Urban Narrative: Computational Linguistic Interpretation of Large Format Public Participation for Urban Infrastructure

Dyer, Mark; Weng, Min-Hsien; Wu, Shaoqun; Ferrari, Tomás García; Dyer, Rachel

doi:10.17645/up.v5i4.3208

Cited by 5 publications

(5 citation statements)

References 24 publications

(24 reference statements)

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“…It is also important to ensure the spread of democratic planning, protection, and support for marginalized sections of the population, including women, children, the poor, and people with disabilities, by building capacity for their inclusion in a functional public transport system [5].…”

Section: Resultsmentioning

confidence: 99%

Socio-cultural factors of the evolution of the urban transport system

Shumilina,

Antsiferova

2024

E3S Web Conf.

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The sociocultural urban space of a modern city is undergoing some transformational changes due to the high level of population mobility. The daily need to develop urban space determines the importance of the transport system in sociocultural interactions, the participants of which value safety, confidentiality, and rationality. In realizing their purpose, urban transport structures not only operate with economic, social and communication resources, but also involve citizens in the process of organizing urban life. This research aims to study the socio-cultural factors in the evolution of the urban transport system by systematizing theoretical scientific concepts, comparative analysis of dynamic changes in the use of public transport and summarizing some gaps in transport infrastructure. Identification of the correlation between sociocultural factors, passenger needs and the quality of transport services determines the need to develop a communicative culture, maintain moral values and overcome sociocultural barriers, which will subsequently lead to a constructive transformation of the urban transport system.

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Section: Resultsmentioning

confidence: 99%

Socio-cultural factors of the evolution of the urban transport system

Shumilina,

Antsiferova

2024

E3S Web Conf.

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“…Policy interventions should generally aim to improve the travel experiences of all passengers. It is also essential to ensure that any change is made possible through the spread of democratic planning by listening to the most marginalized sections of the population including the women and the poor while finding ways to incorporate these concerned into making a functional public transport system [101]. Therefore, to guarantee a functional public transport that is free from the stigma-defining variables (as this study identifies), responsive to the gender needs and adaptive to local contexts e.g., DMA in Saudi Arabia, it is necessary to:…”

Section: Discussionmentioning

confidence: 99%

Influence of Socio-Cultural Attributes on Stigmatizing Public Transport in Saudi Arabia

et al. 2021

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Several factors over the years have contributed to stigma in public transport. Many studies have highlighted the need to make the transport system more equitable both from economic and gender perspectives. This study attempts to demonstrate how the perceptions of public transport users and non-users are stigmatized from social and cultural standpoints. Thus, it identifies the social and cultural stigma-induced barriers embedded with the use and people’s general perception about the public bus service, taking SAPTCO (Saudi Public Transport Company) as a case study. The study results suggest that privacy concern is the primary cause of stigma. Most of the users are unwilling to ride with their families as SAPTCO does not account for gender needs (e.g., privacy, travel convenience, safety, comfort, etc.). Moreover, people from the high-income classes are more stigmatized against this ridership. A fuzzy inference system (FIS) model is used to analyze the survey questionnaire responses and understand what stigma means for the public bus service. Expert opinions are employed to generate “if–then” rules of the FIS models. Sensitivity of the defined fuzzy model is conducted to different aspects of the ridership. The study results further suggest that “inconvenience” poses the highest impact while “feeling safe”, “privacy”, “fare”, “timing”, and “comfort” are found to be the medium impact-making variables for stigma. The stigma-defining variables would be critical for the public bus service to improve its service quality and help (re-)design the policies that would attract a high amount of ridership. Some solutions are suggested in the end that would complement, strengthen, and promote the current SAPTCO service. The demonstrated methodology of this study would be relevant and adaptive to any relevant context to improve public transportation service and pertaining policies.

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“…Here, we attempt to provide a web-based topic analysis tool that allows researchers to explore the current topics in a research area and discover the methods and data that are deployed in the investigation. Urban planning is the study of interest in this paper as our team has received funding (New Zealand National Scientific Challenge for Better Built Homes, Towns and Cities) for research in this area and published several publications [45][46][47][48]. In this study, we focus on research articles in the area of using AI and machine learning in urban governance/design/planning/study with urban data.…”

Section: Methodsmentioning

confidence: 99%

Identification and Visualization of Key Topics in Scientific Publications with Transformer-Based Language Models and Document Clustering Methods

Weng

Dyer

2022

Applied Sciences

Self Cite

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With the rapidly growing number of scientific publications, researchers face an increasing challenge of discovering the current research topics and methodologies in a scientific domain. This paper describes an unsupervised topic detection approach that utilizes the new development of transformer-based GPT-3 (Generative Pretrained Transformer 3) similarity embedding models and modern document clustering techniques. In total, 593 publication abstracts across urban study and machine learning domains were used as a case study to demonstrate the three phases of our approach. The iterative clustering phase uses the GPT-3 embeddings to represent the semantic meaning of abstracts and deploys the HDBSCAN (Hierarchical Density-based Spatial Clustering of Applications with Noise) clustering algorithm along with silhouette scores to group similar abstracts. The keyword extraction phase identifies candidate words from each abstract and selects keywords using the Maximal Marginal Relevance ranking algorithm. The keyword grouping phase produces the keyword groups to represent topics in each abstract cluster, again using GPT-3 embeddings, the HDBSCAN algorithm, and silhouette scores. The results are visualized in a web-based interactive tool that allows users to explore abstract clusters and examine the topics in each cluster through keyword grouping. Our unsupervised topic detection approach does not require labeled datasets for training and has the potential to be used in bibliometric analysis in a large collection of publications.

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Urban Narrative: Computational Linguistic Interpretation of Large Format Public Participation for Urban Infrastructure

Cited by 5 publications

References 24 publications

Socio-cultural factors of the evolution of the urban transport system

Socio-cultural factors of the evolution of the urban transport system

Influence of Socio-Cultural Attributes on Stigmatizing Public Transport in Saudi Arabia

Identification and Visualization of Key Topics in Scientific Publications with Transformer-Based Language Models and Document Clustering Methods

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