Transport-Node-Based Performance Indicators and Tourism Infrastructure Strategies in Historic Cultural Districts

Abstract

Historic and cultural districts serve as important carriers of urban heritage, but increasing tourist flows have placed growing demands on the capacity of their tourism infrastructure. This study constructs a node-based evaluation framework to assess tourism infrastructure within such districts and applies it to three transport nodes in the Ciqikou Historic and Cultural District of Chongqing. Drawing on Lynch’s theory of spatial nodes and an analytic hierarchy process, the framework integrates field-based infrastructure observations with tourist perception data to generate objective node-level evaluations and to validate their consistency with visitor experience. The results reveal substantial differences in infrastructure performance across nodes, with transport accessibility, information services, and environmental hygiene emerging as key factors shaping satisfaction. The comparison of objective scores and tourist perceptions also highlights mismatches at specific nodes, underscoring the need to align facility provision with actual visitor experience. Based on these findings, the study proposes targeted improvement measures addressing transport connectivity, signage systems, basic service facilities, and public safety. The node-based approach offers a practical tool for diagnosing infrastructure gaps in historic cultural districts and provides empirical guidance for refined management and sustainable tourism development.

1. Introduction

Historic and Cultural Districts (HCDs) constitute vital components of urban heritage systems. As urban cores, they preserve abundant cultural resources and play central roles in heritage conservation and tourism development [1]. Internationally, such areas are referred to as “historic districts,” “historic urban areas,” or “sites.” Despite variations in terminology, they share historical, cultural, and architectural significance and are recognized for their outstanding universal value [2]. In China, HCDs are defined by the concentration of cultural relics, the continuity of historic buildings, and the authenticity of spatial patterns [3]. By 2024, more than 1200 districts had been identified and designated as priority areas for protection and tourism development [4].

Tourism has become a strategic tool for revitalizing HCDs, promoting economic growth, and fostering cultural exchange [1]. These districts have emerged as major destinations for heritage tourism, yet increasing visitor flows have imposed pressure on infrastructure and public services [5]. Within the framework of tourism competitiveness, infrastructure and quality of life are essential for sustainable development [6]. Investment in infrastructure not only enhances visitor satisfaction but also contributes to long-term economic benefits [7]. To meet these challenges, the Chinese government’s 14th Five-Year Plan emphasized expanding tourism facilities and public service systems to ensure accessibility across urban and rural areas [8].

Tourism infrastructure is generally defined as the system of public services that supports the functioning of tourism, encompassing economic, social, and environmental facilities [9]. Infrastructure mediates the relationship between tourism growth and the urban environment, linking visitor needs with governance and sustainability planning [10]. Thus, systematic evaluation of infrastructure is essential for the sustainable management of HCDs. However, most existing studies address tourism broadly, emphasizing destination-level competitiveness, visitor satisfaction, or environmental adaptability. For instance, Li et al. [11] examined spatial adaptability for commercial regeneration but did not analyze infrastructure components. Lu et al. [6] developed a competitiveness model using multi-source data but focused on destination-level indicators. Yu et al. [12] highlighted tourist satisfaction with spatial layout and signage, yet without connecting such perceptions to formal evaluation frameworks.

To address these gaps, this study introduces a tourism infrastructure evaluation framework grounded in the concept of spatial nodes. In urban planning, nodes are regarded as elements of spatial structure that concentrate flows and activities, such as plazas, transport intersections, and cultural hubs [13]. In HCDs, nodes also include areas with dense service facilities and intensive tourist activity [14]. Applying nodes as analytical units facilitates understanding of infrastructure distribution and its interaction with visitor behavior.

Based on this framework, this study addresses two main research questions:

(1)

How can the concept of spatial nodes be applied to tourism infrastructure planning in Historic and Cultural Districts?

(2)

How can existing tourism infrastructure be systematically analyzed and evaluated to support service quality improvement and promote the sustainable development of these districts?

This study has two main objectives:

• To develop the concept of tourism infrastructure evaluation based on spatial nodes within the context of Historic and Cultural Districts.
• To propose a systematic methodology for evaluating the existing tourism infrastructure at selected spatial nodes within Historic and Cultural Districts.

2. Literature Review

Tourism infrastructure is generally defined as an interconnected system of legal, physical, and environmental components sustaining tourism activities [15]. Scholars regard it as the foundation of tourism development and a key factor enhancing destination attractiveness [16]. Empirical studies also show that high-quality infrastructure raises tourist satisfaction and repeat visitation, thereby reinforcing competitiveness [17]. These outcomes explain the sustained scholarly interest in infrastructure within tourism research. Core elements of tourism infrastructure include transportation, accommodation, communication networks, tourist information, and recreational or sports facilities [18]. Infrastructure also shapes perceptions of destination image. Kutlu and Ayyıldız [19] demonstrate that visitors’ assessments of attractions, cultural heritage, transport, accommodation, shopping, entertainment, hygiene, and safety collectively influence overall evaluations of destination image. Thus, infrastructure indirectly affects the memorability and appeal of tourist experiences.

In heritage contexts, particularly compact Historic and Cultural Districts (HCDs), the spatial distribution of facilities strongly influences tourist movement and length of stay [20]. Tourists frequently cluster at facility-dense areas, such as museums, visitor centers, parking zones, and restaurants [21]. Infrastructure density is highly correlated with zones of concentrated tourist activity [22]. Despite this, existing studies often focus on single facilities or adopt destination-level perspectives, thereby neglecting micro-spatial variations [23]. In dense HCDs, however, spatial configuration directly shapes tourist experiences, suggesting that perception data from visitors can usefully complement evaluations of infrastructure distribution.

The concept of nodes originates from Lynch’s [13] theory of the urban image, where nodes are defined as key convergence points of movement and activities. In tourism research, nodes have been used to analyze hubs such as transport terminals, shopping centers, and city cores [24]. Building on this, Lohmann and Pearce [25] propose that nodes may function as origins, gateways, hubs, stopovers, or final destinations depending on form and behavior.

Within cultural heritage tourism, service facility locations closely relate to tourist movement patterns [20]. Hotspots often occur in cultural–commercial areas such as historic streets and landmark clusters [26]. Studies using geolocated social media confirm that high-frequency activity points often overlap with transport and service facility zones [27]. These findings suggest that tourist flows are shaped not only by infrastructure distribution but also by the functional significance of nodes [28].

Although previous research seldom defines nodes explicitly as planning units, many studies imply their role in structuring infrastructure and organizing behavior. The relationships among nodal concepts, facility distribution, and tourist experience provide a theoretical basis for node-based evaluation frameworks.

To operationalize such an approach, this study employs the Analytic Hierarchy Process (AHP), a multi-criteria decision-making method that structures problems hierarchically and applies pairwise comparisons to assign weights [29]. Owing to its clarity and adaptability, AHP is widely applied in tourism and urban planning.

In tourism studies, AHP has been used to evaluate attractiveness, service quality, and satisfaction. Göksu and Kaya [30] combined AHP with TOPSIS to rank destinations and assess the relative significance of transport, services, and appeal. Qiao et al. [31] integrated AHP with fuzzy control to evaluate cultural heritage, developing multidimensional models encompassing historical value, identity, and spatial protection. Despite its strengths, AHP involves subjectivity in expert judgment [32]. To address this, the present study integrates field investigations and expert scoring to build judgment matrices and validates results with tourist perception data. This combined approach enhances objectivity and provides a robust framework for evaluating tourism infrastructure in HCDs.

3. Methods

3.1. Defining the Spatial Scope of Node Evaluation

This study defines the service area of each node by combining two dimensions: walkability and visual perception. In terms of walkability, the node center is used as the reference point, and the reachable area is defined as the zone within a 120 m walking distance. This range corresponds to approximately 2–3 min of continuous walking. Jacobs [33] emphasized that excessively long block lengths reduce pedestrian mobility and spontaneous interactions. He suggested that block lengths should remain below 400 feet (approximately 120 m) to maintain vibrant street life. This distance standard has become a widely accepted reference in walkable city studies, particularly in high-density urban environments. Secondly, for visual perception, a circular area with a 150 m diameter centered on the node is used to simulate the range of human spatial perception. Based on extensive field observations, Gehl [34] found that within a 100–150 m range, people perceive street environments, social activity, and spatial details most clearly. Beyond this range, environmental influence declines significantly.

Therefore, the service area of each node is defined as the spatial overlap of these two constraints: the pedestrian diameter of 120 m and the visual perception range of 150 m. Only spaces that meet both conditions are included in the evaluation. Tourism infrastructure elements are recorded only within this boundary. Locations that are geographically close but inaccessible due to terrain or lack of pathways are excluded. This ensures that the evaluation space reflects tourists’ actual perception, thereby improving the accuracy of infrastructure assessment in the tourism environment.

3.2. Selection of Tourism Infrastructure Indicators

In node-based tourism assessment for Historic and Cultural Districts, infrastructure conditions are fundamental to visitor satisfaction and sustainable development.

To effectively assess the condition of infrastructure within each node’s service area, this study establishes a multi-level indicator framework, including both a criteria level and an indicator level. The criteria layer draws on two sources, the first being normative guidance from international and national organizations. UNESCO highlights infrastructure as a key determinant in tourism development, with tourist spending concentrated in transport, accommodation, food, and retail [35]. China’s latest national standards similarly emphasize mobility, information access, sanitation, and public safety [36]. The second source synthesizes academic literature on tourism public services and built environments in heritage sites. Existing studies underline infrastructure’s role in shaping visitor experience and supporting spatial functionality. For example, Wang et al. [20] explored the impact of service facilities on spatial behavior and perception. Dalimunthe et al. [9] evaluated infrastructure readiness for sustainable tourism. Synthesizing both sources, this study classifies tourism infrastructure into six categories aligned with key visitor needs: accessibility, hygiene, information, experience, commercial convenience, and safety. The six categories are as follows:

B1: Transport Infrastructure—Facilities for movement and transit connections.

B2: Environmental Infrastructure—Facilities ensuring cleanliness, sanitation, and waste management.

B3: Information Service Infrastructure—Signage, consultation points, and informational tools for navigation and guidance.

B4: Cultural and Leisure Infrastructure—Spaces and amenities that support cultural engagement, recreational activity, and immersive experiences.

B5: Commercial Service Infrastructure—Lodging, dining, snack stalls, tea houses, and retail facilities.

B6: Public Safety Infrastructure—Facilities for emergency response, medical care, fire safety, and security.

To support indicator selection, a structured literature search was conducted in Web of Science for publications from 2015 to 2025. The search keywords were: (TS = “tourism infrastructure” OR “tourism facilities”) AND (“indicator” OR “evaluation”) AND (“framework” OR “model”). After screening titles and abstracts, 187 articles were reviewed in full. A final set of sources was selected based on relevance to extract measurable components. This process identified 17 indicators and 19 observation items. Figure 1 presents the resulting indicator framework.

Tourism infrastructure in historic districts can be organized into six domains, each contributing to visitor experience and sustainability. Transport infrastructure underpins accessibility, covering both external transport and internal circulation. Efficient transport stations, parking, and internal signage shape travel decisions in space-constrained heritage destinations [37,38]. This is especially important in heritage sites where spatial constraints limit mobility. Clear wayfinding and safe pedestrian connections are essential for maintaining tourism flow and reducing congestion [39].

Equally important are environmental and informational infrastructures, which ensure both physical comfort and cognitive orientation. Facilities such as trash bins and public toilets support cleanliness and hygiene, influencing tourist behavior and ecological protection [40,41]. Mensah and Enu-Kwesi [42] also stress their relevance to community health and sustainable tourism. Information service infrastructure, including signage, visitor centers, and smart facilities, helps tourists navigate independently and engage with heritage content. According to ISO standards [43], these services must be accurate, accessible, and user-friendly. With the advent of smart tourism, as outlined by the Ministry of Culture and Tourism of China [44], destinations must integrate mobile and multilingual platforms to remain competitive.

Cultural and leisure, commercial, and public safety infrastructures complete the overall service system. Cultural and leisure infrastructure provides spaces for rest, engagement, and cultural immersion, fulfilling Carr et al.’s [45] framework of ideal public space experiences. Rest areas, cultural displays, and interactive installations enrich visitor experiences and reinforce local identity [46,47]. Commercial infrastructure comprising dining, retail, accommodation, and self-service options supports economic sustainability and extends tourist stays [48,49]. Finally, public safety infrastructure, including security systems, emergency medical stations, and fire safety mechanisms, is critical for ensuring a safe tourism environment. As UNESCO and allied bodies suggest [50], these systems also protect heritage assets and ensure long-term site operability. Together, these six categories provide a comprehensive and adaptable framework for evaluating and enhancing tourism infrastructure within historic and cultural districts.

3.3. Research Framework

In response to the research questions, this study develops an evaluation framework for tourism infrastructure at transport nodes within Historic and Cultural Districts. Figure 2 presents the overall methodological workflow. First, a system of six criteria and seventeen indicators was established through a systematic literature review and expert assessment, using the AHP to determine the corresponding weights. Second, on-site surveys were conducted in the Ciqikou Historic and Cultural District in Chongqing to document the types and quantities of infrastructure within each node, thereby generating objective evaluation results. Finally, a visitor satisfaction survey was used to compare and validate the objective assessment outcomes, based on which targeted strategies for improving tourism infrastructure were proposed. The following section provides a detailed explanation of these methods.

3.4. Study Area

This study takes the Ciqikou Historic and Cultural District in Chongqing as the empirical case. Ciqikou is located in the western Shapingba District of Chongqing, adjacent to the Jialing River, and exemplifies a typical “interwoven mountain–water” urban landscape. In 2009, Ciqikou was designated as a National Historic and Cultural District. It preserves dense clusters of traditional dwellings and a well-preserved network of historic alleys. With annual tourist visits exceeding ten million, it is one of the most representative cultural heritage destinations in western China (see Figure 3 for study area location).

3.5. AHP-Based Assessment Framework

This study employs a combination of the Analytic Hierarchy Process (AHP) and expert scoring to evaluate tourism infrastructure at nodes within Historic and Cultural Districts and to determine the relative weights of evaluation indicators. Prior to evaluation, experts were provided with detailed descriptions of the characteristics of the node type being assessed (i.e., transport nodes). Transport nodes were defined as key transition points linking external transportation systems with internal spaces, to ensure a consistent conceptual understanding among experts. The specific methodological steps are as follows:

Data collection was conducted through a Structured Questionnaire Survey. A panel of fifteen experts, including professionals in tourism planning, heritage site management, and urban infrastructure (see Supplementary Materials, Table S1 for expert composition), was invited to independently complete the AHP-based evaluation. The panel size aligns with typical AHP applications, where expert groups commonly range from 3 to 25 members depending on the study scope [51], making a sample of 15 appropriate for this evaluation. Each expert performed pairwise comparisons of six primary evaluation criteria using the standard Saaty 1–9 scale. Based on the responses, pairwise comparison matrices were constructed and aggregated using the geometric mean method. To incorporate the user’s perspective, one experienced long-term visitor was included in the panel, while the remaining members were professionals whose expertise ensured that expert judgment remained the dominant basis of the evaluation. Consistency ratios (CRs) were calculated to ensure the logical validity of judgments; all matrices met the accepted threshold (CR < 0.1).

Under the six criteria levels, 17 sub-indicators were identified. Experts were asked to allocate a total of 10 points within each criterion to reflect the relative importance of each sub-indicator. The assigned scores were normalized to obtain the local weights of each indicator within its corresponding criterion. Finally, each indicator’s local weight was multiplied by the weight of its corresponding criterion to derive the overall (global) weight. These calculated weights serve as the basis for applying weighted scoring in the empirical assessment.

Steps for weight analysis of each indicator:

Step 1: Determining the Weights of the Criterion Layer

A panel of 15 experts in tourism planning and heritage management was invited to conduct pairwise comparisons among the six primary criteria using the Saaty 1–9 scale. Each expert produced a 6 × 6 judgment matrix, denoted as $A^{\left(e\right)}=\left[a_{ij}^{\left(e\right)}\right]$. The aggregated judgment matrix $A=\left[a_{ij}\right]$ was derived using the geometric mean method.

$$a_{ij}={\left(\prod _{e=11}^{15}{a}_{ij}^{\left(e\right)}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$15$}\right.}, a_{ji}=1/a_{ij},a_{ii}=1$$

Eigenvector Derivation and Consistency Check:

The principal eigenvalue ${\lambda }_{max}$ and its corresponding eigenvector ${\rm X}={\left[{\mathcal{x}}_1,\dots ,{\mathcal{x}}_6\right]}^{\mathsf{{\rm T}}}$ were computed from the aggregated judgment matrix $A$. The resulting eigenvector was then normalized to obtain the weight vector ${\mathcal{w}}_{{{\rm B}}_k}$ for the criterion layer.

$${\mathcal{w}}_{{{\rm B}}_k}={\displaystyle \frac{{\mathcal{x}}_k}{{\mathit{\Sigma }}_{i=1}^6{ \mathcal{x}}_i}},k=1,\dots ,6$$

Consistency Index and Consistency Ratio:

$$CI={\displaystyle \frac{{\lambda }_{max}-6}{6-1}},RI\left(6\right)=1.24,CR=CI∕RI.$$

The results of the consistency test were as follows:

$${ \lambda }_{max}=6.1651, CI=0.0330, CR=0.0266<0.10.$$

The judgment matrix therefore satisfied the consistency requirement. The resulting criterion weights are presented in

Table 1. Criterion Weights and Consistency Test Results.

Criterion Code	Criterion Name	Criterion Weight	Rank
B1	Transport Infrastructure	0.2314	1
B2	Environmental Infrastructure	0.1060	6
B3	Information Service Infrastructure	0.2176	2
B4	Cultural and Leisure Infrastructure	0.1329	4
B5	Commercial Service Infrastructure	0.1298	5
B6	Public Safety Infrastructure	0.1823	3
λmax	—	6.1651
CI	—	0.0330
CR	—	0.0266

.

Step 2: Allocation of Indicator-Level Weights

Under the six primary criteria, a total of 17 indicators (C1 to C17) were established. For each criterion, experts were asked to distribute a total of 10 points among its corresponding indicators. Let $S_{k,i}^{\left(e\right)}$ denote the score assigned by expert $e$ to indicator $C_{k,i}$, based on which the local weight ${\mathcal{w}}_{C_{k,i}}$ was derived.

$${\overline{s}}_{k,i}={\displaystyle \frac{1}{15}}\sum _{e=1}^{15}{s}_{k,i}^{\left(e\right)},{\mathcal{w}}_{C_{k,i}}={\displaystyle \frac{{\overline{S}}_{k,i}}{10}},\sum _{i=1}^{m_k}{\mathcal{w}}_{C_{k,i}}=1$$

Step 3: Calculation of Global Weights $W_{C_{k,i}}$.

$$W_{C_{k,i}}={\mathcal{w}}_{{{\rm B}}_K}\times {\mathcal{w}}_{C_{k,i}}$$

(1)

Table 2. Criteria and Indicator Weights Distribution.

Criterion		Indicator		Average Score	Local Weight	Criterion Weight	Global Weight	Rank
B1	Transport Infrastructure	C1	Public Transit Facilities	6.80	0.6800	0.2314	0.1574	1
		C2	Internal Transport Facilities	3.20	0.3200	0.2314	0.0740	4
B2	Environmental Infrastructure	C3	Cleaning Facilities	6.07	0.6067	0.1060	0.0643	6
		C4	Restroom Environmental Hygiene Facilities	3.93	0.3933	0.1060	0.0417	12
B3	Information Service Infrastructure	C5	Signage Facilities	4.87	0.4867	0.2176	0.1059	2
		C6	Information Service Facilities	3.13	0.3133	0.2176	0.0682	5
		C7	Smart Facilities	2.00	0.2000	0.2176	0.0435	10
B4	Cultural and Leisure Infrastructure	C8	Rest Facilities	4.60	0.4600	0.1329	0.0611	7
		C9	Interactive Facilities	3.47	0.3467	0.1329	0.0461	9
		C10	Cultural Promotion Facilities	1.93	0.1933	0.1329	0.0257	16
B5	Commercial Service Infrastructure	C11	Dining Facilities	3.20	0.3200	0.1298	0.0415	13
		C12	Retail Service Facilities	2.67	0.2667	0.1298	0.0346	14
		C13	Accommodation Facilities	2.20	0.2200	0.1298	0.0286	15
		C14	Self-Service Facilities	1.93	0.1933	0.1298	0.0251	17
B6	Public Safety Infrastructure	C15	Security Facilities	4.67	0.4667	0.1823	0.0851	3
		C16	Emergency Medical Facilities	3.00	0.3000	0.1823	0.0547	8
		C17	Fire Safety Facilities	2.33	0.2333	0.1823	0.0425	11

presents the final evaluation results after weighting each indicator level.

In the transport node category, within the B-level (criteria-level) indicators, Transport Infrastructure (B1, 0.2314) and Information Service Infrastructure (B3, 0.2176) were assigned the highest weights. They were followed by Public Safety Infrastructure (B6, 0.1823) and Cultural and Leisure Infrastructure (B4, 0.1329). Environmental Infrastructure (B2, 0.1060) received the lowest weight.

Among the C-level (sub-criteria) indicators, the most influential factor was Public Transit Facilities (C1, 0.1574), indicating the dominant role of external accessibility in shaping tourist experience and infrastructure needs. This was followed by Signage Facilities (C5, 0.1059) and Security Facilities (C15, 0.0851). Other indicators also received relatively high weights, including Internal Transport Facilities (C2, 0.0740), Information Service Facilities (C6, 0.0682), and Cleaning Facilities (C3, 0.0643), highlighting the importance of internal mobility, access to tourist information, and environmental hygiene. The importance of other indicators was slightly lower: Rest Facilities (C8, 0.0611), Emergency Medical Facilities (C16, 0.0547), Interactive Facilities (C9, 0.0461), etc.

3.6. Data Collection and Analysis

To verify the applicability of the proposed indicator system, this study conducted field research and data collection at three transport nodes (N1–N3) in the Ciqikou Historic and Cultural District, Chongqing. The spatial distribution of the nodes is shown in Figure 4. These nodes were selected based on their role as gateways linking external urban transportation systems with the internal spatial structure of the district.

Data collection at each node was carried out through direct observation, photographic documentation, and measurement of all predefined indicators within the node’s spatial boundary. The distribution of facilities is illustrated in Figure 5. In addition, the Supplementary Materials (Table S2) provides supplementary on-site photographs that illustrate the spatial layout and key features of the traffic nodes.

Node Profiles:

Node 1 (N1, Southeast Gate Entrance): This is one of the earliest main entrances to the Ciqikou Historic and Cultural District. Upon entry, visitors encounter a narrow corridor-like street (less than 4 m wide) flanked by traditional shops that retain much of their original appearance. Although the original bus stop has been relocated to the periphery due to tourism development, this node remains a vital pedestrian passage because of its historical significance and spatial continuity.

Node 2 (N2, Southwest Gate Entrance): This node features relatively flat terrain and an open view, with good accessibility and spatial legibility. A shaded walkway connects the node to the district interior. Across the street are several restaurants, while a bus stop and metro station are located just outside, making it the district’s main hub for public transportation arrivals.

Node 3 (N3, West Gate Entrance): Located at the western main entry of the district, this node includes a large parking lot and a visitor center, serving as the main gathering point for self-driving and group tourists. Due to significant elevation differences, visitors transition vertically into the main area via elevators and stairs.

Empirical data collected at these three nodes covered the observed metrics corresponding to the 17 evaluation indicators, forming the basis for subsequent quantitative analysis (see Supplementary Materials, Table S3 for observed results). To address unit discrepancies among the indicators, this study applied min-max normalization [52], mapping raw observations to a [0, 1] scale. Remaining values were linearly transformed into standardized scores. These standardized scores were then multiplied by the corresponding global weights of each indicator, resulting in final infrastructure scores for each transport node in Ciqikou.

Table 3. Final Calculation for Each Indicator.

Criterion		N1	N2	N3	Indicator		N1	N2	N3
B1	Transport Infrastructure	0.0370	0.1944	0.1527	C1	Public Transit Facilities	0	0.1574	0.0787
					C2	Internal transport facilities	0.0370	0.0370	0.0740
B2	Environmental Infrastructure	0.0739	0.0214	0.1000	C3	Cleaning Facilities	0.0322	0.0214	0.0643
					C4	Restroom Environmental Hygiene Facilities	0.0417	0	0.0357
B3	Information Service Infrastructure	0.0265	0.0530	0.2176	C5	Signage Facilities	0.0265	0.0530	0.1059
					C6	Information Service Facilities	0	0	0.0682
					C7	Smart Facilities	0	0	0.0435
B4	Cultural and Leisure Infrastructure	0.0782	0.1099	0.0425	C8	Rest Facilities	0.0217	0.0611	0.0041
					C9	Interactive Facilities	0.0307	0.0231	0.0384
					C10	Cultural Promotion Facilities	0.0257	0.0257	0
B5	Commercial Service Infrastructure	0.0716	0.1039	0.0606	C11	Dining Facilities	0.0332	0.0415	0.0249
					C12	Retail Service Facilities	0.0346	0.0087	0.0087
					C13	Accommodation Facilities	0.0038	0.0286	0.0019
					C14	Self-Service Facilities	0	0.0251	0.0251
B6	Public Safety Infrastructure	0.1823	0.1398	0.1398	C15	Security Facilities	0.0851	0.0426	0.0426
					C16	Emergency Medical Facilities	0.0547	0.0547	0.0547
					C17	Fire Safety Facilities	0.0425	0.0425	0.0425
Comprehensive score		0.4694	0.6222	0.7131

presents the specific scores for the three transport nodes in Ciqikou based on linear normalization and weight-based computation.

Figure 6 visually compares the comprehensive infrastructure scores of the three nodes. Evident disparities are observed across indicators, revealing uneven distribution in facility provision and service capacity.

3.7. Comparison and Validation of Evaluation Data

To verify the validity of the evaluation results, we conducted a field questionnaire survey at these three transport nodes (N1–N3). A random sampling method was used to survey visitors at the site. The questionnaire assessed visitor satisfaction with the six evaluation criteria at each transport node (transportation, environment, information services, cultural and leisure, commercial services, and safety management), using a five-point Likert scale (1 = very dissatisfied, 5 = very satisfied). A total of 150 visitors were surveyed, and 137 valid responses were collected (N1 = 45, N2 = 46, N3 = 46). The average score for each criterion at each node was calculated, and the final results of visitor satisfaction at the three transport nodes are presented in Supplementary Materials, Table S4. To ensure consistency with the AHP results, the visitor satisfaction data were processed using the min-max normalization method. The mean scores were scaled to the [0, 1] range based on the 1–5 Likert scale.

Table 4. Normalized Visitor Satisfaction Scores.

Criterion		B1	B2	B3	B4	B5	B6	Overall Average Score
		Transport Infrastructure	Environmental Infrastructure	Information Service Infrastructure	Cultural and Leisure Infrastructure	Commercial Service Infrastructure	Public Safety Infrastructure
Average score	N1	0.6075	0.6950	0.5375	0.7175	0.7025	0.7625	0.6700
	N2	0.7700	0.5625	0.6350	0.7100	0.6625	0.7550	0.6825
	N3	0.7025	0.7075	0.7575	0.6325	0.6750	0.7525	0.7050

presents the normalized satisfaction scores for each node and criterion.

As shown in Table 4, the normalized visitor satisfaction scores were 0.6700 for Node 1 (N1), 0.6825 for Node 2 (N2), and 0.7050 for Node 3 (N3). The average scores across the three nodes showed minimal variation, and the overall ranking of results was consistent with the AHP-derived evaluation. However, discrepancies were observed between subjective and objective assessments for certain criteria. In the visitor satisfaction survey, the score for B5 Commercial Service Infrastructure at Node 1 (0.7025) was slightly higher than that of B2 Environmental Infrastructure (0.6950). At Node 3, B1 Transport Infrastructure (0.7025) scored lower than both B6 Public Safety Infrastructure (0.7525) and B2 Environmental Infrastructure (0.7075). The detailed comparison results are presented in

Table 5. Comparison Between AHP Scores and Visitor Satisfaction Survey Scores.

Node Location	AHP Score Comparison	Tourist Satisfaction Score Comparison
N1	B6(0.1823) > B4(0.0782) > B2(0.0739) > B5(0.0716) > B1(0.0370) > B3(0.0265)	B6(0.7625) > B4(0.7175) > B5(0.7025) > B2(0.6950) > B1(0.6075) > B3(0.5375)
N2	B1(0.1944) > B6(0.1398) > B4(0.1099) > B5(0.1039) > B3(0.0530) > B2(0.0214)	B1(0.7700) > B6(0.7550) > B4(0.7100) > B5(0.6625) > B3(0.6350) > B2(0.5625)
N3	B3(0.2176) > B1(0.1527) > B6(0.1398) > B2(0.1000) > B5(0.0606) > B4(0.0425)	B3(0.7575) > B6(0.7525) > B2(0.7075) > B1(0.7025) > B5(0.6750) > B4(0.6325)
Comprehensive Comparison	N3(0.7131) > N2(0.6222) > N1(0.4694)	N3(0.7050) > N2(0.6825) > N1(0.6700)

.

To further demonstrate the node-level differences, Figure 7 presents the relative performance of the three entrance nodes (N1–N3) across the six facility dimensions (B1–B6), integrating both AHP-based and normalized visitor satisfaction scores. The figure clearly reveals the distinct strengths and weaknesses of each node. Although Node 3 achieves the highest overall performance, each node exhibits differentiated advantages and deficiencies across specific dimensions. Node 1 performs relatively well in cultural and commercial service infrastructure (B4 and B5) but shows a weaker performance in information service (B3). Node 2 performs strongly in transport and safety infrastructure (B1 and B6) but displays a notable deficiency in environmental infrastructure (B2). Node 3 excels in information and safety infrastructure (B3 and B6) yet records comparatively lower satisfaction in cultural and commercial aspects (B4 and B5).

4. Results

Among the three transportation nodes analyzed within the Historic and Cultural District, Node 3 received the highest composite AHP score (0.7131). This node performed best in Information Service Infrastructure (B3, 0.2176), mainly due to hosting the district’s only Tourist Information Center (C6) and clear wayfinding signage (C5), which provide effective orientation and visitor support. It also demonstrated strong performance in Transport Infrastructure (B1, 0.1527), Public Safety Infrastructure (B6, 0.1398), and Environmental Infrastructure (B2, 0.1000). However, visitor satisfaction ratings showed a relatively lower score for Transport Infrastructure (B1), indicating a notable divergence from the AHP-based assessment. This discrepancy may be attributed to the absence of public transit access at this node. While a large parking facility is available, visitors mainly arrive via private vehicles or tour buses, and this limited mode of transportation restricts the perceived convenience of transportation. Commercial Service Infrastructure (B5, 0.0606) and Cultural and Leisure Infrastructure (B4, 0.0425) received comparatively lower scores. The noticeable elevation differences and the semi-enclosed spatial pattern limit spatial openness and constrain the layout of facilities. As a result, commercial services are dispersed, leisure spaces are limited, and pedestrian flows concentrate around the parking area. These factors create local congestion and uneven space utilization, weakening the node’s spatial vitality and functional coherence. They also increase the difficulty of visitor movement, making the perceived level of transport convenience lower than that suggested by the AHP-based transport score. Consequently, visitors perceive the transport environment as less convenient than the objective evaluation indicates, which explains the lower satisfaction rating for this dimension.

Node 2 ranks second in the overall score (0.6222). This node has a strong advantage in Transport Infrastructure (B1, 0.1944). It is the only area in the entire district with a public transit station (C1), and it also has the highest transport infrastructure score among the three nodes. This directly reflects the important impact of accessibility on the value of the node. It is worth noting that the scores for Information Service (B3, 0.0530) and Environmental Infrastructure (B2, 0.0214) are relatively low at this node, and visitor ratings show the same trend. Observations reveal that this node has flat terrain and an open view, with many visitors gathering at the rest corridor near the entrance. However, the number of cleaning facilities (C3) is clearly insufficient. The only toilet facility (D5) is located inside a commercial building, which is far away on foot and inconvenient to use. Information signs (D6) are also sparse and cannot effectively guide visitors in direction and service locations. These spatial and service gaps disrupt the continuity of the visitor experience and reduce information clarity, especially for first-time visitors. The imbalance between high accessibility and limited facilities exposes service shortcomings as visitor flows increase. This situation partly explains the node’s lower scores in the environmental and information dimensions.

Node 1 has a slightly lower overall score (0.4694). It performs weakest in Transport (B1, 0.0370) and Information Service Infrastructure (B3, 0.0265), and both indicators are at a disadvantage compared to the other two nodes. The original bus stop was relocated to the outer edge of the site due to tourism development. Although a parking lot was added later, it is more than 300 m away from the entrance, resulting in poor accessibility. The absence of essential information service facilities (such as C6 and C7) affects visitors’ arrival experience and spatial recognition efficiency. On the other hand, as the earliest main entrance of the district, this node has relatively well-developed Public Safety Infrastructure (B6, 0.1823), with a solid foundation in Security Facilities (C15). Regarding the two dimensions of Commercial Service (B5, 0.0716) and Environmental Infrastructure (B2, 0.0739), tourists’ feedback was divided, especially regarding Commercial Service, which was evaluated more positively. This may be due to the fact that the area provides a rich variety of Dining Facilities (C11) and Retail Service Facilities (C12), including Specialty Stores, Cultural & Creative Shops, Ethnic Clothing Stores and Jewelry Shops. However, despite having diverse commercial and cultural facilities, the overall spatial organization of this node remains fragmented. The long walking distance between the parking lot and the entrance, together with the lack of clear information guidance, reduces visitors’ movement efficiency. The disconnected circulation and dispersed facilities interrupt spatial continuity and result in a low level of functional integration, which makes the overall performance of this node weaker than that of the other nodes.

Overall, the results show that node performance is influenced by the combined effects of spatial layout, facility distribution, and visitor flow. Node 3 achieves the highest overall score because of its well-developed information and safety systems. However, the spatial pattern limits the arrangement of facilities, and concentrated visitor flow leads to local congestion and uneven use. Node 2 has good accessibility, but the lack of environmental and information facilities increases pressure on management and visitor services. Node 1 offers a range of commercial functions, but the long walking distance and unclear direction signs weaken spatial continuity and accessibility. Overall, the AHP results reflect the objective level of facilities at each node, while visitor satisfaction is more influenced by spatial structure and the efficiency of movement. This difference suggests that improving spatial organization, providing facilities in a balanced way, and guiding visitor flow are key approaches to enhancing the spatial quality of historic and cultural districts.

5. Discussion

5.1. Evaluation of Transport-Node Infrastructure Performance

The application of spatial nodes in tourism infrastructure planning is especially relevant in compact and culturally dense contexts such as the Ciqikou Historic and Cultural District in Chongqing. Building on Lynch’s [13] theory of the urban image, this study defines nodes as spatial points of convergence—entryways, intersections, or places of intensified function—that shape how people navigate and perceive the environment. In historic districts, nodes often overlap with key tourist flows, transitions, and service hubs. In Ciqikou, three entrance nodes were examined: Node 1 (Southeast Gate), Node 2 (Southwest Gate), and Node 3 (West Gate). Each node serves distinct functions within the district’s spatial system. This approach aligns with Lynch’s assertion that nodes structure spatial experience and with subsequent research emphasizing their role in concentrating flows and services [20,25].

By employing nodes as planning units, the study responds to limitations in research that emphasizes destination-level infrastructure without acknowledging localized heterogeneity [6,23]. The results reveal that Node 3, despite lacking public transport, achieved the highest infrastructure score (0.7131). Its performance derived from strong information services, including a Tourist Information Center (C6), effective signage (C5), and smart facilities (C7). This finding supports Kutlu and Ayyıldız [19], who argue that information accessibility shapes tourists’ perceptions of destination quality. Conversely, Node 1 scored lowest (0.4694) due to poor transport links and minimal information services, consistent with Le-Klähn and Hall’s [38] observations on the centrality of transport efficiency and wayfinding for tourist satisfaction.

The evaluation integrates AHP with empirical field data to provide a rigorous analytic framework. Six criteria—transport, environmental hygiene, information service, cultural and leisure amenities, commercial services, and public safety—were derived from policy standards [3,36] and academic literature [9,10]. Indicators captured measurable components such as toilets (C4), rest areas (C8), and emergency medical services (C16). Expert weightings prioritized transport (23.14%) and information services (21.76%), affirming Prideaux’s [37] argument that transport underpins accessibility and ISO [43] guidance on signage for orientation.

Cross-validation with tourist surveys confirmed general consistency with AHP scores but also revealed perceptual gaps. Node 3, although objectively strong in information services, was rated lower for transport, reflecting reliance on private vehicles and absence of transit. This underscores Ishizaka and Labib’s [32] call to integrate subjective perceptions with expert-driven evaluations. Node 2’s strong transport accessibility (B1 = 0.1944) was confirmed both objectively and by visitors, aligning with Wang et al.’s [40] emphasis on public mobility as a determinant of tourist flows.

The spatial definition of nodes—using a 120 m walkability radius and 150 m visual perception field—follows Gehl’s [34] and Jacobs’ [33] claims that people experience environments within compact scales. This delineation ensures evaluations reflect lived experience rather than abstract allocations, capturing spatial legibility, visual cues, and micro-scale amenities [21,26].

The Ciqikou case confirms that sustainable infrastructure planning in HCDs must be localized, behaviorally informed, and spatially precise. A node-based approach exposes deficiencies otherwise obscured at destination level and enables replicable planning models. Rather than treating heritage districts as homogenous wholes, the framework recognizes spatial variation and context-specific needs. It also suggests opportunities for extending analysis to commercial, leisure, and ceremonial nodes and for incorporating digital data such as geotagged social media [27].

5.2. Planning and Management Strategies

The differences among the three transport nodes indicate that planning measures should not follow a uniform model. Instead, interventions should be tailored to each node’s functional role and the relative importance of key infrastructure dimensions. As transport and information services are the most influential factors, prioritizing these dimensions provides a clear basis for resource allocation. This approach also supports micro-scale spatial and facility improvements, enhancing visitor experience and operational efficiency.

Node 1 suffers from limited transport connectivity and weak wayfinding, which restrict its effectiveness as an entrance. Improving multimodal access should be a priority. For example, shared-bike stations between the entrance and nearby bus stops can offer flexible transfers for public transport users. A low-speed shuttle linking Node 1 with the main pedestrian corridor can further improve movement efficiency and reduce walking difficulty. To enhance spatial legibility, a layered wayfinding system can be introduced. This system may include bilingual signs, route confirmation markers and QR-code navigation tools. Together, these measures can significantly improve the ability of Node 1 to guide visitors smoothly into the core area of the district.

Node 2 is the only entrance connected to the metro, and it serves as the main gateway for visitor arrival and dispersal. To make full use of this advantage, the imbalance between high accessibility and limited supporting facilities needs to be addressed. Providing well-planned public toilets, waste bins and related facilities can improve basic service capacity and help maintain a stable environmental quality. A layered wayfinding system with bilingual signs and clear visual markers can increase spatial legibility and help visitors navigate more easily. In addition, clearly organized pedestrian routes and well-located resting points can enhance movement continuity and comfort. Overall, these measures can align the transport advantages of Node 2 with improved service capacity, strengthening its role as an efficient gateway.

Although Node 3 shows the strongest overall infrastructure performance, its spatial layout creates problems such as dispersed services, limited leisure space and concentrated pedestrian flows. These issues lead to local congestion and reduce visitor experience, which helps explain the gap between the AHP transport score and the lower level of visitor satisfaction. To address these problems, a low-speed shuttle connecting nearby access points can be introduced to expand travel options and spread visitor movement. Reorganizing resting areas, adding modest commercial facilities and improving the spatial layout around key locations can also distribute visitor activities more evenly and enhance movement continuity. These measures can strengthen the role of Node 3 as a highly integrated and operationally efficient service hub.

6. Conclusions and Recommendations

6.1. Research Conclusions

This study develops and applies a transport-node-based framework for evaluating tourism infrastructure in Historic and Cultural Districts, using Ciqikou in Chongqing as an empirical case. By integrating six criteria and seventeen indicators with AHP-derived weights, the framework provides a structured and operational approach for assessing infrastructure conditions within a node’s walkable and perceivable service boundary. The weighting results highlight the central importance of transport infrastructure and information service infrastructure, with public transit facilities, signage and security identified as the most influential indicators.

The evaluation of the three nodes reveals a clear performance hierarchy, with Node 3 achieving the highest overall score and Node 1 the lowest. This pattern is broadly confirmed by visitor satisfaction data. The perceptual deviation observed for Node 3’s transport dimension further indicates that spatial configuration and movement efficiency can shape visitor judgments, even when facility provision is objectively adequate.

Based on the specific characteristics of each node, the study proposes targeted strategies, including improving accessibility and wayfinding at Node 1, enhancing environmental and information services at Node 2, and optimizing spatial organization and service distribution at Node 3. Overall, the findings demonstrate that transport nodes constitute an effective analytical unit for diagnosing tourism infrastructure performance and provide an evidence-based foundation for planning and management in Historic and Cultural Districts.

6.2. Scientific and Practical Contributions

This study provides two main methodological contributions to the evaluation of tourism infrastructure in historic districts. First, by integrating Lynch’s spatial node theory with an AHP-based indicator system, the study establishes a clear and operable method for assessing traffic nodes as independent functional units. This approach moves beyond broad destination-level assessments and enables planners to identify differences among nodes in terms of transport connections, information support, environmental quality, commercial services, and public safety. The resulting evaluation system translates general requirements for tourism infrastructure into measurable components that can be directly applied in planning and management.

Second, the study reinforces the reliability and practical value of this framework by comparing expert-based assessment results with tourist perception data. This comparison helps reveal where objective conditions and visitor experiences diverge, such as strong information-service capacity but lower perceived transport convenience. Combining expert judgment with user feedback improves the accuracy of node evaluation and ensures that the method reflects issues that visitors actually encounter. This integrated approach provides a practical and transferable way for other historic districts to evaluate and improve their tourism infrastructure more effectively.

6.3. Limitations and Future Research Directions

This study developed an evaluation framework to assess tourism infrastructure at transport nodes in Historic and Cultural Districts. Although the framework offers practical insights for node-based planning and management, several limitations remain.

First, the generalizability of the findings is limited. The case study is based in the Ciqikou Historic and Cultural District, and the context reflects local regulatory requirements and planning practices. Although the framework draws on international normative guidelines such as those of the UNESCO World Heritage Centre, applying it to node-based planning in different cultural and institutional settings would help broaden its applicability.

Second, the evaluation focuses mainly on the physical conditions of tourism infrastructure. While these objective indicators are important for measuring service quality, they do not capture perceptual or experiential dimensions that also influence visitor satisfaction. Future research may incorporate indicators related to service quality, spatial perception, and cultural experience to develop a more comprehensive evaluation framework.

Third, the study focuses on transport nodes, which may limit the applicability of the findings to other types of nodes within the district. Extending the indicator system to commercial, leisure, and other functional nodes would support comparative analysis and contribute to more detailed and comprehensive infrastructure planning.

Finally, the sample size for experts and visitors remains limited. Although the study follows common AHP sampling recommendations, expanding the expert panel to include more disciplines and collecting visitor surveys across different seasons would help enhance the robustness and representativeness of the results.