A method for evaluating development suitability of urban subway freight system

Abstract

The present application belongs to the field of underground space development and application and transportation planning, and particularly relates to a development suitability evaluation method for urban subway freight system, which comprises the following steps: step (1), establishing an analysis framework for development suitability of urban subway freight system; step (2), evaluation indexes and quantitative calculation for development suitability of urban subway freight system; step (3), multi-source data collection; and step (4), establishment of index processing process and evaluation criteria. The present application proposes a method combining qualitative and quantitative methods to evaluate the suitability of comprehensive logistics solutions based on urban subway freight, and the evaluation content is comprehensive and operable, thereby providing reliable decision support for sustainable transition of urban freight transportation from highway dominant mode to railway collaborative transportation.

Description

Technical Field

[0001] This invention belongs to the field of underground space development and application and transportation planning, and specifically relates to a method for evaluating the suitability of urban subway freight system development. Background Technology

[0002] Utilizing subways for underground logistics, known as Metro-based ULS, is currently a primary and preferred approach. Conceptually, this requires modifications to subway operation modes and along-line facilities, leveraging surplus rail capacity and high accessibility to deliver standardized freight units to stations via specially designed new trains. Compared to independently constructing tunnels, subway-based underground logistics is less costly and complex, enabling rapid network formation and maximizing benefits. Using subways as a carrier, through layered network deployment and integrated transport modes, multiple systems can collaborate to provide door-to-door urban delivery services. Integrating underground logistics through subway reconstruction or new construction is currently technically feasible. With subways as the primary mode of urban rail transit, the extensive network infrastructure is expected to be fully utilized, and the comprehensive service capacity of the subway network can be enhanced through rational passenger and freight transport organization. Once a network is formed, subway freight also possesses good compatibility and scalability, holding immense potential to drive a fundamental transformation in urban transportation and logistics models.

[0003] Compared to road freight, subway freight systems offer significant advantages. Firstly, utilizing underground rail transit infrastructure, they provide high-speed, stable transport capabilities, are unaffected by external factors, and offer faster delivery services. Secondly, networked subway freight systems can reduce the negative externalities of surface freight to cities. As a low-carbon and environmentally friendly logistics method, its transportation process does not cause urban traffic congestion or environmental pollution, contributing to urban environmental improvement. Furthermore, the development of intelligent technologies enables real-time tracking and management of goods, facilitating the transformation and upgrading of the logistics industry.

[0004] Therefore, the current situation is that the subway-underground logistics intermodal transport system is a new model for promoting urban logistics planning and development by utilizing urban underground space. Developing a subway-based underground logistics system is an important means to implement and improve urban intelligent and low-carbon logistics infrastructure and build a new comprehensive transportation system. Summary of the Invention

[0005] The purpose of this invention is to provide a method for evaluating the suitability of urban subway freight system development.

[0006] The technical solution to achieve the purpose of this invention is: a method for evaluating the suitability of urban subway freight system development, comprising the following steps:

[0007] Step (1): Establish a suitability analysis framework for the development of urban subway freight systems;

[0008] Step (2): Suitability evaluation indicators and quantitative calculation of urban subway freight system development;

[0009] Step (3): Multi-source data acquisition;

[0010] Step (4): Establishment of indicator processing procedures and evaluation criteria.

[0011] Furthermore, step (1) includes the following steps:

[0012] Step (11): Based on the SWOT method, construct a suitability analysis framework for the development of urban subway freight system, identify the main strengths, weaknesses, opportunities and challenges of urban subway freight system project implementation, and formulate a SWOT matrix;

[0013] Step (12): Using a questionnaire survey, the importance scores of the above indicators are obtained based on the experts' evaluation of the SWOT analysis indicators, and new evaluation indicators are proposed.

[0014] Step (13): Determine the selection of indicators for the suitability evaluation of urban subway freight system development based on the SWOT analysis framework.

[0015] Furthermore, step (2) includes two types of suitability evaluation indicators for the development of urban subway freight systems: benefit-type indicators and cost-type indicators.

[0016] Benefit-oriented indicators specifically include network capacity (C1), infrastructure (C2), demand scale (C3), consumption and market (C4), environmental benefits (C5), and urban economic benefits (C6). 10 The urgency of truck restrictions C 11 By combining the calculation formulas of each indicator, the calculated values ​​of each evaluation indicator are obtained based on the data acquired through multi-source data collection methods;

[0017] The cost-related indicators specifically include: urban logistics hub accessibility (C6), customer proximity (C7), transportation-oriented development (C8), and implementation cost (C9). The calculated values ​​of each evaluation indicator are obtained by combining the calculation formulas of each indicator with data acquired through multi-source data collection methods.

[0018] Furthermore, the quantitative calculation method for benefit-based evaluation indicators is as follows:

[0019] Calculate the network capacity C1, an indicator used to reflect the freight capacity of the urban rail transit network. The calculation formula is as follows:

[0020]

[0021] where λ 11 ∈(0, 1),λ 12 ∈(0 , 1),λ 11 +λ 12 =1

[0022] C1 is a network capacity metric; SA Fmodule It is the total area of ​​shared passenger and freight stations in the urban rail transit network; Ucap FPS This is the capacity per unit area of ​​the installed modules; It is the number of passenger trains associated with each relevant line i; This is the maximum possible number of freight train services; Ucap EC It refers to the freight capacity of the extra carriages; Ucap DT It is the vehicle load of a dedicated freight train; λ 11 and λ 12 These are the weights corresponding to the two items.

[0023] The data for calculating the maximum possible number of freight train services is derived from timetable information for each subway line obtained from the websites of subway operators in various cities. The calculation formula is as follows:

[0024]

[0025] in, This is the end time of passenger service; This is the start time of the last dedicated freight train service; MT i It is the time before the start of the last dedicated freight train service compared to the start of the first passenger service the following day; the constant value δ is the departure frequency of the dedicated freight train; the operation of the dedicated freight train is constrained by HT([Δt]). It is assumed that at most one dedicated freight train service can be added between two adjacent passenger services; HT i (j,j+1) represents the interval between passenger trains j and j+1; i∈Ω represents the set of lines in the urban rail transit network, and j∈M represents the set of passenger train services;

[0026] Infrastructure C2, this indicator reflects the abundance of dedicated railway facilities, and is calculated using the following formula:

[0027] C2 = N station ×N transfer ×URTL

[0028] C2 is an infrastructure indicator; N station N is the number of urban rail transit stations; transfer URTL is the number of urban rail transit transfer stations; URTL is the length of the urban rail transit network.

[0029] Demand Scale C3, this indicator reflects the demand scale of urban freight transport, and the calculation formula is as follows:

[0030] C3 = N EP ×N SMRS

[0031] Where C3 is the demand scale indicator, and N EP N is the number of e-commerce parcels delivered in the city each year. SMRS It represents the total number of shopping malls and retail stores in the city;

[0032] C4, the Consumer and Market indicator, reflects the market size and operating conditions of the urban logistics industry. The calculation formula is as follows:

[0033] C4=λ 41 ×LBI+λ 42 ×RSG,λ 41 ∈(0,1),λ 42 ∈(0,1),λ 41 +λ 42 =1

[0034] Among them, C4 is a consumption and market indicator; LBI is the annual operating revenue of the urban logistics market, reflecting the market size and operating status of the urban logistics industry; RSG is the annual retail sales of social goods, reflecting the purchasing power of citizens and the degree of urban economic prosperity. 41 λ is the relative weight value of LBI. 42 It is the relative weight value of RSG;

[0035] Environmental benefits C5, this indicator is used to reflect the environmental benefits of the subway freight system, and the calculation formula is as follows:

[0036]

[0037] C5 is an environmental benefit indicator for the metro freight system; It is the number of passenger trains associated with each relevant line i; This is the maximum possible number of freight train services; Ucap EC It refers to the freight capacity of the extra carriages; Ucap DT It is the vehicle load of a dedicated freight train; It represents the average daily distance traveled by a single truck; REV represents the utilization rate of electric vehicles in urban logistics activities; Ucap truck It is the average daily cargo volume transported by each truck; α CO It is the cost of treating the carbon monoxide (CO) produced per kilometer of truck travel; It is the cost of treating the carbon dioxide (CO2) produced per kilometer of truck travel; It is the nitrogen oxides (NOx) produced per kilometer of truck travel.X Processing costs; α PM This is the cost of treating particulate matter (PM) generated per kilometer of truck travel; α noise It is the cost of dealing with the noise generated per kilometer of truck travel;

[0038] Urban Economy C 10 This indicator reflects the economic foundation required to implement a subway freight system, and the calculation formula is as follows:

[0039] C 10 =GDP / POP×(λ) 101 ×FGBR+λ 102 ×TIFA),λ 101 ∈(0,1),λ 102 ∈(0,1),λ 101 +λ 102 =1

[0040] Among them, C 10 These are urban economic indicators; GDP is Gross Domestic Product; POP is Population; FGBR is General Budget Revenue; TIFA is Total Fixed Asset Investment. 101 It is the relative weighting value of general budget revenue; λ 102 It is the relative weight value of the total fixed asset investment;

[0041] The urgency of truck restrictions C 11 This indicator reflects the contribution of the subway freight system to urban freight flow, and the calculation formula is as follows:

[0042] C 11 =BanT×BanA×N truck

[0043] Among them, C 11 It is an indicator of the urgency of truck restrictions; BanT is the daily prohibited period for truck travel; BanA is the area of ​​the truck-restricted zone; N truck That is the number of trucks affected.

[0044] Furthermore, the quantitative calculation method for cost-based evaluation indicators is as follows:

[0045] Upstream logistics hub proximity C6 is an indicator used to reflect the spatial proximity between upstream logistics hubs and the subway freight network. The calculation formula is as follows:

[0046]

[0047] Where C6 is the urban logistics hub proximity index, |S| is the number of ULHs, and |z| is the number of ULHs. s -z k| is the Euclidean distance between the upstream logistics hub and the urban rail transit station; upstream logistics hub s∈S; urban rail transit station k∈K; K is the set of all stations. Definition: If the first nearest station belongs to line i, then K′ is defined as the set of stations excluding all stations on line i; if the second nearest station belongs to line j, then K″ is defined as the set of stations excluding all stations on both lines i and j.

[0048] Customer proximity (C7) is an indicator used to reflect the proximity between the urban rail transit network and the final destination of urban delivery. The calculation formula is as follows:

[0049]

[0050] Where C7 is an indicator of the proximity of the urban rail transit network to customers; UR(ε) is the total area of ​​urban areas with a population density exceeding ε; F(ε) is the number of urban rail transit stations within the UR(ε) area; |z q -z k | is the Euclidean distance between the registered post office station q and its nearest station;

[0051] Transportation-oriented development (C8) is an indicator used to reflect the standards for evaluating the suitability of a metro freight system. The calculation formula is as follows:

[0052]

[0053] C8 is a public transport-oriented development indicator, while TOD is a public transport-oriented development index for cities. It is the conversion factor;

[0054] Implementation cost C9, this indicator reflects the economic feasibility of the metro freight system, and the calculation formula is as follows:

[0055]

[0056] Wherein, C9 is the implementation cost indicator; ConsC is the construction cost per kilometer of the most recently operational urban rail transit line; OperaC is the annual operating cost of the urban rail transit; URTL is the length of the urban rail transit network; λ 91 It is the relative weighted value of the construction cost per kilometer of the most recently operational urban rail transit lines; λ 92 It is the relative weighting value of the annual operating cost of urban rail transit based on the network length.

[0057] Furthermore, step (3) specifically includes the following steps;

[0058] Step (31): Obtain relevant data on the city's subway freight system;

[0059] Data related to urban metro freight systems includes railway length, number of lines, trains and services, average daily passenger flow, and the number of urban rail transit stations (N). station The number N of urban rail transit transfer stations transfer Urban rail transit annual operating cost (OperC), total number of trains, daily services, and TOD index data;

[0060] Step (32): Obtain data related to urban transportation and logistics;

[0061] Data related to urban transportation and logistics includes the number of e-commerce parcels N delivered annually in the city. EP Number of trucks affected N truck Number of registered postal service sites N RPS The total number of city shopping malls and retail stores N SMRS Daily prohibited truck travel periods (BanT), area of ​​truck restricted zones (BanA), and average daily travel distance per truck. Urban logistics market annual operating revenue LBI;

[0062] Based on the acquired data, calculate the demand scale C3 indicator and the urgency C of truck restrictions. 11 Indicators, environmental benefits (C5), proximity to upstream logistics hubs (C6), proximity to customers (C7), and urban economic benefits (C) 10 And the attribute values ​​of the C4 indicator for consumption and the market.

[0063] Furthermore, step (4) specifically includes the following steps:

[0064] Step (41): Calculate the weights of each evaluation index using the analytic hierarchy process;

[0065] Step (411): Construct a pairwise comparison matrix using the 1-9 scaling method;

[0066] Step (412): Solve for the pairwise comparison matrix;

[0067] Step (413): Consistency check;

[0068] Step (42): Use the VIKOR method to comprehensively evaluate and determine the suitability level for the development of the urban subway freight system;

[0069] Step (421): Construct the decision matrix and normalize the index values;

[0070] Step (422): Determine the positive ideal solution and the negative ideal solution;

[0071] Step (423): Calculate the group benefit index S i And individual regret indicators Y i ;

[0072] Step (424): Decision index R i calculate;

[0073] Step (43): Comprehensive evaluation criteria for the suitability of urban subway freight system development:

[0074] Using AHP-VIKOR, the comprehensive score and decision index R of the urban metro freight system development suitability indicators were analyzed. i The suitability of urban subway freight system development was evaluated, and the comprehensive score of the indicators and the decision index R were used to assess the suitability. i The order of size indicates the suitability of the alternatives.

[0075] Compared with the prior art, the significant advantages of this invention are:

[0076] This invention provides a quantitative calculation method for the suitability evaluation of urban subway freight system development, applicable to all cities. The data collection method is accurate and convenient; the selection of evaluation indicators comprehensively considers economic and developmental factors; and the standardization basis and weighting of the evaluation indicators have universal applicability, improving the efficiency and reliability of the suitability evaluation of urban subway freight system development. This invention can provide data support and decision-making reference for the deep integration of passenger and freight transportation in urban contexts and the potential merger of subway freight systems and the logistics industry. Attached Figure Description

[0077] Figure 1 Develop a suitability evaluation flowchart for urban subway freight systems;

[0078] Figure 2 SWOT analysis diagram for urban subway freight system;

[0079] Figure 3 Develop a suitability evaluation index measurement chart for urban subway freight systems;

[0080] Figure 4 A map showing the subway network layout of 16 cities for testing;

[0081] Figure 5 A score chart for evaluating the suitability of urban subway freight systems. Detailed Implementation

[0082] The present invention will now be described in further detail with reference to the accompanying drawings.

[0083] A suitability evaluation method for the development of an urban subway freight system includes the following steps:

[0084] Step 1: Establish a suitability analysis framework for the development of urban subway freight systems;

[0085] Step 2: Suitability evaluation indicators and quantitative calculation methods for urban subway freight system development;

[0086] Step 3: Multi-source data acquisition method;

[0087] Step four: Establishing the indicator processing procedure and evaluation criteria;

[0088] Step 5: Application example of the suitability assessment method for the development of a subway freight system.

[0089] Furthermore, in step one, based on the SWOT analysis method, the main driving factors and obstacles to the implementation of the metro freight system project are identified, a suitability analysis framework for the development of the urban metro freight system is constructed, and indicators reflecting the SWOT performance of the metro freight system project are determined.

[0090] 1.1 A suitability analysis framework for the development of urban subway freight systems based on the SWOT method, and the SWOT matrix is ​​as follows: Figure 2 As shown;

[0091] Step 1.1.1: Analyze the impact of large freight demand, government leadership and top-down development strategies, engineering and infrastructure capabilities, social and environmental benefits, efficiency advantages, supply chain integration and profitability, and the flexibility of system organization and expansion on the development of urban subway freight systems from the perspectives of urban economy and urban development, and further analyze the opportunities for the development of urban subway freight systems.

[0092] Step 1.1.2: From the national and regional levels, we will analyze the complexity of urban freight distribution, high initial investment, logistical disadvantages and vulnerabilities, lack of technical standards and planning guidance, market fragmentation and its negative impacts, and incompatibility between urban rail transit and urban logistics, and then analyze the threats to the external environment for the development of urban subway freight systems.

[0093] Step 1.1.3: Advantage analysis for the development of urban subway freight systems, mainly covering stakeholder interests, alignment with national development goals, emerging technologies and market indicators;

[0094] Step 1.1.4: Analysis of the disadvantages of developing urban subway freight systems, mainly covering unreasonable planning and decision-making, ambiguity in public awareness and attitudes, insufficient governance and management, and abuse and blind following of indicators;

[0095] Step 1.2: A questionnaire survey was conducted to invite experts to score each SWOT indicator and propose new indicators.

[0096] Step 1.2.1: Invite nine experts to evaluate 18 indicators based on the city's background, and use a Likert scale to score each indicator, with the score range from 1 (not very relevant) to 5 (most relevant);

[0097] Step 1.2.2: Calculate the final importance score for indicator j, as follows: Figure 2 As shown;

[0098]

[0099] Among them, Score j It is the importance score of the SWOT indicator j; a ij It is the original score given by expert i to indicator j; n ij It is a weighting parameter, which depends on the closeness of the expert's professional knowledge to the knowledge domain of the indicator.

[0100] Step 1.3: Based on the SWOT analysis framework, determine the selection of indicators for the suitability evaluation of urban subway freight system development. The process of selecting evaluation indicators in this invention is as follows: Figure 3 As shown; the engineering and infrastructure capacity indicators can be quantitatively described using network capacity indicators, infrastructure indicators, proximity to customers indicators, and proximity to urban logistics hub indicators.

[0101] A large number of freight demand indicators can be quantitatively described using demand scale indicators;

[0102] Emerging technologies and market indicators can be quantitatively described using consumption and market indicators;

[0103] Social-environmental benefit indicators can be used to quantitatively describe environmental benefit indicators;

[0104] It aligns with national development goals and indicators, and can be quantitatively described using transportation-oriented development indicators;

[0105] Performance advantages, supply chain integration, and profitability indicators can be quantitatively described using implementation cost indicators;

[0106] In addition, the document outlines urgent indicators for urban economic indicators and truck restrictions.

[0107] Furthermore, in step two, the calculation of the suitability evaluation index for the development of the metro freight system includes the following steps:

[0108] 2.1 Calculate the network capacity C1, which is used to reflect the freight capacity of the urban rail transit network;

[0109] Network capacity C1 is represented by two parts: the available space for installing freight-related modules in the passenger-freight shared station and the railway's overall freight capacity.

[0110] 2.1.1 Calculate the available space for installing freight-related modules in the passenger-freight shared station. The calculation formula is as follows:

[0111] S = SA Fmodule ×Ucap FPS

[0112] Among them, the available space value for installing freight-related modules in the S passenger-freight shared station; is SA Fmodule It is the total area of ​​shared passenger and freight stations in the urban rail transit network; Ucap FPS This is the capacity per unit area of ​​the installed modules;

[0113] 2.1.2 The comprehensive freight capacity of the railway is calculated by accumulating the daily freight capacity of line i under the FoT-EC operation mode and the daily freight capacity of line i under the FoT-DT operation mode. The calculation formula is as follows:

[0114]

[0115] Where C represents the total freight volume of the railway; It is the number of passenger trains associated with each relevant line i; Ucap EC It refers to the freight capacity of the extra carriages; Ucap DT It is the vehicle load of a dedicated freight train; The maximum possible number of freight trains can be served, calculated using the following formula:

[0116]

[0117] in, This is the end time of passenger service; It is the start time of the last DFT service; MT i It is the time before the start of the last dedicated freight train service compared to the start of the first passenger service the following day; the constant value δ is the departure frequency of the dedicated freight train; the operation of the dedicated freight train is constrained by HT([Δt]). It is assumed that at most one dedicated freight train service can be added between two adjacent passenger services; HT i (j,j+1) represents the interval between passenger trains j and j+1; i∈Ω Let M represent the set of lines in the urban rail transit network, and j∈M represent the set of passenger train services.

[0118] 2.1.3 Calculate the network capacity C1

[0119]

[0120] where λ 11 ∈(0,1),λ 12 ∈(0,1),λ 11 +λ 12 =1

[0121] C1 is a network capacity metric; SA Fmodule Ucap FPS , Ucap EC Ucap DT The meaning is the same as before; λ 11 and λ 12 These are the weights corresponding to the two items.

[0122] 2.2 Calculate Infrastructure C2, an indicator used to reflect the abundance of dedicated railway facilities, and its calculation formula is as follows:

[0123] C2 = N station ×N transfer ×URTL

[0124] C2 is an infrastructure indicator; N station N is the number of urban rail transit stations; transfer URTL is the number of urban rail transit transfer stations; URTL is the length of the urban rail transit network.

[0125] 2.3 Calculate the demand scale C3, which reflects the demand scale of urban freight transportation. The calculation formula is as follows:

[0126] C3 = N EP ×N SMRS

[0127] Where C3 is the demand scale indicator, and N EP N is the number of e-commerce parcels delivered in the city each year. SMRS It represents the total number of shopping malls and retail stores in the city.

[0128] 2.4 Calculate Consumption and Market C4, an indicator used to reflect the market size and operating conditions of the urban logistics industry. The calculation formula is shown below:

[0129] C4=λ 41 ×LBI+λ 42 ×RSG,λ 41 ∈(0,1),λ 42 ∈(0,1),λ 41 +λ 42 =1

[0130] Among them, C4 is a consumption and market indicator; LBI is the annual operating revenue of the urban logistics market, reflecting the market size and operating status of the urban logistics industry; RSG is the annual retail sales of social goods, reflecting the purchasing power of citizens and the degree of urban economic prosperity. 41 λ is the relative weight value of LBI. 42 It is the relative weight value of RSG.

[0131] 2.5 Calculate the environmental benefits C5. This indicator reflects the environmental benefits of the subway freight system, and its calculation formula is as follows: C5 is an environmental benefit indicator for the metro freight system; It is the number of passenger trains associated with each relevant line i; This is the maximum possible number of freight train services; Ucap EC It refers to the freight capacity of the extra carriages; Ucap DT It is the vehicle load of a dedicated freight train; It represents the average daily distance traveled by a single truck; REV represents the utilization rate of electric vehicles in urban logistics activities; Ucap truck It is the average daily cargo volume transported by each truck; α CO It is the cost of treating the carbon monoxide (CO) produced per kilometer of truck travel; It is the cost of treating the carbon dioxide (CO2) produced per kilometer of truck travel; It is the nitrogen oxides (NOx) produced per kilometer of truck travel. X Processing costs; α PM This is the cost of treating particulate matter (PM) generated per kilometer of truck travel; α noise It is the cost of dealing with the noise generated per kilometer of truck travel.

[0132] 2.6 Calculate the proximity of upstream logistics hubs (C6). This indicator reflects the spatial proximity between upstream logistics hubs and the subway freight network. The calculation formula is as follows:

[0133]

[0134] Where C6 is the urban logistics hub proximity index, |S| is the number of ULHs, and |z| is the number of ULHs. s -z k | is the Euclidean distance between the upstream logistics hub and the urban rail transit station; upstream logistics hub s ∈S; urban rail transit stations k∈K; K is the set of all stations. Definition: If the first nearest station belongs to line i, then K′ is defined as the set of stations excluding all stations on line i; if the second nearest station belongs to line j, then K″ is defined as the set of stations excluding all stations on both lines i and j.

[0135] 2.7 Calculate the proximity to customers C7, an indicator used to reflect the proximity between the urban rail transit network and the final destination of urban delivery. The calculation formula is as follows:

[0136]

[0137] Where C7 is an indicator of the proximity of the urban rail transit network to customers; UR(ε) is the total area of ​​urban areas with a population density exceeding ε; F(ε) is the number of urban rail transit stations within the UR(ε) area; |z q -z k | is the Euclidean distance between the registered post office station q and its nearest station.

[0138] 2.8 Computational Transport-Oriented Development C8, this indicator is used to reflect the standards for evaluating the suitability of a metro freight system, and its calculation formula is as follows:

[0139]

[0140] C8 is a public transport-oriented development indicator, while TOD is a public transport-oriented development index for cities. It is the conversion factor.

[0141] 2.9 Calculate the implementation cost C9. This indicator reflects the economic feasibility of the subway freight system, and its calculation formula is as follows:

[0142]

[0143] Wherein, C9 is the implementation cost indicator; ConsC is the construction cost per kilometer of the most recently operational urban rail transit line; OperaC is the annual operating cost of the urban rail transit; URTL is the length of the urban rail transit network; λ 91 It is the relative weighted value of the construction cost per kilometer of the most recently operational urban rail transit lines; λ 92 It is the relative weighting value of the annual operating cost of urban rail transit based on the network length.

[0144] 2.10 Calculate the city's economic C 10 This indicator reflects the economic foundation required to implement a metro freight system, and its calculation formula is as follows:

[0145] C 10 =GDP / POP×(λ) 101 ×FGBR+λ 102 ×TIFA),λ 101 ∈(0,1),λ 102 ∈(0,1),λ 101 +λ 102 =1

[0146] Among them, C 10 These are urban economic indicators; GDP is Gross Domestic Product; POP is Population; FGBR is General Budget Revenue; TIFA is Total Fixed Asset Investment. 101It is the relative weighting value of general budget revenue; λ 102 It is the relative weight value of the total fixed asset investment.

[0147] 2.11 Calculate the urgency of truck restrictions C 11 This indicator reflects the contribution of the subway freight system to urban freight mobility, and its calculation formula is as follows:

[0148] C 11 =BanT×BanA×N truck

[0149] Among them, C 11 It is an indicator of the urgency of truck restrictions; BanT is the daily prohibited period for truck travel; BanA is the area of ​​the truck-restricted zone; N truck That is the number of trucks affected.

[0150] Furthermore, in step three, the basic data required for the suitability evaluation index of the urban subway freight system development in the area to be studied is obtained through multi-source data collection. After screening and mining all the acquired data, the parameters required for the analysis index are obtained. The multi-source data includes data related to the urban subway freight system and basic data related to the urban population and economy. Specifically, the following steps are included.

[0151] Step 3.1: Obtain relevant data for the city's subway freight system;

[0152] The relevant data includes:

[0153] Railway length, number of lines, trains and services, average daily passenger flow, and number of urban rail transit stations N station The number N of urban rail transit transfer stations transfer The data includes the annual operating cost (OperC) of urban rail transit, the total number of trains, daily services, and the TOD index. Specific details are as follows:

[0154] Step 3.1.1: Obtain the following data from the statistical report released by the Metro Association: railway length, number of lines, trains and services, average daily passenger flow, TOD index, and number of urban rail transit stations N. station The number N of urban rail transit transfer stations transfer The annual operating cost (OperC) data for urban subway freight can be used to calculate the attribute values ​​of the infrastructure (C2), implementation cost (C9), and transportation-oriented development (C8) indicators.

[0155] Step 3.1.2: Obtain timetable data for each subway line from the websites of subway operators in various cities; the timetable data includes passenger service end times. Start time of the last dedicated freight train service The departure frequency δ of dedicated freight trains, and the interval HT of passenger trains j and j+1. i (j,j+1);

[0156] The operation of dedicated freight trains is constrained by HT([Δt]), and a maximum of one additional DFT service can be added between two adjacent passenger services. Based on the collected metro timetable data, calculate the maximum possible number of freight train services. The calculation formula is as follows: in, This is the maximum possible number of freight train services. This is the end time of passenger service; It is the start time of the last DFT service; MT i δ is the time between the start time of the last dedicated freight train service and the start time of the first passenger service the following day; δ is the departure frequency of the dedicated freight train; HT i (j,j+1) is the interval between passenger trains j and j+1; when HT i When (j,j+1)≥[Δt], ξ ij It is 1, otherwise ξ ij It is 0.

[0157] Passenger service time The network capacity C1 is calculated using the following formula:

[0158]

[0159] where λ 11 ∈(0 , 1),λ 12 ∈(0 , 1),λ 11 +λ 12 =1

[0160] C1 is a network capacity metric; SA Fmodule This is a rough estimate of the total area of ​​FPSs in the urban rail transit network; Ucap FPS This is the capacity per unit area of ​​the installed modules; It is the number of passenger trains associated with each relevant line i; Ucap EC This refers to the freight capacity of the extra carriages; This is the maximum possible number of freight train services; Ucap DT It is the vehicle load of DFTs;

[0161] Step 3.2: Obtain urban traffic and logistics data;

[0162] The relevant data includes: N, the number of e-commerce parcels delivered annually in the city.EP Number of trucks affected N truck Number of registered postal service sites N RPS The total number of city shopping malls and retail stores N SMRS Daily prohibited truck travel periods (BanT), area of ​​truck restricted zones (BanA), and average daily travel distance per truck. The annual operating revenue (LBI) of the urban logistics market is as follows:

[0163] Step 3.2.1: Obtain the number N of e-commerce parcels delivered annually in the city based on the published "Statistical Bulletin on the Development of Urban Postal Industry in xx Year". EP Number of trucks affected N truck Annual operating revenue (LBI) of urban logistics market; Number of registered postal service stations (N) RPS Using the acquired data, the attribute value of the demand scale C3 indicator can be calculated;

[0164] Step 3.2.2: Obtain the total number N of city shopping malls and retail stores based on the published "Statistical Yearbook of Retail and Catering Chain Enterprises in xx Year". SMRS data;

[0165] Step 3.2.3: Referencing the freight policies published by the traffic management departments of sample cities, determine the daily prohibited truck travel period (BanT) and the area (BanA) of the truck restricted zone. Using the obtained data, the urgency (C) of truck restrictions can be calculated. 11 The attribute values ​​of the indicator;

[0166] Step 3.2.4: Calculate the average daily driving distance of a single truck based on the published "Annual Report on Urban Transportation Development". Numerical values: Using the relevant data obtained, the attribute values ​​of the environmental benefit C5 indicator can be calculated;

[0167] Step 3.2.5: An empirical survey was conducted to obtain the remaining statistical data that were not yet fully recorded;

[0168] Step 3.2.6: Obtain the geographic coordinates of POIs through data mining technology via the Baidu Maps application programming interface. The POIs include "subway stations", "distribution centers" and "express delivery". After cleaning the raw data, a distance matrix is ​​established. Using the obtained relevant data, the attribute values ​​of the upstream logistics hub proximity C6 and the customer proximity C7 indicators can be calculated.

[0169] Step 3.3 Obtain data related to urban population and socio-economic conditions;

[0170] The relevant data includes: population, gross domestic product, total fixed asset investment, general budget revenue, annual retail sales of consumer goods, and population density exceeding 60 people / hm². 2 The total land area is as follows:

[0171] Step 3.3.1: Obtain data on population (POP), GDP, general budget revenue (FGBR), total fixed asset investment (TIFA), and annual retail sales of consumer goods (RSG) from the published "XX City Statistical Yearbook". Using the obtained data, the city's economic C can be calculated. 10 And the attribute values ​​of the C4 indicator for consumption and the market;

[0172] Step 3.3.2: Obtain the real-time population heat map of the sample city from 7:00 to 8:00 on December 26, 2021, by querying the location-based service (LBS) data platform on the Baidu Maps API; identify areas with a population density exceeding 60 people / hm². 2 The total land area UR is the threshold for high-density areas.

[0173] Furthermore, in step four, the weights of each evaluation index are calculated using the entropy-weighted analytic hierarchy process, and the decision index is calculated using the VIKOR comprehensive evaluation method; specifically, this includes the following steps:

[0174] Step 4.1 Calculate the weights of each evaluation index using the analytic hierarchy process (AHP);

[0175] Step 4.1.1 Construct a pairwise comparison matrix using the 1-9 scaling method. The pairwise comparison matrix B is as follows:

[0176]

[0177] Where, matrix element b ij Indicates evaluation index b i For evaluation index b j The relative importance of each is determined by selecting specific values ​​from Table 41 below;

[0178] Table 41. Values ​​of the Pairwise Comparison Matrix for Evaluation Indicators

[0179]

[0180] Step 4.1.2 Solve for the pairwise comparison matrix;

[0181] Calculate the relative weights of each evaluation index, and then use the constructed pairwise comparison matrix to calculate its largest eigenvalue λ. max The corresponding eigenvector W = [w i ] T W represents the relative weight of the corresponding evaluation index.

[0182] Step 4.1.3 Consistency check;

[0183] To determine whether the calculated weights are reasonable, a consistency check needs to be performed on the pairwise comparison matrix. If the check passes, a decision can be made based on the calculated weight vector; otherwise, a new judgment matrix needs to be constructed and the calculation repeated. Typically, consistency indices CI, RI (random consistency index), and CR (random consistency ratio) are introduced to measure this.

[0184] The formula for calculating the random consistency ratio CR is as follows:

[0185] CR = CI / RI

[0186] Where CR is the random consistency ratio; RI is the random consistency index; and CI is the consistency index. When the CR value does not exceed 0.1, it indicates that the weight calculation results meet the consistency requirements, and the normalized eigenvector can be used as the weight vector; otherwise, the judgment matrix needs to be reconstructed, and a needs to be adjusted. ij This continues until the calculation results meet the consistency requirements.

[0187] The formula for calculating the consistency index (CI) is as follows:

[0188] CI=(λ max -n) / (n-1)

[0189] Where CI is the consistency index; λ max represents the largest eigenvalue of the judgment matrix, n represents the order of the judgment matrix; RI is the average random consistency index of the judgment matrix.

[0190] For judgment matrices of orders 1-11, the values ​​of the random consistency index RI are shown in the table below:

[0191] Table 41 Random Consistency Index (RI) Values

[0192]

[0193] Step 4.2 The improved VIKOR method is used to comprehensively evaluate and determine the suitability level for the development of the urban subway freight system;

[0194] 4.2.1 Construct a decision matrix and normalize the index values;

[0195] Different indicators have different dimensions and cannot be directly compared. Therefore, it is necessary to normalize the original indicators and construct a decision matrix H:

[0196]

[0197] Among them, h ij As an alternative plan p i Compared to the evaluation index qj Normalized measure;

[0198] 4.2.2 Determine the positive and negative ideal solutions;

[0199] To increase the comparability of the results, the constructed decision matrix H is standardized. Alternative solutions p i The results are represented by a benefit index and a cost index. A higher benefit index value indicates a better solution, calculated using the benefit formula. Conversely, a lower cost index value indicates a better solution. Based on these principles, the final alternative solution p is determined. i positive ideal solution and negative ideal solution function:

[0200]

[0201]

[0202] 4.2.3 Calculate the group benefit index S i And individual regret indicators Y i ;

[0203] The group benefit index S i The calculation formula is as follows:

[0204]

[0205] Among them, w j The attribute q represents the output of the analytic hierarchy process. j The weights; h ij As an alternative plan p i Compared to the evaluation index q j Normalized measure;

[0206]

[0207] The individual regret index Y i The calculation formula is as follows:

[0208]

[0209] Among them, w j The index q represents the output of the analytic hierarchy process. j The weights satisfy h ij As an alternative plan p i Compared to the evaluation index q j Normalized measure;

[0210] 4.2.4 Decision Index R i calculate;

[0211] Considering the varying impacts of different indicators on alternative solutions, the calculated weights are incorporated into the VIKOR comprehensive evaluation calculation, and the alternative solution p is calculated using the following formula. i The compromise decision index R i :

[0212]

[0213] in, Let μ = 0.5, where μ represents the relative importance between group utility and individual regret;

[0214] Step 4.3: Comprehensive Evaluation Criteria for the Suitability of Urban Subway Freight System Development

[0215] Decision index R based on the suitability of the urban subway freight system development i The suitability of urban subway freight system development is optimized by ranking the options based on suitability, with a decision index R. i The order of size indicates the suitability and merit of the alternatives.

[0216] Furthermore, in step five, 16 cities are selected as options. Based on the developed evaluation model, the comprehensive score of the indicators is calculated. According to the evaluation criteria, the suitability of the development of the urban subway freight system is determined, and the cities are divided into tiers, providing an application example for the developed subway freight system development suitability assessment method.

[0217] The comprehensive scoring formula for the suitability of the urban subway freight system development is as follows:

[0218]

[0219] Among them, R j It is the comprehensive calculated value of the j-th alternative; w j The index q is output using the Analytic Hierarchy Process (AHP). j The weights; h ij It is the alternative plan p i Compared to the evaluation index q j The normalized exponent value;

[0220] The comprehensive scores of the 16 alternative solutions in this embodiment are shown below. Figure 5 .

[0221] Example 1

[0222] This embodiment, combining the metro freight systems of 16 advanced cities, provides a suitability evaluation method for the development of urban metro freight systems. The overall process diagram is shown below. Figure 1 As shown, the specific steps include:

[0223] Step 1: Construct a suitability analysis framework for the development of an urban subway freight system;

[0224] Based on the SWOT method, this paper conducts a qualitative analysis of the strengths, weaknesses, opportunities and threats of urban subway freight system development, establishes a suitability evaluation index system for urban subway freight system development, and constructs a suitability analysis framework for urban subway freight system development.

[0225] 1.1 A suitability analysis framework for the development of urban subway freight systems based on the SWOT method, and the SWOT matrix is ​​as follows: Figure 2 As shown;

[0226] Step 1.1.1: Analyze the impact of large freight demand, government leadership and top-down development strategies, engineering and infrastructure capabilities, social and environmental benefits, efficiency advantages, supply chain integration and profitability, and the flexibility of system organization and expansion on the development of urban metro freight systems from the perspectives of urban economy and urban development, and then analyze the opportunities for the development of urban metro freight systems.

[0227] Step 1.1.2: From the national and regional levels, we will analyze the complexity of urban freight distribution, high initial investment, logistical disadvantages and vulnerabilities, lack of technical standards and planning guidance, market fragmentation and its negative impacts, and incompatibility between urban rail transit and urban logistics, and then analyze the threats to the external environment for the development of urban subway freight systems.

[0228] Step 1.1.3: Advantage analysis for the development of urban subway freight systems, mainly covering stakeholder interests, alignment with national development goals, emerging technologies and market indicators;

[0229] Step 1.1.4: Analysis of the disadvantages of developing urban subway freight systems, mainly covering unreasonable planning and decision-making, ambiguity in public awareness and attitudes, insufficient governance and management, and abuse and blind following of indicators;

[0230] Step 1.2: A questionnaire survey was conducted to invite experts to score each SWOT indicator and propose new indicators.

[0231] Step 1.2.1: Invite nine experts to evaluate 18 indicators based on the city's background, and use a Likert scale to score each indicator, with the score range from 1 (not very relevant) to 5 (most relevant);

[0232] Step 1.2.2: Calculate the final importance score for indicator j, as follows: Figure 2 As shown;

[0233]

[0234] Among them, a ijIt is the original score given by expert i to indicator j; n ij It is a weighting parameter, which depends on the closeness of the expert's expertise to the knowledge domain of the indicator, n ij ∈[0.2,3],

[0235] Step 1.3: Based on the SWOT analysis framework, determine the selection of indicators for the suitability evaluation of urban subway freight system development. The process of selecting evaluation indicators in this invention is as follows: Figure 3 As shown.

[0236] The engineering and infrastructure capacity indicators can be quantitatively described using network capacity indicators, infrastructure indicators, proximity to customers indicators, and proximity to urban logistics hub indicators.

[0237] The aforementioned large number of freight demand indicators can be quantitatively described using demand scale indicators;

[0238] The emerging technologies and market indicators mentioned above can be quantitatively described using consumption and market indicators;

[0239] The aforementioned social-environmental benefit indicators can be quantitatively described using environmental benefit indicators;

[0240] The indicators that align with national development goals can be quantitatively described using transportation-oriented development indicators.

[0241] The aforementioned performance advantages, supply chain integration, and profitability indicators can be quantitatively described using implementation cost indicators.

[0242] In addition, additional indicators of urban economic indicators and the urgency of truck restrictions were proposed.

[0243] Step 2: Suitability evaluation indicators and quantitative calculation methods for urban subway freight system development;

[0244] Indicator types include cost-based and benefit-based. For cost-based indicators, the smaller the value, the better; for benefit-based indicators, the larger the value, the better.

[0245] The cost-related indicators include: urban logistics hub accessibility (C6), customer proximity (C7), transportation-oriented development (C8), and implementation cost (C9); the benefit-related indicators include: network capacity (C1), infrastructure (C2), demand scale (C3), consumption and market (C4), environmental benefits (C5), and urban economic benefits (C6). 10 The urgency of truck restrictions C 11 ;

[0246] 2.1 Calculate the network capacity C1, which is used to reflect the freight capacity of the urban rail transit network;

[0247] Network capacity C1 is represented by two parts: the available space for installing freight-related modules in the passenger-freight shared station and the railway's overall freight capacity.

[0248] 2.1.1 Calculate the available space for installing freight-related modules in the passenger-freight shared station. The calculation formula is as follows:

[0249] S = SA Fmodule ×Ucap FPS

[0250] Where S is the available space in the passenger-freight shared station for installing freight-related modules; SA Fmodule It is the total area of ​​shared passenger and freight stations in the urban rail transit network; Ucap FPS This is the capacity per unit area of ​​the installed modules;

[0251] 2.1.2 The comprehensive freight capacity of the railway is calculated by accumulating the daily freight capacity of line i under the FoT-EC operation mode and the daily freight capacity of line i under the FoT-DT operation mode. The calculation formula is as follows:

[0252]

[0253] Where C represents the railway's overall freight capacity; It is the number of passenger trains associated with each relevant line i; Ucap EC It refers to the freight capacity of the extra carriages; Ucap DT It is the vehicle load of a dedicated freight train; The maximum possible number of freight trains can be served, calculated using the following formula:

[0254]

[0255] in, This is the end time of passenger service; It is the start time of the last DFT service; MT i It is the time before the start of the last dedicated freight train service compared to the start of the first passenger service the following day; the constant value δ is the departure frequency of the dedicated freight train; the operation of the dedicated freight train is constrained by HT([Δt]). It is assumed that at most one dedicated freight train service can be added between two adjacent passenger services; HT i (j,j+1) represents the interval between passenger trains j and j+1; i∈Ω Let M represent the set of lines in the urban rail transit network, and j∈M represent the set of passenger train services.

[0256] 2.1.3 Calculate the network capacity C1 based on the available space for installing freight-related modules in the passenger-freight shared station and the railway's comprehensive freight capacity;

[0257]

[0258] where λ 11 ∈(0,1),λ 12 ∈(0,1),λ 11 +λ 12 =1

[0259] C1 is a network capacity metric; SA Fmodule Ucap FPS , Ucap EC Ucap DT The meaning is the same as before; λ 11 and λ 12 These are the weights corresponding to the two items.

[0260] 2.2 Calculate Infrastructure C2, an indicator used to reflect the abundance of dedicated railway facilities, and its calculation formula is as follows:

[0261] C2 = N station ×N transfer ×URTL

[0262] C2 is an infrastructure indicator; N station N is the number of urban rail transit stations; transfer It represents the number of urban rail transit transfer stations; URTL represents the length of the urban rail transit network.

[0263] 2.3 Calculate the demand scale C3, which reflects the demand scale of urban freight transportation. The calculation formula is as follows:

[0264] C3 = N EP ×N SMRS

[0265] Where C3 is the demand scale indicator, and N EP N is the number of e-commerce parcels delivered in the city each year. SMRS It represents the total number of shopping malls and retail stores in the city.

[0266] 2.4 Calculate Consumption and Market C4, an indicator used to reflect the market size and operating conditions of the urban logistics industry. The calculation formula is shown below:

[0267] C4=λ 41 ×LBI+λ 42 ×RSG,λ 41 ∈(0,1),λ 42 ∈(0,1),λ 41 +λ 42 =1

[0268] Among them, C4 is a consumption and market indicator; LBI is the annual operating revenue of the urban logistics market, reflecting the market size and operating status of the urban logistics industry; RSG is the annual retail sales of social goods, reflecting the purchasing power of citizens and the degree of urban economic prosperity. 41 λ is the relative weight value of LBI. 42 It is the relative weight value of RSG.

[0269] 2.5 Calculate the environmental benefits C5. This indicator reflects the environmental benefits of the subway freight system, and its calculation formula is as follows:

[0270]

[0271] C5 is an environmental benefit indicator for the metro freight system; It is the number of passenger trains associated with each relevant line i; This is the maximum possible number of freight train services; Ucap EC It refers to the freight capacity of the extra carriages; Ucap DT It is the vehicle load for registered postal service stations; It represents the average daily distance traveled by a single truck; REV represents the utilization rate of electric vehicles in urban logistics activities; Ucap truck It is the average daily cargo volume transported by each truck; α CO It is the cost of treating the carbon monoxide (CO) produced per kilometer of truck travel; It is the cost of treating the carbon dioxide (CO2) produced per kilometer of truck travel; It is the nitrogen oxides (NOx) produced per kilometer of truck travel. X Processing costs; α PM This is the cost of treating particulate matter (PM) generated per kilometer of truck travel; α noise It is the cost of dealing with the noise generated per kilometer of truck travel.

[0272] 2.6 Calculate the proximity of upstream logistics hubs (C6). This indicator reflects the spatial proximity between upstream logistics hubs and the subway freight network. The calculation formula is as follows:

[0273]

[0274] Where C6 is the urban logistics hub proximity index, |S| is the number of ULHs, and |z| is the number of ULHs. s -z k| is the Euclidean distance between the upstream logistics hub and the urban rail transit station; upstream logistics hub s∈S; urban rail transit station k∈K; K is the set of all stations. Definition: If the first nearest station belongs to line i, then K′ is defined as the set of stations excluding all stations on line i; if the second nearest station belongs to line j, then K″ is defined as the set of stations excluding all stations on both lines i and j.

[0275] 2.7 Calculate the proximity to customers C7, an indicator used to reflect the proximity between the urban rail transit network and the final destination of urban delivery. The calculation formula is as follows:

[0276]

[0277] Where C7 is an indicator of the proximity of the urban rail transit network to customers; UR(ε) is the total area of ​​urban areas with a population density exceeding ε; F(ε) is the number of urban rail transit stations within the UR(ε) area; |z q -z k | is the Euclidean distance between the registered post office station q and its nearest station.

[0278] 2.8 Computational Transport-Oriented Development C8, this indicator is used to reflect the standards for evaluating the suitability of a metro freight system, and its calculation formula is as follows:

[0279]

[0280] C8 is a public transport-oriented development indicator, while TOD is a public transport-oriented development index for cities. It is the conversion factor.

[0281] 2.9 Calculate the implementation cost C9. This indicator reflects the economic feasibility of the subway freight system, and its calculation formula is as follows:

[0282]

[0283] Wherein, C9 is the implementation cost indicator; ConsC is the construction cost per kilometer of the most recently operational urban rail transit line; OperaC is the annual operating cost of the urban rail transit; URTL is the length of the urban rail transit network; λ 91 It is the relative weighted value of the construction cost per kilometer of the most recently operational urban rail transit lines; λ 92 It is the relative weighting value of the annual operating cost of urban rail transit based on the network length.

[0284] 2.10 Calculate the city's economic C 10 This indicator reflects the economic foundation required to implement a metro freight system, and its calculation formula is as follows:

[0285] C 10 =GDP / POP×(λ) 101 ×FGBR+λ 102 ×TIFA),λ 101 ∈(0,1),λ 102 ∈(0,1),λ 101 +λ 102 =1

[0286] Among them, C 10 These are urban economic indicators; GDP is Gross Domestic Product; POP is Population; FGBR is General Budget Revenue; TIFA is Total Fixed Asset Investment. 101 It is the relative weighting value of general budget revenue; λ 102 It is the relative weight value of the total fixed asset investment.

[0287] 2.11 Calculate the urgency of truck restrictions C 11 This indicator reflects the contribution of the subway freight system to urban freight mobility, and its calculation formula is as follows:

[0288] C 11 =BanT×BanA×N truck

[0289] Among them, C 11 It is an indicator of the urgency of truck restrictions; BanT is the daily prohibited period for truck travel; BanA is the area of ​​the truck-restricted zone; N truck That is the number of trucks affected.

[0290] Step 3: Multi-source data acquisition;

[0291] Step 3.1: Obtain relevant data for the city's subway freight system;

[0292] The relevant data includes railway length, number of lines, trains and services, average daily passenger flow, and the number of urban rail transit stations (N). station The number of urban rail transit transfer stations, N transfer The annual operating cost (OperC) of urban rail transit, the total number of trains, and the daily service data (TOD index) are as follows:

[0293] Step 3.1.1 In this embodiment, the route map is obtained based on 16 sample urban rail transit systems, such as... Figure 4 As shown.

[0294] Step 3.1.2: Obtain the following data from the statistical report (year) published by the Metro Association: railway length, number of lines, trains and services, average daily passenger flow, TOD index, and number of urban rail transit stations N. stationThe number of urban rail transit transfer stations, N transfer The annual operating cost (OperC) data for urban subway freight transport is shown in Table 31 below. This embodiment selects relevant data for the subway freight transport systems of 16 cities.

[0295] Table 31 Urban Rail Transit Data Collection Table

[0296]

[0297]

[0298] Using the relevant data obtained, the attribute values ​​of the infrastructure C2, implementation cost C9, and transportation-oriented development C8 indicators can be calculated;

[0299] Step 3.1.3: Obtain timetable data for each subway line from the websites of subway operators in various cities; the timetable data includes passenger service end times. Start time of the last dedicated freight train service The departure frequency δ of dedicated freight trains, and the interval HT of passenger trains j and j+1. i (j,j+1);

[0300] The operation of dedicated freight trains is constrained by HT([Δt]). A maximum of one dedicated freight train service can be added between two adjacent passenger services. Based on collected subway timetable data, the duration of passenger service is calculated. The calculation formula is as follows:

[0301]

[0302] in, This is the maximum possible number of freight train services. This is the end time of passenger service; It is the start time of the last DFT service; MT i δ is the time between the start time of the last dedicated freight train service and the start time of the first passenger service the following day; δ is the departure frequency of the dedicated freight train; HT i (j,j+1) is the interval between passenger trains j and j+1; when HT i When (j,j+1)≥[Δt], ξ ij It is 1, otherwise ξ ij It is 0.

[0303] Based on the time of passenger service The network capacity C1 is calculated using the following formula:

[0304]

[0305] where λ 11 ∈(0,1),λ 12 ∈(0,1),λ 11 +λ 12 =1

[0306] C1 is a network capacity metric; SA Fmodule This is a rough estimate of the total area of ​​FPSs in the urban rail transit network; Ucap FPS This is the capacity per unit area of ​​the installed modules; It is the number of passenger trains associated with each relevant line i; Ucap EC This refers to the freight capacity of the extra carriages; This is the maximum possible number of freight train services; Ucap DT It is the vehicle load for registered postal service stations;

[0307] Step 3.2: Obtain urban traffic and logistics data;

[0308] The relevant data includes: N, the number of e-commerce parcels delivered annually in the city. EP Number of trucks affected N truck Number of registered postal service sites N RPS The total number of city shopping malls and retail stores N SMRS Daily prohibited truck travel periods (BanT), area of ​​truck restricted zones (BanA), and average daily travel distance per truck. The annual operating revenue (LBI) of the urban logistics market is shown in Table 32 below, which contains traffic and logistics-related data for the subway freight systems of 16 cities selected in this example.

[0309] Table 32 Urban Transportation and Logistics Data Collection Table

[0310]

[0311]

[0312] Step 3.2.1: Obtain the number N of e-commerce parcels delivered annually in the city based on the published "Statistical Bulletin on the Development of Urban Postal Industry in xx Year". EP Number of trucks affected (N) truck Annual operating revenue (LBI) of urban logistics market; Number of registered postal service stations (N) RPS Using the acquired data, the attribute value of the demand scale C3 indicator can be calculated;

[0313] Step 3.2.2: Obtain the total number N of city shopping malls and retail stores based on the published "Statistical Yearbook of xx Retail and Catering Chain Enterprises". SMRS data;

[0314] Step 3.2.3: Referencing the freight policies published by the traffic management departments of sample cities, determine the daily prohibited truck travel period (BanT) and the area (BanA) of the truck restricted zone. Using the obtained data, the urgency (C) of truck restrictions can be calculated. 11 The attribute values ​​of the indicator;

[0315] Step 3.2.4: Calculate the average daily driving distance of a single truck based on the published "Annual Report on Urban Transportation Development". Numerical values: Using the relevant data obtained, the attribute values ​​of the environmental benefit C5 indicator can be calculated;

[0316] Step 3.2.5: An empirical survey was conducted to obtain the remaining statistical data that were not yet fully recorded;

[0317] Step 3.2.6: Obtain the geographic coordinates of POIs through data mining technology via the Baidu Maps application programming interface. The POIs include "subway stations", "distribution centers" and "express delivery". After cleaning the raw data, a distance matrix is ​​established. Using the obtained relevant data, the attribute values ​​of the upstream logistics hub proximity C6 and the customer proximity C7 indicators can be calculated.

[0318] Step 3.3 Obtain data related to urban population and socio-economic conditions;

[0319] The relevant data includes: population, gross domestic product, total fixed asset investment, general budget revenue, annual retail sales of consumer goods, and population density exceeding 60 people / hm². 2 The total land area, and the urban population and economic data of the 16 cities selected in this embodiment are shown in Table 33 below:

[0320] Table 33: Data Collection Table on Urban Population and Economy in Cities

[0321]

[0322]

[0323] Step 3.3.1: Obtain data on population (POP), GDP, general budget revenue (FGBR), total fixed asset investment (TIFA), and annual retail sales of consumer goods (RSG) from the published "XX City Statistical Yearbook". Using the obtained data, the city's economic C can be calculated. 10 And the attribute values ​​of the C4 indicator for consumption and the market;

[0324] Step 3.3.2: Obtain real-time population heat maps of 16 sample cities from 7:00 to 8:00 on [date] by querying the Location-Based Services (LBS) data platform on the Baidu Maps API; identify cities with a population density exceeding 60 people / m². 2 The total land area UR is the threshold for high-density areas.

[0325] Step four: Establishing the indicator processing procedure and evaluation criteria;

[0326] Step 4.1 Calculate the weights of each evaluation indicator using a combination of the analytic hierarchy process (AHP) and expert group decision-making.

[0327] Step 4.1.1 Experts conducted pairwise comparisons of the relative importance of the 11 indicators, quantifying the comparison results on a scale of 1-9, and constructing a pairwise comparison matrix B:

[0328]

[0329] Where, matrix element b ij Indicates evaluation index b i For evaluation index b j The relative importance of each is determined by selecting specific values ​​from Table 41 below;

[0330] Table 41. Values ​​of the Pairwise Comparison Matrix for Evaluation Indicators

[0331]

[0332] Step 4.1.2 Solve for the pairwise comparison matrix

[0333] Calculate the relative weights of each evaluation index, and then use the constructed pairwise comparison matrix to calculate its largest eigenvalue λ. max The corresponding eigenvectors, W = [w i ] T W represents the relative weight of the corresponding evaluation index.

[0334] Step 4.1.3 Consistency Check

[0335] To determine whether the calculated weights are reasonable, a consistency check needs to be performed on the pairwise comparison matrix. If the check passes, a decision can be made based on the calculated weight vector; otherwise, a new judgment matrix needs to be constructed and the calculation repeated. Typically, a general consistency index (CI), an average random consistency index (RI), and a random consistency ratio (CR) are introduced to measure this.

[0336] The formula for calculating the random consistency ratio CR is as follows:

[0337] CR = CI / RI

[0338] Wherein, CR is the random consistency ratio; RI is the random consistency index; and CI is the consistency index.

[0339] The formula for calculating the consistency index (CI) is as follows:

[0340] CI=(λ max -n) / (n-1)

[0341] Where CI is the consistency index; λ max represents the largest eigenvalue of the judgment matrix, n represents the order of the judgment matrix; RI is the average random consistency index of the judgment matrix.

[0342] The average random consistency index RI for judgment matrices of orders 1-11 is shown in the table below:

[0343] Table 41 Random Consistency Index (RI) Values

[0344]

[0345] When the CR value does not exceed 0.1, it indicates that the weight calculation results meet the consistency requirements, and the normalized eigenvector can be used as the weight vector; otherwise, the judgment matrix needs to be reconstructed and a adjusted. ij This continues until the calculation results meet the consistency requirements.

[0346] Based on the consistency test results of the judgment matrix, the weight vector W of the urban subway freight system development suitability evaluation index system is determined as follows:

[0347] W=[0.067,0.139,0.09,0.076,0.174,0.077,0.068,0.048,0.155,0.057,0.049];

[0348] Step 4.2 Use the VIKOR method to comprehensively evaluate and determine the suitability level for the development of the urban subway freight system;

[0349] 4.2.1 Construct a decision matrix and normalize the index values;

[0350] Different indicators have different dimensions and cannot be directly compared. Therefore, it is necessary to normalize the original indicators and construct a decision matrix H:

[0351]

[0352] Among them, h ij As an alternative plan p i Compared to the evaluation index q j The normalized measure.

[0353] 4.2.2 Determining the positive and negative ideal solutions

[0354] To increase the comparability of the results, the constructed decision matrix H is standardized. Alternative solutions p i The results are represented by a benefit index and a cost index. A higher benefit index value indicates a better solution, calculated using the benefit formula. Conversely, a lower cost index value indicates a better solution. Based on these principles, the final alternative solution p is determined. i positive ideal solution and negative ideal solution function:

[0355]

[0356]

[0357] in,

[0358] 4.2.3 Calculate the group benefit index S i And individual regret indicators Y i ;

[0359] The group benefit index S i The calculation formula is as follows:

[0360]

[0361] Among them, w j The attribute q represents the output of the analytic hierarchy process. j The weights; h ij As an alternative plan p i Compared to the evaluation index q j Normalized measure;

[0362]

[0363] The individual regret index Y i The calculation formula is as follows:

[0364]

[0365] Among them, w j The attribute q represents the output of the analytic hierarchy process. j The weights satisfy h ij As an alternative plan p i Compared to the evaluation index q j Normalized measure;

[0366] 4.2.4 Decision Index R i calculate;

[0367] Considering the varying impacts of different indicators on alternative solutions, the calculated weights are incorporated into the VIKOR comprehensive evaluation calculation, and the alternative solution p is calculated using the following formula. i The compromise decision index R i :

[0368]

[0369] in, Let μ = 0.5, where μ represents the relative importance between group utility and individual regret;

[0370] The group benefit index S for 16 alternative options was calculated using the VIKOR method. i Individual regrettable indicators Y i Decision index R i The values ​​and results are shown in Table 42.

[0371] Table 42 Group Benefit Indicator S of the Evaluation Scheme i Individual regret index Y i Decision Index R i value

[0372]

[0373]

[0374] Step 4.3: Comprehensive Evaluation Criteria for the Suitability of Urban Subway Freight System Development

[0375] Decision index R based on the suitability of the urban subway freight system development i The suitability of urban subway freight system development is optimized by ranking the options based on suitability, with a decision index R. i The order of size indicates the suitability and merit of the alternatives.

[0376] Using the decision index R in the VIKOR method i The ranking results of the index show that the order of the alternatives is as follows: A is the most suitable city for developing an urban subway freight system, followed by B, and then D, E, G, C, H, I, N, F, L, K, P, J, M, and O.

[0377] Step 5: Application example of the suitability assessment method for the development of a subway freight system;

[0378] In step five, 16 cities are selected as options. Based on the developed evaluation model, the comprehensive score of the indicators is calculated. According to the evaluation criteria, the suitability of the development of the urban subway freight system is determined, and the cities are divided into tiers. This provides an application example for the developed subway freight system development suitability assessment method.

[0379] The comprehensive scoring formula for the suitability of the urban subway freight system development is as follows:

[0380]

[0381] Among them, R j It is the comprehensive calculated value of the j-th alternative; w j The index q is output using the Analytic Hierarchy Process (AHP). j The weights; h ij It is the alternative plan p i Compared to the evaluation index q j The normalized exponent value;

[0382] The comprehensive score of the suitability index for the development of the subway freight system in the 16 cities was calculated, see [link / reference]. Figure 5 ;

[0383] Based on the evaluation criteria, the suitability tiers for developing urban subway freight systems are divided. In this embodiment, the comprehensive evaluation criteria for the suitability of urban subway freight system development for the alternative schemes are shown in Table 5.

[0384] Table 5 Comprehensive Evaluation Criteria for the Suitability of Urban Subway Freight System Development

[0385]

[0386] Based on the comprehensive score and scoring criteria for the suitability of urban subway freight system development, a comprehensive assessment of the suitability of subway freight system development in 16 cities was conducted, and the city tier classification results are as follows:

[0387] (1) A and B belong to the first tier of cities;

[0388] (2) Cities D, E, G, C, and H belong to the second tier;

[0389] (3) Cities I, N, and F belong to the third tier;

[0390] (4) L, K, P, J, M, and O belong to the fourth tier of cities.

[0391] Based on the comprehensive evaluation value and referring to the evaluation criteria, it can be concluded that cities A and B selected in the example have higher development suitability, while the suitability of cities D, E, G, C, H, I, N, F, L, K, P, J, M, and O decreases in that order. In this invention, based on the calculation results of each indicator, corresponding optimization reference suggestions can be found. For example, promoting smart blocks, intelligent transportation systems (ITS), and the digital economy, improving the suitability of subway freight system projects through the development of smart facilities and smart technologies, and through policy incentives, subway expansion, and industrial upgrading.

[0392] This invention is not limited to the embodiments described above. The above description of specific embodiments is intended to illustrate and explain the technical solutions of this invention. The specific embodiments described above are merely illustrative and not restrictive. It should be noted that, without departing from the spirit and scope of the claims, those skilled in the art can make several improvements and specific modifications under the guidance of this invention, and these all fall within the scope of protection of this invention.

Claims

1. A method for evaluating the suitability of urban subway freight system development, characterized in that, Includes the following steps: Step (1): Establish a suitability analysis framework for the development of urban subway freight systems; Step (2): Suitability evaluation indicators and quantitative calculation of urban subway freight system development; Step (3): Multi-source data acquisition; Step (4): Establishment of indicator processing procedures and evaluation criteria; Step (1) includes the following steps: Step (11): Based on the SWOT method, construct a suitability analysis framework for the development of urban subway freight system, identify the main strengths, weaknesses, opportunities and challenges of urban subway freight system project implementation, and formulate a SWOT matrix; Step (12): Using a questionnaire survey, the importance scores of the above indicators are obtained based on the experts' evaluation of the SWOT analysis indicators, and new evaluation indicators are proposed. Step (13): Determine the selection of indicators for the suitability evaluation of urban subway freight system development based on the SWOT analysis framework; Step (2) The suitability evaluation indicators for the development of urban subway freight systems include two categories: benefit-based indicators and cost-based indicators; Benefit-oriented indicators specifically include network capacity (C1), infrastructure (C2), demand scale (C3), consumption and market (C4), environmental benefits (C5), and urban economic benefits (C6). 10 The urgency of truck restrictions C 11 By combining the calculation formulas of each indicator, the calculated values ​​of each evaluation indicator are obtained based on the data acquired through multi-source data collection methods; The cost-related indicators specifically include: urban logistics hub accessibility (C6), customer proximity (C7), transportation-oriented development (C8), and implementation cost (C9). The calculated values ​​of each evaluation indicator are obtained by combining the calculation formulas of each indicator with data acquired through multi-source data collection methods. Step (3) specifically includes the following steps; Step (31): Obtain relevant data on the city's subway freight system; Data related to urban metro freight systems includes railway length, number of lines, trains and services, average daily passenger flow, and the number of urban rail transit stations (N). station The number N of urban rail transit transfer stations transfer Urban rail transit annual operating cost (OperC), total number of trains, daily services, and TOD index data; Step (32): Obtain data related to urban transportation and logistics; Data related to urban transportation and logistics includes the number of e-commerce parcels N delivered annually in the city. EP Number of trucks affected N truck Number of registered postal service sites N RPS The total number of city shopping malls and retail stores N SMRS Daily prohibited truck travel periods (BanT), area of ​​truck restricted zones (BanA), and average daily travel distance per truck. Urban logistics market annual operating revenue LBI; Based on the acquired data, calculate the demand scale C3 indicator and the urgency C of truck restrictions. 11 Indicators, environmental benefits (C5), proximity to upstream logistics hubs (C6), proximity to customers (C7), and urban economic benefits (C) 10 And the attribute values ​​of the C4 indicator for consumption and the market; Step (4) specifically includes the following steps: Step (41): Calculate the weights of each evaluation index using the analytic hierarchy process; Step (411): Construct the pairwise comparison matrix using the 1-9 scaling method; the pairwise comparison matrix B is as follows: Where, matrix element b ij Indicates evaluation index b i For evaluation index b j The relative importance; Step (412): Solve for the pairwise comparison matrix; Step (413): Consistency check; Step (42): Use the VIKOR method to comprehensively evaluate and determine the suitability level for the development of the urban subway freight system; Step (421): Construct the decision matrix, perform index value normalization, and construct the decision matrix H as follows: Among them, h ij As an alternative plan p i Compared to the evaluation index q j Normalized measure; Step (422): Determine the positive ideal solution and the negative ideal solution; Step (423): Calculate the group benefit index S i And individual regret indicators Y i ; Step (424): Decision index R i calculate; Step (43): Comprehensive evaluation criteria for the suitability of urban subway freight system development: Using AHP-VIKOR, the comprehensive score and decision index R of the urban metro freight system development suitability indicators were analyzed. i The suitability of urban subway freight system development was evaluated, and the comprehensive score of the indicators and the decision index R were used to assess the suitability. i The order of size indicates the suitability of the alternatives.

2. The method according to claim 1, characterized in that, The quantitative calculation method for benefit-based evaluation indicators is as follows: Calculate the network capacity C1, an indicator used to reflect the freight capacity of the urban rail transit network. The calculation formula is as follows: C1 is a network capacity metric; SA Fmodule It is the total area of ​​shared passenger and freight stations in the urban rail transit network; Ucap FPS This is the capacity per unit area of ​​the installed modules; It is the number of passenger trains associated with each relevant line i; This is the maximum possible number of freight train services; Ucap EC It refers to the freight capacity of the extra carriages; Ucap DT It is the vehicle load of a dedicated freight train; λ 11 and λ 12 These are the weights corresponding to the two items; The data for calculating the maximum possible number of freight train services is derived from timetable information for each subway line obtained from the websites of subway operators in various cities. The calculation formula is as follows: in, This is the end time of passenger service; This is the start time of the last dedicated freight train service; MT i It is the time when the last dedicated freight train service starts earlier than the first passenger service on the following day; the constant value δ is the departure frequency of the dedicated freight train; the operation of the dedicated freight train is constrained by HT ([Δt]); it is assumed that at most one dedicated freight train service can be added between two adjacent passenger services; HT i (j,j+1) represents the interval between passenger trains j and j+1; i∈Ω represents the set of lines in the urban rail transit network, and j∈M represents the set of passenger train services; Infrastructure C2, this indicator reflects the abundance of dedicated railway facilities, and is calculated using the following formula: C2=N station ×N transfer ×URTL C2 is an infrastructure indicator; N station N is the number of urban rail transit stations; transfer URTL is the number of urban rail transit transfer stations; URTL is the length of the urban rail transit network. Demand Scale C3, this indicator reflects the demand scale of urban freight transport, and the calculation formula is as follows: C3=N EP ×N SMRS Where C3 is the demand scale indicator, and N EP N is the number of e-commerce parcels delivered in the city each year. SMRS It represents the total number of shopping malls and retail stores in the city; C4, the Consumption and Market indicator, reflects the market size and operating conditions of the urban logistics industry. The calculation formula is as follows: c4=λ 41 ×LBI+λ 42 ×RSG,λ 41 ∈(0,1),λ 42 ∈(0,1),λ 41 +λ 42 =1 Among them, C4 is a consumption and market indicator; LBI is the annual operating revenue of the urban logistics market, reflecting the market size and operating status of the urban logistics industry; RSG is the annual retail sales of social goods, reflecting the purchasing power of citizens and the degree of urban economic prosperity; λ 41 λ is the relative weight value of LBI. 42 It is the relative weight value of RSG; Environmental benefits C5, this indicator is used to reflect the environmental benefits of the subway freight system, and the calculation formula is as follows: C5 is an environmental benefit indicator for the metro freight system; It is the number of passenger trains associated with each relevant line i; This is the maximum possible number of freight train services; Ucap EC It refers to the freight capacity of the extra carriages; Ucap DT It is the vehicle load of a dedicated freight train; It represents the average daily distance traveled by a single truck; REV represents the utilization rate of electric vehicles in urban logistics activities; Ucap truck It is the average daily cargo volume transported by each truck; α CO It is the cost of treating the carbon monoxide (CO) produced per kilometer of truck travel; It is the cost of treating the carbon dioxide (CO2) produced per kilometer of truck travel; It is the amount of nitrogen oxides (NOx) produced per kilometer of truck travel. X Processing costs; α PM It is the cost of treating particulate matter (PM) generated per kilometer of truck travel; α noise It is the cost of dealing with the noise generated per kilometer of truck travel; Urban Economy C 10 This indicator reflects the economic foundation required to implement a subway freight system, and the calculation formula is as follows: C 10 =GDP / POP×(λ 101 ×FGBR+λ 102 ×TIFA),λ 101 ∈(0,1),λ 102 ∈(0,1),λ 101 +λ 102 =1 Among them, C 10 These are urban economic indicators; GDP is Gross Domestic Product; POP is Population; FGBR is General Budget Revenue; TIFA is Total Fixed Asset Investment; λ 101 It is the relative weighting value of general budget revenue; λ 102 It is the relative weight value of the total fixed asset investment; The urgency of truck restrictions C 11 This indicator reflects the contribution of the subway freight system to urban freight flow, and the calculation formula is as follows: C 11 =BanT×BanA×N truck Among them, C 11 It is an indicator of the urgency of truck restrictions; BanT is the daily prohibited period for truck travel; BanA is the area of ​​the truck-restricted zone; N truck That is the number of trucks affected.

3. The method according to claim 2, characterized in that, The quantitative calculation method for cost-based evaluation indicators is as follows: Upstream logistics hub proximity C6 is an indicator used to reflect the spatial proximity between upstream logistics hubs and the subway freight network. The calculation formula is as follows: Where C6 is the urban logistics hub proximity index, ||S|| is the number of ULHs, and |z s -z k | is the Euclidean distance between the upstream logistics hub and the urban rail transit station; upstream logistics hub s∈S; urban rail transit station k∈K; K is the set of all stations; stipulation: if the first nearest station belongs to line i, then define K′ as the set of stations excluding all stations on line i; if the second nearest station belongs to line j, then define K″ as the set of stations excluding all stations on lines i and j. Customer proximity (C7) is an indicator used to reflect the proximity between the urban rail transit network and the final destination of urban delivery. The calculation formula is as follows: Where C7 is an indicator of the proximity of the urban rail transit network to customers; UR(ε) is the total area of ​​urban areas with a population density exceeding ε; F(ε) is the number of urban rail transit stations within the UR(ε) area; |z q -z k | is the Euclidean distance between the registered post office station q and its nearest station; Transportation-oriented development (C8) is an indicator used to reflect the standards for evaluating the suitability of a metro freight system. The calculation formula is as follows: C8 is a public transport-oriented development indicator, while TOD is a public transport-oriented development index for cities. It is the conversion factor; Implementation cost C9, this indicator reflects the economic feasibility of the metro freight system, and the calculation formula is as follows: Wherein, C9 is the implementation cost indicator; ConsC is the construction cost per kilometer of the most recently operational urban rail transit line; OperaC is the annual operating cost of the urban rail transit; URTL is the length of the urban rail transit network; λ 91 It is the relative weighted value of the construction cost per kilometer of the most recently operational urban rail transit lines; λ 92 It is the relative weighting value of the annual operating cost of urban rail transit based on the network length.

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