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In a rapidly evolving and highly competitive transportation market, increasing customer satisfaction and ridership retention are essential for transit agencies. Understanding how different market segments perceive transit services can help service providers to identify potential priority areas and develop specialised strategies to address their varying travel concerns. However, traditional studies have predominantly investigated the variations across socioeconomic cohorts or travel characteristics but ignored the complex effect of spatial, temporal, and user heterogeneity. Recently social media data has attracted growing interest from academia as an alternative way to compensate public attitude surveys with a high volume of semantic, spatial, and temporal information. This study collected 177,807 microblogs from Sina Weibo to understand how customer satisfaction varies among different market segments characterised by social, temporal, and spatial heterogeneity. Methodologically, this study applies sentiment analysis as a real-time measurement of customers' satisfaction towards transit services, covering safety, crowdedness, reliability, personnel behaviour, and comfort. A beta regression model is then applied to identify the most important explanatory factors and the extent to which explanatory factors affect customers' satisfaction, respectively. The result indicates that age, gender, travel mode, time, and space significantly contribute to customers' satisfaction with the transit system. Their varying impacts on different service attributes are also identified in this study. This study also reveals the highly polarised nature of online sentiment, explaining gendered attitudes. Our research framework could be used as a benchmark for other service industries to conduct similar market segment analysis and integrate it into the policy decision process. • We examine social media data from Weibo to understand customer satisfaction. • We use sentiment analysis to estimate customer satisfaction. • We use beta regression model to identify key influential factors of satisfaction. • We find that age, gender, travel mode, time and geography are key factors. • The identified factors have varying impacts on different service categories. [ABSTRACT FROM AUTHOR]

Location-based social media data can offer useful insights on the spatial and temporal dynamics of public attitudes. In this study, we aim to investigate the gendered attitudes toward transit services in China, utilizing the case of Shenzhen. We collected 44,257 Weibo microblogs, a major source of social media data in China, and applied a series of text mining and visualization techniques to examine the gender differences among our focused themes. The microblogs reveal a distinct gender gap in terms of quantity, as nearly 74% are posted by women. While women tend to be more concerned about the comfort of transit environment (e.g., temperature, crowdedness, and safety, especially at night), men tend to be more interested in transit systems' e-payment services and reporting traffic incidents. Overall, this study presents a methodological framework and empirical case study about how we can utilize certain social media mining techniques to investigate gendered, subjective travel experiences, providing researchers and practitioners with an innovative way to gather customer service feedback and build more inclusive service systems. • Women's voices are often neglected or suppressed in traditional transport planning. • We examine social media data from Weibo to understand gendered perceptions. • We use Latent Dirichlet Allocation model to extract latent "topics" from the posts. • Women differ from men regarding the perceived safety concerns and comfort levels. • Gendered perceptions toward transit services vary across space and time. [ABSTRACT FROM AUTHOR]

Hierarchical hollow porous self-supported flexible NF/Co 3 O 4 /NiCo 2 O 4 electrode was fabricated by in situ growing of CoNi-Asp derived NiCo 2 O 4 nanotubes on ZIF-67 derived Co 3 O 4 3D skeleton and directly used for methanol electrocatalytic oxidation with enhanced activity and without significant electrochemical performance decay after curved, bent and twisted. • The morphology of Co-Ni based MOFs were optimized by organic linkers. • ZIF-67 derived Co 3 O 4 arrays were in situ grown on NF and uses as 3D skeleton. • CoNi-Asp derived NiCo 2 O 4 nanotubes were in situ grown on Co 3 O 4 3D skeleton. • Binder free NF/Co 3 O 4 /NiCo 2 O 4 electrode exhibit enhanced activity and flexibility. Most of the metal organic frameworks (MOFs) derived metal oxides are powdery, which cannot be directly used for electrocatalysis. In this report, zeolitic imidazole framework-67 (ZIF-67) derived hollow porous Co 3 O 4 nanospindle arrays were in situ grown on nickel foam (NF) substrate (NF/Co 3 O 4) and used as 3D skeleton. Cobalt nickel-aspartic acid coordination precursors (CoNi-Asp) derived NiCo 2 O 4 nanotubes were in situ grow on the NF/Co 3 O 4 3D skeleton to formed the hierarchical hollow porous self-supported flexible NF/Co 3 O 4 /NiCo 2 O 4 electrode, which can be directly used for methanol electrocatalytic oxidation. The 3D Co 3 O 4 skeleton can not only afford more surface area and strong adhesion for the in situ growth of NiCo 2 O 4 nanotubes but also enhance the activity of NiCo 2 O 4. More strikingly, the NF/Co 3 O 4 /NiCo 2 O 4 exhibited good flexibility, which have potential application in flexible direct methanol fuel cell (DMFC). [ABSTRACT FROM AUTHOR]

In the post‐pandemic era, tourism has emerged as the most popular form of recreation in densely populated areas, driven by the pursuit of improved quality of life and the younger generation's growing enthusiasm for connecting with nature or history. Accurate measurement of the accessibility of tourist attractions has become crucial due to this trend. However, existing methods for measuring accessibility, such as the widely used two‐step floating catchment area (2SFCA) method, show limitations in multi‐modal transport networks as it overlooks multi‐modal travels and assumes uniform access for each transport mode within the catchment area. In this article, we present a novel multi‐modal network‐based two‐step floating catchment area (MMN‐2SFCA) method, which incorporates a super‐network to explicitly model the multi‐modal travels. The proposed method emphasizes the modeling of transfer behavior, resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The proposed method emphasizes the modeling of transfer behavior, travel mode choice, and travel demand estimation by multi‐source big data (including mobile phone signaling data, travel survey data, subway smart card data, and bike‐sharing data), resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The results of the case study in Suzhou (a famous tourism city in China) demonstrate the effectiveness of the proposed MMN‐2SFCA method. The method can rectify the imbalance in accessibility distribution caused by considering only single‐modal trips, and avoid overestimation of accessibility by accounting for transfer behavior. This study contributes to advancing accessibility measurement for multi‐modal transport networks. Moreover, the MMN‐2SFCA method offers excellent extensibility, enabling authorities to optimize and coordinate multi‐modal transport networks for improving the accessibility of tourist attractions and other facilities. [ABSTRACT FROM AUTHOR]

Background: The urban built environment (BE) has been globally acknowledged as one of the main factors that affects the spread of infectious disease. However, the effect of the street network on coronavirus disease 2019 (COVID-19) incidence has been insufficiently studied. Severe acute respiratory syndrome coronavirus 2, which causes COVID-19, is far more transmissible than previous respiratory viruses, such as severe acute respiratory syndrome coronavirus, which highlights the role of the spatial configuration of street network in COVID-19 spread, as it is where humans have contact with each other, especially in high-density areas. To fill this research gap, this study utilized space syntax theory and investigated the effect of the urban BE on the spatial diffusion of COVID-19 cases in Hong Kong. Method: This study collected a comprehensive dataset including a total of 3815 confirmed cases and corresponding locations from January 18 to October 5, 2020. Based on the space syntax theory, six space syntax measures were selected as quantitative indicators for the urban BE. A linear regression model and Geographically Weighted Regression model were then applied to explore the underlying relationships between COVID-19 cases and the urban BE. In addition, we have further improved the performance of GWR model considering the spatial heterogeneity and scale effects by adopting an adaptive bandwidth. Result: Our results indicated a strong correlation between the geographical distribution of COVID-19 cases and the urban BE. Areas with higher integration (a measure of the cognitive complexity required for a pedestrians to reach a street) and betweenness centrality values (a measure of spatial network accessibility) tend to have more confirmed cases. Further, the Geographically Weighted Regression model with adaptive bandwidth achieved the best performance in predicting the spread of COVID-19 cases. Conclusion: In this study, we revealed a strong positive relationship between the spatial configuration of street network and the spread of COVID-19 cases. The topology, network accessibility, and centrality of an urban area were proven to be effective for use in predicting the spread of COVID-19. The findings of this study also shed light on the underlying mechanism of the spread of COVID-19, which shows significant spatial variation and scale effects. This study contributed to current literature investigating the spread of COVID-19 cases in a local scale from the space syntax perspective, which may be beneficial for epidemic and pandemic prevention. [ABSTRACT FROM AUTHOR]

Daily mobility of disadvantaged social groups, particularly low-income people, has gained increasing interest since the daily life of these people may encounter significant constraints that lead to certain social issues such as social exclusion and segregation. The concept and measure of activity space have been applied to measure the spatial barriers and inequalities faced by the low-income group in their daily life. However, research on how the daily mobility of different social strata has evolved over a longer period in parallel with urban development has been very limited. This study aims to address these gaps by investigating the activity space of the low-income group in comparison with other income groups in Hong Kong from 2002 to 2011, a period when several subway lines were opened. Four indicators were employed to capture the spatial extent and diversity of activity space. Propensity score matching was used to compare the activity space and mobility of the low-income group with those of the below-average and higher-income groups, while controlling for several confounding factors. The findings indicate that the activity space of the low-income people has not improved over the decade, despite some marked expansion of public transport infrastructure and urban development. In addition, a persistent gap in activity space between the low-income group and other income groups has remained stark in both urban areas and the new towns. Policy recommendations are put forward with a view to improving the daily mobility of the low-income group, with avenues for future research identified as well. • Activity space of the low-income group in Hong Kong of 2002 and 2011 is examined. • The spatial extent and diversity of activity space are measured in four indicators. • The activity space of the low-income group has not improved during the study period. • A persistent gap exists between the low-income group and other income groups. • The discrepancy along the income line exists in both urban areas and new towns. [ABSTRACT FROM AUTHOR]