CN110516869B - Air-railway combined transportation hub network planning method aiming at freight transportation field - Google Patents
Air-railway combined transportation hub network planning method aiming at freight transportation field Download PDFInfo
- Publication number
- CN110516869B CN110516869B CN201910777279.7A CN201910777279A CN110516869B CN 110516869 B CN110516869 B CN 110516869B CN 201910777279 A CN201910777279 A CN 201910777279A CN 110516869 B CN110516869 B CN 110516869B
- Authority
- CN
- China
- Prior art keywords
- city
- transportation
- node
- air
- hub
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000013439 planning Methods 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 28
- 239000002131 composite material Substances 0.000 claims abstract description 26
- XEEYBQQBJWHFJM-UHFFFAOYSA-N iron Substances [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 20
- 229910052742 iron Inorganic materials 0.000 claims description 19
- 238000011156 evaluation Methods 0.000 claims description 17
- 238000010276 construction Methods 0.000 claims description 12
- UONOETXJSWQNOL-UHFFFAOYSA-N tungsten carbide Chemical compound [W+]#[C-] UONOETXJSWQNOL-UHFFFAOYSA-N 0.000 claims description 12
- 230000005484 gravity Effects 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000010845 search algorithm Methods 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 7
- 238000012847 principal component analysis method Methods 0.000 claims description 3
- 230000008901 benefit Effects 0.000 abstract description 20
- 238000005457 optimization Methods 0.000 abstract description 8
- 230000006872 improvement Effects 0.000 abstract description 4
- 230000006870 function Effects 0.000 description 19
- 239000000047 product Substances 0.000 description 12
- 238000011160 research Methods 0.000 description 8
- 238000012216 screening Methods 0.000 description 8
- 238000012546 transfer Methods 0.000 description 8
- 238000011161 development Methods 0.000 description 6
- 238000013461 design Methods 0.000 description 5
- 238000009826 distribution Methods 0.000 description 5
- 238000013440 design planning Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000003187 abdominal effect Effects 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- NJFMNPFATSYWHB-UHFFFAOYSA-N ac1l9hgr Chemical compound [Fe].[Fe] NJFMNPFATSYWHB-UHFFFAOYSA-N 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000003012 network analysis Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an air-railway combined transportation hub network planning method aiming at the field of freight transportation, which constructs an air-railway combined transportation composite network through effective connection of high-speed railway and aviation lines, integrates the technical advantages and market advantages of high-speed railway and aviation transportation, and realizes the advantage complementation of the high-speed railway and the aviation transportation. From a microscopic view, the service level of the express industry is improved; in the macroscopic aspect, reasonable configuration of transportation resources, improvement of transportation efficiency and structural optimization of a comprehensive transportation system are realized.
Description
Technical Field
The invention belongs to the technical field of transportation network design, and particularly relates to an air-railway combined transportation hub network planning method aiming at the field of freight transportation.
Background
The air-iron combined transportation mode is a combined transportation mode that air transportation and railway transportation are combined and cooperated with each other, combines the advantages of multiple stations, high speed, door-to-door transportation and the like of high-speed rail express transportation with the characteristics of long-distance air transportation, strong timeliness and the like, effectively links up through a high-speed railway and an air line, constructs an air-iron combined transportation network, complements the advantages of the two, realizes reasonable allocation of transportation resources and promotes transportation efficiency. And the optimization of the comprehensive transportation system structure realizes the development of regional socioeconomic development.
The development of air-railway combined transportation is an important component and a main development direction of the construction of a transportation system in China, an air-railway combined transportation hub network is constructed, the technical advantages and the market advantages of a high-speed railway and air transportation are integrated, and the advantage complementation of the two is realized. Microcosmically, the aims of improving the service level of the transportation industry, improving the road traffic environment and the ecological environment and the like can be achieved; the reasonable allocation of transportation resources, the improvement of transportation efficiency and the structural optimization of a comprehensive transportation system can be realized in the macroscopic aspect, so that the coordinated development of regional social and economic development is realized.
The existing optimization construction method of the air-rail transport network mainly comprises the following steps:
(1) constructing an air-rail transport network planning model by using a multi-distribution hub meso-position problem model and a mixed set planning method, and solving the network planning problem model by using NCL (network communications and communications) and POEM (point of presence algorithm); the scheme utilizes the NCL language to establish a mathematical logic model of the distribution hub median problem in the air-railway transport network, and the NCL language is small in inclusion of model parameters, is difficult to describe complex networks such as air-railway transport and the like, and is only suitable for construction of a smaller transportation hub network;
(2) from the perspective of passengers, constructing an air-railway transport network optimization design model by taking the sum of the running cost of the time-sensitive passengers and the fare cost spent by the price-sensitive passengers as a minimum objective function, and solving by using AIMMS software to obtain an air-railway transport network for the time-sensitive passengers and the price-sensitive passengers; in the design scheme, the passenger transportation and the cargo transportation have great difference under the environments of transportation requirements, transportation conditions and the like, and the air-rail intermodal network under the consideration of a single freight rate is not suitable for the cargo transportation with multiple SKUs, so the adaptability is low;
(3) constructing an air-railway transport network with the aim of maximizing the consumer surplus of intercity passengers in China, and searching the optimal combination of operation capacity allocation among airports and the spatial layout of a high-speed rail network; the scheme starts from the airport, and the air-rail combined transportation network is constructed by seeking the traffic capacity and the spatial layout of the airport, so that the airport air-rail combined transportation network has geographical position limitation and low universality.
The research on constructing the air-railway combined transportation network by the conventional design scheme is mainly concentrated in the passenger transportation field, has less research on the cargo transportation field, and is mainly concentrated in theoretical research levels of demonstrating air-railway combined transportation feasibility and the like.
Disclosure of Invention
Aiming at the defects in the prior art, the network planning method for the air-railway combined transportation hub in the freight transportation field solves the problems of low efficiency, high cost and no systematically integrated airline and railway resources for combined transportation to adapt to different transportation environments when single air or railway transportation goods are used.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a network planning method for an air-rail transport hub in the field of freight transportation comprises the following steps:
s1, determining node cities of the candidate air-railway combined transport network, and building an air-railway combined transport composite network according to the geographical position of the candidate air-railway combined transport network;
s2, evaluating the node city in the air-railway combined transportation composite network by using the transportation distance as an index by using a central node theory, and determining a hub city;
s3, forecasting the air-railway combined transportation express delivery quantity between OD pairs of node cities in the air-railway combined transportation composite network;
s4, constructing an air-railway combined transportation hub network planning model according to the determined air-railway combined transportation express quantity between the hub city and each node city OD pair by taking the lowest total operation cost of the air-railway combined transportation composite network as a target;
and S5, solving the air-railway transport hub network planning model through a traversal search algorithm to obtain an air-railway transport hub network planning result.
Further, the step S3 is specifically:
s31, forecasting freight share rate of air-railway intermodal transportation between any two node cities in the air-railway intermodal transportation composite network through a Logit model according to historical express delivery data;
s32, calculating freight attraction strength between OD pairs of node cities in the air-railway combined transport composite network through a gravity model according to the express industry related index data of the node cities;
and S33, forecasting the air-railway combined transportation express delivery between OD pairs of each node city according to the freight sharing rate and the freight attraction strength.
Further, in step S31, the freight share rate P (i) of the air-iron intermodal transportation is:
wherein EXP (-) is a function of utility value of the transportation product;
Dithe utility value of the product is transported for air-iron combined transportation;
Djthe utility value of the non-air-rail transport product is obtained.
Further, the step S32 is specifically:
s32-1, determining express service evaluation indexes of node cities through a principal component analysis method;
s32-2, constructing a gravity model by taking express service evaluation indexes of each node city as a travel occurrence amount and an arrival attraction amount and taking a linear distance between any two node cities as traffic impedance;
s32-3, determining the freight attraction strength between the OD pairs of each node city based on the constructed attraction model.
Further, in the step S32-1, the express service evaluation index SiThe calculation formula of (2) is as follows:
in the formula etaαIs the variance contribution rate of the principal component alpha,
Fiαa score of a principal component α for node city i;
n is the total number of the principal components;
the gravity model constructed in the step S32-2 is:
in the formula, gijThe express service contact value between the node city i and the node city j is obtained;
Siand SjExpress service evaluation indexes of a node city i and a node city j respectively;
RTijthe straight-line distance between the node city i and the node city j is obtained;
in step S32-3, the calculation formula of the freight strength attraction strength between each node city OD pair is:
in the formula, GijAnd the express attraction strength value of the node city j to the node city i is obtained.
Further, the step S33 is specifically:
s33-1, taking the historical freight volume of each node city as the input of a BP neural network, and predicting to obtain the express delivery service volume of the corresponding node city;
s33-2, determining the proportion of the allopatric services in each node city and the average weight of the express, and predicting the express quantity between OD pairs of each node city by combining the freight share rate and the express attraction strength between the OD pairs of each node city.
Further, the air-rail transport hub network planning model constructed in the step S4 is:
in the formula, C is an objective function with the lowest total operation cost of the air-rail combined transport composite network;
min (-) is a minimum function;
Further, the air-rail transport hub network planning model comprises the following constraint conditions:
(1) and (3) central hub city number constraint of the air-rail transport hub network planning model:
in the formula, ykThe method comprises the following steps of (1) providing a central hub city arranged in an air-rail transport network;
p is the number of the set central hub cities;
(2) the number of the central hub cities passing through the air-railway transport hub network is restricted:
(3) the number of the non-hub nodes passing through the air-rail transport hub network is restricted:
in the formula，ymThe method comprises the following steps of (1) providing a non-central hub city arranged in an air-rail transport network;
(4) transportation mode constraints between two node cities:
(5) when the transfer transportation through the central hub city is adopted, the transfer only occurs in the central hub city for restriction:
in the formula, Yk、YmAll the decision variables are whether the hub city k or the hub city m is selected as the central hub city, if so, Yk1 or Ym1, otherwise Yk0 or Ym＝0；
(6) And (3) constant constraint of cargo transportation flow:
further, the step S5 is specifically:
s51, determining a candidate central hub city set M according to the N hub cities;
the candidate hub city set is as follows: m ═ M1,m2,m3,...,mN,}；
S52, selecting P junction cities from the candidate central junction city set M as central junction cities to obtain
Planting center hub city selection schemes, and numbering each selection scheme as t;
the selection scheme numbering
Selecting P selection schemes corresponding to the central hub cities from the N hub cities, wherein P is the number of the selection schemes<N；
S53, setting t to 1, calculating the optimal objective function value C under the current selection scheme1And taking the initial optimal solution C as the initial optimal solution C, and calculating C1Ratio to C
S54, increasing the number of t by 1 to obtain the objective function value C under the current selection schemet+1And record the current
A value of (d);
s55, repeating the step S54 until
The output is minimized
And
corresponding objective function value CtThe central hub city combination, the transportation path and the transportation mode are used as the network planning result of the air-railway combined transportation hub.
The invention has the beneficial effects that:
the network planning method for the air-railway combined transportation hub aiming at the freight transportation field constructs the air-railway combined transportation composite transportation network through the effective connection of the high-speed railway and the aviation line, integrates the technical advantages and the market advantages of the high-speed railway and the aviation transportation, and realizes the advantage complementation of the high-speed railway and the aviation transportation. From a microscopic view, the service level of the express industry is improved; in the macroscopic aspect, reasonable configuration of transportation resources, improvement of transportation efficiency and structural optimization of a comprehensive transportation system are realized.
Drawings
Fig. 1 is a flowchart of a network planning method for an air-rail transport hub in the freight transportation field according to the present invention.
Fig. 2 is a schematic view of a node city OD inter-pair transportation mode provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1:
as shown in fig. 1, a method for planning an air-rail transport hub network in the freight transportation field includes the following steps:
s1, determining node cities of the candidate air-railway combined transport network, and building an air-railway combined transport composite network according to the geographical position of the candidate air-railway combined transport network;
s2, evaluating the node city in the air-railway combined transportation composite network by using the transportation distance as an index by using a central node theory, and determining a hub city;
s3, forecasting the air-railway combined transportation express delivery quantity between OD pairs of node cities in the air-railway combined transportation composite network;
s4, constructing an air-railway combined transportation hub network planning model according to the determined air-railway combined transportation express quantity between the hub city and each node city OD pair by taking the lowest total operation cost of the air-railway combined transportation composite network as a target;
and S5, solving the air-railway transport hub network planning model through a traversal search algorithm to obtain an air-railway transport hub network planning result.
In the embodiment of the present invention, when determining the node city of the air-rail transport network nationwide in step S1, the node city is determined by setting a series of screening criteria and screening steps, and the screening criteria are:
standard 1: at present, high-speed rail and airport cities are opened, the administrative unit is above grade city, and GDP is located in province 1;
standard 2: the national/regional logistics policy of high-speed rail and airport is provided for inclined cities;
standard 3: provincial city with high-speed rail and airport;
the screening criteria were:
the first step is as follows: meet all three screening criteria;
the second step is that: the standard 1 and the standard 2 are met;
the third step: meets criteria 1, 3 (there are no cities meeting both criteria 1 and 3 after the first and second steps);
the fourth step: the standard 2 and the standard 3 are met;
the screening standard and the screening steps are applied to all cities in China, and 28 node cities which meet the standard can be obtained, namely Harbin, Changchun, Beijing, Tianjin, Shijiazhu, Nanjing, Hangzhou, Taiyuan, Zhengzhou, Wuhan, Changsha, Nanning, Xian, Chengdu, Chongqing, Guiyang, Lanzhou, Kunming, Dalian, Qingdao, Xiamen, Shenzhen, Shenyang, Jinan, Fuzhou, Guangzhou, Shanghai and Hefei.
In the embodiment of the invention, compared with the efficiency, cost and other advantages of a common transportation mode, the multi-type intermodal transportation hub network has the advantages that the efficiency, cost and other advantages are greatly generated due to the existence of the network hub nodes, and goods are gathered at the hub and then sent to the next destination, so that the transportation of the goods based on the hub has certain transportation scale benefits, and the goods are transferred and dispatched at the hub to enable the operation of the whole network to be smooth. Therefore, the reasonable layout of the hubs in the multimodal transport network will have a significant impact on the operation of the whole network. A central hub city is arranged at a reasonable position in the network, so that a network transportation path based on a hub is optimized; the pivots with the appropriate number are arranged, so that the network setting cost is kept reasonable, and the smooth operation of the network can be guaranteed. In the embodiment, a static network centrality index system is constructed by using a complex network correlation theory, and a good-bad solution distance method is used for comprehensive evaluation, so that a hub city is obtained and used as a candidate hub city. The centrality reflects the relative importance of each node in the air-rail combined transport composite network, and is an important basic theory for complex network research. In graph theory and network analysis, five indexes of degree centrality, betweenness centrality, proximity centrality, k-shell method and node capacity are selected as node centrality indexes of a screening hub city, high-speed rail transportation network node importance indexes and air transportation network node importance indexes are compounded, and the transportation distance is considered as an important influence factor of transportation mode selection, so that the inter-city transportation distance is considered as the weight of a comprehensive evaluation index. Selecting eight cities of the top-ranked 28 node cities to enter a candidate hub center city set according to the evaluation result of the composite network node centrality comprehensive index; namely, the air-iron transport network hub cities are Beijing, Shanghai, Zhengzhou, Wuhan, Nanjing, Jinan, Xian and Changsha respectively.
In the embodiment of the present invention, in step S3, the air-railway intermodal transportation express delivery amount between node city OD pairs is predicted, starting from the air-railway intermodal transportation freight sharing rate between node city OD pairs, the freight attraction strength, and the freight delivery amount of each node city.
In the embodiment of the invention, on the basis of obtaining the express delivery between all node city OD pairs and determining the hub city, the air-railway transport network design planning problem is converted into the planning problem of the multilevel transport hub network of the known center hub city candidate set in the steps S4-S5, wherein the problem is a typical P-hub median problem, the problem is solved based on a traversal search algorithm, and the center candidate city in the air-railway transport network planning model and the specific transport mode during the transportation between each node city OD pair are determined.
Example 2:
the step S3 in the above embodiment 1 specifically includes:
s31, forecasting freight share rate of air-railway intermodal transportation between any two node cities in the air-railway intermodal transportation composite network through a Logit model according to historical express delivery data;
s32, calculating freight attraction strength between OD pairs of node cities in the air-railway combined transport composite network through a gravity model according to the express industry related index data of the node cities;
and S33, forecasting the air-railway combined transportation express delivery between OD pairs of each node city according to the freight sharing rate and the freight attraction strength.
In the embodiment of the invention, when the freight share rate is determined through the Logit model, the key point is to determine a linear equation of a characteristic function, and in the embodiment of the invention, the characteristic function in the Logit model is improved by using a utility value function of a transport product on the basis of analyzing the possibility of a shipper to the transport product, namely, the satisfaction degree; before forecasting the node urban freight share rate, the invention assumes that:
(1) the transportation distance is the simplified workload, and the linear transportation distance between two points is taken;
(2) in general, high-speed rail-air combined transportation generally treats high-speed rail transportation as branch transportation of air transportation, namely high-speed rail freight transportation is a line supplement to air freight transportation and is not a competitor. In addition, the transportation of the 'next day' with the strongest timeliness can be realized in terms of logistics products regardless of air freight transportation or high-speed rail-air combined transportation;
(3) the intermodal network planning in the embodiment of the invention allows the direct transportation between any cities, namely allows the non-hub cities to be directly connected through high-speed rail or air transportation;
based on the three points, the air-iron combined transport, the air transport and the high-speed rail freight transport are considered in a combined mode and are collectively called as the air-iron combined transport; therefore, the air-rail transport in the invention essentially comprises air-rail transport and road transport; specifically, when a utility value function of a transport product is determined, 30 express enterprises across the country are investigated, and various basic indexes of the utility value function are determined: the safety index of the aviation and railway combined transportation is 0.9, the freight rate utility value is 0.012, the transportation timeliness utility value is 0.1 when the freight distance is less than 500km, the transportation timeliness utility value is 0.6 when the freight distance is more than 500km, and the GDP utility value is 1; the safety index of road transportation is 0.7, the freight rate utility value is 1, the transportation timeliness utility value is 0.8 when the freight distance is less than 500km, the transportation timeliness utility value is 0.3 when the freight distance is more than 500km, and the GDP utility value is 0.9;
according to the indexes, the utility value A1 of the product obtained by the combined transportation of the aviation and the railway is as follows:
A1＝B1×(C1+D1)×(E1+F1)
wherein, B1 is the freight rate utility value of the combined transportation of air and railway;
c1 is the effectiveness value of the time efficiency of the combined transportation of the aviation and the railway;
d1 is a starting point node city GDP;
d2 is a destination node city GDP;
the utility value A2 of the product of the combined air and railway transportation is as follows:
A2＝B2×(C2+D2)×(E2+F2)×0.9
therefore, the freight share rate P (i) of the air-iron intermodal transportation in step S31 is obtained as:
wherein EXP (-) is a function of utility value of the transportation product;
Dithe utility value of the product is transported for air-iron combined transportation;
Djthe utility value of the non-air-rail transport product is obtained.
In the embodiment of the invention, the gravitation model is a widely applied space interaction capability model, namely a mathematical model for analyzing and predicting the space interaction capability, and the gravitation model applied to the traffic logistics direction is used for establishing a future traffic distribution prediction model according to the fact that the freight volume between any two node city OD pairs is in direct proportion to the departure place cargo diffusion capability and the arrival place cargo absorption capability and in direct proportion to the travel time, the cost, the distance and the like between two intervals; therefore, the step S32 is specifically:
s32-1, determining express service evaluation indexes of node cities through a principal component analysis method;
wherein, express service evaluation index SiThe calculation formula of (2) is as follows:
in the formula etaαThe variance contribution rate of the alpha-th principal component,
Fiαscore of the α -th principal component of node city i; fiα＝Qα+λi，QαIs a specific constitution of the alpha-th principal component, λiAn evaluation index matrix of the node city i;
n is the total number of the principal components;
s32-2, constructing a gravity model by taking express service evaluation indexes of each node city as a travel occurrence amount and an arrival attraction amount and taking a linear distance between any two node cities as traffic impedance;
the principle method of the gravity model applied to the traffic logistics direction is a future traffic distribution prediction model established according to the relationship that the freight volume between any two OD pairs is in direct proportion to the cargo generating capacity of the departure place and the cargo attracting capacity of the arrival area and in inverse proportion to the travel time (or cost, distance and the like) between the two areas. According to a gravity model formula, each city express service evaluation index calculated in the previous step is used as a travel occurrence amount and an arrival attraction amount, and the straight line distance between any two node cities is used as traffic impedance, so that a gravity model is constructed as follows:
in the formula, gijThe express service contact value between the node city i and the node city j is obtained;
Siand SjExpress service evaluation indexes of a node city i and a node city j respectively;
RTijthe straight-line distance between the node city i and the node city j is obtained;
s32-3, determining the freight attraction strength between the OD pairs of each node city based on the constructed attraction model.
The calculation formula of the freight strength attraction strength between the OD pairs of each node city is as follows:
in the formula, GijAnd the express attraction strength value of the node city j to the node city i is obtained.
In an embodiment of the present invention, the step S33 is specifically:
s33-1, taking the historical freight volume of each node city as the input of a BP neural network, and predicting to obtain the express delivery service volume of the corresponding node city;
when the prediction method is selected, the traditional prediction methods including an exponential smoothing method, a trend prediction method, a gray prediction model and the like are considered to generally make prediction only according to the change trend of data, and the BP neural network has the function of realizing complex non-mapping and can carry out autonomous learning, so that the reduction of errors is realized, and the method is very suitable for index budget under the combined action of multiple factors. Therefore, the embodiment of the invention selects the BP neural network to predict the express business volume, and considers that the express industry is taken as a service type industry, the express business volume has great correlation with national economic level of a region, social consumer goods retail gross, population, freight volume and other regional national economic indexes, and when the BP neural network is used for data prediction, the convenience and the accuracy of data acquisition are considered; therefore, in order to improve the accuracy of the prediction result, besides the historical freight volume as an input, the historical national production total value of the node city, the retail total amount of the social consumer goods and the like are also input into the BP neural network as reference input quantities, so that the predicted value of the express business volume is obtained.
S33-2, determining the proportion of the allopatric services in each node city and the average weight of the express, and predicting the express quantity between OD pairs of each node city by combining the freight share rate and the express attraction strength between the OD pairs of each node city.
In the embodiment of the invention, the higher the freight share rate based on a certain transport mode is, the stronger the competitiveness of the certain transport mode between OD pairs is (the greater the attraction strength to the flow of goods) is, that is, the larger the share of the freight market is (the more express services between OD pairs of each node city are), the express attraction strength between OD pairs can be known by the freight share rate of air-railway combined transport, and the express forecast quantity between the OD pairs of air-railway combined transport can be calculated by adding the express forecast quantity of main node cities in the air-railway combined transport network.
Example 3:
based on the above contents, the express quantities among OD pairs of a plurality of hub cities and a plurality of node cities are obtained, and further based on the express quantities, an air-rail transport hub network planning model is constructed and solved; specifically, when a model is constructed and solved, the air-rail transport network design planning problem is converted into a multi-type transport hub network optimization design problem of a known hub candidate set, namely a typical P-hub median problem.
The research of the multi-level interconnection network is generally abstracted into a weighted network graph N ═ I, L, H, C, and further the research is carried out by applying the correlation theory of graph theory. I denotes a node set, I ═ 1, 2.., n }; l represents a transportation mode set, and in the air-railway intermodal research, only the main transportation between large cities is considered, and the distribution and the collection transportation are not considered, so that L is { L ═ L in the invention1,L2In which L is1Denotes high-speed rail transport, L2Representing air transport. H represents the cost consumed by transportation between network nodes; c represents the capacity of each node and connection in the network. The design planning problem of the air-rail transport network is abstracted to be the shortest cost problem on the premise of meeting the constraint conditions. For better compliance with the actual situation and ease of model solution, the following assumptions are made:
(1) as shown in fig. 2, for the transport between any two OD nodes there may be: a. direct connection; b. transferring different transportation shifts of another transportation mode or the same transportation mode to a final destination through a hub node; c. and the destination is reached through two transportation nodes and two transfers.
(2) Supposing that one pivot node exists in two nodes in a certain section of transportation, rho is taken as a discount factor generated due to scale benefit; two nodes in a certain section of transportation are both pivot nodes, and the discount factor generated due to scale benefit is gamma; two nodes in a certain section of transportation are non-pivot nodes without discount, and gamma is not less than rho and is less than 1.
(3) The transfer and connection cost in the air-rail combined transportation process is contained in the operation cost of the hub, and an operation average value with unit of yuan/ton is taken, namely, the difference of transfer and connection cost and the difference of hub hardware conditions are not considered temporarily when the airplane is converted into the airplane, the airplane is converted into the high-speed rail, the high-speed rail is converted into the airplane and the like.
(4) The capacity limitations of the network connections are not considered for the moment.
The network planning model of the air-rail transport hub constructed in step S4 is thus obtained as:
in the formula, C is an objective function with the lowest total operation cost of the air-rail combined transport composite network;
min (-) is a minimum function;
Based on the assumptions, the network planning model for the air-rail transport hub is set to comprise the following constraint conditions:
(1) and (3) central hub city number constraint of the air-rail transport hub network planning model:
in the formula, ykThe method comprises the following steps of (1) providing a central hub city arranged in an air-rail transport network;
p is the number of the set central hub cities;
(2) the number of the central hub cities passing through the air-railway transport hub network is restricted:
(3) the number of the non-hub nodes passing through the air-rail transport hub network is restricted:
in the formula, ymThe method comprises the following steps of (1) providing a non-central hub city arranged in an air-rail transport network;
(4) transportation mode constraints between two node cities:
(5) when the transfer transportation through the central hub city is adopted, the transfer only occurs in the central hub city for restriction:
in the formula, Yk、YmAll the decision variables are whether the hub city k or the hub city m is selected as the central hub city, if so, Yk1 or Ym1, otherwise Yk0 or Ym＝0；
(6) And (3) constant constraint of cargo transportation flow:
example 4:
the traversal search algorithm in step S5 of embodiment 1 is simple in principle, convenient to implement, and higher in calculation accuracy than the heuristic algorithm, and today when the personal computer processor has a certain calculation capability, it is reasonable to apply the traversal search algorithm to process some scientific problems with lower complexity.
The basic idea of using the traversal search algorithm to solve the model is as follows: firstly, according to a determined candidate hub central node city set M; secondly, selecting P cities as the most pivotal nodes from the candidate pivotal node center city set, traversing and calculating the shortest cost (including fixed cost, operation cost, time cost and the like of pivotal node construction) between each OD pair when the current pivotal node is used as the network transit center by utilizing a traversal search algorithm, and obtaining the optimal solution when each city in the pivotal node candidate set is used as a pivotal node through cyclic iteration, wherein the minimum optimal solution is the solution; and finally, analyzing the solving result to obtain a specific intermodal path between each city pair of the air-railway intermodal network.
Therefore, step S5 in this embodiment of the present invention specifically includes:
s51, determining a candidate central hub city set M according to the N hub cities;
the candidate hub city set is as follows: m ═ M1,m2,m3,...,mN,}；
S52, selecting P junction cities from the candidate central junction city set M as central junction cities to obtain
Planting center hub city selection schemes, and numbering each selection scheme as t;
the selection scheme numbering
Selecting P selection schemes corresponding to the central hub cities from the N hub cities, wherein P is the number of the selection schemes<N；
S53, setting t to 1, calculating the optimal objective function value C under the current selection scheme1And taking the initial optimal solution C as the initial optimal solution C, and calculating C1The ratio to C;
wherein the content of the first and second substances,
s54, increasing the number of t by 1 to obtain the objective function value C under the current selection schemet+1And record the current
A value of (d);
s55, repeating the step S54 until
The output is minimized
And
corresponding objective function value CtThe central hub city combination, the transportation path and the transportation mode are used as the network planning result of the air-railway combined transportation hub.
Example 5:
in this embodiment, an example of planning and solving the international air-railway transport hub network by the above scheme is provided:
aiming at the construction cost of the logistics hub, the existing data are less, the invention refers to the newly constructed Wuluqizi and Xuzhou national level logistics hub, and the fixed construction cost of the central hub is 3 multiplied by 108And (5) Yuan. According to the research, the average operation cost of the central hub is 20 yuan/ton. Express delivery D between urban ODsijAs determined in S33. According to the global air freight price index, the air freight price is about 9 yuan/ton/kilometer, and through investigation, the high-speed rail freight price (at the high-speed rail consignment price) is about 2.5 yuan/ton/kilometer; in addition, assuming that two nodes in a certain section of transportation are both central hubs, the freight rate discount is 0.4, one of the central hubs is that the freight rate discount is 0.6, and the operation cost C between any two nodes in two transportation modes can be obtained by combining the distance matrix between the nodesl ij，Cl ik，Cl km，Cl mj. The value of goods transported by air is usually much higher than that of goods transported by roads, the unit express value of the national express industry in 2018 is 536.97 yuan/kg, for convenience of calculation, the current unit express value is 600 yuan/kg, the daily rate of value loss of the goods is 0.04%, namely 10 yuan/ton/hour, the speed per hour (including the time of loading and unloading goods, security inspection and the like) of express transportation of high-speed rails is 200km/h, the speed per hour (including the time of loading and unloading goods, security inspection and the like) of the transport of air freight is 700km/h, and the time cost of two transportation modes of each node can be obtained by combining the distance matrix of each node. Suppose that the air-iron combined transportation has a connection time of 4 hours, namely the connection transportation cost is 4 x 10 to 40 yuan/ton.
According to the data, the network planning model is solved by utilizing Matlab software, and the air-rail transport mode and the route of the national hub city air-rail transport mode are obtained as follows:
table 1: transportation route and transportation mode between main cities
According to the planning result of the air-rail transport network, the following main conclusions can be drawn from the overall macro network: when P is 4, 4 cities are selected as central hubs, namely Beijing, Zhengzhou, Wuhan and Xian respectively; in the urban dense areas of the coastal areas of eastern and eastern China, the main large cities such as Beijing, Shanghai, Nanjing, Wuhan, Jinan, Hangzhou and the like basically can realize direct high-speed rail transportation between the two cities; because Wuhan and Zheng Zhou are respectively positioned in the centers of the geographical positions of the south and the north of China, the hubs which are hollow in the air-iron combined transportation network and have the most frequent iron transportation are Wuhan and Zheng Zhou. At present, both cities begin to build the layout of air-railway combined transportation freight hubs; due to the fact that the northeast is far away from the abdominal region of the country, express delivery traffic is small, air-railway combined transportation of most cities and northeast cities such as Shenyang, Changchun and Harbin needs to be transferred for 2 times and is basically idle.
From the hub city, taking the wuhan with the most frequent air-iron combined transportation as an example, the wuhan is located in the geographic center region of the whole country and mainly takes the air-iron combined transportation tasks of air-iron transportation of goods in southeast-southwest, northeast-southwest and eastern-southwest as main air-iron transportation, and the air-iron transportation tasks mainly take the air-iron transportation as main part, so that the condition of a small amount of idle air or iron-iron transportation exists.
From non-hub cities, taking Chengdu as an example, high-speed rails are adopted for direct transportation of peripheral cities such as Xian, Chongqing and Kunming. In the aspect of cargo output, the cargos sent to the coastal areas and east China areas in the south east arrive in Wuhan through air transportation, are transferred in Wuhan and are transferred to high-speed rails to be transported to the destination; goods sent to the North China area are mainly transferred through the Western-style transportation and transferred to the aircraft to the destination after reaching the Western-style transportation through the high-speed rail; the goods sent to the northeast region need to be transferred twice, and transfer hubs are respectively in Xian and Beijing; in the aspect of goods input, the goods input mode is similar to the goods output mode, goods in North China and northeast China are transferred to high-speed rail transportation to Chengdu after being delivered to the west-safety area by air transportation, and goods in the southeast coast and the east China are transferred to Wuhan by high-speed rail transportation and transferred to the airplane transportation to Chengdu.
The invention has the beneficial effects that:
the network planning method for the air-railway combined transportation hub aiming at the freight transportation field constructs the air-railway combined transportation composite transportation network through the effective connection of the high-speed railway and the aviation line, integrates the technical advantages and the market advantages of the high-speed railway and the aviation transportation, and realizes the advantage complementation of the high-speed railway and the aviation transportation. From a microscopic perspective, the service level of the express industry is improved; in the macroscopic aspect, reasonable configuration of transportation resources, improvement of transportation efficiency and structural optimization of a comprehensive transportation system are realized.
Claims (3)
1. A network planning method for an air-rail transport hub in the field of freight transportation is characterized by comprising the following steps:
s1, determining node cities of the candidate air-railway combined transport network, and building an air-railway combined transport composite network according to the geographical position of the candidate air-railway combined transport network;
s2, evaluating the node city in the air-railway combined transportation composite network by using the transportation distance as an index by using a central node theory, and determining a hub city;
s3, forecasting the air-railway combined transportation express delivery quantity between OD pairs of node cities in the air-railway combined transportation composite network;
s4, constructing an air-railway combined transportation hub network planning model according to the determined air-railway combined transportation express quantity between the hub city and each node city OD pair by taking the lowest total operation cost of the air-railway combined transportation composite network as a target;
s5, solving the air-railway transport hub network planning model through a traversal search algorithm to obtain an air-railway transport hub network planning result; the step S3 specifically includes:
s31, forecasting freight share rate of air-railway intermodal transportation between any two node cities in the air-railway intermodal transportation composite network through a Logit model according to historical express delivery data;
s32, calculating freight attraction strength between OD pairs of node cities in the air-railway combined transport composite network through a gravity model according to the express industry related index data of the node cities;
s33, forecasting the air-railway combined transportation express quantity between OD pairs of each node city according to the freight sharing rate and the freight attraction strength;
the step S32 specifically includes:
s32-1, determining express service evaluation indexes of node cities through a principal component analysis method;
s32-2, constructing a gravity model by taking express service evaluation indexes of each node city as a travel occurrence amount and an arrival attraction amount and taking a linear distance between any two node cities as traffic impedance;
s32-3, determining freight attraction strength between OD pairs of each node city based on the constructed attraction model;
the express service evaluation index S in the step S32-1iThe calculation formula of (2) is as follows:
in the formula etaαIs the variance contribution rate of the principal component alpha,
Fiαa score of a principal component α for node city i;
n is the total number of the principal components;
the gravity model constructed in the step S32-2 is:
in the formula, gijThe express service contact value between the node city i and the node city j is obtained;
Siand SjExpress service evaluation indexes of a node city i and a node city j respectively;
RTijthe straight-line distance between the node city i and the node city j is obtained;
in step S32-3, the calculation formula of the freight attraction strength between each node city OD pair is:
in the formula, GijThe freight attraction strength of the node city j to the node city i is defined;
the step S33 specifically includes:
s33-1, taking the historical freight volume of each node city as the input of a BP neural network, and predicting to obtain the express delivery service volume of the corresponding node city;
s33-2, determining the proportion of the allopatric services in each node city and the average weight of express delivery, and predicting to obtain the express delivery amount between OD pairs of each node city by combining the freight transportation sharing rate and the freight transportation attraction strength between OD pairs of each node city;
the air-rail transport hub network planning model constructed in the step S4 is:
in the formula, C is an objective function with the lowest total operation cost of the air-rail combined transport composite network;
min (-) is a minimum function;
2. The method for planning a network of air-rail terminal for the freight transportation field according to claim 1, wherein the freight allocation rate P (P) of air-rail terminal in step S31 is:
wherein EXP (-) is a function of utility value of the transportation product;
Dpthe utility value of transporting the product by adopting air-iron combined transport p;
Dqfor transporting products by non-air-to-rail intermodal qUtility value.
3. The method for planning an air-rail transport terminal network in the freight transportation field according to claim 1, wherein the step S5 specifically includes:
s51, determining a candidate central hub city set M according to the N hub cities;
the candidate hub city set is as follows: m ═ M1,m2,m3,...,mN,}；
S52, selecting P junction cities from the candidate central junction city set M as central junction cities to obtain
Planting center hub city selection schemes, and numbering each selection scheme as t;
the selection scheme numbering
Selecting P selection schemes corresponding to the central hub cities from the N hub cities, wherein P is the number of the selection schemes<N；
S53, setting t to 1, calculating the optimal objective function value C under the current selection scheme1And taking the initial optimal solution C as the initial optimal solution C, and calculating C1Ratio to C
S54, increasing the number of t by 1 to obtain the objective function value C under the current selection schemet+1And record the current
A value of (d);
s55, repeating the step S54 until
The output is minimized
And
corresponding objective function value CtThe central hub city combination, the transportation path and the transportation mode are used as the network planning result of the air-railway combined transportation hub.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910777279.7A CN110516869B (en) | 2019-08-22 | 2019-08-22 | Air-railway combined transportation hub network planning method aiming at freight transportation field |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910777279.7A CN110516869B (en) | 2019-08-22 | 2019-08-22 | Air-railway combined transportation hub network planning method aiming at freight transportation field |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110516869A CN110516869A (en) | 2019-11-29 |
| CN110516869B true CN110516869B (en) | 2022-05-03 |
Family
ID=68627127
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910777279.7A Active CN110516869B (en) | 2019-08-22 | 2019-08-22 | Air-railway combined transportation hub network planning method aiming at freight transportation field |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110516869B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639790B (en) * | 2020-05-09 | 2023-10-17 | 天津市政工程设计研究总院有限公司 | Multi-mode intermodal scheduling method and system for guaranteeing freight reliability and safety |
| CN112685864B (en) * | 2020-12-25 | 2024-03-29 | 长安大学 | Double-layer high-speed rail dependent network construction method and system based on reality destruction factors |
| CN113743671B (en) * | 2021-09-08 | 2023-04-07 | 西南交通大学 | High-speed rail express special train transportation network optimization method and system |
| CN114707918B (en) * | 2021-12-07 | 2023-04-07 | 西南交通大学 | Railway hierarchical node transportation method |
| CN115271260B (en) * | 2022-09-26 | 2022-12-13 | 西南交通大学 | Road-rail transport capacity prediction method, device, equipment and readable storage medium |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5863203A (en) * | 1996-01-26 | 1999-01-26 | Dowling College | Intermodal transportation simulation system |
| JP2005145573A (en) * | 2003-11-11 | 2005-06-09 | Nippon Steel Corp | Transportation program planning method |
| CN105405090A (en) * | 2015-12-20 | 2016-03-16 | 北京民航信息科技有限公司 | Airway and railway combined transpiration system |
| CN107563947B (en) * | 2017-09-04 | 2021-01-05 | 昆明理工大学 | Traffic and transportation integrated comprehensive service method and system |
| CN108399464A (en) * | 2017-09-27 | 2018-08-14 | 圆通速递有限公司 | A kind of multimodal transport method for optimizing route and system |
-
2019
- 2019-08-22 CN CN201910777279.7A patent/CN110516869B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110516869A (en) | 2019-11-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110516869B (en) | Air-railway combined transportation hub network planning method aiming at freight transportation field | |
| Gulamov et al. | Planning of new transit corridors-New opportunities for the development of transit in Uzbekistan | |
| Bi et al. | Express delivery with high-speed railway: Definitely feasible or just a publicity stunt | |
| Liang et al. | Parcels and mail by high speed rail–A comparative analysis of Germany, France and China | |
| Wang et al. | Optimization models for high-speed train unit routing problems | |
| Shen et al. | Real-time customized bus routes design with optimal passenger and vehicle matching based on column generation algorithm | |
| Yu et al. | Research on High‐Speed Railway Freight Train Organization Method considering Different Transportation Product Demands | |
| Du et al. | A scheme for passenger service-like backhaul for China railway express trains | |
| Huang et al. | Approach and application on high-speed train stop plan for better passenger transfer efficiency: the China case | |
| Zuo et al. | Analysis of a collaborative transport model mixing passengers with freights in metro system | |
| Yu et al. | Network design and delivery scheme optimisation under integrated air-rail freight transportation | |
| He et al. | Parcel delivery by collaborative use of truck fleets and bus-transit vehicles | |
| Li et al. | Optimization of high-speed railway line planning with passenger and freight transport coordination | |
| Pokrovskaya et al. | Northern latitudinal railway project: priorities and drivers | |
| Li et al. | A two-stage stochastic programming model for rail-truck intermodal network design with uncertain customer demand | |
| Wang et al. | A passenger flow routing model for high-speed railway network in different transportation organization modes | |
| Xu et al. | Optimal capacity allocation for high-speed railway express delivery | |
| CN109978464A (en) | A kind of material-flow method and system | |
| Ning | Study on the Optimization of the Multimodal Transport Scheme of Railway Containers | |
| Wang et al. | Research on the choice of multimodal transportation emission reduction schemes based on different carbon tax models | |
| Zhongzhen et al. | Network optimization of RPTB express based on Ant Colony Algorithm | |
| Cheng | The method to select the transport path based on the multimodal cost | |
| Zhang et al. | Selection Conditions Analysis of Passenger Transport Mode for Fast Passenger Network | |
| Zhang et al. | [Retracted] Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology | |
| Wang et al. | Research on the Network Operation Mode of High-Speed Rail Express |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |