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Article

Aesthetic Design and Evaluation of Public Facilities in Railway Stations under the Background of Sustainable Development: A Case of an Information Counter at Xiong’an Railway Station

by
Xuan-Hui Xie
1,
Hongyang Zhu
1,
Yunpeng Xu
1,
Huiran Yan
1,
Shilin Guo
1 and
Qiang Liu
2,*
1
School of Industry Design, Hubei University of Technology, Wuhan 430068, China
2
Academy of Arts & Design, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(12), 5021; https://doi.org/10.3390/su16125021
Submission received: 27 February 2024 / Revised: 4 June 2024 / Accepted: 8 June 2024 / Published: 12 June 2024
(This article belongs to the Special Issue Smart Product-Service Design for Sustainability)
Browse Figures
Figure 1
<p>The photos of Xiong’an railway station and Xiong’an ecological environment.</p> "> Figure 2
<p>Research framework.</p> "> Figure 3
<p>Product design principles under the background of sustainable development.</p> "> Figure 4
<p>Examples and analysis of the research object.</p> "> Figure 5
<p>Aesthetic cognitive layer analysis.</p> "> Figure 6
<p>Indicator system of the aesthetic element.</p> "> Figure 7
<p>SD method diagram.</p> "> Figure 8
<p>DFA method diagram.</p> "> Figure 9
<p>Kansei image feature extraction method process.</p> "> Figure 10
<p>AHP-FCE model for aesthetic design.</p> "> Figure 11
<p>Typical visual image samples.</p> "> Figure 12
<p>Typical visual image samples.</p> "> Figure 13
<p>Gravel diagram.</p> "> Figure 14
<p>Diagram of each factor component.</p> "> Figure 15
<p>Design format analysis of experimental results.</p> "> Figure 16
<p>Statistical chart of design format analysis.</p> "> Figure 17
<p>Formation process of the design scheme.</p> "> Figure 18
<p>Design scheme effect.</p> "> Figure 19
<p>Fuzzy evaluation vector of each scheme.</p> "> Figure 20
<p>Weight analysis diagram of aesthetic element indicators.</p> "> Figure 21
<p>Aesthetic design principles of public facilities in railway stations.</p> "> Figure 22
<p>Ergonomics/human factors analysis of the design scheme.</p> "> Figure 23
<p>Final design scheme effect.</p> ">
Versions Notes

Abstract

:
Sustainable development is an important trend for railway stations, and public facilities are essential parts of railway stations. With the sustainable development of railway station construction, the aesthetic design of public facilities is a problem that needs to be solved in the field of industrial design. In this context, this study proposed an aesthetic design and evaluation method for public facilities in railway stations. This method is constructed by combining the Kansei image and AHP (analytic hierarchy process)–FCE (fuzzy comprehensive evaluation) model, and takes the information counter at Xiong’an railway station as an example to illustrate the method. JACKTM is applied to evaluate the ergonomics of the design scheme. The results are as follows. (1) Ecological culture is an important source of Kansei images for aesthetic designs in the context of sustainable development. Kansei words, such as understated, delicate, dynamic, and others, which reflect original simplicity and original nature, are typical semantic features. Simple and smooth shapes are typical form features. (2) An aesthetic design is a system of various elements; the core content of an aesthetic design is to reflect the original aesthetic feeling. On this basis, the elements of simple, harmonious, humanized, and natural constitute the aesthetic design principle. This method is suitable for the aesthetic design and evaluation of public facilities in railway stations, which could provide valuable guidance for the aesthetic design of public facilities in railway stations under the background of sustainable development.

1. Introduction

With the development of China’s rail system, the total length of railways in China has extended 159,000 km; China has built the world’s largest railway network [1]. Sustainable development is one of the important research trends in the field of railway stations [2]. Xiong’an railway station is representative of a sustainable railway station [3]. Xiong’an railway station is located in Xiong’an new district, Hebei Province, China [4]. The architectural design of Xiong’an railway station fully reflects the development direction of the railway station under the background of sustainable development. The overall shape of the railway station is an oval; the architectural design style and interior design style adopt the concept of integration. Sunlight shines into the interior through the gap at the top of the station building. The interior structure of the station is decorated with clear concrete columns, which reflect the natural, simple, and elegant design style. Xiongan new area has a good ecological environment, with rivers and canals, developed water systems, and widespread lakes. Based on data analysis from Xiong’an’s official website, the photos of Xiong’an railway station and the regional river system are shown in Figure 1.
As an important component of railway stations, the public facilities design has an important impact on railway stations; its aesthetic design and evaluation is an important challenge for sustainable railway station development. How to effectively improve the aesthetic effect of public facilities in railway stations and establish an objective evaluation method is one of the problems that need to be solved in the context of railway sustainable development.
This study aims to summarize the aesthetic design points in the context of sustainable development, and it proposes a design and evaluation method. It takes the information counter at Xiong’an railway station as an example to explain the method. The Kansei image method is combined with the AHP-FCE model to analyze the factors that enhance the aesthetic effect of the information counter design under the background of sustainable development. JACKTM is used to analyze the ergonomics of the design scheme. The research framework of this paper is shown in Figure 2.

2. Literature Review

With the wide application of sustainable development in the field of transportation, it has gradually become an important trend in the research field of railways in recent years. Liu Xinyu et al. studied the transformation and development of Jingmen high-speed railway station infrastructure from the perspective of sustainable development and the circular economy and analyzed the development strategy of high-speed railway station infrastructure in the context of sustainable development [5]. Vilotijevic, Milica et al. analyzed the problems with sustainable railway infrastructure and the specific environment in the Republic of Serbia and pointed out that the railway infrastructure needs to meet the requirements of society, economy, protection, and improvement of the environment [6]. Alessandra Bianchi et al., by evaluating the discussed railway cultural heritage and analyzing its adaptive transformation methods on the basis of cases, provided a reference standard for the sustainable adaptive reuse of railway cultural heritage [7]. Agnes Wanjiku Wangai et al. studied the foresight method applicable to railways’ sustainable development in developing countries [8].
The Kansei image is important research content in industrial design theory [9], product emotional design [10], Kansei Engineering [11], and other fields. It serves as a crucial factor for design scheme creation [12] and users’ cognitive processes of product design [13]. The concept of an “image” is a psychological domain, which refers to the emotional cognition of individuals regarding objects. The relevant research on the Kansei image mainly discusses the emotional cognitive process of product design [14], aiming to effectively quantitatively analyze the emotional information of product design [15]. Kansei image cognitive cues can be decomposed into visual cues and semantic cues [16,17]. With the emergence of interdisciplinary trends in industrial design theory, the research methods on the Kansei image encompass both quantitative and qualitative approaches. The quantitative research method mainly combines factor analysis, design format analysis, and other methods to extract the emotional information of products [18]. The qualitative research methods are combined with cognitive psychology [19], prototype theory [20], grounded theory [21], and others. The Kansei image is an important theoretical tool for analyzing product emotional design.
Scheme evaluation is one of the important contents of decision-making research [22], among which the AHP is a common and simple decision method. The AHP (analytic hierarchy process) is a decision analysis method that is extensively applied in management, program evaluation, scheme optimization, and other research fields [23]. Analyzing various indicators and elements of product design, the AHP provides an importance ranking for each element and index [24]. The AHP is used in combination with other methods to solve decision problems with multiple influence factors, and such combinations include the AHP with fuzzy comprehensive evaluation [25] and the Kano model [26], which provide references for decision-making problems.
The FCE (fuzzy comprehensive evaluation) is a multi-factor evaluation method that considers the influence of various factors, aiming to achieve an effective and objective evaluation for design schemes. The FCE method is widely utilized in diverse domains, including product user demand analysis [27], automobile form design [28], plan analysis for engineering scheme design [29], architectural space evaluation [30], infrastructure evaluation [31], and others.
The AHP-FCE model is a combination of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). It is used for quantitative evaluation and analysis of design schemes [32,33]. Tao Wang et al. used the AHP-FCE model to explore the creative design of 2022 World Cup lamps. Using the AHP-FCE model, the design scheme is quantitatively evaluated, and the relevant evaluation information of the design method is obtained [34]. Hu Sun et al. used the AHP-FCE model to study reusable takeaway containers and proposed design suggestions [33]. Kucuk, Pelin Ofluoglu et al. used the AHP and FCE to analyze the satisfaction of student residential apartment building environments [35]. Fang, XS et al. used the AHP and FCE methods to evaluate the health status of Guangdong Xinhui national wetland park and provided a health evaluation index system for the rapidly developing wetland park [36].
Aesthetic design and evaluation are one of the research hotspots in the field of industrial design. Product aesthetic design is primarily involved in the exploration of people’s aesthetic cognitive activities and the principles of product aesthetic design [37]. Studying the aesthetic perception of product design provides an effective reference for designers to create design schemes [38]. In aesthetic design and evaluation, the AHP and FCE methods are widely used [39,40]. Feng M M used the AHP–fuzzy comprehensive evaluation to study the aesthetic feeling of urban plant landscape and took Hangzhou regional landscape as the object to explain the effectiveness of the method [41]. Zhang N et al. used the AHP to study the aesthetic image of the man–machine interface form element layout design [42].
Based on the above literature analysis, it can be seen that the concept of sustainable development is an important development direction in the field of transportation. Kansei image theory and the AHP-FCE model are widely used in the research of aesthetic evaluation. However, relevant theories and research methods are not applied in the public facilities of railway passenger stations.

3. Materials and Methods

3.1. Analysis of Research Object

Aesthetics are an important factor for product design. Users elicit psychological responses through visual stimuli, subsequently perceiving the aesthetic perception of the design effect [43,44]. The aesthetics of product design is both the embodiment of the explicit visual characteristics of the product exterior and the implicit emotional characteristics expression of the product connotations.
The content of product design aesthetics in the context of sustainable development includes reliable usability, good man–machine interaction, simple product upgrading, and product design to reflect simple social values. In this context, the product design style presents a unique aesthetic feeling. Combined with the analysis of various representative sustainable product design styles, its aesthetics present that product design recovers original simplicity and returns to original nature.
The core concept of product design aesthetics under the background of sustainable development is the harmonious development of products and various factors, reflecting the original aesthetic characteristics of products. For example, the rationality of product use, the simplicity of product upgrade, the rationality of product human–computer interaction, and lower resource consumption constitute the aesthetic design characteristics in the context of sustainable development, as shown in Figure 3.
The information counter is an important public facility in railway stations [45,46]. Elements of the information counter include display screens, stand columns, and platforms. The design elements of the information counter include form, color, material, decorative elements, and others; the combination of design elements and component elements constitutes the foundation for aesthetic design in the information counter. At the same time, the aesthetic design of the information counter is influenced by various factors, including regional culture, station architectural style, and others, as shown in Figure 4.
The factors of aesthetic design can be categorized into explicit and implicit levels. The explicit level includes design features and a combination of design elements, and the implicit level refers to emotional information.
To sum up, aesthetic design can be divided into feature, structure, and concept layers. The feature layer aesthetic mainly refers to the aesthetic impact of form, color, and other concrete elements within the design scheme. The structure layer aesthetic mainly refers to the aesthetic feeling generated by combinatorial relations in the design elements and components for the information counter appearance. The concept layer aesthetic mainly refers to implicit aesthetic feelings, such as cultural characteristics and symbolic traits, as shown in Figure 5.
Based on the analysis of product aesthetic trends in the context of sustainable development and relevant research [47], information counter aesthetic element indicators can be divided into the feature layer, structure layer, and concept layer. The aesthetic evaluation system as shown in Figure 6. The meaning analysis of each aesthetic index is shown in Table 1.

3.2. Kansei Image and Kansei Engineering

The Kansei image is the emotional cognition and response of the design concept, which is a relatively abstract description of the initial state of the design. The specific description of the Kansei image primarily includes visual images and semantic words, which represent explicit features and implicit features, respectively.
Kansei Engineering is a quantitative research method for the emotional cognition of products, and it was proposed by Mitsuo Nagamachi in the 1970s [48,49]. Kansei Engineering involves the transformation of emotional elements into design elements through analysis of users’ perceptual cognition to help designers create schemes. This method was widely applied in different domains, such as product design, clothing design, and others, and was often used to evaluate design schemes, extract cultural features, and analyze user images [50,51].
Kansei Engineering is frequently utilized for feature extraction of Kanei images by the Kansei word scale. Yuedi Huang [52] et al. used Kansei Engineering to extract intangible cultural heritage features. Hartono, M [53] used a modified Kansei Engineering method to explore service demand attraction and applied it to sustainable service design. Kansei Engineering has become an important method for emotional feature extraction.
The semantic differential method (SD method) [54] is a quantitative analysis about people’s emotional cognition; the SD method uses a pair of adjectives to describe a person’s perception. Kansei Engineering extracts crucial perceptual information by Kansei words [55], as shown in Figure 7.
The design format analysis (DFA) [56,57] is a quantitative analysis of appearance features and was proposed by Anders Warell in 2001. Firstly, the DFA method needs to collect a series of descriptions of explicit form features, such as circles, squares, and others. Secondly, the feature correlation of the samples was scored based on the form feature description. Finally, the form feature of samples was analyzed by mathematical statistical methods (Equation (1)) [58].
X ¯ i j k = ∑ k = 1 n X i j k n
where i represents the number of samples, j represents the number of features, and n represents the number of participants in the experiment. The horizontal column represents the features of each product and the vertical column represents each product sample, and the experimental results were obtained by calculating scores. The principal analysis is shown in Figure 8.
In this study, the critical Kansei image feature (such as semantic features and form features) is obtained through the factor analysis method and design format analysis method based on a typical Kansei image sample; it provides guidance on semantic and form features for designers to create a design scheme, as shown in Figure 9.

3.3. AHP Method

The AHP proposed by T.L. Saaty in 1988 combines qualitative and quantitative analysis with multi-dimensional index analysis methodology [59,60].
To determine the weight value of each element index, a judgment matrix was constructed through comparison, and the importance evaluation of two indexes was conducted using the 1–9 scale, as presented in Table 2.
The fuzzy judgment matrix M was established based on the scale values obtained from expert rating data. The relative importance of index i compared to index j was denoted as αij. Thus, the expression for the fuzzy judgment matrix M is as described in Equation (2).
M = α 11 α 12 ⋅ ⋅ ⋅ α 1 n α 21 α 22 ⋅ ⋅ ⋅ α 2 n ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ ⋅ α n 1 α n 2 ⋅ ⋅ ⋅ α n n
The geometric average method is employed to determine the weight value of each index. The procedure is as follows:
  • Calculate the product Mi for each row value in the fuzzy judgment matrix M.
M i = ∏ j = 1 n α i j i = 1 , 2 , … , n
2.
The geometric mean value of the fuzzy judgment matrix is derived.
W i ¯ = M i n ( i = 1 , 2 , … , n )
3.
After the process of normalization, the relative weight Wi is derived in the following manner:
W i = W i ¯ ∑ i = 1 n W ¯ i i = 1 , 2 , … , n
4.
The consistency index is calculated according to the maximum eigenvalue λ max.
CI = λ max − n n − 1
5.
The consistency ratio, CR, can be calculated as follows:
C R = C I R I
Based on Table 3, the random consistency coefficient value RI corresponding to the order n of the matrix is retrieved. If the obtained CR value is less than 0.1, it indicates an acceptable level of consistency in the judgment matrix; otherwise, readjustment of the judgment matrix is required.

3.4. FCE Method

The FCE (fuzzy comprehensive evaluation) is a comprehensive evaluation method based on fuzzy set theory [61,62]; the concept of fuzzy mathematics was proposed by Professor L.A. Zadeh in 1965 [63]. It used the maximum membership principle to conduct a quantitative comprehensive evaluation.
The main calculation process is as follows:
  • Establish the evaluation factor index set U, U = {U1, U2, U3, …}.
  • Create an evaluation set. The evaluation set is V, V = {V1, V2, …,Vp}, and evaluation level is categorized into 5 levels: excellent, good, general, poor, and very poor. The 5 grades of excellent, good, general, poor, and very poor translate to scores of 10, 8, 6, 4, and 2.
  • Establish the fuzzy matrix R. The percentage method is used to calculate the membership and determine the fuzzy matrix R, as described in Equation (8).
R = r 11 r 12 ⋯ r 1 m r 21 r 22 ⋯ r 2 m ⋯ ⋯ ⋯ ⋯ r p 1 r p 2 ⋯ r pm
4.
Determine the weight vector of evaluation factors, W = (a1, a2,...,ap).
5.
According to the determined weight vector W and matrix R, the fuzzy evaluation vector X was calculated, as described in Equation (9).
X = W   ·   R
6.
Calculate the comprehensive score Y, as described in Equation (10).
Y = X   ·   V

3.5. AHP-FCE Model

The AHP-FCE model integrates the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE); the model used the AHP to calculate the weight and the FCE method to evaluate the design scheme comprehensively.
The AHP-FCE model for the information counter design is shown in Figure 10. Firstly, the weight ranking of each aesthetic element index is determined using the AHP. Secondly, based on aesthetic weight analysis, combined with Kansei image features, the design schemes are created. Finally, the design schemes are evaluated by the FCE method, and the resulting analysis is utilized for optimizing the scheme.

4. Research Case

4.1. Kansei Image Features Extraction

Through the data analysis of Xiong’an railway station, the architectural design of the railway station is inspired by water droplets on lotus leaves in Baiyangdian Lake [61], as shown in Figure 11. Therefore, this study takes Xiong’an ecological culture as the theme, collecting relevant visual images and Kansei words.
Firstly, ecological and cultural data related from Xiong’an district (as referenced on the website of Xiong’an China) were gathered. By systematically analyzing the Xiong’an ecological culture, nine typical visual image samples (M1–M9) were selected from concept level, style level, and feature level, as shown in Figure 12.
Through the investigation of historical and cultural data of Xiong’an district, eight typical Kansei word groups were obtained and formulated opposite descriptive phrases, as presented in Table 4. Table 5 is an example of the Kansei cognition questionnaire using the SD method; the positive Kansei word meaning is represented on the right side of the scale, while the negative Kansei word meaning is depicted on the left side. The scores at both ends (−3 and 3) signify a strong correlation.
In order to obtain crucial Kansei information, Kansei image cognition experiments were conducted on the nine samples (Figure 12, M1–M9) in conjunction with the utilization of the image cognition questionnaire (Table 5). The experiment rating data come from 40 people (the 40 people all know Xiong’an culture), and the average scores obtained are shown in Table 6.
The experimental results were factor analyzed using SPSS 22 software. The KMO is 0.577, and Bartlett’s test of sphericity is 0.000, indicating that the results can be factor analyzed. The total variance results are presented in Table 7, and gravel diagram analysis is shown in Figure 13. The rotated component matrix is displayed in Table 8. The 3D component diagram analysis is shown in Figure 14.
According to Table 7 and Figure 13, the first three factors can explain 94.610% of implicit Kansei information of Xiong’an ecological culture. In Table 8 and Figure 14, we can obtain the Kansei word group included in each factor.
In factor 1, S1 (elegant–vulgar), S2 (introverted–extraverted), S3 (gentle–hard), S6 (concise–trivial), and S7 (exquisite–rough) have a greater load, which mainly reflect the refined and understated aesthetic of Xiong’an ecological culture.
In factor 2, S4 (delicate–rough) and S5 (light–heavy) have a greater load, which mainly reflect the delicate aesthetics of Xiong’an ecological culture.
In factor 3, S8 (flow–quiet) has a greater load, which mainly reflects the dynamic aesthetic of Xiong’an ecological culture.
To sum up, the culture of Xiong’an can be summarized as elegant, gentle, and intelligent. According to the analysis of the factor analysis total variance explained (Table 7) and the induction of each semantic, the variance contribution rate/cumulative variance contribution rate is used to calculate the weight of each factor, as shown in Table 9.
In order to extract the form feature description of typical visual image samples, the explicit form feature information of typical visual image samples (M1–M9) was extracted by the DFA method.
Based on nine visual image samples (M1–M9), six phrases of form features description were collected, as shown in Table 10. The form feature description is derived from both the overall shape and detailed shape levels, such as square, circular, abstract, and others. The scale is 1–3 points, and higher scores indicate a strong correlation of corresponding features.
The experiment involved a total of 40 adults. The experimental results are calculated based on Equation (1) and the DFA method principle (Figure 8) to form experimental results, as shown in Figure 15, and the statistical results are shown in Figure 16.
According to Figure 15, features F1, F5, and F6 have higher scores, which shows that its feature recognition is good, and the features can be used as the main sources of form features. Visual images M2, M4, and M6 have higher scores, which shows that their recognition is good, and the visual images can be used as important image sources.
Figure 16 illustrates the score fluctuation of F1-F6 features. Most of the image scores of F2 and F4 are lower than two points, indicating that F2 and F4 have poor feature recognition. This shows that square and straight turns are not typical.
To sum up, the form feature of Xiong’an ecological cultural samples can be described as follows. In the overall shape level, the main features are circles, streamlines, and abstractions. In the detail form level, an arc shape with smooth contours is observed. In the form transition level, rounded corners are evident. Visual images M2, M4, and M6 can be used as important image sources. The analysis of each feature is shown in Table 11.

4.2. Aesthetic Weight Analysis Based on the AHP

Based on the aesthetic index (Table 1 and Figure 6), the AHP is used to calculate the weight of aesthetic elements (Equations (2)–(7)), and the rating data come from 10 experts (the experts are from the research field of industrial design). The results are shown in Table 12, Table 13, Table 14 and Table 15.
The calculation results satisfy the criterion CR < 0.1 in Table 12, Table 13, Table 14 and Table 15, which passes the consistency test. According to the aforementioned formula, the weight values of indicators for all layers of aesthetic elements can be derived, as shown in Table 16.
Based on Table 16, the aesthetic weight of the first-level index is as follows: feature layer A > structure layer B > concept layer C. In the feature layer, A1 > A2 > A3 > A4. In the structure layer, B4 > B2 > B3 > B1. In the conceptual layer, C1 > C4 > C2 > C3.

4.3. Design Scheme Creation

The design scheme is formulated by integrating Kansei image features and aesthetic importance analysis. Subsequently, comprehensive evaluation analysis uses the FCE method, and the process as shown in Figure 17.
Based on the analysis of Xiong’an Kansei image features and the aesthetic weight ranking in the previous study, we have created four information counter design schemes in the railway station (K1–K4), as shown in Figure 18.

4.4. FCE Method for Design Schemes

In order to further optimize the scheme, the FCE method is used to evaluate the aesthetic effect of each design scheme. The evaluation level is categorized into five levels: excellent, good, general, poor, and very poor, and the rating data come from thirty persons.
Based on the statistical data presented, the fuzzy comprehensive evaluation matrix R1–R4 for the evaluation index of the design scheme K1–K4 is as follows.
R 1 = 14 / 30 13 / 30 3 / 30 0 / 30 0 / 30 13 / 30 14 / 30 3 / 30 0 / 30 0 / 30 12 / 30 13 / 30 5 / 30 0 / 30 0 / 30 14 / 30 13 / 30 3 / 30 0 / 30 0 / 30 10 / 30 15 / 30 5 / 30 0 / 30 0 / 30 9 / 30 15 / 30 6 / 30 0 / 30 0 / 30 8 / 30 13 / 30 9 / 30 0 / 30 0 / 30 7 / 30 17 / 30 6 / 30 0 / 30 0 / 30 13 / 30 10 / 30 7 / 30 0 / 30 0 / 30 8 / 30 11 / 30 11 / 30 0 / 30 0 / 30 7 / 30 7 / 30 16 / 30 0 / 30 0 / 30 11 / 30 15 / 30 4 / 30 0 / 30 0 / 30
R 2 = 21 / 30 8 / 30 1 / 30 0 / 30 0 / 30 16 / 30 9 / 30 5 / 30 0 / 30 0 / 30 15 / 30 8 / 30 7 / 30 0 / 30 0 / 30 16 / 30 8 / 30 6 / 30 0 / 30 0 / 30 19 / 30 7 / 30 4 / 30 0 / 30 0 / 30 24 / 30 4 / 30 2 / 30 0 / 30 0 / 30 18 / 30 9 / 30 3 / 30 0 / 30 0 / 30 23 / 30 5 / 30 2 / 30 0 / 30 0 / 30 19 / 30 7 / 30 4 / 30 0 / 30 0 / 30 13 / 30 10 / 30 7 / 30 0 / 30 0 / 30 15 / 30 8 / 30 17 / 30 0 / 30 0 / 30 22 / 30 5 / 30 3 / 30 0 / 30 0 / 30
R 3 = 11 / 30 13 / 30 6 / 30 0 / 30 0 / 30 9 / 30 14 / 30 7 / 30 0 / 30 0 / 30 12 / 30 12 / 30 6 / 30 0 / 30 0 / 30 10 / 30 13 / 30 7 / 30 0 / 30 0 / 30 13 / 30 11 / 30 6 / 30 0 / 30 0 / 30 12 / 30 10 / 30 8 / 30 0 / 30 0 / 30 18 / 30 11 / 30 3 / 30 0 / 30 0 / 30 9 / 30 16 / 30 5 / 30 0 / 30 0 / 30 10 / 30 13 / 30 7 / 30 0 / 30 0 / 30 8 / 30 15 / 30 17 / 30 0 / 30 0 / 30 9 / 30 12 / 30 9 / 30 0 / 30 0 / 30 12 / 30 13 / 30 5 / 30 0 / 30 0 / 30
R 4 = 16 / 30 8 / 30 6 / 30 0 / 30 0 / 30 15 / 30 10 / 30 5 / 30 0 / 30 0 / 30 15 / 30 9 / 30 6 / 30 0 / 30 0 / 30 15 / 30 10 / 30 5 / 30 0 / 30 0 / 30 14 / 30 9 / 30 7 / 30 0 / 30 0 / 30 13 / 30 8 / 30 9 / 30 0 / 30 0 / 30 18 / 30 9 / 30 3 / 30 0 / 30 0 / 30 14 / 30 7 / 30 9 / 30 0 / 30 0 / 30 16 / 30 8 / 30 7 / 30 0 / 30 0 / 30 19 / 30 9 / 30 2 / 30 0 / 30 0 / 30 16 / 30 9 / 30 3 / 30 0 / 30 0 / 30 15 / 30 12 / 30 3 / 30 0 / 30 0 / 30
According to Equation (9), we can obtain the fuzzy evaluation vector X1a–X1c for the feature layer, structure layer, and concept layer of design scheme K1.
X 1 a = W × R 1 a = ( 0.5566 ,   0.2608 ,   0.1053   ,   0.0774 ) • 0.47 0.43 0.10 0 0 0.43 0.47 0.10 0 0 0.40 0.43 0.17 0 0 0.47 0.43 0.10 0 0 = ( 0.451 , 0.442 , 0.107 , 0 , 0 )
X 1 b = W × R 1 b = ( 0.0914 ,   0.3482 ,   0.1625   ,   0.3847 ) • 0.33 0.50 0.17 0 0 0.300 0.50 0.20 0 0 0.27 0.43 0.30 0 0 0.23 0.57 0.20 0 0 = ( 0.268 , 0.508 , 0.211 , 0 , 0 )
X 1 c = W × R 1 c = ( 0.4844 ,   0.1742 ,   0.0835   ,   0.2579 ) • 0.43 0.33 0.23 0 0 0.27 0.37 0.37 0 0 0.23 0.23 0.53 0 0 0.37 0.50 0.13 0 0 = ( 0.370 , 0.374 , 0.256 , 0 , 0 )
The total fuzzy evaluation vector X1 of design scheme k1 is calculated as follows:
X 1 = W × R ′ 1 = ( 0.6281 ,   0.2675 ,   0.1006 )   •     0.451 0.442 0.107 0 0 0.268 0.508 0.211 0 0 0.370 0.374 0.256 0 0 = ( 0.392 , 0.451 , 0.149 , 0 , 0 )
Using the same method, the fuzzy evaluation vector and the total fuzzy evaluation vector for schemes K2–K4 can be calculated.
The fuzzy evaluation vector X2a–X2c and the total fuzzy evaluation vector X2 for scheme K2 are as follows:
X 2 a = W × R 2 = ( 0.623 ,   0.275 ,   0.102 ,   0 ,   0 )
X 2 b = W × R 2 = ( 0.729 ,   0.181 ,   0.077 ,   0 ,   0 )
X 2 c = W × R 2 = ( 0.613 ,   0.236 ,   0.151 ,   0 ,   0 )
X 2 = W × R ′ 2 = ( 0.648   0.255 ,   0.100 ,   0 ,   0 )
The fuzzy evaluation vector X3a–X3c and the total fuzzy evaluation vector X3 for scheme K3 are as follows:
X 3 a = W × R 3 a = ( 0.350 ,   0.439 ,   0.211 ,   0 ,   0 )
X 3 b = W × R 3 b = ( 0.392 ,   0.414 ,   0.192 ,   0 ,   0 )
X 3 c = W × R 3 c = ( 0.336 ,   0.442 ,   0.222 ,   0 ,   0 )
X 3 = W × R ′ 3 = ( 0.359 ,   0.431 ,   0.206 ,   0 ,   0 )
The fuzzy evaluation vector X4a–X4c and the total fuzzy evaluation vector X4 for scheme K4 are as follows:
X 4 a = W × R 4 = ( 0.519 ,   0.293 ,   0.189 ,   0 ,   0 )
X 4 b = W × R 4 = ( 0.471 ,   0.259 ,   0.257 ,   0 ,   0 )
X 4 c = W × R 4 = ( 0.542 ,   0.310 ,   0.159 ,   0 ,   0 )
X 4 = W × R ′ 4 = ( 0.506 ,   0.284 ,   0.203 ,   0 ,   0 )
According to Equation (10), by converting the five grades of excellent, good, general, poor, and very poor to scores of 10, 8, 6, 4, and 2, respectively, the comprehensive evaluation score Y1–Y4 for design scheme K1–K4 was calculated.
Y 1 = 0.392   ×   10 + 0.451   ×   8   + 0.149   ×   6 + 0   ×   4 + 0   ×   2 = 8.422
Y 2 = 0.648   ×   10   +   0.255   ×   8   +   0.100   ×   6   + 0   ×   4 + 0   ×   2 = 9.120
Y 3 = 0.359   ×   10   +   0.431   ×   8   +   0.206   ×   6 + 0 × 4 + 0 × 2 = 8.274
Y 4 = 0.506   ×   10   +   0.284   ×   8   +   0.203   ×   6   +   0 × 4 + 0 × 2 = 8.550
By comparing the scores, it can be determined that the comprehensive evaluation of K2 is the best.

5. Result and Discussion

5.1. Evaluation Result Analysis

According to the above analysis of the fuzzy evaluation vector for schemes K2–K4, the fuzzy evaluation vector of each scheme is shown in Figure 19.
According to Figure 19, the fuzzy evaluation vector of design scheme K2 in various aspects is excellent and has high membership. In order to analyze the design information of scheme K2, the scores of scheme K2 in each feature level can be calculated.
The comprehensive evaluation score for design scheme K2 is presented as follows.
Evaluation vector of feature layer A.
Y a = 10 × 0.623 + 8 × 0.275 + 6 × 0.102 + 4 × 0 + 2 × 0 = 9.092
Evaluation vector of structure layer B.
Y b = 10 × 0.729 + 8 × 0.181 + 6 × 0.077 + 4 × 0 + 2 × 0 = 9.200
Evaluation vector of concept layer C.
Y c = 10 × 0.613 + 8 × 0.236 + 6 × 0.151 + 4 × 0 + 2 × 0 = 8.924
The scores of each secondary indicator score are calculated using the same method, and the evaluation score is presented in Table 17.
According to Table 17, the design scheme K2 has a great structure and aesthetic feeling (B = 9.200, B1 = 9.00, B2 = 9.47, B3 = 9.00, B4 = 9.40), and the secondary indicator scoring value is greater than 9 points, which reflects a strong sense of unity, equilibrium, rhythm, and orderliness in design elements and components. The equilibrium feeling B2 receives the highest rating, which indicates the good coordinate and sequence relationship in the design elements of this scheme. In feature layer A, the A1 score is 9.33, which shows that the aesthetic effect of form design is good. In concept layer C, the C1 score is 9.00 and the C4 score is 9.27, which shows that the design reflects the human and local cultural characteristics very well, and its design effect is simple, indicating that its manufacturing cost is low, reflecting an excellent economy.

5.2. Kansei Image Analysis of Aesthetic Design under the Background of Sustainable Development

Ecological culture is an important theme for the product aesthetic design in the context of sustainable development. In the ecological culture data, the regional plant is an important source of visual image, by analyzing the Kansei image by combining the factor analysis and design format analysis methods, designers can achieve effective visualization of image information and extract critical Kansei image features. The Kansei features of the information counter in the railway station is analyzed as follows:
(1)
In the semantic feature level, Kansei words such as elegant, introverted, and other words that reflect refined, understated, delicate, and dynamic aesthetics can be used as an aesthetic design guide in the context of sustainable development.
(2)
In the form features level, the simple and smooth form can better make people perceive the sustainable design aesthetic characteristics.

5.3. Aesthetic Elements Analysis under the Background of Sustainable Development

The weight values of indicators for each layer of aesthetic elements are shown in Figure 20. According to the ranking of the weight scores of indicators in each layer, the importance of aesthetics is analyzed as follows:
(1)
In the first-level evaluation indicators, the visual feature of appearance have the most significant influence on aesthetics, which shows that the basis of aesthetic perception is the explicit appearance design features, such as form, color, material, and others.
(2)
In the feature layer, simple and natural forms exert a significant influence on public facilities in railway stations, which shows that simple forms, humanized colors, natural materials, and natural decorations have an important impact on the aesthetic perception of sustainable design.
(3)
In the structure layer, the sense of unity and equilibrium have a great influence on aesthetic feeling. For the public facilities in railway stations under the background of sustainable development, the harmony and unity between the design elements are important factors in structural aesthetics.
(4)
In the concept layer, humanized design and a sense of simplicity are important contents of sustainable design aesthetics. In addition, the natural aesthetic and the harmony of the station environment are also important factors.
Based on the above analysis, the aesthetic design principles of public facilities in railway stations under the sustainable background are shown in Figure 21. The original aesthetic is the core content of the design principle of railway station public facilities. On this basis, an integral combination system of original aesthetic, simple aesthetic, humanized aesthetic, harmonious aesthetic, and natural aesthetic constitutes the aesthetic design principles of public facilities in railway stations under the background of sustainable development.

5.4. Design Scheme Improvement

Based on the above analysis, information counter aesthetic design also needs to meet ergonomic standards. The man–machine adaptability of the design case is simulated and verified by using JACKTM.
In the ergonomic analysis, the 95th percentile of Chinese adult males and the 95th percentile of Chinese females were selected for human factors analysis, and the ergonomic design of each region was good. The height of the design scheme is in line with the normal view of the passenger and the workspaces for at least two staff members to pass. The desk height of the counter can meet the normal inquiries of passengers, and there are accessible areas for wheelchair users to make inquiries. Figure 22 shows the static simulation of ergonomics.
Based on the above analysis, the design scheme K2 is taken as a prototype, combined with the indoor environment constraints of Xiongan railway station, and its local details are modified and improved to be used in the engineering scheme; the real picture is shown in Figure 23.

6. Conclusions

This study proposed the aesthetic design principles of public facilities in railway stations under the background of sustainable development based on the Kansei image and AHP-FCE model, and it takes the information counter design of Xiong’an railway station as an example to explain the method. The principal findings of the aesthetic design of public facilities in railway stations under the background of sustainable development are as follows:
(1)
The typical semantic features of the Kansei words and the typical form features of the visual images are obtained through the factor analysis and design format analysis methods, respectively. The plant elements in the ecological environment can be used as the Kansei image source for aesthetic design under the background of sustainable development. At the semantic feature level, Kansei words, such as understated, delicate, dynamic, and others, which reflect original simplicity and original nature, are typical semantic features. At the level of form features, simple and smooth shapes are typical form features.
(2)
Aesthetic design is a system composed of various elements. The core content of an aesthetic design is to reflect the original aesthetic feeling, such as simple, harmonious, and organic form design, low manufacturing costs, humane design, harmony with the station environment, and a natural sense of decoration, and these constitute the design aesthetic principles.
(3)
The AHP-FCE model is an effective design evaluation method. The AHP method can effectively determine the weight ranking of various aesthetic elements. The FCE method enables an objective evaluation of the design scheme, and it can quantitatively evaluate various aspects of the design scheme and provide an objective reference for designers to optimize the scheme. The combination of the AHP-FCE model and the Kansei image method can provide guidance for the creation and improvement of public facilities design in railway stations and offer a comprehensive and objective evaluation method for aesthetic designs. The ergonomic analysis of the design scheme can be combined with JACKTM. Future research can integrate computer-aided designs and psychological cognitive experiments to conduct quantitative investigations on Kansei image information and aesthetic elements.

Author Contributions

Conceptualization and methodology, X.-H.X.; data curation, H.Z.; formal analysis, Y.X.; writing—review and editing, H.Y.; visualization, S.G.; writing—review and editing and design, X.-H.X.; project administration, Q.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Sichuan Modern Design and Culture Research Center (project number MD23E020) and the Hubei University of Technology 2023 Doctoral Research Foundation Project (XJ2023005901).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets used during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The photos of Xiong’an railway station and Xiong’an ecological environment.
Figure 1. The photos of Xiong’an railway station and Xiong’an ecological environment.
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Figure 2. Research framework.
Figure 2. Research framework.
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Figure 3. Product design principles under the background of sustainable development.
Figure 3. Product design principles under the background of sustainable development.
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Figure 4. Examples and analysis of the research object.
Figure 4. Examples and analysis of the research object.
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Figure 5. Aesthetic cognitive layer analysis.
Figure 5. Aesthetic cognitive layer analysis.
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Figure 6. Indicator system of the aesthetic element.
Figure 6. Indicator system of the aesthetic element.
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Figure 7. SD method diagram.
Figure 7. SD method diagram.
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Figure 8. DFA method diagram.
Figure 8. DFA method diagram.
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Figure 9. Kansei image feature extraction method process.
Figure 9. Kansei image feature extraction method process.
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Figure 10. AHP-FCE model for aesthetic design.
Figure 10. AHP-FCE model for aesthetic design.
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Figure 11. Typical visual image samples.
Figure 11. Typical visual image samples.
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Figure 12. Typical visual image samples.
Figure 12. Typical visual image samples.
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Figure 13. Gravel diagram.
Figure 13. Gravel diagram.
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Figure 14. Diagram of each factor component.
Figure 14. Diagram of each factor component.
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Figure 15. Design format analysis of experimental results.
Figure 15. Design format analysis of experimental results.
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Figure 16. Statistical chart of design format analysis.
Figure 16. Statistical chart of design format analysis.
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Figure 17. Formation process of the design scheme.
Figure 17. Formation process of the design scheme.
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Figure 18. Design scheme effect.
Figure 18. Design scheme effect.
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Figure 19. Fuzzy evaluation vector of each scheme.
Figure 19. Fuzzy evaluation vector of each scheme.
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Figure 20. Weight analysis diagram of aesthetic element indicators.
Figure 20. Weight analysis diagram of aesthetic element indicators.
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Figure 21. Aesthetic design principles of public facilities in railway stations.
Figure 21. Aesthetic design principles of public facilities in railway stations.
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Figure 22. Ergonomics/human factors analysis of the design scheme.
Figure 22. Ergonomics/human factors analysis of the design scheme.
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Figure 23. Final design scheme effect.
Figure 23. Final design scheme effect.
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Table 1. Definitions of indicators.
Table 1. Definitions of indicators.
LevelAesthetic ElementMeaning
Feature layerFormForm aesthetic feeling is the sustainable design aesthetics for the product form design
ColorColor aesthetic feeling is the sustainable design aesthetics for the product color design
MaterialMaterial aesthetic feeling is the sustainable design aesthetics for the product material design
Decorative elementDecorative element aesthetic feeling is the sustainable design aesthetics for the product decorative design
Structure
layer		SequenceSequence feeling refers to the arrangement of relationships in the design elements and components for the information counter appearance
EquilibriumEquilibrium is a stabilization of the relationship between the design elements and components for the information counter appearance
RhythmRhythm feeling refers to regular patterns of changes in the design elements and components for information counter appearance. This regular pattern of changes can help to make spirituality
UnityUnity feeling is coherence and wholeness of relationships in the design elements and components for the information counter appearance
Concept
layer		Humanization and cultureHarmony with the humanization of local culture
Natural and ecologicalHarmony with the local natural environment
railway station environmentHarmony with the railway station environment
Economical and simpleSimplicity of processing and manufacturing
Table 2. The 1–9 scale values and meaning.
Table 2. The 1–9 scale values and meaning.
Scale ValuesSignificanceMeaning
1Equally importantBoth elements are equally important
3Slightly importantThe former element is slightly more important than the latter
5Significantly importantThe former element is clearly more important than the latter
7Very importanceThe former element is strongly more important than the latter
9Absolutely importantThe former element is absolutely more important than the latter
2, 4, 6, 8MidpointIndicates the median of the above two elements
1, 1/2,…, 1/9Inverse comparisonThe latter element is the inverse of the above value when compared to the former element
Table 3. Random consistency index.
Table 3. Random consistency index.
n1234567891011
RI000.580.901.121.241.321.411.451.491.51
Table 4. Collection of Kansei words.
Table 4. Collection of Kansei words.
NumberKansei WordsNumberKansei Words
S1Elegant–VulgarS5Light–Heavy
S2Introverted–ExtravertedS6Concise–Trival
S3Gentle–HardS7Exquisite–Rough
S4Delicate–RoughS8Flow–Quiet
Table 5. Kansei image cognition questionnaire.
Table 5. Kansei image cognition questionnaire.
Kansei Words−3−2−10123Kansei Words
ExtremelyQuiteSlightlyNeutralSlightlyQuiteExtremely
S1 Elegant Vulgar
S2 Introverted Extraverted
S3 Gentle Hard
S4 Delicate Rough
S5 Lithe Heavy
S6 Concise Cumbersome
S7 Exquisite Rough
S8 Flow Quiet
Table 6. Average score of the imagery perception experiment.
Table 6. Average score of the imagery perception experiment.
S1S2S3S4S5S6S7S8
M1−2.275−1.125−1.525−1.650−1.025−1.900−2.125−1.250
M2−1.950−2.050−1.825−1.825−2.275−2.150−1.075−2.300
M3−0.9251.125−0.5751.2000.9001.1751.350−2.150
M4−2.025−2.100−2.125−1.525−1.125−2.250−1.550−2.450
M5−0.9502.3502.600−1.250−1.0502.1502.550−2.350
M6−1.8251.475−1.3751.4250.0752.1502.300−2.575
M7−2.500−2.450−2.400−2.350−1.225−2.150−2.125−2.600
M8−2.350−2.175−1.725−1.250−1.825−1.875−1.475−2.050
M9−2.400−2.500−2.425−2.400−1.175−2.550−2.175−2.150
Table 7. Factor analysis of total variance explained.
Table 7. Factor analysis of total variance explained.
ComponentsInitial EigenvalueExtract the Sum of Squares of LoadsThe Sum of the Squares of Rotating Loads
TotalPercentage of VarianceAccumulate %TotalPercentage of VarianceAccumulate %TotalPercentage of VarianceAccumulate %
15.46768.33668.3365.46768.33668.3363.74146.75946.759
21.09413.67082.0071.09413.67082.0072.70533.81680.575
31.00812.60494.6101.00812.60494.6101.12314.03694.610
Table 8. Rotated component matrix.
Table 8. Rotated component matrix.
Main Ingredients
123
S10.8350.392−0.060
S20.8350.528−0.039
S30.991−0.0110.042
S40.2990.908−0.115
S50.2090.9210.022
S60.7700.580−0.192
S70.7970.510−0.284
S8−0.066−0.0510.992
Table 9. Weight analysis of each factor.
Table 9. Weight analysis of each factor.
SemanticDescription of Factor CharacteristicsWeightRanking by Importance
factor 1S1 (elegant–vulgar)
S2 (introverted–extraverted)
S3 (gentle–hard)
S6 (concise–trival)
S7 (exquisite–rough)		refined aesthetic, understated aesthetic0.4941
factor 2S4 (delicate–rough)
S5 (light–heavy)		delicate aesthetic0.3572
factor 3S8 (flow–quiet)dynamic aesthetic0.1483
Table 10. Collection of form feature description and score.
Table 10. Collection of form feature description and score.
NumberFrom Feature DescriptionUnrelated (1)Correlated (2)Strong Correlated (3)
F1shape is a circle
F2shape is a square
F3shape turning arc
F4shape turning straight line
F5shape is an abstraction
F6shape is smooth
Table 11. The analysis of typical form features.
Table 11. The analysis of typical form features.
Feature ClassificationFeature DescriptionVisual Image Screening
Overall shapeThe main features are circles, streamlines, and abstractionsSustainability 16 05021 i001
Detail shapeThe main features are the smooth arc shapeSustainability 16 05021 i002
Shape transitionRounded corners are evidentSustainability 16 05021 i003
Table 12. Judgment matrix and weight value of the first layer.
Table 12. Judgment matrix and weight value of the first layer.
ABCωλmaxCICR
A13.64.60.64813.090890.045450.07836
B0.3013.40.2513
C0.200.2910.1006
Table 13. Judgment matrix and weight value of the A layer.
Table 13. Judgment matrix and weight value of the A layer.
AA1A2A3A4ω (Ai)λmaxCICR
A114.34.15.40.55664.21310.071040.07982
A20.2313.34.60.2608
A30.240.3011.430.1053
A40.190.220.7010.0774
Table 14. Judgment matrix and weight value of the B layer.
Table 14. Judgment matrix and weight value of the B layer.
BB1B2B3B4ω (Bi)λmaxCICR
B110.290.720.180.09144.18340.061140.06869
B23.4513.40.760.3482
B31.400.3010.720.1625
B45.651.321.3910.3847
Table 15. Judgment matrix and weight value of the C layer.
Table 15. Judgment matrix and weight value of the C layer.
CC1C2C3C4ω (Ci)λmaxCICR
C114.83.81.930.48444.24110.080360.09030
C20.2113.90.520.1742
C30.260.2610.320.0835
C40.521.943.12510.2653
Table 16. Weight of aesthetic elements.
Table 16. Weight of aesthetic elements.
First-Level IndicatorsWeight
(W1)		SortingSecond-Level IndicatorsWeights (W2)Sorting
A0.62811A10.55661
A20.26082
A30.10533
A40.07744
B0.26752B10.09144
B20.34822
B30.16253
B40.38471
C0.10063C10.48441
C20.17423
C30.08354
C40.25792
Table 17. Comprehensive evaluation score of each indicator.
Table 17. Comprehensive evaluation score of each indicator.
First-Level Indicator ScoreSecond-Level Indicator Score
Feature layer A9.092Form A19.33
Color A28.73
Material A38.53
Decorative element A48.67
Structure layer B9.200Sequence B19.00
Equilibrium B29.47
Rhythmical B39.00
Unity B49.40
Concept layer C8.924Humanization and culture C19.00
Natural and ecological C28.40
railway station environment C38.53
Economical and simple C49.27
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Xie, X.-H.; Zhu, H.; Xu, Y.; Yan, H.; Guo, S.; Liu, Q. Aesthetic Design and Evaluation of Public Facilities in Railway Stations under the Background of Sustainable Development: A Case of an Information Counter at Xiong’an Railway Station. Sustainability 2024, 16, 5021. https://doi.org/10.3390/su16125021

AMA Style

Xie X-H, Zhu H, Xu Y, Yan H, Guo S, Liu Q. Aesthetic Design and Evaluation of Public Facilities in Railway Stations under the Background of Sustainable Development: A Case of an Information Counter at Xiong’an Railway Station. Sustainability. 2024; 16(12):5021. https://doi.org/10.3390/su16125021

Chicago/Turabian Style

Xie, Xuan-Hui, Hongyang Zhu, Yunpeng Xu, Huiran Yan, Shilin Guo, and Qiang Liu. 2024. "Aesthetic Design and Evaluation of Public Facilities in Railway Stations under the Background of Sustainable Development: A Case of an Information Counter at Xiong’an Railway Station" Sustainability 16, no. 12: 5021. https://doi.org/10.3390/su16125021

APA Style

Xie, X. -H., Zhu, H., Xu, Y., Yan, H., Guo, S., & Liu, Q. (2024). Aesthetic Design and Evaluation of Public Facilities in Railway Stations under the Background of Sustainable Development: A Case of an Information Counter at Xiong’an Railway Station. Sustainability, 16(12), 5021. https://doi.org/10.3390/su16125021

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