating a transmission probability P Oo of the departure station and the station o by using formulas (6) and (7), and a cumulative transmission probability CP Oo:
wherein car o is the average daily load number at departure station o, The average daily loading number of all stations in the administrative area where the station o is located is the sum of the average daily loading numbers of all stations; /(I)Is the sum of the daily average loading numbers of the 1 st to the o-th stations (1, 2, …, o);
Calculating an arrival probability P Dd of the station d-th station and an accumulated arrival probability CP Dd using (8) and (9):
where POP d is the population of station d buffer b, The population total number of all site buffer areas in the administrative area where the site d is located is b, and the buffer area b is the radiation influence range of the site d; and
Obtaining minute gate OD data of fine scale, i.e. a certain traffic-way traffic of o site of administrative district i to d site department s of administrative district j, using formulas (10) - (12)
Wherein,Is the traffic of the section s between the region i and the region j,/>Is a single delivery, N is the number of simulations, F o is the departure site selection parameter, F d is the arrival site selection parameter, where
Wherein, CP Oo-1 is the cumulative transmission probability of the (o-1) th site, [ CP Oo-1,CPOo ] is the interval distribution of site o in the social cumulative element function, R o is a uniformly distributed random number between 0 and 1, when R o∈[CPOo-1,CPOo ], the goods departure site is o site;
wherein, CP Dd-1 is the cumulative arrival probability of the d-1 site, [ CP Dd-1,CPDd ] is the interval distribution of site d in the social cumulative element function, R d is the uniformly distributed random number between 0 and 1, when R d∈[CPDd-1,CPDd ], the goods reach site d.
2. The method of constructing and tracking traffic segment door cargo communication data according to claim 1, wherein: the inter-zone OD data includes inter-zone OD data for a plurality of transportation modes including rail transportation, road transportation, water transportation, and air transportation.
3. The method of constructing and tracking traffic segment door cargo communication data according to claim 1, wherein: the input-output table is a multi-region input-output table and comprises four quadrants, wherein the first quadrant is the industrial association of departments among the regions; the second quadrant represents the final use of the products and services provided by the departments of each area; the third quadrant shows the initial input amount of each department in each region; the fourth quadrant is empty.
4. The method of constructing and tracking traffic segment door cargo communication data according to claim 1, wherein: step 1) performing division downscaling on OD data between goods areas based on the total output of goods of the division doors and the input-output relation of different departments between areas of input-output table division doors, and constructing the goods transportation quantity of the division between the areas of a certain transportation mode, as shown in a formula (1):
Wherein, Is the certain traffic of departments s between the areas i and j, T ij is the certain traffic of all cargoes between the areas i and j,/>The ratio of the transportation quantity of the certain transportation mode of the departments s from the region i to the region j in the output table to the transportation quantity of the certain transportation mode of all departments is calculated;
Wherein, Calculated according to the formula (2):
Wherein, The weight of the certain transportation mode transportation input from the region i to the region j of the production table is input;
Wherein, Calculated according to the formula (3):
Wherein, The economic input of departments s between the region i and the region j in the output table is input, and IO s is the total economic output of the departments s of the input-output table; f s is the total capacity of all departments, C s is the delivery coefficient of department s for the certain mode of transportation;
Wherein the yield coefficient C s is calculated according to equation (4) when the certain transportation means traffic and the cargo yield are known, and is calculated according to equation (5) when the certain transportation means traffic and the cargo yield are unknown:
Where T s is the transport capacity of a certain transportation means described by section s, F s is the total yield of section s; the total volume of cargo for a certain transportation mode described in T total, F total is the corresponding total volume of cargo.
5. The method of constructing and tracking traffic segment door cargo communication data according to claim 1, wherein: the step 3) comprises the following steps: based on freight road line network and line operation energy limit, using Di Jie Style algorithm to search transportation path for reduced-scale sub gate station OD data, and comparingOptimizing to obtain a transportation path between the station o and the station d, wherein the transportation path comprises a road section e od,eod,…,eodn; wherein n is a natural number greater than 1;
Wherein the flow f odn of the segment e odn is calculated as follows formulas (13) and (14):
fodn≤Codn (14)
Where f odn is the flow through segment e odn, Is the traffic of the o site of administrative district i to the d site department s of administrative district j, C odn is the performance limit of road segment e odn, and when the flow through road segment e odn is greater than the performance limit of road segment e odn, the road segment e odn is deleted from the freight road route network and the traffic is re-paired/>Optimizing until the operation energy limit is met; when the station o to the station d pass through the section e odn, t odn =1, otherwise t odn =0.
6. The method for constructing and tracking traffic segment door cargo communication data according to claim 1 or 2, wherein: and further comprises the step of comparing and verifying by using the statistical values after the steps 1) and 3) to verify the reliability of the data of the inter-area division gate OD obtained in the step 1) and the freight flow network obtained in the step 3).
7. The utility model provides a traffic subsection door goods exchange data builds and device of tracking which characterized in that: comprising the following steps:
the division downscaling module is used for performing division downscaling based on the input-output relation of the division doors of each region in the input-output table and downscaling the OD data between the regions to the OD data of the division doors between the regions;
The spatial downscaling module is used for performing spatial downscaling based on the socioeconomic data and downscaling the inter-regional division OD data to the refined spatial scale division OD data; and
The path optimizing module is used for establishing a traffic freight flow network by utilizing a shortest path algorithm based on the OD data of the sub-division of the refined space scale and simultaneously realizing path tracking of the OD data of the sub-division;
The space downscaling module downscaling the administrative section division OD data to the site dimension division OD data by utilizing an attraction model and a simulation technology based on social elements, wherein the social elements comprise the daily loading number of sites, the daily unloading number of sites, the population number of site radiation and the GDP of site radiation;
The spatial downscaling module calculates the transmission probability P Oo of the departure station and the cumulative transmission probability CP Oo of the station o by using formulas (6) and (7):
wherein car o is the average daily load number at departure station o, The average daily loading number of all stations in the administrative area where the station o is located is the sum of the average daily loading numbers of all stations; /(I)Is the sum of the daily average loading numbers of the 1 st to the o-th stations (1, 2, …, o);
The spatial downscaling module calculates the arrival probability P Dd of the site d site and the cumulative arrival probability CP Dd by using (8) and (9):
where POP d is the population of station d buffer b, The population total number of all site buffer areas in the administrative area where the site d is located is b, and the buffer area b is the radiation influence range of the site d; and
The spatial downscaling module obtains refined scale division gate OD data, i.e., the amount of transportation of a certain traffic from o site in administrative district i to d site department s in administrative district j, using formulas (10) - (12)
Wherein,Is the traffic of the section s between the region i and the region j,/>Is a single delivery, N is the number of simulations, F o is the departure site selection parameter, F d is the arrival site selection parameter, where
Wherein, CP Oo-1 is the cumulative transmission probability of the (o-1) th site, [ CP Oo-1,CPOo ] is the interval distribution of site o in the social cumulative element function, R o is a uniformly distributed random number between 0 and 1, when R o∈[CPOo-1,CPOo ], the goods departure site is o site;
wherein, CP Dd-1 is the cumulative arrival probability of the d-1 site, [ CP Dd-1,CPDd ] is the interval distribution of site d in the social cumulative element function, R d is the uniformly distributed random number between 0 and 1, when R d∈[CPDd-1,CPDd ], the goods reach site d.
8. An electronic device, comprising: a memory and one or more processors;
the memory is used for storing one or more programs;
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101593320A (en) * | 2009-03-27 | 2009-12-02 | 北京交通大学 | A kind of multiple transportation modes combination capacity optimization method based on the transportation demand feature |
| JP2010231557A (en) * | 2009-03-27 | 2010-10-14 | Toyota Motor Corp | Traffic information providing system, information processing apparatus, and navigation apparatus |
| CN105160889A (en) * | 2015-09-29 | 2015-12-16 | 中山大学 | Multi-source-point collaborative dispersion method for road network traffic flow in urban waterlogging situation |
| KR20180107017A (en) * | 2018-05-03 | 2018-10-01 | 주식회사 핸즈 | Method for providing possibility information of encounter and server thereof |
| CN108665097A (en) * | 2018-05-02 | 2018-10-16 | 重庆交通大学 | A kind of freight demand simulating and predicting method, device and storage medium |
| CN112036662A (en) * | 2020-09-10 | 2020-12-04 | 四川大学 | Method for establishing regional flow prediction model and regional flow prediction method |
| CN112529292A (en) * | 2020-12-11 | 2021-03-19 | 重庆邮电大学 | Traffic network line optimization method based on improved Floyd algorithm |
| CN113643535A (en) * | 2021-08-02 | 2021-11-12 | 宝方云科技(浙江)有限公司 | Road traffic prediction method, device, equipment and medium based on smart city |
| CN115358561A (en) * | 2022-08-16 | 2022-11-18 | 北京师范大学 | Multi-region input-output evaluation method based on multi-scenario analysis cause analysis |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10984498B2 (en) * | 2015-06-15 | 2021-04-20 | International Business Machines Corporation | Managing transportation deployment using customer activity |
| WO2022063839A1 (en) * | 2020-09-22 | 2022-03-31 | Swiss Reinsurance Company Ltd. | Monitoring and risk index measuring system based on measured ecosystem services depending on sector-based economic performances, and corresponding method |
-
2023
- 2023-03-09 CN CN202310225694.8A patent/CN116187887B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101593320A (en) * | 2009-03-27 | 2009-12-02 | 北京交通大学 | A kind of multiple transportation modes combination capacity optimization method based on the transportation demand feature |
| JP2010231557A (en) * | 2009-03-27 | 2010-10-14 | Toyota Motor Corp | Traffic information providing system, information processing apparatus, and navigation apparatus |
| CN105160889A (en) * | 2015-09-29 | 2015-12-16 | 中山大学 | Multi-source-point collaborative dispersion method for road network traffic flow in urban waterlogging situation |
| CN108665097A (en) * | 2018-05-02 | 2018-10-16 | 重庆交通大学 | A kind of freight demand simulating and predicting method, device and storage medium |
| KR20180107017A (en) * | 2018-05-03 | 2018-10-01 | 주식회사 핸즈 | Method for providing possibility information of encounter and server thereof |
| CN112036662A (en) * | 2020-09-10 | 2020-12-04 | 四川大学 | Method for establishing regional flow prediction model and regional flow prediction method |
| CN112529292A (en) * | 2020-12-11 | 2021-03-19 | 重庆邮电大学 | Traffic network line optimization method based on improved Floyd algorithm |
| CN113643535A (en) * | 2021-08-02 | 2021-11-12 | 宝方云科技(浙江)有限公司 | Road traffic prediction method, device, equipment and medium based on smart city |
| CN115358561A (en) * | 2022-08-16 | 2022-11-18 | 北京师范大学 | Multi-region input-output evaluation method based on multi-scenario analysis cause analysis |
Non-Patent Citations (1)
| Title |
|---|
| 基于运输通道的货运交通需求预测理论与方法研究;冯小虎;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20120115;27-59 * |
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