Prioritizing Subway Station Entrance Attributes to Optimize Passenger Satisfaction in Cold Climate Zones: Integrating Gradient Boosting Decision Trees with Asymmetric Impact-Performance Analysis

Self Cite

Post-industrial neighborhoods are valued for their historical and cultural significance but often contend with challenges such as physical deterioration, social instability, and cultural decay, which diminish residents’ satisfaction. Leveraging urban renewal as a catalyst, it is essential to boost residents’ satisfaction by enhancing the environmental quality of these areas. This study, drawing on data from Shenyang, China, utilizes the combined strengths of gradient boosting decision trees (GBDTs) and asymmetric impact-performance analysis (AIPA) to systematically identify and prioritize the built-environment attributes that significantly enhance residents’ satisfaction. Our analysis identifies twelve key attributes, strategically prioritized based on their asymmetric impacts on satisfaction and current performance levels. Heritage maintenance, property management, activities, and heritage publicity are marked as requiring immediate improvement, with heritage maintenance identified as the most urgent. Other attributes are categorized based on their potential to enhance satisfaction or their lack of immediate improvement needs, enabling targeted and effective urban revitalization strategies. This research equips urban planners and policymakers with critical insights, supporting informed decisions that markedly improve the quality of life in these distinctive urban settings.

Section: Analysis Techniques For the Priority Assessment Of Attributesmentioning

confidence: 99%

Section: Analysis Techniques For the Priority Assessment Of Attributesmentioning

confidence: 99%

Prioritizing Environmental Attributes to Enhance Residents’ Satisfaction in Post-Industrial Neighborhoods: An Application of Machine Learning-Augmented Asymmetric Impact-Performance Analysis

Ji,

Shang,

Liu

et al. 2024

Self Cite

Investigating the Multifaceted Impact of Urban Environment on Winter Green Travel in Cold Regions: An Empirical Study of Shenyang, China

Du,

Wang,

Dou

et al. 2024

Green travel offers significant benefits, including reducing pollution and alleviating traffic congestion. Despite these advantages, green travel is less popular in cold regions, particularly during winter, due to comfort, convenience, and safety concerns. While climate factors are immutable, enhancing the urban environment can promote winter green travel. This paper examines the impact of urban environments on residents’ willingness to engage in green travel in cities located in cold regions during winter. Using data from Shenyang, China, a comprehensive structural equation model based on the stimulus–organism–response (S-O-R) theory was constructed, revealing the causal relationships and underlying structure between environmental factors and green travel willingness. The model demonstrates that social, built, natural, and travel environments collectively shape residents’ willingness to engage in green travel, with the social environment emerging as the most impactful factor. Additionally, this study identified two crucial mediating variables, travel perception evaluation and attitude towards green travel, which indirectly influence green travel willingness. This study also identifies nine critical factors—often underappreciated in traditional analyses—that should be prioritised in urban planning. These findings advance the understanding of green travel behaviour in winter cities by illustrating the complex interplay between environmental factors and individual attitudes while providing actionable guidance for fostering supportive social environments and strategically enhancing built and travel environments to promote green travel in cold climates.

Boosting Winter Green Travel: Prioritizing Built Environment Enhancements for Shared Bike Users Accessing Public Transit in the First/Last Mile Using Machine Learning and Grounded Theory

Du,

Ji,

Dou

et al. 2024

Shared bikes are widely used in Chinese cities as a green and healthy solution to address the First/Last Mile issue in public transit access. However, usage declines in cold regions during winter due to harsh weather conditions. While climate factors cannot be changed, enhancing the built environment can promote green travel even in winter. This study uses data from Shenyang, China, to investigate how built environment attributes impact the travel satisfaction of shared bike users who utilize bikes as a First/Last Mile solution to access public transit in winter cities. By employing machine learning algorithms combined with Asymmetric Impact-Performance Analysis (AIPA) and grounded theory, we systematically identify the key attributes and rank them based on their asymmetric impact and urgency of improvement. The analysis revealed 19 key attributes, 17 of which are related to the built environment, underscoring its profound influence on travel satisfaction. Notably, factors such as the profile design of cycling paths and safety facilities along routes were identified as high priorities for improvement due to their significant potential to enhance satisfaction. Meanwhile, features like barrier-free access along paths and street greenery offer substantial opportunities for improvement with more modest efforts. Our research provides critical insights into the nuanced relationship between built environment features and travel satisfaction for First/Last Mile shared bike users. By highlighting priority improvements, we offer urban planners and policymakers a framework for creating livable, sustainable environments that support green travel even in harsh winter conditions.