Research on Urban-Rural Integration Planning Guidance and Path Realization in Suburban Areas of Metropolis Based on AI Models

How to deal with the relationship between urban and rural areas is one of the core issues that must be faced in national development [1-2]. The unbalanced development of urban and rural areas and the inadequate development of rural areas are still the centralized manifestation of the main contradictions in society, and it is necessary to comply with the general trend of urban-rural integration and development, break down the systematic barriers that impede the equal exchange of urban and rural factors and the two-way flow of factors, promote the development of factors and various types of services to go to the countryside more, and take the lead to break down the urban-rural dichotomy within the county area [3-5]. Promoting the integrated development of urban and rural areas is an important proposition of the times, and it is necessary to explore a new path of development that is compatible with China’s national conditions and meets the demands of local development [6-7].

In recent years, all parts of the country have actively carried out pilot experiments on urban-rural integration in the light of local realities, and have achieved certain results [8]. However, as a whole, the urban-rural dualistic pattern is still obvious, and the development gap between urban and rural areas is still large, and even tends to expand, especially in the western region, the industrial linkage between counties, towns and villages is weak, and the integrated development pattern of counties and villages has not yet been formed [9-10]. The long-standing urban-rural dual household registration system restricts the integration of urban and rural development, the poor flow of land and other resource elements, the weak linkage between urban and rural industries, the exodus of the rural population, the insufficient supply of talents, the difficulty of access to financial resources in rural areas, the weakness of grass-roots organizations, the weakness of financial resources, as well as the growing ecological problems of the countryside, etc., has become a practical obstacle to the realization of the integrated development of urban and rural areas [11-14]. At the same time, from remote rural areas to urban-rural crossroads to urban areas, the flow and combination of the elements of “people” and “land” and their cyclic effects have driven the urban-rural relationship to evolve from a low level of urban-rural integration to a high level of urban-rural integration [15-16]. The suburbs of metropolises are located in the urban-rural ecotone, which is not only the “leading belt” of urban expansion and construction, but also the “pilot area” of rural urbanization, and the frontier of urban-rural integration, and the regional system of human-land relationship is highly complex and dynamic [17-18]. It is an important practical necessity and urgency to study the problems and development paths of urban-rural integration development in peri-urban villages of metropolitan cities [19-20].

China’s long-standing urban-rural division has led to insufficient urban-rural linkage in suburban development, and the concept of city-centered development is deeply rooted. As an urban-rural combination, the mixed symbiosis of city and countryside is the basic characteristic of the suburbs, but in the development, they are often troubled by the attributes of city or countryside, and it is difficult to jump out of the historical constraints of the urban-rural division, to realize the integrated urban-rural development in the suburbs, and to be applied to the suburbs in the era of metropolitan urbanization Urban-rural integration development in the era of metropolitan urbanization [21-24].

This paper proposes a multidimensional evaluation index system for urban-rural integration in metropolitan peri-urban areas with reference to the theory of urban-rural integration development and empirical related research, combined with the basic principles and ideas for the construction of the evaluation index system. After normalizing the collected sample data indicators, the best projection direction value of each dimension data is analyzed using the projection objective function, and the optimal projection direction solving result is optimized using the RAGA method. Then according to the optimal projection direction of the urban-rural integration of each evaluation index assignment processing, calculate the comprehensive evaluation level of urban-rural integration development. Subsequently, each evaluation factor is combined with the A* heuristic search optimization algorithm and the MCC CLightGBM model to design the urban-rural integration planning guidance and path realization scheme for metropolitan suburban areas based on the AI model. This study takes City N as a case study object, evaluates and analyzes the urban-rural integration development of typical suburban townships in the city, and uses the model to plan the optimal path to explore the implementation effect of the AI model in terms of changes in the level of urban-rural integration development.

2

Multi-dimensional evaluation methods for urban-rural integration

2.1

Construction of evaluation index system

Based on the principles and ideas of the evaluation index system construction, this study mainly adopts the literature combing method to determine and select the indicators for measuring the level of urban-rural integration development in suburban metropolitan areas. First, the core literature on the theoretical and empirical research on urban-rural integration development in recent years is sorted out, and the structure of the indicator system for measuring the level of urban-rural integration development in suburban metropolitan areas is constructed from the existing scholars’ research views and research analysis. Secondly, based on the combing and summarization of relevant literature, the indicator system is constructed in three dimensions: economic, spatial, and social, according to the principles of scientific, systematic, rational, and operability in the selection of indicators, and based on the data related to urban-rural integration development in suburban areas of the metropolis. The specific indicator system for measuring the level of urban-rural integration development in the suburban areas of the metropolis is shown in Table 1. The selection of indicators and the design of the system of urban-rural integration development level in the suburban areas of the metropolis are mainly based on the consideration of the differences between urban and rural areas in terms of economic and spatial dimensions, so as to reflect the degree of urban-rural integration development in the suburban areas of the metropolis, and then provide a certain basis for the quantitative analysis of urban-rural integration development in the suburban areas of the metropolis. Promoting urban-rural economic integration is the prerequisite and driving force for urban-rural integrated development. Economic indicators such as the ratio of per capita consumption level of urban and rural residents, the ratio of disposable income between urban and rural areas, and the Tel Index reflect the degree of difference in income and consumption between urban and rural areas. Urban spatial expansion and built-up area can, to a certain extent, reflect the vitality of regional development and the intensity and change of urban spatial expansion. Urban-rural social integration is conducive to promoting the harmonious development of urban-rural relations and the sharing of the fruits of economic development by urban and rural residents. Indicators such as the ratio of expenditure and income of the urban basic medical insurance fund, the number of urban and rural residents enrolled in social pension insurance, the expenditure of the urban and rural residents’ social pension insurance fund, and the ratio of the number of urban and rural health center beds were selected, focusing mainly on the level of disparity between urban and rural medical care.

Table 1.

Urban and rural integrated development level measure system

Criterion layer	Secondary index	Index number
Urban and rural economic integration (A)	Household per capita consumption level	A1
	Urban and rural disposable income ratio	A2
	Non-farm industry ratio	A3
	Urban and rural GDP per capita	A4
	Urbanization rate	A5
	Thie index	A6
Urban and rural space integration (B)	Private car ownership of urban and rural residents	B1
	Urban space expansion	B2
	Passenger turnover	B3
	Construction area	B4
	Telephone penetration	B5
Urban and rural social integration (C)	Urban basic health insurance fund expenditure ratio	C1
	Residents of urban and rural residents	C2
	Urban and rural residents’ social pension fund	C3
	Urban and rural clinics	C4

2.2

Methodology for evaluating the level of urban-rural integration

2.2.1

Sample data normalization methods

The initial sample set ${X_{(i, j)}^{'} | i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., p}$ of each indicator is constructed with the object of urban-rural integration in the suburban areas of the studied metropolis, where $n$ is the number of samples, $p$ is the number of indicators, and $X_{(i, j)}^{'}$ denotes the value of the $j$ th indicator in the $i$ th sample city.

In order to eliminate the influence of the scale on the measurement results, the sample data are normalized [25], if the attribute of this indicator is positive, there is: (1) $X_{(i, j)} = \frac{{X^{'}}_{(i, j) -} X_{m i n (j)}}{X_{m a x (j)} - X_{m i n (j)}}$

If the attribute of this indicator is negative, then there is: (2) $X_{(i, j)} = \frac{{X^{'}}_{\max (j) -} X_{(i, j)}}{X_{\max (j)} - X_{\min (j)}}$

Where $X_{\max (j)}$ is the maximum value of the $j$ nd indicator, $X_{\min (j)}$ is the minimum value of the $j$ th indicator, $X_{(i, j)}$ is the result of the normalization process of the $j$ th indicator value of the $i$ th sample city, and ${X_{(i, j)}}$ is the set of indicator values after the initial sample set ${X_{(i, j)}^{'}}$ normalization process.

2.2.2

Constructing the projection objective function

The 1-dimensional data ${X_{(i, j)} | i = 1, 2, 3, ..., n; j = 1, 2, 3, ..., p}$ is synthesized into a $p$ -dimensional projection value $Z_{(i)}$ with $a = {a (1), a (2), a (3), ..., a (p)}$ as the best projection direction using the projection tracing model [26], and: (3) $Z_{(i)} = \sum_{j = 1}^{p} a (j) X_{(i, j)}, (i = 1, 2, 3, ..., n)$ where $a$ is a unit length vector.

In order to make the local projection points as dense as possible, the indicators are clustered to form a number of scattered clusters of points, so the projection objective function $Q_{(a)}$ is created [27]: (4) $Q_{(a)} = S_{z} D_{z}$ (5) $S_{z} = \sqrt{\frac{\sum_{i = 1}^{n} {[Z_{(i)} - E_{(z)}]}^{2}}{n - 1}}$ (6) $D_{Z} = \sum_{i = 1}^{n} \sum_{j = 1}^{n} [R - r (i, j)] * u [R - r (i, j)]$ where $S_{z}$ is the standard deviation of the projection value $Z_{(i)}$ , $D_{Z}$ is the local density of the projection value $Z_{(i)}$ , $E_{(z)}$ is the mean of the projection value $Z_{(i)}$ , $R$ is the radius of the window of the local density, and $r (i, j) = | Z_{(i)} - Z_{(j)} |$ , the distance between the samples.

In practice, in order to avoid the sliding average error is too large, you can optimize the value of $R$ to ensure that the number of projection points in the window is sufficient through multiple experiments. $u [R - r (i, j)]$ denotes the unit-order transcendental function, and $R \leq r (i, j)$ , when $r (i, j) = 0; R \geq r (i, j)$ , $r (i, j) = 1$ .

2.2.3

Optimization of the projection objective function

For the determined set of sample indicators, the change of projection direction $a$ will lead to the change of projection indicator function $Q_{(a)}$ projection indicator function to take the maximum value of the corresponding vector reflects the optimal projection direction, that is, for the maximum probability of exposure of multi-dimensional data structure characteristics of the projection direction, the optimal projection direction is obtained by solving the nonlinear optimum of the high global using the RAGA method [28]. After debugging, the initial population size of the parent generation N=400 is selected in this study, setting the crossover probability $P = 0.9$ , the mutation probability $P_{m} = 0.1$ , the number of excellent individuals $n = 21$ , and the number of acceleration is 64 times. The optimal projection vector $a$ is obtained after the operation, and it is brought into the equation (3) to get the optimal projection value of each index $Z_{(i)}$ .

2.2.4

Integrated evaluation methodology

The optimal projection direction of each indicator of the level of urban-rural integration and development in the peri-urban areas of the metropolis has been derived as mentioned above $z_{(i)}$ , and the scalar value of $z_{(i)}$ reflects the degree of influence of the indicator on the composite index of the level of urban-rural integration and development, and then the weight value of the indicator is calculated according to the formula in (7) $e$ : (7) $e = \frac{Z_{(i)}}{\sum_{i = 1}^{n} Z_{(i)}}$

The result of the comprehensive evaluation level of urban-rural integration development is: (8) $U = \sum_{i = 1}^{n} e * x_{i j}$

After studying the division methods of related scholars [29], based on the actual development of urban-rural integration in China, this study divides urban-rural integration of metropolitan peri-urban areas into six stages. That is, the separation stage of urban-rural integration, the beginning stage of urban-rural integration, the primary stage of urban-rural integration, the intermediate stage of urban-rural integration, the advanced stage of urban-rural integration, and the high-quality urban-rural integration stage. According to the evaluation system scores, the corresponding scores for each of the six stages are urban-rural integration separation stage (0-0.15), urban-rural integration starting stage (0.15-0.30), urban-rural integration primary stage (0.30-0.60), urban-rural integration intermediate stage (0.60-0.80), urban-rural integration advanced stage (0.80-0.95), and high-quality urban-rural integration stage (0.95-1).

3

Realization strategy of urban-rural integration path planning based on AI modeling

The A* algorithm is a heuristic search algorithm based on Dijkstra’s algorithm and the breadth-first search (BFS) algorithm The algorithm calculates the surrogate value of each neighboring node and uses a heuristic function to guide the search in order to find the optimal path and at the same time improve the search efficiency. The search process generally includes the following steps.

1)

Input start node and target node. Before starting the search, it is necessary to specify the location of the start and target nodes of the search.

2)

Define the heuristic function. The heuristic function is used to evaluate the priority of each search node. It estimates the cost from the current node to the target node so that the algorithm can prioritize the expansion of the node with the lowest cost:

3)

Initialize the starting point. Create open list and close list, open list stores the current node and the expansion node, close list stores the expansion node with the smallest heuristic cost, start the path search from the starting node, put the starting node in the open list, close list is empty at this time.

4)

Iterate expansion nodes. Search for extension nodes adjacent to the current node (up to 8), put in the open list, and then select a node with the smallest heuristic function value from the open list to expand, that is, calculate its neighboring nodes and add these nodes to the open list, the current node is removed from the open list and put in the close list.

5)

Update the cost of the node. Determine whether there is a feasible node in the current open list, if not, there is no feasible path between the starting point and the goal node, conversely, update the node-inspired cost.

6)

Judge the target node. When a node is expanded, it is necessary to determine whether it is the target node. If it is, the search ends and the path is returned. Otherwise, continue to iteratively expand the node.

7)

Retrace the path. When the target node is searched, the path from the start node to the target node can be reconstructed by backtracking the parent node of each node.

Therefore, this paper constructs the urban-rural integration planning guidance and path realization scheme for metropolitan suburban areas based on the AI model, and the specific realization process is shown in Figure 1. In this study, in order to analyze the urban-rural integration planning guide and path of metropolitan peri-urban areas at different stages, the multidimensional comprehensive evaluation indexes of urban-rural integration proposed above are used in order to assess and determine the level of urban-rural integration development of the metropolitan peri-urban areas under its study, and the impact of different indexes on the level of urban-rural integration development is analyzed. In the previous research on urban-rural integration path planning, the spatial differences between urban and rural environments are ignored, so this study builds an intelligent optimization model based on the improved A* algorithm and the MCC CLightGBM hybrid model to match different urban and rural environments, generates the importance of the different factors affecting the level of urban-rural integration and development of the metropolitan peri-urban areas in the study area, and then outputs the optimal planning plan for the paths of urban-rural integration. Assist relevant government staff to effectively carry out urban-rural integration construction.

4

Path planning model application effect analysis

4.1

Overview of cases and data sources

4.1.1

Overview of the study population

N City is the capital of J Province, located in the core area of the Yangtze River Delta city cluster, and is an important central city in the developed region of eastern China. According to the data of N City Statistical Yearbook, the city area was 6,892 square kilometers at the end of 2023, with 12 districts, 110 streets and towns and 1,326 village units under its jurisdiction, with a gross domestic product (GDP) of 1,245.2 billion yuan, and a per capita GDP of 132,600 yuan for the resident population. With a household population of 6,825,400 and a resident population of 8,153,600, the urbanization rate of the resident population has reached 78.23%, with a disposable income per capita of 56,324 yuan for urban residents and 22,365 yuan for rural residents, the city has entered the late stage of urbanization and industrialization as a whole, and belongs to the more typical developed metropolitan areas in China. The study of urban-rural integration development in its peri-urban areas can provide a reference for the planning of urban-rural integration paths in the peri-urban areas of similar mature metropolitan areas and growing metropolitan areas.

It should be noted that, if defined in terms of regional functional linkages or spatial patterns, the spatial scope of the N metropolitan area should extend beyond its administrative jurisdiction, and many of the neighboring sub-cities have in fact formed very close linkages with the N city, or even a high level of co-citizenship. However, due to the administrative system, these regional sub-centers, although closely connected to the city, are clearly different from the sub-centers within the city’s jurisdiction, especially the inter-provincial differences. Therefore, for the sake of the unity of economic, social and policy environments, the consistency of urban-rural integration planning and implementation entities, and the ease of research, this paper limits the study boundary of peri-urban areas to the administrative jurisdiction of N city, which is by default the scope of the metropolitan area.

4.1.2

Data sources

The data used in the analysis of this paper include two parts: information about urban-rural integration in the peri-urban area of N city metropolis and data on sample administrative villages/communities in the peri-urban area. Among them, the analysis of the evolution of the formation of the peri-urban area of N city metropolis mainly applies the literature analysis method, historical analysis method, map analysis method and field research method. The data used include survey data, historical information, network information, established planning results and some valuable related studies. The basic data about the sample administrative villages/communities in the peri-urban areas of the metropolis are mainly obtained through field research. In this paper, eight administrative villages and agriculture-related communities are screened as cases within the entire N city peri-urban area, and the development characteristics of different types of urban-rural integration in the N city peri-urban area are generally grasped through interviews with community staff, questionnaire surveys, and on-site surveys of each rural community. The content of attention is based on the multidimensional evaluation index system of urban-rural integration proposed above, including the basic situation of urban areas and villages, the development of economy and industry, the employment and population flow of residents, and the access of residents to public services.

4.2

Analysis of the level of integrated urban and rural development

4.2.1

Determination of evaluation indicator weights

The results of the weighting analysis of the evaluation index system of urban-rural integration development level in suburban metropolitan areas are shown in Table 2. It can be seen that A “urban-rural economic integration” is the most important for urban-rural integrated development, with the total value of indicator weights accounting for 46.1%, followed by B “urban-rural spatial integration” dimension, accounting for 32.5%. Compared with the dimension “social integration between urban and rural areas”, which was previously considered to have a greater impact on the level of urban-rural integration and development, the weighted value of this measurement is only 21.4%, which is the third highest. Among the three levels of indicators, the A6 “Thiel Index” has a higher weighting of 0.114. This is followed by A1 “per capita consumption level” (0.098) and B3 “passenger turnover” (0.096), which indicates that the per capita consumption level and passenger turnover have an important impact on the urban-rural integration development of metropolitan suburban areas, and that higher per capita consumption level and passenger turnover are conducive to improving the well-being of residents in urban and rural areas. Higher per capita consumption and passenger turnover are conducive to enhancing the sense of well-being of residents in urban and rural areas.

Table 2.

The optimal projection direction and weight value of the evaluation index

Criterion layer	Index weight	Secondary index	Index number	Optimum projection direction	Index weight
Urban and rural economic integration (A)	0.461	Household per capita consumption level	A1	0.156	0.098
		Urban and rural disposable income ratio	A2	0.053	0.065
		Non-farm industry ratio	A3	0.124	0.038
		Urban and rural GDP per capita	A4	0.061	0.08
		Urbanization rate	A5	0.05	0.066
		Thie index	A6	0.158	0.114
Urban and rural space integration (B)	0.325	Private car ownership of urban and rural residents	B1	0.321	0.064
		Urban space expansion	B2	0.251	0.032
		Passenger turnover	B3	0.366	0.096
		Construction area	B4	0.212	0.07
		Telephone penetration	B5	0.175	0.063
Urban and rural social integration (C)	0.214	Urban basic health insurance fund expenditure ratio	C1	0.22	0.02
		Residents of urban and rural residents	C2	0.087	0.058
		Urban and rural residents’ social pension fund	C3	0.277	0.064
		Urban and rural clinics	C4	0.192	0.072

4.2.2

Results of the evaluation of the level of urban-rural integration and development in N city

This study first evaluates and analyzes the urban-rural integration development level of cities and townships in eight peri-urban districts of N. The actual evaluation results of the urban-rural integration development level of the peri-urban districts of N during the period of 2013-2020 are shown in Table 3. From the evaluation results of urban-rural integration development in 2013, there is a great difference between the urban-rural integration development level of township 2 (0.006) and township 6 (0.182) in peri-urban areas. And there has been a large gap in the level of urban-rural integration development among the eight representative townships in the peri-urban area of Metropolis N during the eight years. However, it can be seen that urban-rural interaction between townships is gradually increasing, and under the guidance of relevant policies, the flow of all kinds of urban and rural factors has begun to gradually tilt toward rural areas. On the one hand, infrastructure and public services in urban areas are being optimized, while on the other hand, investment in suburban rural areas is gradually increasing.

Table 3.

The urban and rural integration level analysis of 8 suburbs in N city

Rural township number	2013	2014	2015	2016	2017	2018	2019	2020
1	0.056	0.247	0.29	0.359	0.361	0.536	0.623	0.826
2	0.006	0.104	0.236	0.307	0.379	0.497	0.597	0.694
3	0.092	0.235	0.284	0.324	0.331	0.574	0.613	0.690
4	0.067	0.294	0.335	0.342	0.442	0.567	0.59	0.612
5	0.094	0.27	0.277	0.3	0.304	0.317	0.414	0.452
6	0.182	0.184	0.23	0.262	0.304	0.369	0.516	0.545
7	0.092	0.183	0.334	0.48	0.512	0.647	0.71	0.723
8	0.044	0.19	0.219	0.305	0.383	0.441	0.506	0.654

Also based on the six stages of development level of urban-rural integration summarized above, namely, the stage of separation of urban-rural integration (0-0.15), the stage of beginning urban-rural integration (0.15-0.30), the stage of primary urban-rural integration (0.30-0.60), the stage of intermediate urban-rural integration (0.60-0.80), the stage of advanced urban-rural integration (0.80-0.95), and the stage of high-quality urban-rural integration (0.95-1). Bringing in the evaluation results of the development of the actual urban-rural integration level, we get the urban-rural integration development level of the townships in the peri-urban area of N city in 2020 as shown in Figure 2. The comprehensive evaluation level of urban-rural integration development in the peri-urban area of N city in 2020 is 0.6495, and the whole is in the intermediate stage of urban-rural integration, among which township 1 (0.826) belongs to the advanced stage of urban-rural integration, township 7 (0.723), township 2 ( 0.694), Township 3 (0.690), Township 8 (0.654), a total of four townships have a level of urban-rural integration that is higher than the overall average level of urban-rural integration in the peri-urban areas of N City, and the level of urban-rural integration in three peri-urban areas, including Township 4, is lower than that of N City as a whole, so the road to urban-rural integration and development in the peri-urban areas of N City is still arduous.

4.3

Analysis of the effectiveness of the implementation of the AI model-based path planning program

This paper applies the urban-rural integration planning scheme based on the AI model for metropolitan peri-urban areas to the urban-rural integration planning of eight typical townships in the peri-urban areas of N city, carries out the urban-rural integration planning implementation based on the optimal planning scheme given by the model, and analyzes the changes in the level of development of urban-rural integration in the typical townships and explores the effectiveness of the implementation of the pathway planning scheme.The urban-rural integration of the townships of the peri-urban areas of N city from 2020 to 2023 level evaluation results are shown in Figure 3, with (a) and (b) representing the analysis results of townships 1-4 and townships 5-8, respectively. Since the implementation of the AI model-based urban-rural integration development planning path program in late 2020, the evaluation level of urban-rural integration development in the peri-urban areas of Metropolis N has shown a growing trend.In 2021, the urban-rural integration development levels of townships 1, townships 2, and townships 3 in the peri-urban areas have increased from 0.826, 0.694, and 0.690, respectively, in 2020 to 0.869, 0.724, and 0.748, and this trend of change is due to the fact that the real industrial economy, public facilities, and the life of local residents in peri-urban cities continue to migrate to peri-urban townships, showing a trend of good urban-rural integration. At the same time, the increasing sophistication of metropolitan peri-urban areas and the rise of regional brand economies have changed the traditional living habits of local residents, a situation that has further led to an increase in the level of urban-rural integration in metropolitan peri-urban areas. In addition, from the analysis results of suburban townships5 and townships6, which had a lower level of urban-rural integration and development at the beginning of 2020, the level of urban-rural integration and development rises from 0.452 and 0.545 in 2020 to 0.863 and 0.923 in 2021, a change that is on the one hand due to the fact that the development of industrial parks, new towns in peri-urban areas or cultural, creative and tourism projects, and large-scale enterprises promotes the development of land and industrial This change is due on the one hand to the development of industrial parks, suburban new towns or cultural, creative and tourism projects, and large enterprises, which promotes land and industrial urbanization, and on the other hand to the fact that the development of industries attracts employment in the local area and the surrounding areas, and the continuous gathering of the population and the “demand for housing” promotes the development of real estate in suburban towns and cities, which promotes the quality of the urbanization of suburban areas from the perspective of the industry, land, and population.

5

Conclusion

After constructing the evaluation index system of urban-rural integration development in this paper, the projection tracing model is used to assign weights to each evaluation index and calculate the evaluation results of urban-rural integration development in metropolitan peri-urban areas. Then, it combines the improved A* algorithm and the MCC CLightGBM hybrid model to construct the urban-rural integration path planning practice program based on the AI model. The results of the case study show that the urban-rural interaction of the eight typical townships in the peri-urban areas of N city is gradually increasing during 2013-2020, but the gap between the urban-rural integration and development level is extremely large, and the urban-rural integration level of three peri-urban townships (0.612, 0.545, 0.452) is lower than that of N city as a whole (0.6495) in 2020, which makes the path of urban-rural integration and development of the peri-urban areas of N city very arduous. After planning the development of urban-rural integration in the city’s peri-urban area townships using the AI model proposed in this paper with the key elements in the evaluation index of urban-rural integration development, the analysis of the effectiveness of the implementation shows that the level of urban-rural integration and development of the lower level of urban-rural integration in peri-urban area townships 5 and townships 6, the level of urban-rural integration and development increased from 0.452 and 0.545 in 2020 to 0.863 and 0.923 in 2021.It indicates that the real estate in peri-urban townships has been effectively developed, and the dimensions of industry, land, and population have contributed to the improvement of the quality of urbanization in peri-urban areas.

In summary, the application of the AI intelligent planning model proposed in this paper can promote the integrated development of urban and rural environments in peri-urban areas of metropolitan cities, promote urban-rural sharing, and promote the sustainable development of urban and rural economies and societies, among others.

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Research on Urban-Rural Integration Planning Guidance and Path Realization in Suburban Areas of Metropolis Based on AI Models

Shiwei Hu

Xinyi Mao

Guohua Zhao

Yuhan Qi

Yujia Fu

Publicado en línea: 24 sept 2025

Recibido: 09 ene 2025

Aceptado: 01 may 2025

DOI: https://doi.org/10.2478/amns-2025-0981

Palabras claveEvaluation system, Machine learning, AI model, Urban-rural integration, Path planning

© 2025 Shiwei Hu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Evaluation system, Machine learning, AI model, Urban-rural integration, Path planning