Urban freight management and control area division method based on hierarchical management and control

Abstract

The invention discloses an urban freight management and control area division method based on hierarchical management and control. The method comprises the following steps: 1) setting a first-level index and a second-level index; and 2) quantifying the indexes, determining the weights of the first-level and second-level indexes, obtaining a final freight management and control evaluation system index total score, and performing management and control hierarchy matching. According to the invention, by constructing a freight management and control area evaluation system and quantifying indexes, the freight management and control degrees of different areas are evaluated, the weight of each index is determined by using an analysis method, and an index quantification and management and control hierarchy matching method is adopted to divide a city area into an encouraging and guiding area, a general management and control area and a strict management and control area. The method is beneficial for reasonably organizing and managing freight activities in a city, reducing traffic congestion, improving traffic efficiency and improving environment quality, can provide solving strategies according to local conditions for characteristics of different areas in actual projects, and provides beneficial reference for urban sustainable development and management of the traffic transportation industry.

Description

Technical Field

[0001] The present invention belongs to the technical field of truck traffic control, and particularly relates to a method for dividing urban freight control areas based on hierarchical control. Background Art

[0002] With the continuous advancement of urbanization and the sustainable development of the economy, urban freight transportation plays an increasingly important role. However, urban freight transportation also faces some challenges in the development process, such as transportation costs, traffic congestion, environmental pollution, resource waste, etc. In this context, in order to improve the efficiency and sustainability of urban freight transportation, the planning and management of freight transportation need to move towards intelligence, refinement, and systematization. It has become an inevitable trend to formulate different freight control strategies according to the characteristics of different regions. As a new traffic management concept, the hierarchical control mode has gradually attracted the attention of urban planners and traffic experts. In the organization and planning of urban freight transportation, by dividing the city into different levels of freight control areas and carrying out matching refined control and traffic organization optimization, the traffic operation efficiency can be improved, traffic congestion can be reduced, energy consumption can be lowered, and the traffic environment can be improved. This mode fully considers the needs and interests of different traffic participants and adopts differential management strategies, which can better adapt to the diversity and complexity of urban freight transportation.

[0003] In terms of urban freight transportation planning, domestic research on the division of urban freight control areas and policy research has been increasingly emphasized. Yu Sanlin used the grey system prediction theory to predict the freight volume in Beijing and studied the future policies and freight organization at the same time. Wang Yingjie took the freight transportation system planning in the Hunnan area of Shenyang as an example and summarized the technical route of urban freight transportation system planning from the perspective of the planning of freight areas and channels. Yang Niannian analyzed the freight transportation management policies of each city and explored the problems existing in China's urban freight transportation management policies, such as single purpose, incomplete system, and extensive and vague management methods. Wang Yongqing clarified six specific indicators and quantitative measurement paths that need to be controlled in the management of the urban freight transportation system under the national territorial space planning system. Foreign research mainly focuses on establishing models to explore freight management policies. Kai Martins-Turner et al. modeled and simulated the planning of freight trips based on the Agent traffic model to provide decision-making for managers. Renata A.M. Bandeira et al. established a fuzzy multi-criteria evaluation model with economic and social benefits as the goals to ensure the sustainable development of urban freight.

[0004] In the research on hierarchical control models, hierarchical control models are commonly used in fields such as financial management, regional planning, and plan formulation. Tao Yanliang summarized and analyzed the characteristics of the progress hierarchical plan and control model to solve problems such as the decision-making of the development cycle in the project establishment stage and the representation of the development process in the development stage of complex equipment development projects. Xiao Da et al. took the mineral energy space as an example and proposed an idea for compiling the overall land use plan for hierarchical management of composite national land space. Yang Mengli et al. tried to apply this idea to construct a land use planning review system suitable for Nanjing, achieving full coverage of the whole region with the overall plans at the city, sub-district, and town levels and the detailed plans inside and outside the development boundary, as well as village plans, and strengthening the linkage and conduction with the detailed plans through constructing a two-level special plan system. Li Xiaohua et al. proposed a digital twin system for aircraft final assembly lines for hierarchical transparent control to solve problems such as poor integration of the business system of aircraft final assembly lines, opaque assembly processes, and untimely handling of abnormalities.

[0005] However, how to achieve the efficient, safe, and sustainable development of urban freight transportation under the guidance of the hierarchical control model remains a difficult point in the industry. Summary of the Invention

[0006] In order to solve the technical problems existing in the prior art, the purpose of the present invention is to provide a method for dividing urban freight control areas based on hierarchical control.

[0007] To achieve the above purpose and reach the above technical effects, the technical solution adopted by the present invention is as follows:

[0008] A method for dividing urban freight control areas based on hierarchical control includes the following steps:

[0009] 1) Set primary indicators and secondary indicators;

[0010] 2) Quantify the above indicators, determine the weights of the primary and secondary indicators, obtain the total score of the final freight control evaluation system indicators, and perform control level matching.

[0011] Further, in step 1), there are five primary indicators, namely economy, efficiency, demand, environment, and policy; there are 13 secondary indicators. Among them, the economic indicators include the unit freight vehicle transportation cost and the unit freight vehicle transportation time; the efficiency indicators include the road network topological connectivity, the "peak area congestion index", and the average speed in the peak area; the demand indicators include the proportion of industrial storage land, the daily average freight volume, and the density of the current freight channels; the environmental indicators include the total daily carbon emissions, the requirements for ecological protection, and the impacts on farmland and forest land; the policy indicators include the existing freight traffic restriction policies and other management and protection policies.

[0012] Further, in step 2), the steps for determining the weights of the primary and secondary indicators include:

[0013] 21) Construct the hierarchical structure

[0014] Define "the criticality of regional freight control" as the target layer; 5 first-level indicators and 13 second-level indicators as the criterion layer; the alternative scheme layer is each region or each group of regions in the city that actually needs to be studied and analyzed;

[0015] 22) Construct the judgment matrix

[0016] Compare the importance of each indicator, use "1 - 9" for scaling, and construct the judgment matrix according to the comparison results;

[0017] 23) Consistency test

[0018] Use Python for programming to solve and obtain the maximum eigenvalue λ of the matrix max , and further calculate the consistency index CI and the consistency ratio CR;

[0019] 24) Normalize the weights

[0020] Normalize the eigenvector values to obtain the weights of the first-level and second-level indicators;

[0021] 25) Assign values and quantify the indicators

[0022] The indicator quantification adopts a ten-point system. According to the actual situation of each second-level indicator, different values from 2 to 10 are given, and finally the quantified scores of each level of indicators are obtained as the basis for judging the classification of the regional control level;

[0023] 26) Match the control levels

[0024] Match the control levels according to the total score of the final freight control evaluation system indicators.

[0025] Furthermore, in step 22), the judgment matrix is as follows:

[0026]

[0027] Furthermore, in step 23), the calculation formulas for the consistency index CI and the consistency ratio CR are:

[0028]

[0029] where RI is the average random consistency index.

[0030] Furthermore, in step 24), the weights of the first-level indicators are shown in Table 1:

[0031] Table 1

[0032] Indicator Economy Efficiency Demand Environment Policy Weight w 0.14 0.10 0.24 0.34 0.18 .

[0033] Further, in step 24), the weights of the secondary indicators are shown in Table 2:

[0034] Table 2

[0035]

[0036]

[0037] Further, in step 25), for yes / no questions, if there is no policy restriction, the score is 10 points; if there are suggestion / guidance policy restrictions, the score is 6 points; if there are mandatory policy restrictions, the score is 2 points. For quantitative data questions, by comparing with the average level of the area, a normalization model is used for scoring. For judgment questions, they are divided into five levels: better, good, average, poor, and very poor, with scores of 10 points, 8 points, 6 points, 4 points, and 2 points respectively.

[0038] Further, in step 26), according to the total score of the freight control evaluation system indicators, the control level matching is carried out with the set strict control area, general control area, and encouragement and guidance area.

[0039] Further, the principle of control level matching is as follows:

[0040] Encouragement and guidance area: In principle, the score should be greater than 7 points, and 1 to 2 freight transit channels and several internal freight logistics channels are planned;

[0041] General control area: In principle, the score is 4 - 7 points, and 1 freight transit channel and 1 - 4 internal freight logistics channels are planned;

[0042] Strict control area: In principle, the score is less than 4 points, no more than 1 freight transit channel, and no internal freight logistics channels are set up.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] By constructing a freight control area evaluation system and quantifying indicators, the present invention aims to evaluate the freight control intensity of different areas, reasonably organize and manage the freight activities within the city, reduce traffic congestion, improve traffic efficiency, and improve environmental quality. The weights of each indicator are determined by the analysis method, and the methods of indicator quantification and control level matching are adopted to divide the urban area into the encouragement and guidance area, general control area, and strict control area, providing a scientific basis and operation guide for urban freight traffic management.

[0045] The method for dividing urban freight control areas proposed by the present invention has certain feasibility and practicality, can be fully applied in actual projects, can provide solutions according to local conditions for the characteristics of different areas, and provides useful reference and reference for the sustainable development of the city and the management of the transportation industry. Brief Description of the Drawings

[0046] Figure 1 is a flowchart of the present invention;

[0047] Figure 2 is a heat distribution map of freight volumes in each area of Huqiu District in Embodiment 1 of the present invention;

[0048] Figure 3 is an IPA analysis chart in Embodiment 1 of the present invention;

[0049] Figure 4 is a total score chart of the index of the freight control evaluation system in Embodiment 1 of the present invention;

[0050] Figure 5 is a freight control plan chart in Embodiment 1 of the present invention. Detailed Description of the Invention

[0051] The present invention will be described in detail below so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making the protection scope of the present invention more clearly defined.

[0052] The following gives a brief overview of one or more aspects to provide a basic understanding of these aspects. This overview is not an exhaustive survey of all contemplated aspects, and is neither intended to identify key or decisive elements of all aspects nor to attempt to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that follows.

[0053] As Figures 1 - 5 shown, a method for dividing urban freight control areas based on hierarchical control includes the following steps:

[0054] 1) Set primary indicators and secondary indicators to construct a freight control area evaluation system

[0055] There are five primary indicators, namely "economy", "efficiency", "demand", "environment" and "policy";

[0056] There are 13 secondary indicators. Among them, the "economy" indicator includes "unit freight vehicle transportation cost" and "unit freight vehicle transportation time"; the "efficiency" indicator includes "road network topological connectivity", "peak area congestion index" and "peak area average vehicle speed"; the "demand" indicator includes "proportion of industrial storage land", "daily average total freight volume" and "current freight channel density"; the "environment" indicator includes "daily average total carbon emissions", "ecological protection requirements" and "impact on farmland and forest land"; the "policy" indicator includes "existing freight vehicle traffic restriction policies" and "other management and protection policies";

[0057] 2) Quantify the above indicators, determine the weights of the first- and second-level indicators, and obtain the indicators of the freight control evaluation system

[0058] 21) Construct the hierarchical structure

[0059] Define "the key degree of regional freight control" as the target layer; the above 5 first-level indicators and 13 second-level indicators are the criterion layer; the alternative scheme layer is each region or each group of regions of the city that actually needs to be studied and analyzed;

[0060] 22) Construct the judgment matrix

[0061] Use the expert scoring method to compare the importance of each indicator, scale with "1-9", and construct the judgment matrix according to the comparison results. The obtained judgment matrix is as follows:

[0062]

[0063] 23) Consistency test

[0064] Use Python for programming and solving to obtain the maximum eigenvalue λ of the matrix max = 5.146, and further calculate the consistency index CI and the consistency ratio CR:

[0065]

[0066] Among them, RI is the average random consistency index;

[0067] After solving: CI = 0.0366; CR = 0.0326 < 0.1, and the consistency test passes;

[0068] 24) Normalized weights

[0069] Normalize the eigenvector values to obtain the weights of each indicator. Table 1 shows the weights of the first-level indicators:

[0070] Table 1

[0071] Indicator Economy Efficiency Demand Environment Policy Weight w 0.14 0.10 0.24 0.34 0.18

[0072] The above can obtain the weights of each first-level indicator. Similarly, the weights of the second-level indicators can be obtained, and the total weight distribution is shown in Table 2 below:

[0073] Table 2

[0074]

[0075] 25) Index assignment and quantification

[0076] The index quantification adopts a ten-point system. According to the actual situation of each secondary index, different values from 2 to 10 points are given, and finally the quantified scores of each level of indicators are obtained, which are used as the basis for judging the classification of regional control levels;

[0077] For yes / no questions, such as whether there are environmental protection requirements, freight traffic restriction policies, etc., if there is no policy restriction, the assigned value is 10 points; if there are suggestion and guidance policy restrictions, the assigned value is 6 points; if there are mandatory policy restrictions, the assigned value is 2 points;

[0078] For quantified data questions, such as total freight volume, transportation cost and time, congestion index, average vehicle speed, etc., by comparing with the average level of the area, a normalization model is used for assignment;

[0079] For judgment questions, such as road network topological connectivity, etc., it is divided into five levels: better, good, average, poor and very poor, and the assigned values are 10 points, 8 points, 6 points, 4 points and 2 points respectively;

[0080] 26) Control level matching

[0081] According to the total score of the final freight control evaluation system indicators, it is matched with the set strict control area, general control area and encouragement and guidance area. The lower the total score, the less conducive the area is to large-scale freight traffic, and corresponding control is required:

[0082] Encouragement and guidance area: In principle, the score should be greater than 7 points, and 1 to 2 freight transit channels and several internal freight logistics channels are planned;

[0083] General control area: In principle, the score is 4 - 7 points, and 1 freight transit channel and 1 - 4 internal freight logistics channels are planned;

[0084] Strict control area: In principle, it is less than 4 points, no more than 1 freight transit channel, and no internal freight logistics channels are set.

[0085] Example 1

[0086] Analysis of the current situation of regional freight transportation:

[0087] Taking into account factors such as land use nature, street division, and natural barriers, the Huqiu District (High-tech Zone) of Suzhou City is divided into 13 control areas.

[0088] Through a combination of enterprise census and driver spot checks, a total of 2,548 transportation enterprises and 267 truck drivers were surveyed, and a freight volume research and analysis was conducted on each plate of the area. Its daily average freight volume distribution is as Figure 2 shown.

[0089] Collect traffic operation data, regional land use data, and relevant freight traffic restrictions and environmental protection policies through an online platform, and collect data such as the freight volume and transportation efficiency of regional enterprises through on-site investigations.

[0090] Obtain the scores of each sub-region in each dimension, and use the IPA analysis method to obtain Figure 3 the scatter plot shown below:

[0091] Different regions have different performances in indicators according to their land use and development types. The points falling in the fourth quadrant indicate that the evaluation results of these regions in these dimensions are not ideal. For example, the environmental protection requirements of scenic spots are relatively high, and residential areas perform poorly in traffic operation and carbon emissions.

[0092] The IPA analysis chart established by each secondary indicator can determine the scores of the first-level indicators of each region and the total score of the freight control evaluation system indicators as Figure 4 shown below, and then obtain the division results of the control regions:

[0093] Encouraged guidance areas: Shishan Industrial Park, Huxin Industrial Park, and Science and Technology Industrial Park, with scores of 7.43, 7.33, and 7.25 respectively;

[0094] General control areas: Taihu Science City, Financial Town Area, Shishan Business District, Zhenhu Sub-region, Yangshan Scenic Area, Science and Technology City Sub-region, Shishan Scenic Area, and Rail Transit Business District, with scores ranging from 5.47 to 6.91;

[0095] Strict control areas: Yangshan Residential Area and Huguan Old City, with scores of 3.97 and 3.40 respectively.

[0096] According to the control region division, formulate different truck traffic regulations, as shown in Table 3.

[0097] Table 3

[0098]

[0099] For freight transit channels, both yellow-plate and blue-plate trucks can pass through 24 hours a day, but are only allowed to drive on this channel. Entering the corresponding control areas requires following the internal traffic regulations; for internal freight logistics channels, in principle, follow the traffic regulations within the control areas to which they belong. For internal channels with strong evacuation capabilities and large distribution demands, the requirements can be appropriately relaxed according to the actual situation.

[0100] Combined with the analysis of the current situation data, the control region division, and the optimization of freight traffic organization, the freight control plan for Huqiu District, Suzhou City is as Figure 5 shown below.

[0101] In view of the problems such as traffic congestion and low efficiency existing in urban freight management, as well as the requirements of environmental protection and urban sustainable development, based on the concept of hierarchical control, this invention studies the division method of urban freight control areas and the optimization strategy of traffic organization. A freight control area evaluation system is constructed, the analytic hierarchy process is used to determine the weights of each index, and the index quantification and control level matching method are adopted to evaluate the control criticality of each area and match the control levels. Then, taking Huqiu District of Suzhou City as a case study, the feasibility and effectiveness of the proposed method are verified. Finally, truck passing regulations for different levels of control areas are formulated to optimize freight traffic organization, improve urban freight efficiency, and provide a scientific basis and operation guide for urban freight traffic management. The practical results show that the research results of this invention have reference significance and practical application value in improving the urban freight environment and enhancing freight efficiency.

[0102] For parts or structures not specifically described in this invention, existing technologies or existing products can be used, and no further elaboration will be made here.

[0103] The above are only embodiments of this invention, and do not limit the patent scope of this invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of this invention's specification, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of this invention.

Claims

1. A method for dividing urban freight control areas based on hierarchical control, characterized in that, It includes the following steps: 1) Set primary indicators and secondary indicators; 2) Quantify the above indicators, determine the weights of the primary and secondary indicators, obtain the total score of the final freight control evaluation system indicators, and perform control level matching.

2. The method for dividing urban freight control areas based on hierarchical control according to claim 1, wherein, In step 1), there are five primary indicators, namely economy, efficiency, demand, environment, and policy; there are 13 secondary indicators. Among them, the economic indicators include the unit freight transportation cost per truck and the unit freight transportation time per truck; the efficiency indicators include the road network topological connectivity, "peak area congestion index", and average speed in the peak area; the demand indicators include the proportion of industrial storage land, the daily average total freight volume, and the density of the existing freight channels; the environmental indicators include the total daily carbon emissions, ecological protection requirements, and impacts on farmland and forest land; the policy indicators include the existing freight traffic restriction policies and other management and protection policies.

3. A method for dividing urban freight control areas based on hierarchical control according to claim 1, characterized in that, In step 2), the steps to determine the weights of the primary and secondary indicators include: 21) Construct a hierarchical structure Define "regional freight control criticality" as the target layer; the 5 primary indicators and 13 secondary indicators as the criterion layer; the alternative scheme layer is each region or each group of regions in the city that actually needs to be studied and analyzed; 22) Construct a judgment matrix Compare the importance of each indicator, use a scale of 1 - 9, and construct a judgment matrix according to the comparison results; 23) Consistency check Programming and solving using Python to obtain the maximum eigenvalue λ of the matrix max , and further calculating the consistency index CI and the consistency ratio CR; 24) Normalize the weights Perform normalization processing on the eigenvector values to obtain the weights of the primary and secondary indicators; 25) Quantify the indicator assignment The indicator quantification adopts a ten-point system. According to the actual situation of each secondary indicator, different assignments of 2 - 10 points are given, and finally the quantified scores of each level of indicators are obtained as the basis for judging the regional control level division; 26) Control level matching Perform control level matching according to the total score of the final freight control evaluation system indicators.

4. A method for dividing urban freight control areas based on hierarchical control as claimed in claim 3, wherein, In step 22), the judgment matrix is as follows:

5. A method for dividing urban freight control areas based on hierarchical control according to claim 3, characterized in that, In step 23), the calculation formulas for the consistency index CI and the consistency ratio CR are: where RI is the average random consistency index.

6. The method for dividing urban freight control areas based on hierarchical control according to claim 3, characterized in that, In step 24), the weights of the primary indicators are shown in Table 1: Table 1 。 7. A method for dividing urban freight control areas based on hierarchical control according to claim 3, characterized in that In step 24), the weights of the secondary indicators are shown in Table 2: Table 2 8. A method for dividing urban freight control areas based on hierarchical control according to claim 3, characterized in that, In step 25), for yes / no questions, assign 10 points if there is no policy limit, 6 points if there are suggestion and guidance type policy limits, and 2 points if there are mandatory policy limits; for quantitative data type questions, compare with the average level of the area and use a normalization model for assignment; for judgment type questions, divide them into five levels: better, good, general, poor, and very poor, and assign 10 points, 8 points, 6 points, 4 points, and 2 points respectively.

9. A method for dividing urban freight control areas based on hierarchical control according to claim 3, characterized in that, In step 26), according to the total score of the final freight control evaluation system indicators, perform control level matching with the set strict control area, general control area, and encouragement and guidance area.

10. A method for dividing urban freight control areas based on hierarchical control as claimed in claim 9, characterized in that, The principle for performing control level matching is: Encouragement and guidance area: In principle, the score should be greater than 7 points, and 1 - 2 freight transit channels and several internal freight logistics channels are planned; General control area: In principle, the score is 4 - 7 points, and 1 freight transit channel and 1 - 4 internal freight logistics channels are planned; Strict control area: In principle, the score is less than 4 points, no more than 1 freight transit channel, and no internal freight logistics channels are set up.

Images