CN115796620A - High-speed rail station influence area scale prediction method based on node-site model - Google Patents
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Abstract
The invention discloses a scale prediction method for a high-speed rail station influence area based on a node-place model, which comprises the following steps of 1) determining a research object, selecting a relatively mature and operated high-speed rail station and analyzing characteristic data of the station; step 2), constructing a node-site index system; step 3), index weight confirmation is carried out, and index weight is obtained through an entropy weight method and an analytic hierarchy process; step 4), carrying out non-dimensionalization treatment on each index data; step 5), evaluating and analyzing the node value and the site value of the selected high-speed rail station; and 6) carrying out regression analysis to obtain a node-site model for predicting the scale of the influence area of the newly-built high-speed rail station. According to the method, weight determination is carried out on indexes of all levels according to the self condition of the high-speed rail station by combining an analytic hierarchy process and an entropy weight method, the node value and the site value of each station are evaluated, a regression model is established, the site value of the high-speed rail station can be accurately predicted, and theoretical and method bases are provided for planning and newly-built scale prediction of the high-speed rail station influence area.
Description
Technical Field
The invention relates to the technical field of high-speed rail station value prediction and evaluation, in particular to a high-speed rail station influence area scale prediction method based on a node-site model.
Background
The node-site model developed by the netherlands scholars Bertolini in the 1996 era looked at the node and location functions of the train stations as a whole, followed the same reasoning as the general traffic-land use feedback loop, and aimed at further exploring the potential relationship (particularly their equilibrium or equilibrium state) between the two functions. Bertolini and his colleagues have comprehensively studied the node functions and location functions of the train station and advocated a balanced development between them. In another related paper, chorus and Bertolini further applied node-site models to explore the spatial development dynamics of the Tokyo station area. Despite their enormous efforts, their method of computing node and site values seems to be somewhat arbitrary, as different scholars can define and evaluate node and site values in different ways. Thus, although the assumptions about the development of the balance between the train station nodes and the site seem reasonable, how to quantify these values remains a huge challenge and is not yet specified so far. Bei Tuoli ni has the disadvantage of lacking a basic index of coverage for some important land use and transport capacity. Furthermore, the relative importance of different indices is ignored, while some indices may be more important than others. Reuser et al improved the indices of the node site model in his study and then investigated their ranking. Studies have shown that the change is not significant when the ratio of each index is increased. Many studies have attempted to refine the node-site model by introducing new indices and additional dimensions to better reflect the building environment characteristics of the terminal area and fully describe the station area. Zemp et al perfected the node-location model by adding new indices (e.g., passenger frequency distributions) that reflect local travel characteristics in switzerland. Kamruzzaman et al used six TOD indicators (employment density, dwelling density, land use diversity, intersection density, mustache density, and public transportation accessibility).
Further, although there are many studies on the characteristics of station node sites, the relationship between traffic nodes and a wider area is not considered. As can be seen from an analysis of the current study, the study analysis or project is typically focused on the area around the traffic node (typically a maximum distance is set at 800 meters-roughly equivalent to a 10 minute walk, considered an acceptable time of ingress and egress for the traffic node), whereas in small cities or suburbs many origins and destinations are far away. In these cases, the following key factors must be considered: the traffic node is reached by different travel modes, and the starting place and the destination of the city center position (travel) are determined under the wider geographical background.
CN 113256964A discloses a method for designing the capacity of an urban switching center based on a node-place model, which comprises the following steps: determining functional area division of an urban switching center, and determining the scale of each functional area according to a capacity calculation method of each functional area of the urban switching center; introducing a node-site model, selecting indexes related to the scale of the functional area of the urban switching center, and performing normalization processing on the selected indexes so as to conveniently determine the cooperative state of the node by combining the node-site model; and adjusting the capacity of the unbalanced node according to the cooperative state of the node, applying the ratio of the node in a balanced state to the unbalanced node, adjusting the scale ratio of the functional areas of the unbalanced node to make the functional areas tend to be in a balanced state, and adding the scales of the functional areas in the balanced state to obtain the total capacity of the urban switching center. The method can reduce the capacity calculation error of the traditional method, is beneficial to realizing the cooperation among the scales of all functional areas, and promotes the coordinated development of the overall structure of the urban switching center. The method mainly solves the problem that the capacity design of traffic nodes in cities provides reference, and the node-place model is difficult to predict the value of the largest traffic node-high-speed rail station between cities.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for predicting the scale of the high-speed rail station influence area based on a node-site model.
In order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows:
a high-speed rail station influence area scale prediction method based on a node-site model comprises the following steps:
step 1), determining a research object, selecting a high-speed rail station which is established to operate, namely a mature high-speed rail station, as the research object, and acquiring characteristic data required by analysis;
step 2), constructing a node index system and a site index system;
step 3), index weight confirmation is carried out, and index weight is obtained through an analytic hierarchy process and an entropy weight process;
step 4), carrying out dimensionless treatment on each index data, carrying out normalization treatment on each index, setting the highest scoring station as 1 and the lowest scoring station as 0 on the index values after the treatment between 0 and 1;
step 5), evaluating and analyzing the node value and the site value of the selected high-speed rail station;
step 6), carrying out regression analysis on the node value and the site value of the site; and obtaining a node-site model for predicting the scale of the influence area of the newly-built high-speed rail station.
Further, the normalization processing in step 4) includes performing linear transformation on the data indexes to make the data indexes fall into a specified interval, eliminating the variance between the variables while not changing the co-integration relationship between the variables, so that indexes with different units can be evaluated and compared at the same level, and in order to accurately reflect the influence of each index on the node value and eliminate the error of each index data caused by different dimensions, self-variation or large numerical value difference, each index needs to be subjected to non-dimensionalization processing, and the original data is transformed by adopting an extreme method to make all indexes fall into a (0,1) interval in a non-dimensionalization manner, and the indexes are worse when being closer to 0; the closer to 1, the better the performance, and the entropy weight method is combined to determine the index weight of the node value so as to obtain the specific value of the node value corresponding to each node.
Further, the node index system in the step 2) specifically comprises two first-level indexes of city external accessibility and city internal accessibility, wherein the city external accessibility comprises two second-level indexes of service route number and daily service frequency; the accessibility in the city comprises three secondary indexes of the number of bus service routes in 1KM, the distance from the bus to the city center and the time from the bus to the city center.
Further, the first-level indexes of the accessibility outside the city and the accessibility inside the city are determined by an analytic hierarchy process. The external accessibility of the city comprises two secondary indexes of the number of service routes and the daily service frequency; the accessibility in the city comprises three secondary indexes of the number of bus service routes in 1KM, the distance between the bus and the city center and the time between the bus and the city center, and the index weight is determined by an entropy weight method.
Further, the specific calculation steps of the entropy weight method in step 3) are as follows:
firstly, the proportion y of the j index of the ith project to the j index of all projects is calculated ij ：
m is the total number of items substituted into the formula, x ij For the jth index of the ith item, calculating the entropy value e of the jth index j And the value coefficient d j Entropy value coefficient d j Can measure the difference between the indexes, the entropy value e j The smaller the difference coefficient d between the indices j The larger the index, the more important the index is;
d j ＝1- e j
in the formula: constant n is total number of items, then weight W of jth index j Comprises the following steps:
further, measuring and calculating the node value in the step 5): the outside-city reachability weighting system is 0.84 and the inside-city reachability weighting coefficient is 0.16, wherein the outside-city reachability comprises the number of service routes of 0.32 and the daily service frequency of 0.68; the urban interior reachability includes 0.57 bus service route number, 0.33 bus-to-city center distance and 0.11 bus-to-city center time within 1 KM.
Further, the estimation formula of the site value in the step 5) is as follows:
i.e. site value = design size site function
In the formula: s i The land utilization scale of the high-speed rail station area is expressed by the occupied area; beta is a in The field value coefficient is formed by multiplying a design scale coefficient and a function coefficient.
Further, the linear regression manner of the node-site model in step 6) is as follows: y =1.5158x +0.1302 linear regression of R 2 And (5) carrying out reverse-deduction to obtain a predicted value of the scale of the high-speed rail station influence area according to a formula of the predicted site value, the site value and the design scale, wherein y is 0.724 and x is the node value.
Compared with the prior art, the method has the advantages that an index system is constructed according to the self condition of the high-speed rail station, the indexes of all levels are subjected to weight determination by combining an analytic hierarchy process and an entropy weight method, and the node-site value evaluation result is subjected to normalized analysis. The data indexes of the method are easy to obtain, the node-site value of the high-speed rail station can be accurately predicted, and an important theoretical basis can be provided for the scale prediction of the high-speed rail station influence area planned.
Drawings
FIG. 1 is a flow chart of a method for predicting the scale of the high-speed rail station influence area based on a node-site model.
FIG. 2 is a node architecture diagram of the present invention.
Fig. 3 is a functional deconstruction schematic diagram of a high-speed rail station area.
Fig. 4 is a high-speed rail station area node value map.
Fig. 5 is a high-speed rail station area site value map.
Fig. 6 is a high-speed rail station area node-site model linear regression diagram.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings and the embodiment. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the steps of this embodiment:
step 1), determining a research object, selecting a high-speed rail station which is already established to operate as the research object, and acquiring characteristic data required by analysis;
step 2), constructing a node index system and a site index system;
step 3), index weight confirmation is carried out, and index weight is obtained through an analytic hierarchy process and an entropy weight process;
step 4), carrying out dimensionless treatment on each index data, carrying out normalization treatment on each index, setting the highest scoring station as 1 and the lowest scoring station as 0 on the index values after the treatment between 0 and 1;
step 5), evaluating and analyzing the node value and the site value of the selected high-speed rail station;
step 6), carrying out regression analysis on the node value and the site value of the site; and obtaining a node-site model for predicting the scale of the influence area of the newly-built high-speed rail station.
The following is an example implementation:
in the present embodiment, the study subject is selected from existing studies related to the construction of high-speed rail station areas, and the scholars focus on the study subject in cities along the railway such as the Jingguang railway and the Jinghun railway, or mainly select the high-speed rail station areas located in the large cities such as the Beijing south station as excellent cases for analysis. The embodiment aims to summarize the general city scale growth of the high-speed rail station area from the construction condition of the high-speed rail station area at the present stage, and analyze the node value and the site value of the high-speed rail station area. Therefore, the selection of the research object should have universality, so that the land development is mature, the research area meeting the requirement should cover a large number of high-speed rail station areas, and the characteristics of the samples in the high-speed rail station areas should be diversified, and the samples should include station areas belonging to different passenger transport grades, having different scales in different lines and in the cities so as to reflect the diversity of the samples and the universality of the analysis result. Therefore, it is not suitable to select a specific high-speed rail line along a high-speed rail station area or a high-speed rail station area discontinuous in the area as an analysis object.
According to the scheme of integrated development planning of the Yangtze river delta area officially approved for implementation in 12 months in 2019, the Yangtze river delta area comprises the whole range of the three provinces and the first city, namely Shanghai city, jiangsu province, zhejiang province and Anhui province. The high-speed rail in the area is built earlier, high-speed rail lines are more, and the economic foundation is better. High-iron plays an important role in promoting industrial aggregation and transfer. And selecting the Tianjin Xian station, the Shenzhen North station, the Shenzhen station, the Guangzhou south station, the Chengdu Dongdu station, the Chengdu station, the Chongqing West station, the Chongqing North station, the Shenyang station and the Dalian station which are developed more maturely and are built for a long time as supplements. Based on this, in this embodiment, five long-triangle high-speed rail lines and a high-speed rail station developed in the domestic part are selected as specific research objects. The following table is selected and arranged:
and partial city site information table (obtainable through government website, baidu, etc.)
The data source and processing in this embodiment are as follows:
in the node value index measurement and calculation data, the external traffic service capacity data of the station comes from a 12306 website, wherein the parking number information comes from a Xinlang net and a 12306 website, and the data time is 2021 years or 2022 years and 4 months; the urban traffic connection capability data is derived from a location retrieval and routing service in a Baidu map web service. In the place value index measuring and calculating data, station grade and design scale data come from Wikipedia and Baidu encyclopedia, and the function positioning of the station refers to the diversity degree of the function and the industry of the high-speed rail station influence area, and is obtained by information arrangement of government websites, baidu encyclopedia, baidu maps, google and other search platforms.
The data is standardized according to the principle that the data indexes are subjected to linear transformation and fall into a specified interval, so that the heteroscedasticity between the variables is eliminated while the co-integration relation between the variables is not changed, and the indexes in different units can be evaluated and compared at the same level conveniently. In order to accurately reflect the influence of each index on the node value and eliminate the error of each index data caused by different dimensions, self variation or larger numerical value difference, each index needs to be subjected to dimensionless processing. Original data is transformed by adopting an extremum method, so that all indexes fall into a (0,1) interval in a dimensionless manner, and the closer to 0, the worse the performance is; the closer to 1, the better the performance, and the entropy weight method is combined to determine the index weight of the node value so as to obtain the specific value of the node value corresponding to each node. The standard formula for the range method is as follows:
as shown in fig. 2, a node index system is constructed, specifically including two first-level indexes of city external reachability and city internal reachability, where the city external reachability includes two second-level indexes of service route number and daily service frequency; the accessibility in the city comprises three secondary indexes of the number of bus service routes in 1KM, the distance from the bus to the city center and the time from the bus to the city center.
Determining the index weight combining the qualitative and the quantitative:
and determining index weights by an analytic hierarchy process according to two primary indexes of the accessibility outside the city and the accessibility inside the city. The external accessibility of the city comprises two secondary indexes of the number of service routes and the daily service frequency; the accessibility in the city comprises three secondary indexes of bus service route number in 1KM, distance between buses and city centers and time between the buses and the city centers, and the index weight is determined by an entropy weight method.
In order to reduce and avoid subjective factors as much as possible and solve certain objective limitations in the weight determination process, the embodiment adopts an index weight determination method combining qualitative and quantitative methods, namely an analytic hierarchy process and an entropy weight method.
The analytic hierarchy process is a systematic and hierarchical analytic process combining qualitative analysis and quantitative analysis, and the node index system constructed in the embodiment has obvious hierarchical characteristics and is more suitable for adopting the analytic hierarchy process. The core of the analytic hierarchy process is to determine the weight size by comparing the relative importance between indexes and between criteria, and by constructing a contrast matrix, and finally determine the importance degree between every two indexes through big data experimental data.
A node index AHP analysis table obtained by an analytic hierarchy process:
| item(s) | Feature vector | Weight coefficient w |
| Outside accessibility in cities | 1.672 | 0.84 |
| City is interior accessibility | 0.328 | 0.16 |
The entropy weight method comprises the following specific calculation steps:
firstly, the proportion y of the j index of the ith project to the j index of all projects is calculated ij ：
m is the total number of items substituted into the formula, x ij For the jth index of the ith item, calculating the entropy value e of the jth index i And the value coefficient d j Entropy value coefficient d j Can measure the difference between the indexes, the entropy value e i The smaller the difference coefficient d between the indices j The larger the index, the more important the index is;
d j ＝1-e j
in the formula: constant n is total number of items, then weight W of jth index j Comprises the following steps:
the node index entropy weight method analysis table is as follows:
the final weight determination of the node index results in the following:
and (3) measuring and calculating the node value:
the node value measurement system table is as follows:
and the table represents that if the high-speed rail station is located in a county-level administrative district, relevant index statistics are carried out according to a county-level city, and if the high-speed rail station is located in a district-level administrative unit, relevant index statistics are carried out according to a direct district scope of a prefecture-level city. The distance to the central urban area is calculated for the study according to the vector geographic information data.
The node values of the high-speed rail stations are measured and calculated according to the table, and the node values of the high-speed rail stations are represented by performing standardized processing and sequencing on the results, as shown in the following table and figure 4, measured and calculated data are arranged from large to small, and then it is found that the urban scale is positively correlated with the node values, and the higher the urban scale is, the higher the node values are. In addition, the node values are more unevenly distributed among stations in large cities such as Guangzhou south station, nanjing south station, hangzhou east station and Chengdu east station, and are obviously higher than tin-free stations, suzhou stations, tianjin west stations and the like.
And (3) measuring and calculating the place value:
as shown in fig. 3, due to the differences in the geographical location, station grade, design scale and floor area of the high-speed railway stations, there will be great differences in the functional location of the urban areas where they are located. The peripheral industrial door types of high-speed railway station are abundant, mainly include living in, amusement and recreation, commercial affairs, trade, tourism, commodity circulation, headquarter economy, cultural intention, finance, high and new technology, exhibition, information service, electronic commerce, research and development, outsourcing, education and training, to the high-speed railway station district, the peripheral industrial door types of its parcel are abundant more, and the function of undertaking is more, and the drive city development function that plays is just more showing. For the influence plots of high-speed railway stations with important geographic positions, high station grade and design scale and large occupied areas, the corresponding positioning of the influence plots is also high, such as city centers or city depocenter. For a high-speed railway station influence land parcel with common geographic position, medium station grade and design scale and common floor area, the corresponding positioning is also common, such as a city or an area portal, a function demonstration area or an industrial platform. For a high-speed railway station with poor geographic position, low station grade and design scale and small floor area, the corresponding positioning is also low, such as a transportation junction or an isolated transportation station.
The embodiment calculates and calculates the site value of the high-speed rail station area by comprehensively considering the design scale of the high-speed rail station, the land scale and the function positioning of the high-speed rail station area, the industrial development profile and other indexes, and the specific calculation formula is as follows: comprises the following steps:
i.e. site value = design size site function
In the formula: s. the i The land utilization scale of the high-speed rail station area is expressed by the occupied area; beta is a in The field value coefficient is composed of a design scale coefficient and a function coefficient. The design scale factor is formed by multiplying a design scale coefficient and a function coefficient (specific coefficient assignment is shown in a table), wherein the design scale comprises two parts of station platform and line quantity, which represent a division standard for self passenger transportation quantity and technical workload, the more the platform and line quantity is, the larger the value is, the more the place function coefficient represents the regional function diversification degree born by a high-speed rail station area, and the more the function types are, the larger the value is. The design scale comprises the number of the months and the number of the lines of the sites, and the number of the months, the number of the lines and the design scale of the sites form positive correlation.
As shown in the following table and fig. 5, in the place value calculation and settlement sorting, the place values of the south beijing station, the south guangzhou station, the east hangzhou station, the shenyang station and the north Chongqing station are obviously higher than those of other high-speed railway stations, and the high-speed railway stations of the north chanzhou station, the south Zhenjiang station, the distance station, the south Jiaxing station and the south Kunshan station are smaller in scale and lower in place value. Most of high-speed rail stations have low site value generally, the difference is not obvious, the development of the high-speed rail station areas in China is reflected to be in a low-degree development state generally, and the government has limited effect on guiding the development of the station area industry. In addition, there is no significant relationship between the urban size of the area where the high-speed rail station is located and the value of the urban site. In some medium and small cities, the development intensity and scale of the industry in high-speed rail station areas even exceed those of large cities. For example, although the metropolitan stations are located in the city center, the site value is relatively low due to the limitation of places and the extremely limited development area of high-speed rail station areas. The size of the Yiwu city where the Yiwu station is located is far different from that of Shenzhen city and Suzhou city, but the size of the high-speed rail station area is far larger than that of the Wu station, the Suzhou station, the Shenzhen station and the like.
The present example was evaluated:
the multi-index evaluation system needs to perform correlation test on indexes and judge whether the relation and regularity between the indexes meet the conventional understanding. The correlation test aims to analyze whether causal relationship exists among variables and judge the correlation degree and direction of the variables according to the coefficient value.
The common test parameter is a Pearson (Pearson) correlation coefficient or a Spearman (Spearman) correlation coefficient, if the correlation coefficient between the variables is positive, the change trends of the two variables are the same, and if the correlation coefficient is negative, the change trends of the two variables are opposite. The check value is between-1 and 1, and the closer the absolute value is to 1, the more closely the two variables are related. Correlation coefficients greater than 0.6 are considered significant correlations between the indices.
And (4) performing relevance test on the indexes subjected to dimensionality division according to the node value and the site value by using a Pearson relevance coefficient, and taking the average value of the dimensionality indexes as a value index. The inspection result shows that the Pearson correlation coefficient value of the selected station is 0.794, which shows that a significant positive correlation exists between the node value and the site value, and a continuous and positive feedback mechanism exists between the node value and the site value of the high-speed rail station area. The result accords with the relation logic and the expected direction of the node-place model, and the index can be used for constructing the node-place initial model.
As shown in fig. 6, by evaluating the model,and (3) measuring and calculating a node-site model linear regression graph of the high-speed rail station, wherein the linear regression mode is as follows: y =1.5158x +0.1302 linear regression of R 2 And =0.724, y is a site value, x is a node value, a predicted value of the scale of the affected area of the high-speed rail station is obtained by reverse estimation according to a formula of the predicted site value and the design scale, and the future high-speed rail station is analyzed and predicted through linear regression.
In addition, such a station located in a large city is easier to reduce a part of lines, and the reduced accessibility can cause the reduction of the node value, and for a high-speed rail station in a node imbalance state, the high-speed rail station area may be just in a balanced development state.
The node-site value model evaluation can be performed on the planned and constructed high-speed rail station through the embodiment:
the embodiment takes a planned and constructed Nanjing Beijing station as a case, the Nanjing Beijing station is one of Nanjing main hub passenger stations, integrates various traffic forms such as railways, highways, urban rails and public transport, mainly undertakes long-distance motor train transportation in Shanghai and Subei areas, and simultaneously communicates peripheral areas such as Hening, ninghuai and Ningbao. The functional location is that the Beijing station of Beijing along the river railway in the economic zone of Yangtze river is one of the main hub passenger stations of Nanjing, integrates various traffic modes such as railway, highway, urban rail and public traffic, mainly undertakes long-distance motor train transportation in Shanghai and Subei areas, and simultaneously communicates peripheral areas such as Hening, ninghuai and Ningpeng. The functional positioning is an important node of the north Yangtze river railway in the economic zone of Yangtze river and is also an important comprehensive hub in the Beijing area of south China. The method comprises the steps of taking an as-yet-built Nanjing Beijing station as a research object, and predicting the influence scale of future high-speed rail station areas of the Nanjing Beijing station through linear regression of node-site model evaluation results of the high-speed rail station areas.
After the built Nanjing and Beijing station is deduced by the upper node value measuring and calculating method, the node value of the Nanjing and Beijing station area is substituted into a linear regression equation through normalization processing to be 0.175, wherein the Nanjing and Beijing station refers to the external accessibility result (8 stations 16 lines) of the Nanjing station as the node index of the Nanjing and Beijing station for external connection because the Nanjing and Beijing station is not built. And substituting the normalized data into a linear regression equation to deduce that the station area site value is about 0.0296. The site value is brought into a formula of site value = design scale and site function, the scale of the high-speed rail station influence area is 8.5-12.5 square kilometers in consideration of statistical sampling errors, the actual situation is that the hub economic area of the Nanjing Beijing station is planned to be 8.7 square kilometers, and the planning of the hub economic area is reflected to be reasonable to a certain extent.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (10)
1. A high-speed rail station influence area scale prediction method based on a node-site model is characterized by comprising the following steps:
step 1), determining a research object, selecting a high-speed rail station which is already established to operate as the research object, and acquiring characteristic data required by analysis;
step 2), constructing a node index system and a site index system;
step 3), index weight confirmation is carried out, and index weight is obtained through an analytic hierarchy process and an entropy weight process;
step 4), carrying out dimensionless treatment on each index data, carrying out normalization treatment on each index, setting the highest scoring station as 1 and the lowest scoring station as 0 on the index values after the treatment between 0 and 1;
step 5), evaluating and analyzing the node value and the site value of the selected high-speed rail station;
step 6), carrying out regression analysis on the node value and the site value of the site; and obtaining a node-site model for predicting the scale of the influence area of the newly-built high-speed rail station.
2. The method for predicting the scale of the influence area of the high-speed rail station based on the node-site model according to claim 1, wherein the node index system in the step 2) specifically comprises two primary indexes of urban external accessibility and urban internal accessibility, wherein the urban external accessibility comprises two secondary indexes of service route number and daily service frequency; the accessibility in the city comprises three secondary indexes of the number of bus service routes in 1KM, the distance from the bus to the city center and the time from the bus to the city center.
3. The method for predicting the scale of the influence area of the high-speed rail station based on the node-site model as claimed in claim 2, wherein the first-level indexes of the outside-city accessibility and the inside-city accessibility are determined by an analytic hierarchy process.
4. The method for predicting the scale of the high-speed rail station influence area based on the node-site model according to claim 2, wherein the urban external accessibility comprises two secondary indexes of the number of service routes and the daily service frequency; the accessibility in the city comprises three secondary indexes including the number of bus service routes in 1KM, the distance between the bus and the city center and the time between the bus and the city center, and the index weight is determined by the secondary indexes through an entropy weight method.
5. The method for predicting the scale of the influence area of the high-speed rail station based on the node-site model according to claim 5, wherein the site value system in the step 2) specifically comprises a design scale and site functions, wherein the design scale comprises 10 lines and more, 6 lines from 10 lines to 10 lines (excluding), 4 lines from 8 lines to 6 lines (excluding), 2 lines from 4 lines to 4 lines (excluding), and 2 lines below 4 lines, and values of the values are 1.0, 0.6, 0.4, 0.2 and 0.1; the site functions respectively comprise a city center or a subsidiary center, a city portal or a new city area, a function demonstration area or an industrial platform, a common transportation hub and an isolated station, and the values of the site functions are 1.0, 0.8, 0.6, 0.2 and 0.
6. The method for predicting the scale of the influence area of the high-speed rail station based on the node-site model according to claim 6, wherein the entropy weight method in the step 3) comprises the following specific calculation steps:
firstly, the proportion y of the j index of the ith project to the j index of all projects is calculated ij ：
m is the total number of items substituted into the formula, x ij For the jth index of the ith item, calculating the entropy value e of the jth index j And the value coefficient d j Entropy value coefficient d j Can measure the difference between the indexes, the entropy value e j The smaller the difference coefficient d between the indices j The larger the index is, the more important the index is;
dj＝1-ej
in the formula: constant n is total number of items, then weight W of jth index j Comprises the following steps:
7. the method for predicting the scale of the influence area of the high-speed rail station based on the node-site model according to claim 1, wherein the node value in the step 3) is measured by: the outside-city reachability weighting system is 0.84 and the inside-city reachability weighting coefficient is 0.16, wherein the outside-city reachability comprises the number of service routes of 0.32 and the daily service frequency of 0.68; the urban interior accessibility comprises the number of bus service routes within 1KM of 0.57 distance from bus to city center
0.33 and time of bus to city center 0.11.
8. The method for predicting the scale of the high-speed rail station influence area based on the node-site model according to claim 1, wherein: the normalization processing in the step 4) comprises that the data indexes are subjected to linear transformation to be in a specified interval, the heteroscedasticity between the variables is eliminated while the coordination relation between the variables is not changed, all the indexes are subjected to dimensionless to be in an interval (0,1), and the indexes are closer to 0 and are worse; the closer to 1, the better the performance, and the entropy weight method is combined to determine the index weight of the node value so as to obtain the specific value of the node value corresponding to each node.
9. The method for predicting the scale of the high-speed rail station influence area based on the node-site model according to claim 1, wherein the site value in the step 5) is calculated by the formula:
i.e. site value = design size site function
In the formula: s i The land utilization scale of the high-speed rail station area is expressed by the occupied area; beta is a in The field value coefficient is formed by multiplying a design scale coefficient and a function coefficient.
10. The application of the node-site model-based high-speed rail station influence area size prediction method according to claim 2, wherein the linear regression manner of the node-site model in the step 6) is as follows:
y =1.5158x+0.1302 linear regression middle R 2 ＝0.724
And y is a site value, x is a node value, a predicted value of the scale of the high-speed rail station influence area is obtained by reverse extrapolation according to the predicted site value and a formula of the site value and the design scale, and the scale of the future high-speed rail station influence area is analyzed and predicted.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113256964A (en) * | 2021-04-19 | 2021-08-13 | 东南大学 | City exchange center capacity design method based on node-place model |
| CN114757447A (en) * | 2022-06-08 | 2022-07-15 | 广东工业大学 | Multi-model mixed passenger transport hub station passenger flow prediction method and system |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113256964A (en) * | 2021-04-19 | 2021-08-13 | 东南大学 | City exchange center capacity design method based on node-place model |
| CN114757447A (en) * | 2022-06-08 | 2022-07-15 | 广东工业大学 | Multi-model mixed passenger transport hub station passenger flow prediction method and system |
Non-Patent Citations (2)
| Title |
|---|
| 庄宇等: "《站城协同 轨道车站地区的交通可达与空间使用》", 同济大学出版社, pages: 62 - 65 * |
| 沈怡辰: ""基于节点-场所模型的高铁站区规划反思与优化路径"", 《中国优秀硕士学位论文全文数据库 工程科技II辑》, no. 06, pages 038 - 1240 * |
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