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Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Mar 4;16:11983. doi: 10.1038/s41598-026-41044-x Search in PMC Search in PubMed View in NLM Catalog Add to search An assessment and collaborative governance of the non-motorized environment potential of commercial TOD based on the improved NPP model Yaqiong Duan Yaqiong Duan 1 School of Architecture, Chang’an University, Xi’an, 710061 China 2 Engineering Research Center of Collaborative Planning of Low-Carbon Urban Space and Transportation, Universities of Shaanxi Province, Xi’an, 710061 China Find articles by Yaqiong Duan 1, 2 , Yujia Ma Yujia Ma 1 School of Architecture, Chang’an University, Xi’an, 710061 China Find articles by Yujia Ma 1 , Yuxin Liu Yuxin Liu 1 School of Architecture, Chang’an University, Xi’an, 710061 China Find articles by Yuxin Liu 1 , Lingda Zhang Lingda Zhang 1 School of Architecture, Chang’an University, Xi’an, 710061 China Find articles by Lingda Zhang 1 , Quanhua Hou Quanhua Hou 1 School of Architecture, Chang’an University, Xi’an, 710061 China 2 Engineering Research Center of Collaborative Planning of Low-Carbon Urban Space and Transportation, Universities of Shaanxi Province, Xi’an, 710061 China Find articles by Quanhua Hou 1, 2, ✉ Author information Article notes Copyright and License information 1 School of Architecture, Chang’an University, Xi’an, 710061 China 2 Engineering Research Center of Collaborative Planning of Low-Carbon Urban Space and Transportation, Universities of Shaanxi Province, Xi’an, 710061 China ✉ Corresponding author. Received 2025 Apr 23; Accepted 2026 Feb 17; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13069116 PMID: 41781542 Abstract Against the backdrop of high-density urban development, the non-motorized environment around rail transit stations, especially commercial metro station areas, faces numerous spatial injustice issues that undermine not only the core functions of urban commercial services but also their role as windows for showcasing a city’s image. Thus, optimizing this environment is crucial. This study, grounded in spatial justice theory, focuses on commercial rail transit station areas in Xi’an and constructs a three-dimensional “Node-Place-Perception” (NPP) evaluation system to address spatial development imbalances and promote station-city integration. Using data from Baidu Maps, Points of Interest (POI), and heatmaps, the study categorizes 14 sample station areas based on node, place, and perception dimensions. The results classify these areas into four types: balanced, completely imbalanced, place-imbalanced, and perception-imbalanced, corresponding to four distinct spatial injustice types. Guided by the principle of "equitable access," optimization strategies are proposed, including improving barrier-free facilities to enhance accessibility, reshaping mixed-use neighborhoods for balanced functional allocation, and integrating cultural perception scenes to enrich cultural appeal and elevate the city’s image. Through theoretical and methodological innovations, this study offers a scientific paradigm for the refined renewal of high-density urban rail transit station areas, fostering synergistic development between efficiency enhancement and social inclusivity. Keywords: Types of spatial injustice; , “Node-Place-Perception” model, Commercial rail transit station area, Non-motorized environment Subject terms: Environmental impact, Psychology and behaviour Introduction With the diversification of residents’ travel modes and the growing awareness of spatial rights, rail transit station areas, as crucial components of urban public spaces, are increasingly recognized for their significance. The quality of the non-motorized environment (NME) in these areas not only directly reflects transportation efficiency but also serves as a vital domain for assessing the practical effectiveness of spatial justice principles. Currently, the concept of spatial justice is gradually becoming a core principle in urban planning and policy formulation. For instance, the European Union’s European Spatial Development Strategy explicitly advocates for equitable, inclusive, and sustainable urban spatial development, ensuring that all residents have equal access to urban resources and services. Similarly, the UK government’s National Spatial Strategy aims to achieve urban spatial equity and inclusion through optimized spatial layouts, improved infrastructure quality, and enhanced community participation. In China, the State Council’s Action Plan for Energy Conservation and Carbon Reduction 2024–2025 outlines targets for the development of non-motorized transportation systems, which essentially constitute a practical response to spatial justice theory. By optimizing the allocation of public space resources, these efforts aim to mitigate issues such as fragmented walking experiences and compromised spatial rights of vulnerable groups in rail transit station areas resulting from high-intensity development. Spatial justice theory emphasizes the equitable distribution of urban resources and the equal participation of all residents 1 . In rail transit station areas, its core demand is not merely the formal equal allocation of spatial resources, but rather ensuring that all residents can substantively and equally enjoy and utilize urban space. The degree and quality of this “enjoyment” must ultimately be verified through users’ subjective experience and perception. Therefore, measuring perceptual elements such as a sense of safety and comfort constitutes the ultimate basis for assessing whether spatial justice has been realized. Particularly in commercial station areas, due to their “transportation-commerce” hybrid functionality, there is a pressing need to optimize the non-motorized environment to achieve coordinated improvements in passenger flow organization efficiency and spatial quality. However, high-intensity passenger flows and complex functional overlays often lead to a decline in walking experience, while the existing “Node-Place” model tends to neglect the user perception dimension, making it difficult to accurately identify renewal needs under a social value orientation 2 . In recent years, the study of urban non-motorized environments has garnered significant attention. Scholars have employed various methods to construct modern cities that are livable, business-friendly, and tourist-friendly, thereby promoting sustainable development. Existing research has significantly enhanced the evaluation accuracy of urban non-motorized environments through technologies such as street view image analysis 3 – 5 , spatiotemporal behavior data 6 – 9 , and machine learning 10 – 12 . In terms of content, scholars have also begun to explore the correlation between user perception and spatial quality, for instance, investigating the positive effects of visual friendliness 13 nighttime lighting, and green coverage 14 on walking experience 15 , 16 and sense of safety 17 – 19 . These studies reveal the intrinsic links between environmental elements, psychological responses, and behavioral intentions, and also provide empirical evidence for understanding spatial injustice. However, existing achievements are mostly confined to a single dimension or localized elements, and have not yet systematically integrated the perception dimension with spatial justice theory. There is a particular lack of an integrated analytical framework capable of simultaneously diagnosing efficiency defects and equity deficits. The key to addressing urban spatial justice lies in whether a theoretical framework can be constructed that organically integrates precise methodological support with profound theoretical concern, and systematically transforms the “perception” dimension into a measurable scale of “rights.” This theoretical gap not only highlights a core lacuna within the existing literature but also establishes a clear problematic starting point and value positioning for this study. Therefore, when formulating renewal strategies for commercial rail transit areas, it is essential to adopt spatial justice principles as the core guiding principle, systematically integrating multidimensional elements such as “transportation function–spatial form–social perception” from the perspectives of humanism, equality, and justice, to reveal issues of rights imbalance behind spatial quality disparities, ensuring that all residents can equally enjoy urban resources and services, and promoting urban spatial equity and inclusivity. To address the aforementioned limitations, this study proposes a three-dimensional “Node-Place-Perception” (NPP) model. Its core lies in establishing a theoretical linkage between “perception” and "justice," treating perceptual elements as proxy variables for measuring spatial “inclusivity” and "dignity." This approach aims to construct a systematic analytical tool capable of simultaneously diagnosing efficiency gaps and fairness deficits, thereby guiding socially value-oriented renewal (Fig. 1 . Dynamic Association Diagram of Node, Place, and Perception Dimensions under the Guidance of Spatial Justice). Fig. 1. Open in a new tab Dynamic association diagram of node, place, and perception dimensions under the guidance of spatial justice. Study area and data sources Overview of the study area Since the operation of the Xi’an Metro began in 2011, a total of 10 metro lines have been opened, with an operational mileage of 403 km and 235 stations, including 34 transfer stations. As of March 2025, the highest daily passenger volume of the Xi’an Metro has reached 5.534 million trips. The commercial development surrounding Xi’an rail transit stations exhibits significant regional concentration, primarily centered around the city’s central area, transportation hubs, and emerging commercial districts. These areas typically feature high densities of business types, population, and land use intensity, providing pedestrians with diverse behavioral choices. In particular, business districts such as Yongningmen and Xiaozhai not only gather a large number of shopping, dining, and entertainment venues but also form unique commercial atmospheres and cultural characteristics. These regions are further interconnected through metro lines, fostering the prosperity of commercial activities. Considering the location of stations, the density of business types surrounding the stations, the number of intersecting rail lines, population density, and the land use conditions conducive to pedestrian diversity, and referring to previous classification standards for rail transit station types in Xi’an 20 , this study selects representative commercial rail transit stations as research objects. Specifically, these include Sajinqiao Station and Yuxiangmen Station on Line 1; Tiyuchang Station, Anyuanmen Station, Dianshita Station, Hangtiancheng Station, Xiaozhai Station, and Yongningmen Station on Line 2; Kejilu Station on Line 3; Dachashi Station, Daminggongbei Station, and Wenjinglu Station on Line 4; and Northwestern Polytechnical University Station and Guangjijie Station on Line 6, totaling 14 commercial rail transit station areas. To enhance the adaptability of the sample to actual traffic conditions, this study considers the impact of the actual road network topology on pedestrian accessibility and delineates the research scope for each rail transit station area based on network analysis. This method more accurately reflects pedestrian accessibility in the actual road network, particularly under complex traffic conditions such as T-junctions and dead ends, thereby demonstrating the scientific and practical nature of the research (Fig. 2 Location Map) . Fig. 2. Open in a new tab Location map. Data sources The data sources for this study include Points of Interest (POI) data, official public data from the Xi’an Metro (March 2025), nighttime lighting data (summer data from September 2024), Weibo check-in data (full-time data from September 2024 to March 2025), street view data of Xi’an from Gaode Maps in 2024, OSM road network data (updated version from April 2024), and street view semantic segmentation data. Among these, the street view data from Gaode Maps were collected within an 800-m radius of commercial rail transit station areas in Xi’an, using time windows of weekdays (September 10–14, 2024) and weekends (September 15–16, 2024). One street view point was collected every 30 m from 8:00 to 20:00 each day, resulting in a total of 13,590 valid sample points to cover the differences in the pedestrian environment during commuting and leisure periods. Research methods and indicator system Research methods This study mainly consists of two parts: first, a quality assessment of commercial rail transit station areas; second, a classification of spatial injustice types based on the assessment results, providing actionable improvement directions for enhancing station area quality. The former involves constructing a multidimensional “Node-Place-Perception” (NPP) model indicator system to measure the potential for renewal of various station areas under different scenarios. The model consists of three dimensions and a seven-type multilayer structure. The Analytic Hierarchy Process (AHP) and entropy weight method are then employed to comprehensively assign weights to each evaluation indicator and derive the final scores for each rail transit station area (Fig. 3 . Framework Diagram of the Research Methodology). Fig. 3. Open in a new tab Framework diagram of the research methodology. Through the analysis of the three-dimensional coordinate system of the “Node-Place-Perception” model, the ratings for "Node," "Place," and “Perception” are each divided into three equal parts: high (H), medium (M), and low (L). Based on this, the interaction of scores across the three dimensions is presented in the form of a Rubik’s Cube, revealing 27 types of station area development states. These are further summarized into six station area types. According to these station area types, the types of spatial injustice are analyzed, primarily including multi-dimensional spatial injustice (dependent station areas, completely imbalanced station areas), node-dimensional spatial injustice (node-imbalanced station areas), place-dimensional spatial injustice (place-imbalanced station areas), and perception-dimensional spatial injustice (perception-imbalanced station areas) (Fig. 4 . State Classification Diagram of the “Node-Place-Perception” Model). Fig. 4. Open in a new tab State classification diagram of the “Node-Place-Perception” model. Establishment of the indicator system The node dimension reflects the transportation service functions and value of rail transit stations as urban transportation nodes, primarily encompassing three types: accessibility of rail transit stations, node centrality, and node radiation. The place dimension evaluates whether the built environment of the station area is conducive to urban renewal, mainly including two types: land use and streetscape semantics. The perception dimension places greater emphasis on user experience, supplementing the "Node-Place" model from the perspective of station area perception. It employs a streetscape semantic segmentation method based on a fully convolutional network with deep learning to obtain streetscape information of the rail transit station area from the user’s perspective, including two types: environmental perception and safety perception (Table 1 ). Table 1. Construction of the indicator system for commercial rail transit station areas. Dimension Type Indicator Explanation Data source Node 1/3 Station accessibility Walking accessibility Measures the convenience of the walking environment around rail transit stations, which is an important manifestation of transportation accessibility in spatial justice. High walking accessibility means residents can more conveniently utilize rail transit resources and reduce travel barriers Gaode Map’s 2024 street view data of Xi’an Bus accessibility Evaluates the connection between rail transit stations and other bus modes, ensuring residents can conveniently reach stations through multiple ways, reflecting transportation diversity and fairness in spatial justice POI of bus stops and rail transit stations in Gaode Map Node centrality Number of entrances/Exits Reflects the central position of rail transit stations in the urban transportation network. A large number of entrances/exits means the station has a wider service range, can attract more people, and promote the fair allocation of spatial resources Xi’an Subway Operation Lines and Timetable (March 2025) Number of service directions Measures the number of transportation lines that the station can serve. A large number of service directions indicates the station’s strong hub role in urban transportation, which helps to improve spatial mobility and accessibility Node radiation Average trip distance of stations Represents the average service distance of the station, reflecting the station’s radiation capability to the surrounding areas. A shorter average trip distance means the station can better serve surrounding residents, reduce travel time and costs, and enhance spatial justice POI of bus stops and rail transit stations in Gaode Map Place 1/3 Land use Analysis of land mixing degree Evaluates the diversity of land use around the station. A high degree of land mixing means the station area has rich functions, can meet the needs of different groups of people, and promote the fair allocation and efficient use of spatial resources 2023 Xi’an Land Use Status Map Street view semantics Road continuity Measures the connectivity of the road network around the station. A continuous road network helps to improve walking and cycling experiences, reduce travel barriers, and reflect the convenience of travel in spatial justice OSM Xi’an Road Vector File (Updated Version of April 2024) Analysis of motor vehicle and non-motor vehicle separation Evaluates the separation of motor vehicle and non-motor vehicle lanes around the station. Good separation helps to improve road safety and protect the rights and interests of pedestrians and cyclists street spatial dimension Represents the ratio of street width to building height. A reasonable spatial scale helps to improve walking comfort and reduce a sense of oppression, which is an important manifestation of environmental comfort in spatial justice Gaode Map’s 2024 street view data of Xi’an Analysis of Spatial Landmarks Evaluates the landmark buildings or landscapes around the station. Areas with strong landmarks can attract more people, enhance the vitality of the station area, and promote the effective use of spatial resources Weibo Check-in Data Perception 1/3 Environmental perception Green looking ratio Measures the proportion of green plants in the street view around the station. A high green view rate helps to improve visual comfort and environmental quality, which is an important manifestation of environmental aesthetics in spatial justice Based on the street view semantic segmentation of the fully convolutional network of deep learning, calculate the proportions of green plants and sky in the street view of the station area Sky-view factor Represents the proportion of sky in the street view around the station. An open sky view helps to improve mood and meet the environmental psychological needs in spatial justice Safety perception Analysis of lighting system Evaluates the night lighting situation around the station. Good lighting helps to improve the safety of night travel, protect residents’ basic rights, and is an important manifestation of safety in spatial justice Night Lighting Data (Summer Data of September 2024) Analysis of accessibility facilities Measures the perfection of accessibility facilities around the station, such as elevators and ramps, which helps to protect the travel rights of disabled people and the elderly, reflecting inclusiveness in spatial justice Gaode Map’s 2024 street view data of Xi’an Open in a new tab The entropy method, as an objective weighting method, measures the relationships and dispersion among indicators through relevant statistical methods, ensuring objectivity and fairness in the results. Therefore, this study uses the entropy method to weight vitality factors. Due to the diversity of indicators, there is an issue of inconsistent dimensions in the data. Hence, data standardization is required before evaluation. Additionally, to avoid the phenomenon where logarithmic calculations become meaningless due to zero values during entropy calculation, this study adopts Zeng Xiangang’s method by uniformly shifting all data by one unit 21 . Results and discussion Analysis of indicator weights A comprehensive evaluation of the non-motorized environment in rail transit station areas was conducted from three dimensions: node, place, and perception. To enhance the accuracy and credibility of the performance evaluation results, this study employed the Analytic Hierarchy Process (AHP) and the entropy weight method to determine the indicator weights. The specific steps are as follows: Determination of Subjective Weights by AHP:To construct a scientifically reasonable weighting system, this study first employed the AHP to obtain subjective weights. The selection of experts adhered to the principles of "domain relevance, experiential authority, and perspective diversity." A total of 20 experts were invited, including 12 scholars in urban planning and transportation engineering (focusing on spatial models and transportation efficiency), 5 researchers in sociology and behavioral geography (concerned with social behavior and spatial equity), and 3 heads of urban management departments (familiar with policy and practical constraints), aiming to integrate academic, social, and policy perspectives. In terms of questionnaire design, a detailed explanation of the evaluation indicator system and the connotation of each indicator was provided before the formal scoring to ensure a consistent understanding among experts of the three-dimensional “Node-Place-Perception” framework and each indicator. Subsequently, an anonymous approach was adopted, asking experts to make pairwise importance comparisons for indicators within the criterion and indicator layers using the 1–9 scale method. After collecting all valid questionnaires, the geometric mean of all expert judgment matrices was calculated to form a comprehensive judgment matrix. The Consistency Ratio (CR) of the comprehensive judgment matrix was calculated (CR = 0.028 < 0.1), passing the test to ensure logical rationality. Determination of Objective Weights by Entropy Method:To compensate for potential bias in subjective judgments and fully exploit the inherent information in the data, this study concurrently employed the entropy weight method to determine objective weights. Based on the raw indicator data matrix composed of multi-source data such as POI, streetscape semantics, and heatmaps, the degree of dispersion in the data is measured by calculating the entropy value ( e j ) for each indicator. The smaller the entropy value, the greater the variation of the indicator across different samples, the more information it provides, and thus the more important its role in the evaluation. Integration of Subjective and Objective Combination Weights:To obtain composite weights that balance expert experiential judgment with objective data information, this study performed a linear weighted combination of the AHP subjective weights and the entropy method objective weights. The weights were integrated using a ratio of "60% subjective weight and 40% objective weight." This allocation is primarily based on the following considerations: on one hand, the AHP weights encapsulate domain experts’ profound understanding of the spatial justice theoretical framework and the relative importance of its dimensions, and thus should play a dominant role in the weighting system. On the other hand, the entropy method weights reflect the objective differences in actual data distribution, effectively correcting potential common biases in subjective judgment. Combining the two enhances the robustness and persuasiveness of the weighting system. Through the steps above, the final composite weights, integrating both subjective and objective information, were derived (Table 2 ). Table 2. Table of weight coefficients for evaluation indicators. Criterion layer Indicator layer AHP subjective weight Objective weight by entropy method Comprehensive Weight Weight of criterion layer % Weight of indicator Layer % Weight of criterion layer % Weight of indicator layer % Weight of criterion layer % Weight of indicator layer % Node A A1 0.35 0.47 0.30 0.45 0.32 0.46 A2 0.25 0.23 0.24 A3 0.10 0.13 0.12 A4 0.08 0.12 0.10 A5 0.10 0.07 0.08 Place B B1 0.38 0.52 0.40 0.56 0.39 0.54 B2 0.15 0.10 0.13 B3 0.09 0.12 0.10 B4 0.14 0.12 0.13 B5 0.10 0.10 0.10 Perception C C1 0.27 0.25 0.30 0.23 0.29 0.24 C2 0.25 0.23 0.24 C3 0.30 0.32 0.31 C4 0.20 0.22 0.21 Open in a new tab Analysis of single-factor evaluation results After standardizing the scores for each dimension of the commercial rail transit station areas in Xi’an and assigning equal weights, we categorized each dimension into three levels: high (H), medium (M), and low (L). This was done to examine the development status of each station area from the perspective of spatial justice. Analysis of node evaluation results The node dimension scores for the commercial rail transit station areas in Xi’an were generally high, with a score range of [2.49, 4.26]. The Dachashi and Guangjijie rail transit station areas scored particularly high in this dimension, with 4.26 and 3.83, respectively. The Dachashi Station, as a transfer hub for Metro Lines 4 and 6, offers convenient transportation and comprehensive facilities, providing efficient travel services for citizens and embodying the principles of traffic accessibility and facility equality in spatial justice. The Guangjijie Station, with its superior geographical location and convenient street network, not only facilitates citizens’ daily commuting but also promotes the deep integration of cultural tourism, demonstrating the positive role of spatial justice in promoting cultural diversity and community vitality. The Anyuanmen Station scored the lowest, at 2.49, mainly due to the scarcity of nearby bus routes and non-motor vehicle parking spots, as well as the limited number of POI around its facilities. The majority of rail transit station areas fell into the medium level in the node dimension, indicating that the current state of Xi’an’s rail transit station areas in this dimension is still insufficient, reflecting an imbalance in spatial resource allocation and the need for improvement in the construction level of rail transit stations (Fig. 5 ). Fig. 5. Open in a new tab Analysis chart of node evaluation results. Analysis of place evaluation results The score range for the place dimension of commercial rail transit station areas in Xi’an is [1.75, 4.15]. The evaluation results for this dimension indicate that most station areas are at a medium level, while high-level station areas such as Tiyuchang Station, Yongningmen Station, and Wenjinglu Station exhibit dual charm of urban vitality and historical culture through large-scale public buildings, high land-use mix, and well-equipped facilities. These station areas not only meet the diversified needs of modern life but also retain the historical features of the ancient city through rational spatial planning and business layout, enhancing the overall quality of the station areas and aligning with the objectives of spatial justice regarding improving public space quality and promoting social integration. However, for station areas at medium to low levels, such as Dianshita Station, issues such as low richness of surrounding facilities, weak road accessibility, traffic congestion, and lack of appropriate street space scale have become bottlenecks restricting their development, violating the principles of spatial justice. To address these challenges, it is recommended to adopt measures such as optimizing traffic organization and improving facility allocation to promote balanced development of station areas and achieve equitable distribution of spatial resources (Fig. 6 ). Fig. 6. Open in a new tab Analysis chart of place evaluation results. Analysis of perception evaluation results The score range for the perception dimension of commercial rail transit station areas in Xi’an is [1.88, 3.65], with relatively low overall scores. The results indicate that a small portion of station areas are at a low level, while the majority are at a medium level, reflecting deficiencies in the overall perceptual quality of commercial rail transit station areas in Xi’an. Station areas such as Anyuanmen, Dachashi, and Daminggong North Station have relatively low overall quality, which may be attributed to factors such as low surrounding building density and floor area ratio, insufficient green open space, and inadequate facilities. There is a certain degree of inequity in the allocation of spatial resources in these station areas, necessitating measures for improvement. Meanwhile, due to regional influences, Anyuanmen Station, positioned as a commercial station despite being a key node of Xi’an’s Ming Dynasty city wall and a subway transportation hub, has received a relatively low evaluation, creating a notable contradiction. This contradiction is closely linked to the ancient city preservation policies. For instance, new residential land is strictly prohibited within the scope of city wall preservation, commercial land must prioritize cultural and tourism functions, and excessive commercialization is strictly restricted. Particularly, as Anyuanmen is located in the core landscape area of the Ming Dynasty city wall, it is imperative to prioritize the protection of the integrity of historical features, making it difficult to plan open commercial plazas around the station. Therefore, while safeguarding the integrity of historical features, it is essential to actively explore ways to plan commercial facilities such as open commercial plazas around the station, achieving a balance between spatial justice in promoting economic development and cultural preservation (Fig. 7 ). Fig. 7. Open in a new tab Analysis chart of perception evaluation results. Analysis of clustering results Based on the scoring results and grade classifications of rail transit station areas across the three dimensions discussed above, and utilizing the " Node-Place-Perception " evaluation model, this study delves into an analysis of the various development states exhibited by commercial rail transit station areas in Xi’an from the perspective of spatial justice, as demonstrated through the three-dimensional synergistic effects. The development states of each rail transit station area are shown in Fig. 7 . There are a total of fourteen commercial rail transit station areas in Xi’an, categorized into four major types: balanced, completely imbalanced, place-imbalanced, and perception-imbalanced. Among these, balanced station areas dominate, with a total of nine, accounting for as high as 64.28% of the total, indicating that these station areas have achieved a relatively balanced state in terms of node connectivity, place vitality, and public perception, effectively embodying the concept of spatial justice. Following these are place-imbalanced and perception-imbalanced station areas, with two each, which exhibit deficiencies in the perfection of place functions and public perception satisfaction, respectively (Fig. 8 ). In future planning, greater emphasis should be placed on the rational allocation of spatial resources and the fulfillment of public needs. There is only one completely imbalanced station area, namely Anyuanmen Station, which scored low across all three dimensions and serves as a typical representative of spatial justice deficiency. It urgently requires comprehensive renovation and enhancement strategies to improve its current development status and promote the comprehensive and coordinated development of station area spaces. Fig. 8. Open in a new tab Analysis diagram of clustering results. By categorizing the commercial rail transit station areas in Xi’an into types of spatial injustice, we identify spatial disparities among different rail transit station areas (Table 3 ). There is one multi-dimensional spatial injustice station area, two place-dimensional spatial injustice station areas, and two perception-dimensional spatial injustice station areas among the commercial rail transit station areas in Xi’an. Specifically, the place-dimensional spatial injustice station areas are the Dianshita Station and the Kejilu Station; the perception-dimensional spatial injustice station areas are Dachashi Station and Daminggong North Station; and the multi-dimensional spatial injustice station area is Anyuanmen Station. Table 3. Analysis of clustering results for commercial rail transit station areas. Station Node evaluation result Place evaluation result Perception evaluation result Station area type Types of spatial injustice SAJINQIAO Station H M M Balanced type No significant spatial injustice type YUXIANGMEN Station M H M Balanced type No significant spatial injustice type TIYUCHANG Station M H M Balanced type No significant spatial injustice type ANYUANMEN Station L M L Completely unbalanced type Multidimensional spatial injustice type DIANSHITA Station M L M Perceptual imbalance type Place-dimensional space injustice type HANGTIANCHENG Station M M M Balanced type No significant spatial injustice type XIAOZHAI Station M M H Balanced type No significant spatial injustice type YONGNINGMEN Station H H M Balanced type No significant spatial injustice type KEJILU Station M L M Place imbalance type Place-dimensional space injustice type DACHAISHI Station H H L Perceptual imbalance type Perceptual -dimensional space injustice type DAMINGGONGBEI Station M M L Place imbalance type Perceptual -dimensional space injustice type WENJINGLU Station M H M Balanced type No significant spatial injustice type XIBEIGONGYEDAXUE Station M M M Balanced type No significant spatial injustice type GUANGJIJIE Station H M M Balanced type No significant spatial injustice type Open in a new tab To gain a deeper understanding of the spatial distribution characteristics of these station areas and the underlying reasons, a study on spatial heterogeneity of the commercial rail transit station areas in Xi’an was conducted (Fig. 9 ). This spatial heterogeneity is primarily influenced by the following factors: Fig. 9. Open in a new tab Spatial distribution diagram of clustering results for commercial rail transit station areas. Geographical Location and Traffic Conditions:** Balanced station areas are relatively evenly distributed within Xi’an. These stations are typically located near the city center or important transportation nodes, enjoying good traffic accessibility and rich urban functions. For example, Sajinqiao Station and Yuxiangmen Station are both situated in the core area of the city, with convenient transportation. In contrast, place-imbalanced station areas are mainly distributed in the periphery of the old city or emerging development areas in Xi’an, where poor transportation conditions result in lower accessibility and convenience. For instance, Anyuanmen Station is restricted by the ancient city protection policy, leading to inadequate transportation facilities and a singular land-use function. Despite its good traffic conditions, Dianshita Station scores low on the place dimension due to the singular land-use function around it. Urban Functions and Business Distribution:Balanced station areas usually integrate various functions such as commerce, office, and residence, offering a diverse range of businesses to meet the needs of different groups of people. For example, Xiaozhai Station, surrounded by thriving commerce and diverse businesses, attracts a large number of people. On the other hand, place-imbalanced station areas may suffer from functional singularity or business scarcity due to factors such as historical preservation and land-use planning restrictions. For instance, Kejilu Station emphasizes high-tech industries but lacks necessary commercial and living facilities, hindering the realization of spatial justice. Pedestrian Perception and Cultural Atmosphere: Pedestrians’ perception of station areas is deeply influenced by multiple factors such as cultural atmosphere, public facilities, and open green spaces. Balanced station areas typically possess a good cultural atmosphere and well-equipped public facilities, meeting the diverse needs of pedestrians and reflecting the role of spatial justice in enhancing public quality of life. However, perception-imbalanced station areas may suffer from low satisfaction on the perception dimension due to issues such as scarce green spaces and insufficient public facilities. For example, although Dachashi Station has complete commercial facilities, the lack of green spaces and public facilities affects pedestrians’ overall perception experience, highlighting the importance of spatial justice in the layout of public spaces and facilities. In summary, the spatial heterogeneity of commercial rail transit station areas in Xi’an is mainly influenced by various factors including geographical location, traffic conditions, urban functions and business distribution, as well as pedestrian perception and cultural atmosphere. These factors interact to cause significant disparities and imbalances in the performance of different types of station areas across three dimensions. Based on the aforementioned evaluation results, corresponding optimization strategies should be formulated from four aspects: balanced, place-imbalanced, perception-imbalanced, and completely imbalanced, in order to enhance the quality of their non-motorized environments. This will provide useful references and insights for improving the non-motorized environments of commercial rail transit station areas in Xi’an. Optimization strategies for slow-traffic space in station areas From the perspective of spatial justice theory, urban spaces should equitably and reasonably meet the needs of different groups, achieving balanced resource allocation and efficient utilization. In response to the various types of imbalances observed in commercial rail transit station areas in Xi’an, targeted optimization strategies are necessary to promote the realization of spatial justice. Node-Imbalanced Station Areas: Improvement aimed at strengthening "traffic interchange efficiency" and "accessibility." For such station areas, transportation facilities should be increased and traffic organization optimized. Non-motorized vehicle parking areas should be reasonably planned around the station area with clear signage to enhance the convenience of transfers. Additionally, traffic flow analysis should be conducted on surrounding roads to optimize signal light timing, guide orderly vehicle movement, and reduce congestion. Meanwhile, AR navigation systems should be utilized to assist individuals with disabilities in barrier-free transfers, ensuring equitable distribution of transportation resources and improving residents’ travel efficiency (Fig. 10 ). Fig. 10. Open in a new tab Optimization diagram for node-imbalanced station areas. Place-Imbalanced Station Areas:Spatial functional remediation aimed at promoting “functional mix” and “vitality sharing”. The issues in such station areas are concentrated on functional singularity and spatial fragmentation. The core strategy lies in functional implantation and spatial transformation. First, systematically supplement basic service facilities to fill the gaps in community-level service functions according to the "15-min living circle" concept. Next, implement integrated street space renovation, comprehensively enhancing key streets by widening sidewalks and increasing greenery and rest facilities (Fig. 11 ). Fig. 11. Open in a new tab Optimization diagram for place-imbalanced station areas. Perception-Imbalanced Station Areas: Micro-space creation oriented toward enhancing "environmental well-being" and "place identity." For such station areas, the first step is to implement "green micro-renovation in interstitial spaces", constructing pocket parks, vertical greening, and other facilities. Additionally, comprehensively improve the humanization level of facilities by upgrading details such as seating and signage. Finally, incorporate local cultural perception elements to shape culturally themed pathways through public art and other means (Fig. 12 ). Fig. 12. Open in a new tab Optimization diagram for perception-imbalanced station areas. Completely Imbalanced Station Areas:Comprehensive integrated renovation centered on promoting “multidimensional system reconstruction" and "coordination between preservation and development." Such station areas exhibit severe deficiencies across all three dimensions—node, place, and perception—and are often subject to additional constraints such as historical preservation, necessitating systematic and holistic planning intervention. Implement phased, multidimensional collaborative renovation: in the short term, prioritize alleviating traffic bottlenecks and supplementing the most essential daily service facilities and barrier-free environments; in the medium to long term, further promote functional mixing, enhance the quality of public spaces, and foster cultural scene-building (Fig. 13 . Optimization Diagram for Completely Imbalanced Station Areas). Fig. 13. Open in a new tab Optimization diagram for completely imbalanced station areas. By implementing these measures and strategies, it is hoped to enhance the quality of the non-motorized environment in commercial rail transit station areas in Xi’an, achieving urban sustainable development guided by spatial justice. Additionally, for rail transit station areas in regions with different potentials, differentiated implementation strategies are recommended: for high-potential areas such as Anyuanmen Station, priority should be given to improving invisible facilities and creating cultural and tourism scenes; for medium-potential areas, a phased approach should be taken to implement mixed-use layouts and optimize transportation systems, promoting regional vitality regeneration; for low-potential areas including Yongningmen Station and Dachashi Station, micro-renewal should be the main approach, combined with a merchant self-governance mechanism, to gradually enhance green landscapes and cultural atmospheres, thereby increasing the attractiveness of the places. Discussion The “Node-Place-Perception” model under the framework of spatial justice This study inherits and integrates two academic traditions: one is the “Node-Place” model, which emphasizes the interaction between transportation and land use 22 , 23 ; the other is spatial justice theory, which focuses on the equitable distribution of spatial resources 24 – 26 . Compared to the former, the breakthrough of the NPP model lies in introducing the “perception” dimension, expanding the model from functional efficiency analysis to social equity evaluation. In contrast to the latter and numerous studies that concentrate on environmental perception, the depth of this research lies in not treating “perception” as an isolated psychological response, but rather systematically interpreting it as a measurement scale for the degree of “rights” realization. This paper constructs a three-dimensional NPP model comprising the three dimensions of “node,” “place,” and “perception,” presenting the developmental status of station areas in the form of a Rubik’s Cube. This intuitive mode of expression aids in better understanding and applying the research findings. It is worth noting that the perception indicators constructed in this paper primarily serve an early-warning function to identify potential spatial justice deficits such as the lack of environmental well-being, rather than directly measuring spatial justice. This innovation not only overcomes the static limitations of traditional two-dimensional models but also aligns more closely with the actual perceptions and needs of urban residents, offering a new theoretical perspective for research on urban non-motorized environments. Limitations and future directions This study has certain limitations in sample selection, mainly reflected in the concentration of cases on specific types of stations within a single city. Nevertheless, it holds significant theoretical and practical value for optimizing the non-motorized environment in high-density urban commercial rail transit station areas. As a typical high-density development city, the spatial injustice issues and resource allocation challenges faced by Xi’an’s commercial station areas are broadly representative among many high-density cities in Asia. The “Node-Place-Perception” three-dimensional model constructed in this study accurately identifies the synergistic and imbalanced relationships among transportation functions, spatial forms, and user experiences within station areas, providing a scientific diagnostic tool for refined governance during the stock renewal stage in high-density urban areas. Although the direct generalizability of the conclusions requires validation through more urban cases, the spatial justice-oriented evaluation framework and optimization strategies proposed in this paper offer actionable renewal paradigms for cities facing similar high-density development pressures. Its significance lies in translating abstract principles of fairness and inclusion into implementable spatial interventions, facilitating the transformation of commercial station areas from mere transportation hubs into efficient and inclusive urban vitality centers. Future research could further enrich the existing three-dimensional model by incorporating additional relevant dimensions and indicators to more comprehensively reflect the complexity and diversity of the non-motorized environment in rail transit station areas. Conclusion In the context of advocating green commuting and promoting urban sustainable development, this study takes the commercial rail transit station areas in Xi’an as the research object, integrates spatial justice theory into the non-motorized environment evaluation system, and innovatively proposes a three-dimensional “Node-Place-Perception” (NPP) model. This model breaks through the static limitations of the traditional two-dimensional "Node-Place" model, constructs a comprehensive evaluation framework, and provides a new perspective for urban non-motorized environment research. This study reveals significant disparities and imbalances in the non-motorized environment of station areas in terms of traffic efficiency, functional layout, and user perception. These disparities and imbalances reflect, to some extent, spatial injustice issues existing in certain station areas, such as lagging infrastructure development, road accessibility deprivation, functional configuration imbalance, and perceived exclusion in areas outside the city wall. Among the commercial rail transit station areas in Xi’an, the balanced-type station areas account for the largest proportion, making up 64.28% of the total. There are only three imbalanced-type station areas, accounting for 21.43%, and all of them are located outside the city wall. Additionally, there is one general-perception-type station area (Yongningmen Station) and one low-perception-type station area(Dachashi Station). Based on the principle of “equitable access” in spatial justice, targeted optimization strategies are proposed. These strategies cover various aspects, including improving barrier-free facilities, reshaping mixed-function neighborhoods, and embedding cultural perception scenarios. The aim is to achieve efficiency improvement and social inclusivity enhancement in the non-motorized environment of station areas through refined renewal. In summary, this study provides new ideas and paradigms for the evaluation and governance of the non-motorized environment in urban rail transit station areas through theoretical and methodological innovations. The research findings not only enrich the theoretical system of urban non-motorized environments but also provide important scientific evidence and practical references for urban planning and traffic management. In the future, with further in-depth research and expansion, it is believed that this study will contribute to promoting urban sustainable development and constructing more equitable and efficient urban spaces. Author contributions Conceptualization, Y.D. and Y.M.; methodology, Y.L. and Y.M.; software, Y.M.; investigation, Y.L.; writing—original draft preparation, Y.L. and Y.M.; writing—review and editing, Y.D., L.Z. and Q.H; project administration, Y.D. All authors have read and agreed to the published version of the manuscript. Funding The author declares that there is no conflict of interest. This research was supported by the Special Funding Project for Basic Scientific Research of Central Universities (Humanities and Social Sciences) (Grant No. 300102414601). Data availability All data supporting the fndings of this study are available within the article. Declarations Competing interests The authors declare that they have no known competing financialinterests or personal relationships that could have appeared to influencethe work reported in this paper. 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