Rail transit network and urban planning fusion evaluation method and system

Abstract

The invention relates to the technical field of rail transit network and urban planning fusion evaluation, in particular to a rail transit network and urban planning fusion evaluation method and system. Predicting and acquiring third rail influence data and fourth rail influence data by combining an urban rail transit evaluation and prediction model; first rail risk data and second rail risk data are obtained by predicting historical disaster data and historical risk data of urban rail transit data; first traffic prediction and evaluation information, second traffic prediction and evaluation information, third traffic prediction and evaluation information and third traffic prediction and evaluation information are calculated and obtained based on the data for evaluation so as to obtain an urban rail transit evaluation result; through the method, the influence evaluation data of the subsequent stage can be obtained on the basis of the existing evaluation data, and the evaluation accuracy of the urban overall planning target is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated assessment of rail transit networks and urban planning, and in particular to a method and system for integrated assessment of rail transit networks and urban planning. Background Art

[0002] The urban rail transit network is a transportation network composed of multiple rail transit lines, such as subways and high-speed railways. These rail transit networks are one of the important public transportation networks in every city. In addition to their role in public transportation, they also play an important role in logistics transportation.

[0003] Urban planning is an important means of coordinating the resources of various urban areas and coordinating the development of urban areas. Through multi-faceted assessments of population, employment, land use rate and ecological environment, it is possible to test the effectiveness of current urban planning, summarize the phased and effectiveness issues in the planning, and make adjustments to achieve healthy and orderly development of the city.

[0004] With the rise of new industries and concepts such as logistics, transportation, and cultural tourism, the evaluation and planning of urban rail transit has become an important part of urban planning. The coverage rate of densely populated areas in the city, the utilization rate of spatial land in the city, and the integration rate of transportation space are all major considerations in the evaluation of the integration of rail transit networks and urban planning.

[0005] At present, the evaluation work in this area has always been based on the existing data, which can only evaluate a series of issues that can reflect the urban development at the current stage; however, the main purpose of the evaluation work is to ensure that the goals of the city's overall plan will not be greatly affected. Since the city and rail transit network are a dynamic development process, the evaluation results based only on the existing data can only evaluate and judge the current stage of development, without considering the impact on the development of subsequent stages; the accuracy of the evaluation of the city's overall plan goals is poor.

[0006] Therefore, a rail transit network and urban planning integrated evaluation method and system are proposed. Summary of the Invention

[0007] The purpose of the present invention is to provide a rail transit network and urban planning integrated assessment method and system, by obtaining the population data of the coverage area of ​​urban rail transit data and the population data of each region, combined with the urban rail transit assessment prediction model to predict and obtain third rail impact data and fourth rail impact data; through the historical disaster data and historical risk data of urban rail transit data, the first rail risk data and the second rail risk data are obtained by prediction; based on these data, the first traffic forecast assessment information, the second traffic forecast assessment information, the third traffic forecast assessment information and the third traffic forecast assessment information are calculated and evaluated to obtain the urban rail transit assessment result; through this method, on the basis of the existing assessment data, the impact assessment data of the subsequent stage can be obtained, thereby improving the accuracy of the analysis of the city's overall planning goals.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] Obtaining rail transit data of the city, and obtaining first rail impact data based on the coverage of the rail transit data; the coverage of the rail transit data is the population data of the urban area covered by each station in the urban planning obtained by the rail transit data, to generate the first rail impact data;

[0010] Obtaining second rail impact data based on population data of each area of ​​the city; setting an urban rail transit assessment and prediction model to predict the first and second rail impact data to obtain third and fourth rail impact data; the urban rail transit assessment and prediction model is a time series data analysis and prediction model;

[0011] Obtaining historical disaster data of the rail transit data, and using the urban rail transit assessment and prediction model to predict future disaster data for first rail risk data of the rail transit data; obtaining second rail risk data through the historical risk data of the rail transit data and the urban rail transit assessment and prediction model;

[0012] Obtaining historical rail transit evaluation information of the city, and obtaining first traffic prediction evaluation information based on the urban rail transit evaluation prediction model and the historical rail transit evaluation information;

[0013] Calculating and acquiring second traffic forecast assessment information based on the third track impact data and the fourth track impact data;

[0014] Furthermore, the second traffic forecast evaluation information is calculated as:

[0015]

[0016] Among them, λ PerThe second traffic prediction evaluation information, ω P is the evaluation weight of rail transit stations, ε P Assessing weight for growth, is the average flow of people at the site, P max is the maximum traffic flow that the site can bear, ε P is the population growth weight, N r is the number of tracks connected to the station, ω Ri Evaluate the weight for the ith track, is the average passenger flow of the ith track, R imax is the maximum passenger flow that the ith track can bear, N A The number of neighboring areas responsible for the urban area of ​​the site in question, is the site scalability evaluation weight of the i-th neighboring area, is the second track impact data of the i-th adjacent area, is the fourth track impact data of the i-th adjacent area, M T The third track impact data for the site, M F The first track impact data of the site, α P is the population growth weight threshold, γ i is the evaluation weight of the i-th neighboring area, α i Evaluate the threshold for the i-th neighboring region;

[0017] The first track risk data and the second track risk data are used to calculate and obtain third traffic prediction assessment information;

[0018] Furthermore, the third traffic prediction evaluation information is calculated as:

[0019]

[0020] Among them, λ Risk The third traffic prediction evaluation information, For the second track risk data, is the first track risk data, ξ kex is the total maintenance cost of the k-th track, The impact time of the second track risk data, T k is the total operating time, is the unit maintenance cost of the i-th second track risk data, exp is the exponential function with e as the base, ξ jR is the maintenance cost of the j-th risk data of the first track;

[0021] Performing an evaluation based on the first traffic forecast evaluation information, the second traffic forecast evaluation information, and the third traffic forecast evaluation information to generate comprehensive traffic forecast evaluation information; obtaining an urban rail transit evaluation result based on the current urban traffic evaluation information and the comprehensive traffic forecast evaluation information;

[0022] Furthermore, the urban rail transit assessment result is calculated as:

[0023] λ CR =λ O +λ P ;

[0024]

[0025] Among them, λ CR is the urban rail transit evaluation result, λ O is the current urban traffic assessment information, λ P is the comprehensive traffic forecast evaluation information, N Pt is the number of rail transit stations, The third traffic prediction evaluation information of the i-th item, is the second traffic prediction evaluation information of the i-th item, λ F is the first traffic prediction evaluation information, α1 and α2 are evaluation weights; α1+α2=1;

[0026] The present invention also provides a rail transit network and urban planning integrated assessment system, including an urban rail assessment data acquisition module, an urban rail population assessment data prediction module, an urban rail disaster assessment data prediction module, an urban rail assessment parameter calculation module, and an urban rail assessment result calculation module, specifically:

[0027] The urban rail assessment data acquisition module acquires urban rail transit data and acquires first rail impact data based on the coverage of the rail transit data; and acquires second rail impact data based on the population data of each area of ​​the city; the coverage of the rail transit data is the population data of the urban area covered by each station in the urban planning, and generates the first rail impact data;

[0028] An urban rail population assessment data prediction module is configured to set an urban rail transit assessment prediction model to predict the first rail impact data and the second rail impact data, and obtain third rail impact data and fourth rail impact data; the urban rail transit assessment prediction model is a time series data analysis and prediction model;

[0029] An urban rail disaster assessment data prediction module obtains historical disaster data of the rail transit data, and uses the urban rail transit assessment prediction model to predict first rail risk data of future disaster data on the rail transit data; and obtains second rail risk data through the historical risk data of the rail transit data and the urban rail transit assessment prediction model;

[0030] An urban rail transit assessment parameter calculation module is configured to obtain historical rail transit assessment information of the city, and obtain first traffic forecast assessment information based on the urban rail transit assessment prediction model and the historical rail transit assessment information; obtain second traffic forecast assessment information based on the third rail impact data and the fourth rail impact data; and obtain third traffic forecast assessment information based on the first rail risk data and the second rail risk data;

[0031] An urban rail transit evaluation result calculation module is configured to evaluate the first traffic forecast evaluation information, the second traffic forecast evaluation information, and the third traffic forecast evaluation information to generate comprehensive traffic forecast evaluation information; and obtain an urban rail transit evaluation result through the current urban traffic evaluation information and the comprehensive traffic forecast evaluation information.

[0032] Furthermore, the urban rail transit assessment results are calculated as follows:

[0033] λ CR =λ O +λ P ;

[0034]

[0035] Among them, λ CR is the urban rail transit evaluation result, λ O is the current urban traffic assessment information, λ P is the comprehensive traffic forecast evaluation information, N Pt is the number of rail transit stations, The third traffic prediction evaluation information of the i-th item, is the second traffic prediction evaluation information of the i-th item, λ F is the first traffic prediction evaluation information, α1 and α2 are evaluation weights; α1+α2=1.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention uses the coverage area of ​​each station in the current urban rail transit data to obtain the impact of regional population data on the carrying capacity of rail transit; through the urban rail transit evaluation and prediction model, the current regional population data of each future urban rail station and the population data of the adjacent area are evaluated to evaluate whether the station can carry more population in the subsequent stage and whether it can be expanded in the face of population growth in other areas; this evaluation method can more accurately grasp the carrying capacity evaluation and scalability evaluation of urban rail in the subsequent stage, so as to provide a detailed evaluation data report, facilitate accurate understanding of the impact of current stage development and subsequent development on the city's overall planning goals, and improve the accuracy of the evaluation.

[0038] 2. The present invention makes predictions based on data on the impact of historical disasters on the tracks of various stations in the current city area, as well as data on the risks that the urban tracks are affected by during operation. The method then evaluates the maintenance costs of the tracks connecting various stations in the city based on the predicted data. This method can be based on the characteristics of the geographical locations of different cities, combined with various emergency events, including natural or human factors, in the current development of the city. By predicting these data, it is possible to evaluate the subsequent development of the urban track from the operating cost data, and further improve the accuracy of the city's overall planning target assessment from the perspective of subsequent risk costs and operating costs.

[0039] 3. The present invention is based on the urban rail transit evaluation results of the current stage of urban rail transit, and further analyzes the evaluation results of the current stage by combining the evaluation data of the subsequent stage. At the same time, the urban rail transit evaluation prediction model uses historical evaluation data to perform evaluation and prediction, and combines the evaluation data of the subsequent stage for calculation to improve the accuracy of the evaluation results of the subsequent stage. This method can ensure the accuracy of the evaluation results of the existing data, and at the same time, it can also combine the evaluation results of the subsequent stage to further enrich the evaluation scope of the evaluation results and improve the accuracy of the urban master plan target evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the method flow of the present invention;

[0041] Figure 2 It is a schematic diagram of the adjacent area of ​​the present invention;

[0042] Figure 3 Schematic diagram of the system flow of the present invention;

[0043] Figure 4 This is a flow chart of the rail transit assessment and prediction model of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] Urban rail is one of the important means of transportation in current urban development. It plays a vital role in both transportation and economy. Therefore, in terms of urban planning assessment, urban rail has also become an important factor in current urban planning assessment. Current methods can only summarize the current development situation, but cannot analyze the impact of the current development situation on subsequent development stages through the assessment results. As a result, the current assessment results have poor accuracy in assessing the overall urban planning goals. To this end, the present invention provides a method for integrating rail transit networks with urban planning. The technical solution is as follows:

[0046] Reference Figure 1 As shown, the rail transit data of the city is obtained, and the first rail impact data is obtained according to the coverage of the rail transit data;

[0047] Obtaining second rail impact data based on population data of each area of ​​the city; setting an urban rail transit assessment and prediction model to predict the first rail impact data and the second rail impact data, and obtaining third rail impact data and fourth rail impact data;

[0048] Obtaining historical disaster data of the rail transit data, and using the urban rail transit assessment and prediction model to predict future disaster data for first rail risk data of the rail transit data; obtaining second rail risk data through the historical risk data of the rail transit data and the urban rail transit assessment and prediction model;

[0049] Obtaining historical rail transit evaluation information of the city, and obtaining first traffic prediction evaluation information based on the urban rail transit evaluation prediction model and the historical rail transit evaluation information;

[0050] Calculating and obtaining second traffic forecast assessment information based on the third track impact data and the fourth track impact data; and calculating and obtaining third traffic forecast assessment information based on the first track risk data and the second track risk data;

[0051] An evaluation is performed in combination with the first traffic forecast evaluation information, the second traffic forecast evaluation information and the third traffic forecast evaluation information to generate comprehensive traffic forecast evaluation information; and an urban rail transit evaluation result is obtained through the current urban traffic evaluation information and the comprehensive traffic forecast evaluation information.

[0052] The present invention analyzes the population data of the coverage areas of each station in the existing urban rail transit network in combination with the population data of the adjacent areas. It uses the urban rail transit assessment and prediction model to predict the population changes in the current area and the adjacent areas. It analyzes and evaluates the population carrying capacity of the current station and the scalability of other areas to achieve the evaluation of the urban population development and the development of the rail transit in the subsequent stage, and improves the evaluation accuracy of the city's overall planning goals from the perspective of population development.

[0053] Based on cost data, this invention makes predictions based on historical disaster and risk data, and evaluates the additional cost of urban rail operations from multiple dimensions, including natural and human factors. This method can conduct detailed assessments based on the geographic location of different cities and combined with operating cost data caused by various emergency situations arising in the current development process. This can identify the gap between urban rail operating costs and the target costs of urban master plans, thereby improving the accuracy of assessments of urban master plan targets from a cost perspective.

[0054] Based on the existing assessment data, a comprehensive assessment is conducted in combination with the urban rail transit assessment prediction model to predict the assessment results of the subsequent stages, the predicted urban rail cost risk data, and the predicted urban rail population development data. This method can ensure the accuracy of the assessment results of the current stage while evaluating the subsequent impact from the two aspects of artificial intelligence data analysis and statistical data analysis. While improving the accuracy of the subsequent impact assessment, it can also improve the assessment accuracy of the city's overall planning goals.

[0055] Example 1

[0056] For the purpose of specific explanation, the present invention is described in conjunction with the following embodiments:

[0057] Obtain the city's rail transit data, and obtain the first rail impact data based on the coverage of the rail transit data; the coverage of the rail transit data is the population data of the urban area that each station is responsible for in urban planning, and generate the first rail impact data; for example, a subway line in City A covers District C and District D, and the line only covers part of District C and part of District D; the population data of this part of the area is the first rail impact data

[0058] The coverage can be calculated by demarcating each area based on expert experience and relevant development planning objectives;

[0059] Alternatively, by counting functional buildings, such as schools, hospitals, residential areas, industrial parks, and factories, the population of each area can be counted according to their functions, and the coverage area of ​​each site can be obtained by combining clustering algorithms.

[0060] Population changes have a significant impact on both urban master plan objectives and rail transit assessments. Since the population carrying capacity of each station in the urban rail transit network is limited, in order to analyze the impact of existing rail transit stations on the development of urban master plan objectives, it is necessary to obtain the urban areas covered by each rail transit station during the assessment process. This analysis is based on the population of the responsible area, providing important basic data for subsequent statistical and model evaluation analyses. This strengthens the data benchmark for the assessment of urban master plan objectives and improves the accuracy of subsequent assessments.

[0061] The second rail impact data is obtained based on the population data of each area of ​​the city. The second rail impact data is the total population data of the corresponding urban district based on the coverage of urban rail transit. For example, if a subway line in City A covers Districts C and D, the total population data of Districts C and D is the second rail impact data.

[0062] An urban rail transit assessment and prediction model is set up to predict the first track impact data and the second track impact data, and obtain the third track impact data and the fourth track impact data. The urban rail transit assessment and prediction model is a time series data analysis and prediction model. Since all data are generated based on time, population data will show positive or negative growth over time, and these positive and negative growth are closely related to the development of urban rail transit and the realization of urban master planning goals. Therefore, analyzing the characteristics of the time series data formed by these data for prediction can accurately grasp the current development situation, provide reliable data for subsequent evaluations, and improve the accuracy of urban master planning goals.

[0063] Obtain historical disaster data of rail transit data, and use the urban rail transit assessment and prediction model to predict future disaster data for the first rail risk data of rail transit data; obtain the second rail risk data through the historical risk data of rail transit data and the urban rail transit assessment and prediction model;

[0064] Obtaining historical rail transit assessment information of the city, and obtaining first traffic prediction assessment information based on the urban rail transit assessment prediction model and the historical rail transit assessment information;

[0065] Obtaining second traffic forecast assessment information by calculation based on the third track impact data and the fourth track impact data;

[0066] Furthermore, the second traffic forecast evaluation information is calculated as:

[0067]

[0068] Among them, λ Per is the second traffic prediction evaluation information, ω Pis the evaluation weight of rail transit stations, ε P Assessing weight for growth, is the average flow of people at the site, P max is the maximum traffic flow that the site can bear, ε P is the population growth weight, N r is the number of tracks connected to the station, ω Ri Evaluate the weight for the ith track, is the average passenger flow of the ith track, R imax is the maximum passenger flow that the ith track can bear, N A The number of neighboring regions in the city area that the station is responsible for, γ Pi is the site scalability evaluation weight of the i-th neighboring area, is the second track impact data of the ith adjacent area, is the fourth track impact data of the i-th adjacent area, M T is the third track impact data of the site, M F is the first track impact data of the site, α P is the population growth weight threshold, γ i is the evaluation weight of the i-th neighboring area, α i Evaluate the threshold for the i-th neighboring region; α P The default value is 2, which can be changed according to the situation; i The value of is set based on the growth of a large amount of historical population data combined with expert experience;

[0069] Neighboring area reference Figure 2 As shown, Figure 3 Circle A, circle B, rectangle C, rectangle D, and circle E are population concentration areas obtained through clustering algorithms and expert experience. The centers of circles A and B each include one station. The determination of neighboring areas is mainly based on the closest distance between the center points of each area.

[0070] Figure 2 Since the middle rectangle D is at the same distance as circles A and B, rectangle D is considered to be adjacent to circles A and B. Rectangle C is close to circle A, so rectangle C is adjacent to circle A. Circle E is close to circle B, so circle E is adjacent to circle B.

[0071] Figure 2 The result is that rectangle C and rectangle D are adjacent to circle A; rectangle D and circle E are adjacent to circle B;

[0072] The population data forecast and evaluation analysis is primarily based on the current carrying capacity of each rail transit station, including the number of people the station itself can accommodate and the number of people transported by the various tracks connected to the station. Since rail transit, such as high-speed rail or subway, drives the development of the surrounding economy, the population will also grow with economic development;

[0073] Therefore, during the evaluation process, it is necessary to obtain the population data of the area currently under the charge of each station, as well as the transportation capacity of the station connecting to each track for evaluation and analysis. Taking into account the subsequent population growth, a growth weight will be added based on the predicted data to provide more accurate evaluation data for subsequent urban master planning goals. On this basis, the present invention also measures the scalability of each station by predicting the population growth of the adjacent area. Through the population development of each region, a more comprehensive assessment of the development of the track and the city is carried out, providing more comprehensive evaluation data for subsequent urban master planning goals, thereby improving the accuracy of urban master planning goals.

[0074] The first track risk data and the second track risk data are calculated to obtain third traffic prediction assessment information;

[0075] Furthermore, the third traffic prediction evaluation information is calculated as:

[0076]

[0077] Among them, λ Risk For the third traffic forecast evaluation information, is the second track risk data, is the first track risk data, ξ kex is the total maintenance cost of the k-th track, is the impact time of the second track risk data, T k is the total operating time, is the unit maintenance cost of the i-th second track risk data, exp is the exponential function with e as the base, ξ jR is the maintenance cost of the jth first track risk data;

[0078] In addition to normal maintenance, urban rail transit systems may be susceptible to natural disasters of varying severity and frequency due to their geographical location, resulting in varying degrees of damage to the urban rail transit network. For example, rail transit in some coastal cities is susceptible to prolonged typhoons in the summer, while heavy rainfall and geographical features in some cities can lead to flooding that impacts the subway system. As cities develop, the increase in rail transit systems and the development of related emergency measures have a significant impact on the assessment of urban development.

[0079] At the same time, as the number of urban rail lines increases, it is inevitable that the operating costs of rail transit will increase due to special reasons such as insufficient systems and power supply, and other non-natural factors. The evaluation of these data is also of great reference significance for the subsequent evaluation of urban rail lines and cities.

[0080] Therefore, by obtaining risk data caused by historical natural disasters and non-natural disasters, we can predict the number of historical disasters and the number of risk data caused by non-natural disasters in the future based on historical data. We can use this data to conduct a weighted analysis based on the cost data of maintenance in each city to obtain an assessment of the additional significant cost investment in subsequent urban rail and rail in the urban development process. This assessment can be used to evaluate rail transit and urban development planning from a cost perspective, which will help to carry out subsequent evaluation work on urban master plan goals and improve the accuracy of urban master plan goal assessment.

[0081] Performing an evaluation by combining the first traffic forecast evaluation information, the second traffic forecast evaluation information, and the third traffic forecast evaluation information to generate comprehensive traffic forecast evaluation information; obtaining an urban rail transit evaluation result through the current urban traffic evaluation information and the comprehensive traffic forecast evaluation information;

[0082] Furthermore, the urban rail transit assessment results are calculated as:

[0083] λ CR =λ O +λ P ;

[0084]

[0085] Among them, λ CR is the evaluation result of urban rail transit, λ O is the current urban traffic assessment information, λ P For comprehensive traffic forecast assessment information, N Pt is the number of rail transit stations, is the third traffic prediction evaluation information of the i-th time, is the second traffic prediction evaluation information of the i-th time, λ F is the first traffic forecast evaluation information, α1 and α2 are evaluation weights; α1+α2=1; the values ​​of α1 and α2 are 0.6 and 0.4, respectively, and the specific values ​​can be modified based on expert experience and actual implementation;

[0086] Because the current assessment data is of a certain reference nature, it is incorporated into the subsequent impact assessment process to improve the accuracy of the city's overall planning objectives. At the same time, retaining the current assessment data will not affect the current assessment process. At the same time, in order to improve the accuracy of subsequent assessments, in addition to the evaluation calculations using statistical methods, the model evaluation results are also incorporated. Through the comprehensive evaluation and analysis of these two results, the evaluation dimensions can be effectively improved to enhance the accuracy of the city's overall planning objectives.

[0087] Example 2

[0088] The present invention also provides a rail transit network and urban planning integrated evaluation system, which can be based on the urban rail transit evaluation prediction model, and can quickly train and predict through existing historical data. At the same time, through the set formulas and model parameters, it can realize automated evaluation work and improve the efficiency of evaluation; Figure 3 As shown, the system includes an urban rail assessment data acquisition module, an urban rail population assessment data prediction module, an urban rail disaster assessment data prediction module, an urban rail assessment parameter calculation module and an urban rail assessment result calculation module, specifically:

[0089] The urban rail assessment data acquisition module obtains the city's rail transit data and obtains the first rail impact data based on the coverage of the rail transit data; and obtains the second rail impact data based on the population data of each area in the city. The coverage of the rail transit data is the population data of the urban area covered by each station in the urban planning, and generates the first rail impact data.

[0090] The urban rail transit population assessment data prediction module sets an urban rail transit assessment prediction model to predict the first track impact data and the second track impact data, and obtains the third track impact data and the fourth track impact data; the urban rail transit assessment prediction model is a time series data analysis and prediction model;

[0091] The rail transit evaluation prediction model includes an evaluation data preprocessing unit, an evaluation data feature extraction unit, and an evaluation data prediction output unit. Figure 4 As shown;

[0092] The evaluation data preprocessing unit mainly sorts all input data in chronological order to form a 1*n time series, and then preprocesses the time series through standardization and noise reduction methods to generate a preprocessed time series;

[0093] The preprocessed time series is input into the evaluation data feature extraction unit to extract time series features and obtain prediction feature data; the time series prediction model selected by the evaluation data feature extraction unit is GRU;

[0094] The evaluation data prediction output unit performs feature fusion analysis based on the prediction feature data to make predictions and obtain prediction results;

[0095] This example uses a GRU model for training and prediction using simulated data. A total of five sets of simulated data are used for analysis in terms of accuracy and computational speed. The five sets of simulated data have different amounts of data. All data are daily passenger flow data collected at a certain subway station over the past few years. The population of the area covered by the station is estimated using a 20-fold relationship based on expert experience. The first set of data is 2 years of data, the second set is 3 years of data, the third set is 4 years of data, the fourth set is 5 years of data, and the fifth set is 6 years of data.

[0096] Each data set was trained using the same computer hardware. Parameters were trained using 80% of the data set as a training set and 20% as a test set. After retaining the best parameters, predictions were made using the same prediction data. Table 1 shows the best results in terms of accuracy and computational speed for the five data sets.

[0097] Table 1. Results of prediction accuracy and calculation rate of GRU model for five groups of data

[0098] Data Type Accuracy Calculation rate Group 1 76.5％ 4 minutes and 32 seconds Group 2 79.2％ 4 minutes and 41 seconds Group 3 82.3％ 4 minutes 51 seconds Group 4 84.6％ 4 minutes 56 seconds Group 5 85.7％ 5 minutes 03 seconds

[0099] From the results in Table 1, we can see that the accuracy of the prediction results is relatively high. As the amount of data increases, the accuracy also gradually increases. Although the calculation efficiency has decreased to a certain extent, the overall speed is still relatively high. The final prediction result has an accuracy of 85.7%, which has a certain reference value for the evaluation of the integrated evaluation system of rail transit network and urban planning.

[0100] The urban rail disaster assessment data prediction module obtains historical disaster data of rail transit data and combines it with the urban rail transit assessment prediction model to predict the first rail risk data of future disaster data for rail transit data; and obtains the second rail risk data through the historical risk data of rail transit data and the urban rail transit assessment prediction model;

[0101] The urban rail transit assessment parameter calculation module obtains the city's rail transit historical assessment information, obtains first traffic forecast assessment information based on the urban rail transit assessment prediction model and the rail transit historical assessment information; obtains second traffic forecast assessment information based on the third rail impact data and the fourth rail impact data; and obtains third traffic forecast assessment information based on the first rail risk data and the second rail risk data;

[0102] The urban rail transit assessment result calculation module combines the first traffic forecast assessment information, the second traffic forecast assessment information, and the third traffic forecast assessment information to perform an assessment and generate comprehensive traffic forecast assessment information; and obtains the urban rail transit assessment result through the current urban traffic assessment information and the comprehensive traffic forecast assessment information;

[0103] Furthermore, the urban rail transit assessment results are calculated as follows:

[0104] λ CR =λ O +λ P ;

[0105]

[0106] Among them, λ CR is the evaluation result of urban rail transit, λ O is the current urban traffic assessment information, λ P For comprehensive traffic forecast assessment information, N Pt is the number of rail transit stations, is the third traffic prediction evaluation information of the i-th time, is the second traffic prediction evaluation information of the i-th time, λ F is the first traffic prediction evaluation information, α1 and α2 are evaluation weights; α1+α2=1.

[0107] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A rail transit network and urban planning integration assessment method, characterized in that: include: Acquire rail transit data of the city, and acquire first rail impact data based on coverage of the rail transit data; Obtaining second track impact data based on population data of each area of ​​the city; Setting an urban rail transit assessment prediction model to predict the first rail impact data and the second rail impact data, and obtaining third rail impact data and fourth rail impact data; Acquire historical disaster data of the rail transit data, and use the urban rail transit assessment and prediction model to predict first rail risk data of future disaster data on the rail transit data; Acquire second rail risk data through the historical risk data of the rail transit data and the urban rail transit assessment and prediction model; Obtaining historical rail transit evaluation information of the city, and obtaining first traffic prediction evaluation information based on the urban rail transit evaluation prediction model and the historical rail transit evaluation information; Calculating and acquiring second traffic forecast assessment information based on the third track impact data and the fourth track impact data; The first track risk data and the second track risk data are used to calculate and obtain third traffic prediction assessment information; An evaluation is performed in combination with the first traffic forecast evaluation information, the second traffic forecast evaluation information and the third traffic forecast evaluation information to generate comprehensive traffic forecast evaluation information; and an urban rail transit evaluation result is obtained through the current urban traffic evaluation information and the comprehensive traffic forecast evaluation information.

2. A rail transit network and urban planning integration assessment method according to claim 1, characterized in that: The coverage of the rail transit data is to obtain the population data of the urban area that each station is responsible for in urban planning, and generate the first rail impact data.

3. The rail transit network and urban planning integration assessment method according to claim 1 is characterized in that: The urban rail transit evaluation and prediction model is a time series data analysis and prediction model.

4. The rail transit network and urban planning integration assessment method according to claim 1 is characterized in that: Calculating and obtaining second traffic forecast evaluation information based on the third track impact data and the fourth track impact data includes: Among them, λ Per The second traffic prediction evaluation information, ω P is the evaluation weight of rail transit stations, ε P Assessing weight for growth, is the average flow of people at the site, P max is the maximum traffic flow that the site can bear, ε P is the population growth weight, N r is the number of tracks connected to the station, ω Ri Evaluate the weight for the ith track, is the average passenger flow of the ith track, R imax is the maximum passenger flow that the ith track can bear, N A The number of neighbors responsible for the urban area of ​​the station, γ Pi is the site scalability evaluation weight of the i-th neighboring area, is the second track impact data of the i-th adjacent area, is the fourth track impact data of the i-th adjacent area, M T The third track impact data for the site, M F The first track impact data of the site, α P is the population growth weight threshold, γ i is the evaluation weight of the i-th neighboring area, α i Evaluate the threshold for the i-th neighborhood.

5. The rail transit network and urban planning integration assessment method according to claim 1 is characterized in that: Calculating the first track risk data and the second track risk data to obtain the third traffic prediction assessment information includes: Among them, λ Risk The third traffic prediction evaluation information, For the second track risk data, is the first track risk data, ξ kex is the total maintenance cost of the k-th track, The impact time of the second track risk data, T k is the total operating time, is the unit maintenance cost of the i-th second track risk data, exp is the exponential function with e as the base, ξ jR is the maintenance cost of the j-th risk data of the first track.

6. The rail transit network and urban planning integration assessment method according to claim 1 is characterized in that: Obtaining the urban rail transit assessment result through the city's traffic assessment information and the comprehensive traffic forecast assessment information includes: l CR =λ O +λ P ; Among them, λ CR is the urban rail transit evaluation result, λ O is the current urban traffic assessment information, λ P is the comprehensive traffic forecast evaluation information, N Pt is the number of rail transit stations, The third traffic prediction evaluation information of the i-th item, is the second traffic prediction evaluation information of the i-th item, λ F is the first traffic prediction evaluation information, α1 and α2 are evaluation weights; α1+α2=1.

7. A rail transit network and urban planning integration evaluation system, characterized by: include: An urban rail transit assessment data acquisition module acquires urban rail transit data and acquires first rail impact data based on the coverage of the rail transit data; Obtaining second track impact data based on population data of each area of ​​the city; An urban rail population assessment data prediction module is configured to set an urban rail transit assessment prediction model to predict the first rail impact data and the second rail impact data, and obtain the third rail impact data and the fourth rail impact data; An urban rail disaster assessment data prediction module obtains historical disaster data of the rail transit data and predicts first rail risk data of the rail transit data with the help of the urban rail transit assessment prediction model based on future disaster data; Acquire second rail risk data through the historical risk data of the rail transit data and the urban rail transit assessment and prediction model; An urban rail transit evaluation parameter calculation module obtains historical rail transit evaluation information of the city, obtains first traffic forecast evaluation information based on the urban rail transit evaluation prediction model and the historical rail transit evaluation information; and obtains second traffic forecast evaluation information based on the third rail impact data and the fourth rail impact data. The first track risk data and the second track risk data are used to calculate and obtain third traffic prediction assessment information; The urban rail transit evaluation result calculation module combines the first traffic forecast evaluation information, the second traffic forecast evaluation information and the third traffic forecast evaluation information to perform evaluation and generate comprehensive traffic forecast evaluation information; and obtains the urban rail transit evaluation result through the current urban traffic evaluation information and the comprehensive traffic forecast evaluation information.

8. The rail transit network and urban planning integration evaluation system according to claim 7 is characterized in that: The coverage of the rail transit data is to obtain the population data of the urban area that each station is responsible for in urban planning, and generate the first rail impact data.

9. The rail transit network and urban planning integration evaluation system according to claim 7, characterized in that: The urban rail transit evaluation and prediction model is a time series data analysis and prediction model.

10. The rail transit network and urban planning integration evaluation system according to claim 7, characterized in that: The urban rail transit evaluation result calculation module obtains the urban rail transit evaluation result through the current urban traffic evaluation information and the comprehensive traffic forecast evaluation information, including: l CR =λ O +λ P ; Among them, λ CR is the urban rail transit evaluation result, λ O is the current urban traffic assessment information, λ P is the comprehensive traffic forecast evaluation information, N Pt is the number of rail transit stations, The third traffic prediction evaluation information of the i-th item, is the second traffic prediction evaluation information of the i-th item, λ F is the first traffic prediction evaluation information, α1 and α2 are evaluation weights; α1+α2=1.

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