Public transit network reconstruction method under urban rail networking operation

Abstract

The invention discloses a public transportation network reconstruction method under urban rail networking operation, and the method comprises the steps: obtaining multivariate travel data, carrying out the data-driven calculation of passenger flow OD, and providing scientific data support for network planning; meanwhile, when the whole line network is established, a backbone network and a connection network are adopted to simplify levels, technical indexes and service standards of all the levels are defined, actual passenger flow distribution is accurately measured and calculated, and the reasonability of line planning is ensured; and meanwhile, by establishing a three-dimensional matching index system, quantifying the difference between the current situation and the ideal network, and by differentiating schemes and implementing priority ranking, lines with large difference are optimized according to priorities, so that gradual optimization of the whole network is realized, large-scale oscillation is avoided, and finally reconstruction of the public transport network is realized. A complementary situation with urban rail transit is formed, redundant waste of public traffic design is avoided, and meanwhile, residents can go out more conveniently by optimizing lines.

Description

Technical Field

[0001] This invention relates to the field of transportation operation and maintenance planning, and in particular to a method for reconstructing a public transport network under the operation of an urban rail transit network. Background Technology

[0002] Regular buses are an important part of urban public transportation. For a long time, they have played an important role in serving citizens' daily commutes, connecting urban functional areas, covering areas not reached by rail transit, and ensuring basic travel rights. In recent years, with the rapid development of urban rail transit, rail transit in some large cities has gradually become the main mode of public transportation. The functional positioning of buses in the public transportation network system has undergone significant changes. At the same time, buses face sustainable development challenges such as continuous loss of passengers, low service quality and operational efficiency, inverted operating costs and increased reliance on subsidies.

[0003] Traditional "minor repairs" approach to bus route optimization is often problem-oriented, relying on diagnostic assessments of existing routes and passively optimizing them from the bottom up. This lacks a top-down, systematic consideration, and the optimization efforts cannot keep pace with the rapid shifts in passenger flow. It fails to fundamentally address issues such as unclear functional positioning of the bus network and insufficient coordination with urban rail transit, making it ill-suited to the new public transportation development pattern of "rail transit as the mainstay, bus as a supplement." Therefore, there is an urgent need for a systematic reconstruction of the bus network within the context of urban rail transit network development. This reconstruction, with urban rail transit as the leading force, aims to restructure the bus network hierarchy, clarify the functional positioning of each level of routes, and strengthen the deep integration of rail transit and bus networks. This is a crucial prerequisite for improving the overall service level of the public transportation system and optimizing resource allocation, and is of great significance for improving citizens' travel experience and promoting sustainable urban transportation development. To this end, a method for bus network reconstruction under the operation of an urban rail transit network is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a method for reconstructing a public transport network under urban rail transit network operation, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for reconstructing a public transport network under urban rail transit network operation, comprising the following steps:

[0006] S1. Multi-source data fusion and travel feature database construction: Residents' travel data is obtained through multiple channels, and OD data is extrapolated based on the travel data. Then, the data is integrated to construct a travel feature database.

[0007] S2. Ideal network hierarchical planning is generated, which plans the entire public transport network into a backbone network and a feeder network. Based on residents' travel data, the routes and vehicle schedules of the backbone network and the feeder network are planned separately, and finally, multiple feeder networks are connected into the backbone network.

[0008] S3. Dynamic matching and incremental optimization of the current and ideal bus network: By fitting and calculating the overlap and passenger flow coordination between the existing bus routes and the ideal planned routes, the routes are divided according to the matching degree. Routes with unsatisfactory matching degree are then modified according to the plan, while routes with satisfactory matching degree remain unchanged.

[0009] Preferably, the entire process of step S1 includes;

[0010] S11. Data collection: Collect multi-source travel data, including resident travel survey data, mobile phone signaling data, public transport IC card data, public transport GPS data, shared bicycle order data, ride-hailing and taxi order data, etc.

[0011] S12. Data processing: Standardize data of different magnitudes using the Z-score standardization method, as shown in the following formula:

[0012]

[0013] in For the first In the class of data, the first Standardized values ​​of the data. For the first In the class of data, the first One set of raw data, For the first The mean of class data, For the first Standard deviation of class data;

[0014] S13. Calculation of travel OD data: Inferring public transportation and bus passenger flow OD by integrating multi-source data, using a weighted fusion algorithm, the formula is as follows;

[0015]

[0016] in To merge from the starting point To the finish line Passenger OD volume; For the first Weights of data sources For the first From the starting point in the class data source To the finish line Passenger OD volume, The number of different types of data sources;

[0017] S14. Construction of the travel feature database: Integrate the data processed in S12 and S13 with the travel OD data to construct a full-scale travel feature database.

[0018] Preferably, the backbone network in step S2 includes corridor identification and backbone line layout during the planning process, wherein corridor identification includes:

[0019] S211 Public transport passenger flow corridor identification: By statistically analyzing the public transport passenger flow of each road corridor during peak hours, when the flow reaches the design planning threshold, it is identified as a public transport passenger flow corridor;

[0020] S212. Identification of oversaturated urban rail corridors: This is achieved by calculating the passenger flow saturation of each section of the rail line during peak hours, using the following formula.

[0021]

[0022] in Track section Passenger flow saturation Track section during peak hours In the Passenger flow within a time interval Track section Design capacity; when At that time, it was identified as an oversaturated corridor for urban rail transit;

[0023] S213. Identification of high-potential corridors not covered by urban rail transit: Statistical analysis of public transport passenger flow intensity in each road corridor in areas without rail transit coverage. When the passenger flow of a corridor reaches 3,000 passengers per kilometer, it is identified as a high-potential corridor not covered by urban rail transit.

[0024] Preferably, the backbone layout process includes the following steps;

[0025] S221. Parameter settings: Determine the constraints for route planning, including route length constraints, repetition coefficient constraints, and platform capacity constraints.

[0026] S222. Alternative Route Generation: Based on the OD matrix and road network structure of the backbone network in the travel feature database, an improved version of Dijkstra's algorithm is used to generate a set of alternative routes between each origin-destination pair. The path weights in the improved shortest path algorithm are calculated as follows:

[0027]

[0028] in For road section The weight, These are the weighting coefficients for distance, time, and congestion level, respectively. ;

[0029] S223. Iterative optimization: Calculate the direct passenger volume of each candidate route, then plan the route layout based on the passenger volume, and constrain the route number and passenger flow satisfaction to finally form the final backbone route planning scheme.

[0030] Preferably, the iterative optimization process includes the following steps:

[0031] S2231. Calculate the number of direct passengers for each alternative route;

[0032] S2232. Prioritize the deployment of routes with the largest direct passenger volume, and then adjust the passenger flow OD matrix after deployment. The formula can be expressed as follows:

[0033]

[0034] in For the already deployed lines Direct passenger volume, For the line The starting set, For the line The final set, To correct from the starting point To the finish line Passenger OD volume; To correct from the starting point To the finish line Passenger OD volume;

[0035] S2233. Modify the shortest path matrix by setting complex numbers for the established line sections. The formula is as follows:

[0036]

[0037] in For the corrected road section The weight, The weights before correction. This is the adjustment factor for the multi-line coefficients, and , For road section The number of bus routes already in operation;

[0038] S2234. Iterate through the loop, repeating steps S2231 to S2233 until the constraints on the number of routes and the degree of passenger flow satisfaction are met (passenger flow coverage rate > 90%). The formula for calculating passenger flow coverage rate is as follows.

[0039]

[0040] in For passenger flow coverage, L represents the set of existing backbone routes. For the line Direct passenger volume, This represents the total OD volume of the backbone network.

[0041] Preferably, the planning process of the connection network in step S2 includes:

[0042] S231. District division: Districts are divided based on urban development, land use, cluster distribution, rail network layout, road network structure, and job-housing distribution.

[0043] S232. The layout of connecting lines shall be handled using the same methods and steps as the layout of backbone lines.

[0044] Preferably, step S3 includes the construction and calculation of a three-dimensional matching index system, line type classification, formulation of differentiated solutions, and determination of implementation priorities, wherein the construction and calculation of the three-dimensional matching index system includes:

[0045] S311. Spatial fit calculation: The overlap length and alignment similarity between the current route and the ideal route are calculated. Then, the overall spatial fit between the two routes is calculated using a weighted average method based on the overlap length and alignment similarity.

[0046] S312. Passenger flow coordination degree calculation: calculate the degree of matching between the passenger flow of each section of the current route and the passenger flow of the corresponding section of the ideal route, as well as the peak period matching degree between the current route and the ideal route. Then, the price averaging method is used to calculate the passenger flow coordination degree.

[0047] S313. Calculate the operating efficiency by calculating the average ratio of the current route travel speed to the ideal route travel speed and the passenger load factor of the current route and the ideal route, and then use the weighted average method to calculate the operating efficiency.

[0048] S314. Matching index calculation: The weighted average method is used to calculate the comprehensive matching index between the current route and the ideal route. The formula is as follows:

[0049]

[0050] Where G is the matching index, SX is the spatial fit, FX is the passenger flow coordination, and EX is the operational efficiency; The weights for spatial fit, passenger flow coordination, and operational efficiency are respectively: .

[0051] Preferably, the line type division is carried out according to the value range of the matching degree index G. If G > 80%, it is a highly matching line; if 50% < G < 80%, it is a partially matching line; if G < 50, it is a non-matching line.

[0052] Preferably, the detailed criteria for formulating the differentiation plan include;

[0053] Highly matching lines; directly retain, maintain the existing line direction, station settings and operation parameters;

[0054] Partially matching lines; optimize by adjusting stations and optimizing the direction;

[0055] Non-matching lines; revoke or replace them with new lines in the ideal line network according to requirements.

[0056] Preferably, the implementation priority is determined by combining the passenger flow sensitivity analysis to determine the implementation priority of line adjustment, and an implementation priority evaluation index system is constructed, including line passenger flow (Q) and transfer dependence (R). The priority index is calculated by the weighted summation method, and the formula is as follows;

[0057]

[0058] Where is the implementation priority, and the larger the value, the higher the priority; are the weights of the line passenger flow and transfer dependence respectively, and , is the daily average passenger flow of the line, is the maximum daily average passenger flow among all the lines to be adjusted, is the proportion of the transfer volume between the line and the rail to the total passenger flow of the line, is the maximum transfer dependence among all the lines to be adjusted;

[0059] First, optimize the lines with a high implementation priority index, that is, the lines with little impact on passenger flow and high synergy benefits, to achieve a smooth transition of the line network.

[0060] The technical effects and advantages of the present invention:

[0061] This method acquires diverse travel data, then uses data-driven fusion of multi-source travel data to calculate passenger flow origin-destination (OD) using mathematical formulas, accurately depicting travel demand and providing scientific data support for network planning. Simultaneously, when constructing the entire network, a simplified hierarchy of "backbone network + connecting network" is adopted, clearly defining the technical indicators and service standards of each level. Accurate calculation of actual passenger flow distribution ensures the rationality of route planning. Furthermore, by establishing a three-dimensional matching indicator system, the differences between the current and ideal network are quantified. Differentiated solutions and implementation priorities are then used to optimize routes with significant differences, achieving gradual optimization of the entire network, avoiding large-scale fluctuations, and ultimately reconstructing the public transport network to complement rail transit, avoiding redundant and wasteful public transport design, and making residents' travel more convenient through route optimization. Attached Figure Description

[0062] Figure 1 This is a flowchart of the overall method of the present invention;

[0063] Figure 2 This is a flowchart illustrating the logic of ideal network planning in the method of this invention.

[0064] Figure 3 This is a schematic diagram of the ideal wire mesh generation process in the method of the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] This invention provides, for example Figures 1 to 3 The method for reconstructing a public transport network under urban rail transit network operation, as shown, includes the following steps:

[0067] S1. Multi-source data fusion and travel feature database construction: Residents' travel data is obtained through multiple channels, and OD data is extrapolated based on the travel data. Then, the data is integrated to construct a travel feature database.

[0068] S2. Ideal network hierarchical planning is generated, which plans the entire public transport network into a backbone network and a feeder network. Based on residents' travel data, the routes and vehicle schedules of the backbone network and the feeder network are planned separately, and finally, multiple feeder networks are connected into the backbone network.

[0069] S3. Dynamic matching and incremental optimization of the current and ideal bus network: By fitting and calculating the overlap and passenger flow coordination between the existing bus routes and the ideal planned routes, the routes are divided according to the matching degree. Routes with unsatisfactory matching degree are then modified according to the plan, while routes with satisfactory matching degree remain unchanged.

[0070] The entire process of step S1 includes:

[0071] S11. Data collection: Collect multi-source travel data, including resident travel survey data, mobile phone signaling data, public transport IC card data, public transport GPS data, shared bicycle order data, ride-hailing and taxi order data, etc.

[0072] S12. Data processing: Standardize data of different magnitudes using the Z-score standardization method, as shown in the following formula:

[0073]

[0074] in For the first In the class of data, the first Standardized values ​​of the data. For the first In the class of data, the first One set of raw data, For the first The mean of class data, For the first Standard deviation of class data;

[0075] S13. Calculation of travel OD data: Inferring public transportation and bus passenger flow OD by integrating multi-source data, using a weighted fusion algorithm, the formula is as follows;

[0076]

[0077] in To merge from the starting point To the finish line Passenger OD volume; For the first Weights of data sources For the first From the starting point in the class data source To the finish line Passenger OD volume, The number of different types of data sources;

[0078] S14. Construction of the travel feature database: Integrate the data processed in S12 and S13 with the travel OD data to construct a full-scale travel feature database.

[0079] The backbone network in step S2 includes corridor identification and backbone layout during the planning process, where corridor identification includes:

[0080] S211 Public transport passenger flow corridor identification: By statistically analyzing the public transport passenger flow of each road corridor during peak hours, when the flow reaches the design planning threshold, it is identified as a public transport passenger flow corridor, which is usually 6,000 passengers per hour.

[0081] S212. Identification of oversaturated urban rail corridors: This is achieved by calculating the passenger flow saturation of each section of the rail line during peak hours, using the following formula.

[0082]

[0083] in Track section Passenger flow saturation Track section during peak hours In the Passenger flow within a time interval Track section Design capacity; when At that time, it was identified as an oversaturated corridor for urban rail transit;

[0084] S213. Identification of high-potential corridors not covered by urban rail transit: Statistical analysis of public transport passenger flow intensity in each road corridor in areas without rail transit coverage. When the passenger flow of a corridor reaches 3,000 passengers per kilometer, it is identified as a high-potential corridor not covered by urban rail transit.

[0085] The backbone network layout process, based on the methodology of "iterative iteration and gradual network formation," includes the following steps;

[0086] S221. Parameter settings: Determine the constraints of the route planning, including the route length constraint (main corridor line 12≤L≤25km, main corridor connecting line 12≤L≤25km), the repetition coefficient constraint (repetition rate of roads passed by other routes ≤60%), and the constraint of the number of lines that a single platform can accommodate (single platform ≤6 lines).

[0087] S222. Alternative Route Generation: Based on the OD matrix and road network structure of the backbone network in the travel feature database, an improved version of Dijkstra's algorithm is used to generate a set of alternative routes between each origin-destination pair. The path weights in the improved shortest path algorithm are calculated as follows:

[0088]

[0089] in For road section The weight, These are the weighting coefficients for distance, time, and congestion level, respectively. ;

[0090] S223. Iterative optimization: Calculate the direct passenger volume of each candidate route, then plan the route layout based on the passenger volume, and constrain it by the number of routes and the degree of passenger flow satisfaction, thereby finally forming the final backbone route planning scheme.

[0091] The iterative optimization process includes the following steps:

[0092] S2231. Calculate the number of direct passengers for each alternative route;

[0093] S2232. Prioritize the deployment of routes with the largest direct passenger volume, and then adjust the passenger flow OD matrix after deployment. The formula can be expressed as follows:

[0094]

[0095] in For the already deployed lines Direct passenger volume, For the line The starting set, For the line The final set, To correct from the starting point To the finish line Passenger OD volume; To correct from the starting point To the finish line Passenger OD volume;

[0096] S2233. Modify the shortest path matrix by setting complex numbers for the existing line sections. The formula is as follows:

[0097]

[0098] in For the corrected road section The weight, The weights before correction. This is the adjustment factor for the multi-line coefficients, and , For road section The number of bus routes already in operation;

[0099] S2234. Iterate through the loop, repeating steps S2231 to S2233 until the constraints on the number of routes and the degree of passenger flow satisfaction are met (passenger flow coverage rate > 90%). The formula for calculating the passenger flow coverage rate is as follows.

[0100]

[0101] in is the passenger flow coverage rate, and \(L_{i}\) is the set of backbone lines that have been deployed. is the line 's direct passenger volume. is the total passenger flow OD volume of the backbone network.

[0102] The planning process of the feeder network in step S2 includes:

[0103] S231. Area division: Divide the area according to urban development, land use, group distribution, rail network layout, road network structure, and job-housing distribution.

[0104] S232. Feeder line layout: Process it using the same method steps as the backbone line layout process.

[0105] Step S3 includes the construction and calculation of a three-dimensional matching index system, line type division, formulation of a differentiation plan, and determination of implementation priority. Among them, the construction and calculation of the three-dimensional matching index system include:

[0106] S311. Calculation of spatial fit: Calculate the overlapping length of the current line and the ideal line's direction and the linear similarity, and then calculate the overall spatial fit between the two using the weighted average method based on the overlapping length and linear similarity.

[0107] S312. Calculation of passenger flow coordination: Calculate the coincidence degree of the passenger flow at each section of the current line and the passenger flow at the corresponding section of the ideal line, as well as the peak period matching degree between the current line and the ideal line, and then calculate the passenger flow coordination using the weighted average method.

[0108] S313. Calculation of operation efficiency: Calculate the average ratio of the travel speed of the current line to the travel speed of the ideal line and the load factor of the current line and the ideal line, and then calculate the operation efficiency using the weighted average method.

[0109] S314. Calculation of the matching degree index: Calculate the comprehensive matching degree index of the current line and the ideal line using the weighted average method. The formula is as follows:

[0110]

[0111] Where \(G\) is the matching degree index, \(SX\) is the spatial fit, \(FX\) is the passenger flow coordination, and \(EX\) is the operation efficiency. are the weights of the spatial fit, passenger flow coordination, and operation efficiency respectively, and .

[0112] The line type is divided according to the value range of the matching degree index \(G\). If \(G>80\%\), it is a highly matching line; if \(50\%<G<80\%\), it is a partially matching line; if \(G<50\), it is a non-matching line.

[0113] The detailed guidelines for developing a differentiated strategy include:

[0114] Highly compatible routes; directly retain existing route directions, station settings, and operating parameters;

[0115] Partially matched routes; optimization is achieved by adjusting stations and optimizing routes.

[0116] Mismatched lines; remove or replace them with new lines in the ideal network as needed.

[0117] The implementation priority is determined by combining passenger flow sensitivity analysis, and an implementation priority evaluation index system is constructed, including line passenger flow (Q) and transfer dependence (R). The priority index is calculated by weighted summation method, as shown in the following formula;

[0118]

[0119] in To implement priority, the larger the value of the number, the higher the priority. The weights are respectively the passenger flow of the line and the transfer dependence, and , The average daily passenger volume of the route, The maximum average daily passenger volume among all routes to be adjusted. This refers to the proportion of transfers between the line and the rail network to the total passenger volume of the line. The maximum transfer dependency among all routes to be adjusted;

[0120] Prioritize optimizing routes with high priority indices, i.e., routes with minimal impact on passenger flow and high synergistic benefits, to achieve a smooth transition of the network.

[0121] Working Principle: This method acquires diverse travel data, then uses data-driven fusion of multi-source travel data to calculate passenger flow origin-destination (OD) using mathematical formulas, accurately depicting travel demand and providing scientific data support for network planning. Simultaneously, when constructing the entire network, a simplified hierarchy of "backbone network + connecting network" is adopted, clarifying the technical indicators and service standards of each level. Accurate calculation of actual passenger flow distribution ensures the rationality of route planning. Furthermore, by establishing a three-dimensional matching indicator system, the differences between the current and ideal network are quantified. Differentiated solutions and implementation priorities are used to optimize routes with significant differences, achieving gradual optimization of the entire network, avoiding large-scale fluctuations, and ultimately reconstructing the public transport network to complement rail transit, avoiding redundant and wasteful public transport design, and making residents' travel more convenient through route optimization.

[0122] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for reconstructing a public transport network under urban rail transit network operation, characterized in that, Includes the following steps: S1. Multi-source data fusion and travel feature database construction: Residents' travel data is acquired through multiple channels, and OD data is extrapolated based on the travel data. Then, the data is integrated to construct a travel feature database. S2. Ideal network hierarchical planning generation: The entire public transport network is planned into a backbone network and a feeder network. Based on residents' travel data, the routes and vehicle schedules of the backbone network and the feeder network are planned separately. Finally, multiple feeder networks are connected into the backbone network. S3. Dynamic matching and incremental optimization of the current and ideal bus network: By fitting and calculating the overlap and passenger flow coordination between the existing bus routes and the ideal planned routes, the routes are divided according to the matching degree. Routes with unsatisfactory matching degree are then modified according to the plan, while routes with satisfactory matching degree remain unchanged.

2. The method for reconstructing a public transport network under urban rail transit network operation as described in claim 1, characterized in that, The entire process of step S1 includes: S11. Data Collection: Collect multi-source travel data, including resident travel survey data, mobile phone signaling data, public transport IC card data, public transport GPS data, shared bicycle order data, ride-hailing and taxi order data; S12. Data Processing: Standardize data of different magnitudes using the Z-score standardization method, as shown in the following formula: ， in For the first In the class of data, the first Standardized values ​​of the data. For the first In the class of data, the first One set of raw data, For the first The mean of class data, For the first Standard deviation of class data; S13. Calculation of Origin and Delivery (OD) Data: The Origin and Delivery of public transportation and bus passenger flow are calculated by integrating multi-source data and using a weighted fusion algorithm. The formula is as follows: ， in To merge from the starting point To the finish line Passenger OD volume; For the first Weights of data sources For the first From the starting point in the class data source To the finish line Passenger OD volume, The number of different types of data sources; S14. Construction of the travel feature database: Integrate the data processed in S12 and S13 with the travel OD data to construct a full travel feature database.

3. The method for reconstructing a public transport network under urban rail transit network operation as described in claim 2, characterized in that, The backbone network in step S2 includes corridor identification and backbone layout during the planning process, wherein corridor identification includes: S211 Public transport passenger flow corridor identification: By statistically analyzing the public transport passenger flow of each road corridor during peak hours, when the flow reaches the design planning threshold, it is identified as a public transport passenger flow corridor; S212. Identification of Oversaturated Corridors in Urban Rail Transit: This is achieved by calculating the passenger flow saturation at various sections of the rail line during peak hours, using the following formula: ， in Track section Passenger flow saturation Track section during peak hours In the Passenger flow within a time interval Track section Design capacity; when At that time, it was identified as an oversaturated corridor for urban rail transit; S213. Identification of high-potential corridors not covered by urban rail: Statistically analyze the public transport passenger flow intensity of each road corridor in areas without rail coverage. When the passenger flow of a corridor reaches 3,000 passengers per kilometer, it is identified as a high-potential corridor not covered by urban rail.

4. The method for reconstructing a public transport network under urban rail transit network operation as described in claim 3, characterized in that, The backbone network layout process includes the following steps; S221. Parameter settings to determine the constraints of route planning: including route length constraints, repetition coefficient constraints, and constraints on the number of routes that can be accommodated at stations. S222. Alternative Route Generation: Based on the OD matrix and road network structure of the backbone network in the travel feature database, an improved version of Dijkstra's algorithm is used to generate a set of alternative routes between each origin-destination pair. The path weights in the improved shortest path algorithm are calculated as follows: ， in For road section The weight, These are the weighting coefficients for distance, time, and congestion level, respectively. ; S223. Iterative optimization: By calculating the direct passenger volume of each candidate route, the route layout is planned according to the passenger volume, and the constraints are imposed by the number of routes and the degree of passenger flow satisfaction, thus forming the final backbone route planning scheme.

5. A method for reconstructing a public transport network under urban rail transit network operation as described in claim 4, characterized in that, The iterative optimization process includes the following steps: S2231. Calculate the number of direct passengers for each alternative route; S2232. Prioritize the layout of the line with the largest direct passenger volume. After the layout, correct the passenger flow OD matrix, and its formula can be expressed as: ， in For the already deployed lines Direct passenger volume, For the line The starting set, For the line The final set, To correct from the starting point To the finish line Passenger OD volume; To correct from the starting point To the finish line Passenger OD volume; S2233. Correct the shortest path matrix, and set a complex number for the cross-section of the established line. The formula is: ， in For the corrected road section The weight, The weights before correction. This is the adjustment factor for the multi-line coefficients, and , For road section The number of bus routes already in operation; S2234. Iterate cyclically, repeating steps S2231 to S2233 until the line number constraint and the passenger flow satisfaction constraint are met. The calculation formula of the passenger flow coverage rate is as follows: ， in For passenger flow coverage, L represents the set of existing backbone routes. For the line Direct passenger volume, This represents the total OD volume of the backbone network.

6. The method for reconstructing a public transport network under urban rail transit network operation as described in claim 1, characterized in that, The planning process of the feeder network in step S2 includes: S231. Area division: Divide the area according to urban development, land use, group distribution, rail network layout, road network structure and employment-residence distribution. S232. Feeder line layout: Use the same method steps as the backbone line layout process for processing.

7. The method for reconstructing a public transport network under urban rail transit network operation as described in claim 1, characterized in that, Step S3 includes the construction and calculation of a three-dimensional matching index system, line type division, formulation of a differentiation plan and determination of implementation priority. The construction and calculation of the three-dimensional matching index system include: S311. Calculation of spatial fitness: Calculate the coincidence length of the current line and the ideal line and the linear similarity, and then calculate the overall spatial fitness of the two by using the weighted average method according to the coincidence length and the linear similarity. S312. Calculation of passenger flow synergy: Calculate the coincidence degree of the passenger flow of each cross-section of the current line and the passenger flow of the corresponding cross-section of the ideal line, and the peak period matching degree of the current line and the ideal line, and then calculate the passenger flow synergy by using the weighted average method. S313. Calculation of operation efficiency: Calculate the average ratio of the travel speed of the current line to the travel speed of the ideal line and the load factor of the current line and the ideal line, and then calculate the operation efficiency by using the weighted average method. S314. Calculation of the matching degree index: Calculate the comprehensive matching degree index of the current line and the ideal line by using the weighted average method. The formula is as follows: ， Where G is the matching index, SX is the spatial fit, FX is the passenger flow coordination, and EX is the operational efficiency; The weights for spatial fit, passenger flow coordination, and operational efficiency are respectively: .

8. A method for reconstructing a public transport network under urban rail transit network operation as described in claim 7, characterized in that, The line type division is carried out according to the value range of the matching degree index G. If G>80%, it is a highly matching line; if 50%<G<80%, it is a partially matching line; if G<50%, it is a non-matching line.

9. A method for reconstructing a public transport network under urban rail transit network operation as described in claim 8, characterized in that, The detailed criteria for formulating the differentiation plan include: Highly matching lines: Keep directly and maintain the existing line direction, station settings and operation parameters. Partially matching lines: Optimize by adjusting stations and optimizing the direction. Non-matching lines: Withdraw or replace with a new line in the ideal line network according to the demand.

10. A method for reconstructing a public transport network under urban rail transit network operation as described in claim 9, characterized in that, The determination of the implementation priority combines the passenger flow sensitivity analysis to determine the implementation priority of line adjustment, constructs an implementation priority evaluation index system, including the line passenger volume (Q) and the transfer dependence degree (R), and calculates the priority index by using the weighted summation method. The formula is as follows: ， in To implement the priority index, the larger the value, the higher the priority; The weights are respectively the passenger flow of the line and the transfer dependence, and , The average daily passenger volume of the route, The maximum average daily passenger volume among all routes to be adjusted. This refers to the proportion of transfers between the line and the rail network to the total passenger volume of the line. The maximum transfer dependency among all routes to be adjusted; Prioritize the optimization of lines with a high implementation priority index, that is, lines with little passenger flow impact and high synergy benefits, to achieve a smooth transition of the line network.

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