In modern cities, urban public spaces, such as parks, gardens, plazas, and streets, play a big role in people’s social activities, physical activities, mental health, and overall well-being. However, the traditional post-occupancy evaluation (POE) process for public spaces such as large urban parks is extremely difficult, especially for long-term user experiences through observations, surveys, and interviews. On the other hand, social media has emerged as a major media outlet recording millions of user experiences to the public, which provides opportunities to inform how public space is used and perceived by users. Furthermore, unlike previous research that primarily presented descriptive characters of park programs, our study employs a network model to elucidate the interactive relationships and intensities among reported park elements, human activities, and experiences. This approach enables us to track the sources within the space that impact people’s perceptions, such as weather conditions, food options, and notable landmarks. The utilization of this network model opens avenues for future research to comprehensively investigate the factors shaping people’s perceptions in public open spaces. This study uses Bryant Park as an example and presents a new analytical framework, POSE (post-occupancy social media evaluation), to support long-term POE studies for large public spaces. Methods such as data automation, descriptive statistics, and social network analysis were used. The identification and quantification of meaningful park activities, scenes, and sentiments as well as their relationships will help optimize the design and management of park programs.

This study investigates the dynamics of passenger satisfaction and sustainable urban mobility within the context of multimodal railway hubs, focusing on travel behaviors at major stations in China. Against the backdrop of rapid urbanization and the nation’s initiatives to improve transportation efficiency, this research employs social media data analysis to assess passenger sentiment across six key transportation hubs in Eastern China. Utilizing methodological approaches such as keyword frequency analysis and semantic categorization of 39,061 Dianping reviews, supplemented by network visualizations with Gephi, this study reveals insights into factors influencing passenger satisfaction beyond travel efficiency. Signage quality, facility availability, queueing, and crowding emerge as significant determinants of passenger behavior. The study underscores the importance of strategic improvements in station design, navigational aids, and facility management, grounded in real-time data analytics and passenger feedback, to enhance overall passenger satisfaction and promote sustainable urban mobility. This research contributes to advancing understanding of passenger behavior and informs efforts aimed at improving urban transportation systems to meet the evolving needs of passengers and cities.

The average daily throughput of large-scale passenger high-speed railway stations is large, and the design of the inbound space connecting with the underground and other modes of transport affects the passengers’ wayfinding behaviour and time spent, which in turn affects the efficiency of the inbound station. How to optimise the design of station entry space and signage arrangement becomes the key to shortening the station entry time. In this paper, eye tracking, spatial syntax, and semantic difference methods are used to evaluate the passenger’s wayfinding process in the underground hub of a large high-speed railway station and the spatial syntax is used to quantify and analyse the wayfinding path segments, to explore the influence of the spatial attributes of different nodes and the spatial arrangement of the guiding signs on the passenger’s wayfinding behaviour data and the difference in attention, and to find out that the connectivity of the wayfinding nodes, the area of the field of view, and the passengers’ The study concludes that the connectivity and visual field area of wayfinding nodes have a strong positive correlation with the passengers’ route choice time, which has less influence on the correct rate of wayfinding and can be taken into less consideration in the subsequent design. While analysing the spatial density of signs and the correct rate of wayfinding in the sample, it is concluded that the density of guide signs is maintained in the interval of 5–11‰, and at the same time, the number is sufficient to point to the destination is a more appropriate interval, and ultimately, the impact of the correct rate of wayfinding of the weighting of the following: signage focus on the time > density of information > density of key information > diameter of the pupil. The study analyses the influencing factors affecting passengers’ wayfinding behaviour from a human factors perspective and provides feedback on the design of underground entry spaces in large passenger high-speed rail stations.

Based on big data, a new public space evaluation method is proposed. Using programming technology to collect visitor reviews from the travel website TripAdvisor to build a database, based on the data of 99,240 words in 1573 visitor reviews in 10 years, the connection between data and reality is established through systematic data classification and visualization. Following an assessment of the Kimbell Art Museum’s functionality, architectural design, and landscape design, along with visitor feedback, a new evaluation methodology was formulated for application to public buildings with landscapes. By utilizing the unique advantages of big data, it provides convenient and efficient analysis methods for public spaces with similar data foundations and opens the way for the optimization of the built environment in the information age.