A classification method for the spatial distribution pattern of urban heavy truck travel

Abstract

The present invention provides a method for classifying the travel spatial distribution pattern of urban heavy trucks. The method includes: dividing the travel points of urban heavy trucks, and obtaining the travel distribution matrix of urban heavy trucks according to the travel chain data between each travel point; dividing the travel points of urban heavy trucks into multiple levels according to the size of the travel volume in the travel distribution matrix of urban heavy trucks, and calculating the travel flow hierarchical index of urban heavy trucks; multiplying the heterogeneity of the heavy truck travel volume at different travel points with the normalized spatial separation index to obtain the urban centrality index of urban heavy trucks; and classifying the travel spatial distribution pattern of urban heavy trucks according to the travel flow hierarchical index and urban centrality index of urban heavy trucks. The method of the present invention can objectively and effectively quantify and classify the overall characteristics of the travel spatial distribution of urban heavy trucks, and provide an important technical method demonstration for urban freight transportation planning and management departments.

Description

Technical Field

[0001] The present invention relates to the technical field of urban freight transportation planning, and in particular to a method for classifying travel space distribution patterns of urban heavy trucks. Background Art

[0002] The urban freight system ensures the production and life of the city and is the foundation of the city's economic development. In recent years, rapid urbanization has increased the volume of freight travel to meet the needs of urban development. However, a series of problems such as traffic congestion, environmental pollution, and safety accidents have also increased the pressure on the urban transportation system. Quantifying the overall characteristics of the spatial distribution of urban heavy truck travel can help grasp the spatial distribution of backbone freight demand in the city, which is conducive to providing scientific suggestions for the formulation of urban freight transportation planning and management strategies, thereby improving the city's freight efficiency and alleviating the pressure on the urban freight transportation system.

[0003] In the existing technical methods, the travel expectation line diagram drawn based on the travel distribution matrix is ​​mainly used to visualize the overall distribution of heavy truck travel space. However, this method cannot objectively and quantitatively describe the overall characteristics of the heavy truck travel space distribution. It is very important to quantify the overall characteristics of the urban heavy truck travel space distribution, because the quantitative results can objectively and uniformly evaluate the urban freight system. Therefore, there is an urgent need for a method and system that can comprehensively quantify the overall characteristics of the urban heavy truck travel space distribution. Summary of the invention

[0004] The embodiments of the present invention provide a method for classifying the travel spatial distribution patterns of urban heavy trucks, and provide guidance and suggestions for formulating freight policies to improve urban freight efficiency and alleviate the pressure on urban freight transportation systems.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme.

[0006] A method for classifying the travel spatial distribution patterns of urban heavy trucks, comprising:

[0007] Divide the travel points of urban heavy trucks, and obtain the travel distribution matrix of urban heavy trucks based on the travel chain data between each travel point;

[0008] According to the travel volume in the travel distribution matrix of urban heavy trucks, the travel points of urban heavy trucks are divided into multiple levels, and a travel flow level matrix is ​​established. The travel flow level index of urban heavy trucks is calculated according to the travel flow level matrix.

[0009] The heterogeneity of heavy truck trips at different travel points and the spatial separation index are calculated, and the urban centrality index of heavy trucks in the city is obtained by multiplying the heterogeneity of heavy truck trips at different travel points with the normalized spatial separation index.

[0010] According to the travel flow hierarchy index and the city centrality index of the urban heavy trucks, the travel spatial distribution pattern of the urban heavy trucks is classified.

[0011] Preferably, the dividing of the travel points of urban heavy trucks and obtaining the urban heavy truck travel distribution matrix according to the travel chain data between the travel points includes:

[0012] The city is divided into multiple traffic zones based on a set area, and each traffic zone is recorded as a travel point for heavy trucks. The travel within each travel point is ignored, and only the travel generation volume, attraction volume and travel distribution volume between each travel point are counted. The travel distribution matrix of urban heavy trucks is established using all statistical travel chain data. The row coordinates of the travel distribution matrix represent the starting point traffic zone, and the column coordinates represent the destination traffic zone. The elements in the travel distribution matrix are the travel distribution volume between a pair of starting and ending points, indicating the travel volume starting from the starting point and arriving at the destination. The travel generation volume is the sum of the row elements of the travel distribution matrix, indicating the total travel volume starting from the starting point corresponding to the row; the travel attraction volume is the sum of the column elements of the travel distribution matrix, indicating the total travel volume starting from other starting points and arriving at the destination corresponding to the column.

[0013] Preferably, the travel points of urban heavy trucks are divided into multiple levels according to the amount of travel in the travel distribution matrix of urban heavy trucks, a travel flow level matrix is ​​established, and the travel flow level index of urban heavy trucks is calculated according to the travel flow level matrix, including:

[0014] Extract the travel volume of travel points in the travel distribution matrix of urban heavy trucks and draw the Lorenz curve of the travel volume;

[0015] A tangent line is obtained at the vertex of the Lorenz curve, and the intersection of the tangent line and the horizontal axis is recorded as the threshold of the first-layer travel points. The travel points falling to the right of the threshold are recorded as the first-layer travel points. The first-layer travel points are removed from the total travel locations, and the second Lorenz curve is drawn according to the travel volume of the remaining travel points. The above process is repeated until the slope of the tangent line of the Lorenz curve at the vertex reaches 1, and the heavy truck travel location is divided into L layers;

[0016] Count the proportion of the travel volume between the p-th layer travel point and the q-th layer travel point to the total travel volume M pq , with M pqAs elements, construct a flow level matrix M of size L×L. The row elements in the flow level matrix M refer to the proportion of the trip volume starting from the travel points of a certain layer to the travel points of other layers in the total trip volume. The column elements in the flow level matrix M refer to the proportion of the trip volume starting from the travel points of other layers to the travel points of a certain layer in the total trip volume.

[0017] The elements of the three diagonal traces in the travel flow level matrix M are summed to obtain the travel flow level index, which is calculated as follows:

[0018]

[0019] In the formula, Φ reflects the interaction intensity between places with similar freight volume. The larger the index value, the stronger the interaction intensity between places with similar freight volume. LL The element located at the Lth row and Lth column in the flow level matrix M represents the proportion of the trip volume starting from the Lth level travel point and arriving at the Lth level travel point in the total trip volume.

[0020] Preferably, the calculation of the heterogeneity of the heavy truck travel volume at different travel points and the spatial separation index, multiplying the heterogeneity of the heavy truck travel volume at different travel points by the normalized spatial separation index to obtain the city centrality index of the city's heavy trucks, includes:

[0021] Count the proportion of heavy truck trips at each trip point i in the city to the total trips in the city. i ;

[0022] The heterogeneity of heavy truck trips at different travel points is calculated using the following formula:

[0023]

[0024] In the formula, n represents the total number of regions in the city, and H represents the heterogeneity of the heavy truck travel volume in the city;

[0025] The column vector S = (s1, s2, s3…s n ) T ;

[0026] Establish the distance matrix D between city travel points, and the element d in the distance matrix D between city travel points ij is the Euclidean distance between the centroids of regions i and j, and all elements on the main diagonal of D are 0;

[0027] The calculation formula of the spatial separation index V is set as follows:

[0028] V=S′×D×S

[0029] In the formula, when all heavy truck trips are concentrated in one area, V is 0; when heavy truck trips are evenly dispersed in the outermost part of the city, the spatial separation index reaches the maximum value V max ; S′ is the transpose of the column vector S consisting of the proportion of heavy truck trips at each trip point to the total trips in the city, and is a row vector;

[0030] The spatial separation index V is normalized and the calculation formula is as follows:

[0031] P=1―V / V max

[0032] In the formula, the closer P is to 0, the more dispersed the spatial distribution of heavy truck travel is; the closer P is to 1, the more concentrated the spatial distribution of heavy truck travel is;

[0033] The heterogeneity of heavy truck trips in different locations is multiplied by the normalized spatial separation index to obtain the city centrality index Ψ, which is calculated as follows:

[0034] Ψ=H·P

[0035] In the formula, the higher the value of Ψ, the higher the degree of centralization of the spatial distribution of urban heavy truck travel; the lower the value of Ψ, the lower the degree of centralization of the spatial distribution of urban heavy truck travel.

[0036] Preferably, the classification of the travel spatial distribution pattern of urban heavy trucks according to the travel flow hierarchy index and the urban centrality index of the urban heavy trucks includes:

[0037] A set number of cities are plotted on a two-dimensional coordinate plane with the flow level index Φ as the vertical axis and the city centrality index Ψ as the horizontal axis. The coordinate position of the city on the two-dimensional coordinate plane is expressed as (the centrality index Ψ value of the city, the flow level index Φ value of the city). Kmeans clustering is performed on the two-dimensional coordinate plane, and the flow level index Φ values ​​and the city centrality index Ψ values ​​of a set number of cities are used as inputs to the KMeans() function in the sklearn library of the Python programming language. The output result of the KMeans() function is the category to which the travel spatial distribution pattern of heavy trucks in each city belongs, which includes concentrated in the central area of ​​the city, surrounding the central area of ​​the city in a surrounding shape and the surrounding shapes are closely connected, surrounding the central area of ​​the city in a surrounding shape, and scattered in various areas of the city.

[0038] It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the method of the present invention can effectively identify the spatial distribution patterns of urban heavy truck travel with different characteristics, help urban freight planning managers to more systematically understand the spatial distribution of backbone freight demand in the city, and provide suggestions for the scientific formulation of urban freight traffic planning and management strategies such as truck road charging policies, truck dedicated lane settings, and spatial layout of warehousing and logistics facilities, thereby improving the city's freight efficiency and alleviating the pressure on the urban freight traffic system.

[0039] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 A processing flow chart of a method for classifying the travel space distribution pattern of urban heavy trucks provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of establishing a travel distribution matrix of a heavy truck in a city provided by an embodiment of the present invention. The left figure is a schematic diagram of a heavy truck traveling in three areas, wherein each dotted line represents a trip, points A, B and C represent travel points recorded after the area is coarsened, and the size of A, B and C represents the travel volume of the travel point. The larger the scatter point, the greater the travel volume; the arrows on A, B and C represent the interaction volume between the travel points. The wider the arrow, the corresponding travel distribution matrix is ​​shown in the right figure;

[0043] Figure 3 Taking Beijing as an example, the first five levels of the travel point hierarchy of urban heavy trucks are shown in the figure.

[0044] Figure 4 Taking Beijing as an example, the first five layers of the travel flow hierarchy matrix of urban heavy trucks are shown;

[0045] Figure 5 The calculation results of the travel flow level index of heavy trucks in 34 Chinese cities in the embodiment;

[0046] Figure 6 The calculation results of the travel volume centrality index of heavy trucks in 34 Chinese cities in the embodiment;

[0047] Figure 7The classification results of the travel space distribution patterns of heavy trucks in 34 Chinese cities in the embodiment;

[0048] Figure 8 Visually display the expected line graphs of heavy truck travel in typical cities under four types: centralized, surrounding type I, surrounding type II and scattered type;

[0049] Fig. 9 A schematic diagram of a travel distribution matrix provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0051] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0052] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0053] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0054] The processing flow of a method for classifying the travel spatial distribution pattern of urban heavy trucks provided by an embodiment of the present invention is as follows: Figure 1 As shown, the following steps are included:

[0055] Step S1, divide the travel points of urban heavy trucks, and obtain the travel distribution matrix of urban heavy trucks according to the travel chain data between the travel points.

[0056] Step S2, divide the travel points of urban heavy trucks into multiple levels according to the travel volume in the travel distribution matrix of heavy trucks, establish a travel flow hierarchy matrix, use the sum of the three diagonal traces in the travel flow hierarchy matrix as the travel flow hierarchy index, and use the travel flow hierarchy index to quantify the overall characteristics of the urban heavy truck travel distribution matrix.

[0057] Step S3, calculate the heterogeneity of heavy truck travel volume at different locations and the spatial separation index, multiply the heterogeneity of heavy truck travel volume at different locations by the normalized spatial separation index to obtain the city centrality index, and quantify the centralization degree of the spatial distribution of urban heavy truck travel volume.

[0058] Step S4: classify the spatial distribution pattern of heavy truck travel in each city according to the calculation results of the heavy truck travel flow level index and the city centrality index of each city.

[0059] Among them, the research examples include 34 cities in China with a population of more than 2 million;

[0060] Furthermore, in one embodiment, step S1 specifically includes:

[0061] The city is divided into multiple traffic zones with a unit of about 1 square kilometer. Each traffic zone is coarsely recorded as a travel point for heavy trucks. The trips within each travel point are ignored. Only the trip generation and attraction at each travel point and the trip distribution between each travel point are counted. The travel distribution matrix of heavy trucks in the city is established using all the statistical travel chain data. The travel distribution matrix is ​​also called the OD matrix (abbreviation for origin-destination). The row coordinates of the matrix represent the traffic zones at the origin, and the column coordinates represent the traffic zones at the destination. The elements in the matrix are the travel distribution between a pair of origin and destination, indicating the travel volume starting from the origin and arriving at the destination. The travel generation volume is the sum of the row elements of the travel distribution matrix, indicating the total travel volume starting from the origin corresponding to the row; the travel attraction volume is the sum of the column elements of the travel distribution matrix, indicating the total travel volume starting from other origins and arriving at the destination corresponding to the column.

[0062] For example, Fig. 9A schematic diagram of a travel distribution matrix provided for an embodiment of the present invention. The value in a box in the first row indicates that the travel distribution volume starting from point A and arriving at point C is 2; the value in another box in the first row indicates that the total travel volume starting from point A (arriving at points B and C) is 3, that is, the travel volume at point A is 3; the value in the first box in the second row indicates that the travel distribution volume starting from point B and arriving at point A is 1; the value in the first box in the fourth row indicates that the total travel volume starting from points B and C and arriving at point A is 2, that is, the travel attraction volume at point A is 2.

[0063] Figure 2 A schematic diagram of establishing a travel distribution matrix of heavy trucks in a city provided by an embodiment of the present invention, Figure 2 The middle left figure is a schematic diagram of a heavy truck traveling in three areas, where each dotted line represents a trip, points A, B, and C represent travel points after the area is coarsened, and the size of A, B, and C represents the travel volume of the travel point. The larger the scatter point, the greater the travel volume; the arrows on A, B, and C represent the interaction between travel points. The wider the arrow, Figure 2 The middle right picture is the corresponding travel distribution matrix.

[0064] Furthermore, in one embodiment, step S2 specifically includes:

[0065] S21, extracting the travel volume of travel points in the travel distribution matrix of urban heavy trucks, and drawing a Lorenz curve of the travel volume;

[0066] S22. Find a tangent line at the vertex of the Lorenz curve. The intersection of the tangent line and the horizontal axis is recorded as the threshold of the first-layer travel point. The travel point falling to the right of the threshold is recorded as the first-layer travel point. Figure 3 This is a schematic diagram of the first five levels of the travel point hierarchy for urban heavy trucks, taking Beijing as an example.

[0067] S23, removing the first-layer travel points from the total travel locations, drawing a second Lorenz curve based on the travel volume of the remaining travel points, and repeating the operation of S22 until the tangent slope of the Lorenz curve reaches 1 at the vertex, at which time the heavy truck travel locations are divided into L layers;

[0068] S24. Count the proportion of the travel volume between the p-th layer travel point and the q-th layer travel point to the total travel volume M pq , with M pq As elements, construct a flow-level matrix M of size L×L. Figure 4The following is a schematic diagram of the first five layers of the travel flow hierarchy matrix of heavy trucks in Beijing. In the previous step, the travel points have been divided into multiple levels according to the travel volume. The row elements in the flow hierarchy matrix M refer to the proportion of the travel volume from the travel points of a certain layer to the travel points of other layers in the total travel volume, and the column elements in the flow hierarchy matrix M refer to the proportion of the travel volume from the travel points of other layers to the travel points of a certain layer in the total travel volume.

[0069] S24, sum the elements of the three diagonal traces in the travel flow level matrix M to obtain the travel flow level index, and the calculation formula is as follows:

[0070]

[0071] In the formula, Φ can reflect the interaction intensity between places with similar freight volume. The larger the index value, the stronger the interaction intensity between places with similar freight volume. LL It is the element located in the Lth row and Lth column in the flow level matrix M, indicating the proportion of the trip volume starting from the Lth level travel point and arriving at the Lth level travel point in the total trip volume.

[0072] For example, taking the heavy truck travel data of 34 cities in China as an example, the heavy truck travel flow level index of these cities is calculated and sorted. The results are as follows: Figure 5 shown.

[0073] Further, in one embodiment, step S3 specifically includes:

[0074] S31. Count the proportion of heavy truck trips at each trip point i in the city to the total trips in the city. i ;

[0075] S32. Calculate the heterogeneity of heavy truck travel volume at different travel points. The calculation formula is as follows:

[0076]

[0077] In the formula, n represents the total number of regions in the city;

[0078] S33, calculating the spatial separation index;

[0079] The calculation method of the spatial separation index is as follows:

[0080] The column vector S = (s1, s2, s3…s n ) T ;

[0081] Establish the distance matrix D between city travel points, whose element d ijis the Euclidean distance between the centroids of regions i and j, and all elements on the main diagonal of D are 0;

[0082] The calculation formula of spatial separation index V is as follows:

[0083] V=S′×D×S

[0084] In the formula, when all heavy truck trips are concentrated in one area, V is 0; when heavy truck trips are evenly dispersed in the outermost part of the city, the spatial separation index reaches the maximum value V max ; S′ is the transpose of the column vector S, which is composed of the proportion of heavy truck travel volume at each travel point to the total travel volume in the city, and is a row vector.

[0085] S34. In order to eliminate the influence of different city shapes and sizes, the spatial separation index V is normalized and the calculation formula is as follows:

[0086] P=1―V / V max

[0087] In the formula, the closer P is to 0, the more dispersed the spatial distribution of heavy truck travel is; the closer P is to 1, the more concentrated the spatial distribution of heavy truck travel is;

[0088] S35. Multiply the heterogeneity of heavy truck trips at different locations by the normalized spatial separation index to obtain the city centrality index, which is calculated as follows:

[0089] Ψ=H·P

[0090] In the formula, the higher the value of Ψ, the higher the degree of centralization of the spatial distribution of urban heavy truck travel; the lower the value of Ψ, the lower the degree of centralization of the spatial distribution of urban heavy truck travel.

[0091] For example, taking the heavy truck travel data of 34 cities in China as an example, the heavy truck travel volume centrality index of these cities is calculated and sorted. The results are as follows: Figure 6 shown.

[0092] Further, in one embodiment, step S4 specifically includes classifying the travel spatial distribution pattern of heavy trucks in the city using the Kmeans clustering algorithm according to the calculation results of the travel flow level index Φ and the city centrality index Ψ of heavy trucks in each city. First, 34 cities are plotted on a two-dimensional coordinate plane with the flow level index Φ value as the vertical axis and the city centrality index Ψ value as the horizontal axis. The coordinate position of the city on the two-dimensional plane is expressed as (the centrality index Ψ value of the city, the flow level index Φ value of the city), and Kmeans clustering is performed on the two-dimensional coordinate plane. Next, the KMeans() function in the sklearn library of the Python programming language is used, and the flow level index Φ value and the city centrality index Ψ value of the 34 cities are used as the input of the KMeans() function, and the output result of the KMeans() function is the category to which each city belongs. The classification results obtained by clustering include information on the categories to which each city belongs for only 34 cities. Among them, Harbin, Chongqing, Wuhan, Nanning, Changchun, and Shenyang are first-category cities; Tianjin, Chengdu, Guangzhou, Nanjing, Xi'an, Qingdao, Kunming, Hangzhou, Jinan, Dalian, Suzhou, Wuxi, Guiyang, and Luoyang are second-category cities; Beijing, Changsha, Zhengzhou, Taiyuan, Nanchang, Hefei, Shijiazhuang, and Urumqi are third-category cities; Shanghai, Shenzhen, Dongguan, Xiamen, Fuzhou, and Ningbo are fourth-category cities, all of which have been mapped. Figure 7 middle.

[0093] like Figure 7 As shown, Figure 7 The figure uses different symbols to show the four types of heavy truck travel spatial distribution patterns in 34 cities.

[0094] For example, Figure 8 The expected line graphs of heavy truck travel in typical cities under four types are shown. According to the expected line graphs, we can see that in Chongqing and Harbin, the spatial distribution of urban heavy truck travel is mainly concentrated in the central area of ​​the city, so this type of city is named concentrated type; in Xi'an and Hangzhou, the spatial distribution of urban heavy truck travel is mainly around the central area of ​​the city and the surrounding shape is closely connected, so this type of city is named surrounding type I; in Changsha and Zhengzhou, the spatial distribution of urban heavy truck travel is also around the central area of ​​the city, but compared with type II, the spatial distribution of urban heavy truck travel in type III is relatively more dispersed, and the travel volume on the surrounding shape is also less, so this type of city is named surrounding type II; in Shanghai and Shenzhen, the spatial distribution of heavy truck travel is scattered in various areas of the city, so type IV is named scattered type.

[0095] In subsequent applications, the relevant data of urban heavy truck travel can be input into the system to obtain the overall characteristics of the urban heavy truck travel distribution matrix and the quantitative results of the centralization degree of the spatial distribution of urban heavy truck travel volume. By comparing the quantitative results of the two indicators with the data source of the present invention, the type of travel of the calculated urban heavy truck can be directly obtained.

[0096] In summary, the embodiment of the present invention comprehensively and accurately quantifies the overall characteristics of the spatial distribution of heavy truck travel through two heavy truck travel characteristic quantification indicators, and proposes a system for quantifying the overall characteristics of the spatial distribution of urban heavy truck travel. The system can quantify the overall characteristics of the heavy truck travel distribution matrix and the degree of centralization of the spatial distribution of urban heavy truck travel volume, and can also identify the spatial distribution patterns of urban heavy truck travel with different characteristics, helping urban freight planning managers to more systematically understand the spatial distribution of backbone freight demand in the city, and provide suggestions for the scientific formulation of urban freight traffic planning and management strategies such as truck road charging policies, truck dedicated lane settings, and spatial location layout of warehousing and logistics facilities, thereby improving the city's freight efficiency and alleviating the pressure on the urban freight traffic system.

[0097] In the existing heavy truck travel research of the embodiment of the present invention, the travel expectation line diagram drawn based on the travel distribution matrix is ​​mainly used to visualize the overall distribution of heavy truck travel space, but this method cannot objectively and quantitatively describe the overall characteristics of the heavy truck travel space distribution. The present invention proposes a method and system for quantifying the overall characteristics of the heavy truck travel space distribution, which can more systematically understand the spatial distribution of backbone freight demand in the city, and provide an important technical method for objectively evaluating the current status of the urban freight system.

[0098] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0099] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0100] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0101] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for classifying the travel spatial distribution patterns of urban heavy trucks, characterized in that: include: Divide the travel points of urban heavy trucks, and obtain the travel distribution matrix of urban heavy trucks based on the travel chain data between each travel point; According to the travel volume in the travel distribution matrix of urban heavy trucks, the travel points of urban heavy trucks are divided into multiple levels, and a travel flow level matrix is ​​established. The travel flow level index of urban heavy trucks is calculated according to the travel flow level matrix. The heterogeneity of heavy truck trips at different travel points and the spatial separation index are calculated, and the urban centrality index of heavy trucks in the city is obtained by multiplying the heterogeneity of heavy truck trips at different travel points with the normalized spatial separation index. Classifying the travel spatial distribution pattern of urban heavy trucks according to the travel flow hierarchy index and the urban centrality index of the urban heavy trucks; The travel points of urban heavy trucks are divided into multiple levels according to the travel volume in the travel distribution matrix of urban heavy trucks, a travel flow level matrix is ​​established, and the travel flow level index of urban heavy trucks is calculated according to the travel flow level matrix, including: Extract the travel volume of travel points in the travel distribution matrix of urban heavy trucks and draw the Lorenz curve of the travel volume; A tangent line is obtained at the vertex of the Lorenz curve, and the intersection of the tangent line and the horizontal axis is recorded as the threshold of the first-layer travel points. The travel points falling to the right of the threshold are recorded as the first-layer travel points. The first-layer travel points are removed from the total travel locations, and the second Lorenz curve is drawn according to the travel volume of the remaining travel points. The above process is repeated until the slope of the tangent line of the Lorenz curve at the vertex reaches 1, and the heavy truck travel location is divided into L layers; Count the proportion of the travel volume between the p-th layer travel point and the q-th layer travel point to the total travel volume M pq , with M pq As elements, construct a travel flow hierarchy matrix M of size L×L. The row elements in the travel flow hierarchy matrix M refer to the proportion of the trip volume starting from a travel point on a certain layer to the travel points on other layers in the total travel volume. The column elements in the travel flow hierarchy matrix M refer to the proportion of the trip volume starting from the travel points on other layers to the travel points on a certain layer in the total travel volume. The elements of the three diagonal traces in the travel flow level matrix M are summed to obtain the travel flow level index, which is calculated as follows: In the formula, Φ reflects the interaction intensity between places with similar freight volume. The larger the index value, the stronger the interaction intensity between places with similar freight volume. LL The element located in the Lth row and Lth column of the travel flow hierarchy matrix M represents the proportion of the trip volume starting from the Lth level travel point and arriving at the Lth level travel point in the total trip volume; The calculation of the heterogeneity of heavy truck travel volume and spatial separation index at different travel points, multiplying the heterogeneity of heavy truck travel volume at different travel points by the normalized spatial separation index, to obtain the city centrality index of urban heavy trucks, includes: Count the proportion of heavy truck trips at each trip point i in the city to the total trips in the city. i ; The heterogeneity of heavy truck trips at different travel points is calculated using the following formula: In the formula, n represents the total number of regions in the city, and H represents the heterogeneity of the heavy truck travel volume in the city; The proportion of heavy truck trips at each travel point to the total trips in the city is formed into a column vector S = (s1, s2, s3…s n ) T ; Establish the distance matrix D between city travel points, and the element d in the distance matrix D between city travel points ij is the Euclidean distance between the centroids of regions i and j, and all elements on the main diagonal of D are 0; The calculation formula of the spatial separation index V is set as follows: V=S′×D×S In the formula, when all heavy truck trips are concentrated in one area, V is 0; when heavy truck trips are evenly dispersed in the outermost part of the city, the spatial separation index reaches the maximum value V max ; S′ is the transpose of the column vector S consisting of the proportion of heavy truck trips at each trip point to the total trips in the city, and is a row vector; The spatial separation index V is normalized and the calculation formula is as follows: P=1―V / V max In the formula, the closer P is to 0, the more dispersed the spatial distribution of heavy truck travel is; the closer P is to 1, the more concentrated the spatial distribution of heavy truck travel is; The heterogeneity of heavy truck trips in different locations is multiplied by the normalized spatial separation index to obtain the city centrality index Ψ, which is calculated as follows: Ψ=H·P In the formula, the higher the value of Ψ, the higher the degree of centralization of the spatial distribution of urban heavy truck travel; the lower the value of Ψ, the lower the degree of centralization of the spatial distribution of urban heavy truck travel; The classification of the travel spatial distribution pattern of urban heavy trucks according to the travel flow level index and the urban centrality index of the urban heavy trucks includes: A set number of cities are plotted on a two-dimensional coordinate plane with the travel flow hierarchy index Φ as the vertical axis and the city centrality index ψ as the horizontal axis. The coordinate position of the city on the two-dimensional coordinate plane is expressed as (the centrality index ψ value of the city, the travel flow hierarchy index φ value of the city). Kmeans clustering is performed on the two-dimensional coordinate plane, and the travel flow hierarchy index φ values ​​and the city centrality index ψ values ​​of the set number of cities are used as inputs to the KMeans() function in the sklearn library of the Python programming language. The output result of the KMeans() function is the category to which the travel spatial distribution pattern of heavy trucks in each city belongs, which includes being concentrated in the central area of ​​the city, being in a surrounding shape around the central area of ​​the city and the surrounding shapes are closely connected, and being in a surrounding shape around the central area of ​​the city and the surrounding shapes are relatively dispersed and scattered in various areas of the city.

2. The method according to claim 1, characterized in that The method of dividing the travel points of urban heavy trucks and obtaining the travel distribution matrix of urban heavy trucks according to the travel chain data between the travel points includes: The city is divided into multiple traffic zones based on a set area, and each traffic zone is recorded as a travel point for heavy trucks. The travel within each travel point is ignored, and only the travel generation volume, attraction volume and travel distribution volume between each travel point are counted. The travel distribution matrix of urban heavy trucks is established using all statistical travel chain data. The row coordinates of the travel distribution matrix represent the starting point traffic zone, and the column coordinates represent the destination traffic zone. The elements in the travel distribution matrix are the travel distribution volume between a pair of starting and ending points, indicating the travel volume starting from the starting point and arriving at the destination. The travel generation volume is the sum of the row elements of the travel distribution matrix, indicating the total travel volume starting from the starting point corresponding to the row; the travel attraction volume is the sum of the column elements of the travel distribution matrix, indicating the total travel volume starting from other starting points and arriving at the destination corresponding to the column.

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