Researchers are increasingly interested in the impact of the built environment on urban walkability.Pedestrian satisfaction is a key indicator of walkability and can elucidate latent factors to improve walking environ...Researchers are increasingly interested in the impact of the built environment on urban walkability.Pedestrian satisfaction is a key indicator of walkability and can elucidate latent factors to improve walking environment.This study develops an assessment framework for evaluating walking satisfaction on sidewalks in commercial districts in Japan according to the built environment and personal attributes.Questionnaire surveys were used to collect data from 963 Japanese residents’impact ratings of built environment variables.Six factors were extracted following exploratory factor analysis.Second-order confirmatory factor analysis was then applied to further explore the relationship between observed variables and latent factors.Latent class analysis was employed to classify the population,and personal attributes served as covariates in the multinomial logistic regression analysis to explore how these attributes affected the latent classes.The results indicated that visual impression,spatial richness,accessibility,comfort,diversity,and security influence pedestrian walking satisfaction on sidewalks in commercial districts.Travel purpose and travel method are important indicators that influence the latent classes of the population.The result presented herein can guide policy makers to optimize the construction of walkable urban environments and enact policies based on the factors and populations that are best suited to urban development.展开更多
There has been a recent line of work to automatically detect the emotions of posts in social media.In literature,studies treat posts independently and detect their emotions separately.Different from previous studies,w...There has been a recent line of work to automatically detect the emotions of posts in social media.In literature,studies treat posts independently and detect their emotions separately.Different from previous studies,we explore the dependence among relevant posts via authors'backgrounds,since the authors with similar backgrounds,e.g.,"gender","location",tend to express similar emotions.However,personal attributes are not easy to obtain in most social media websites.Accordingly,we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection:the Joint Model with Personal Attention Mechanism(JPA)model is used to detect emotion and personal attributes jointly,and capture the attributes-aware words to connect similar people;the Neural Personal Discrimination(NPD)model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection.Experimental results show the usefulness of personal attributes in emotion detection,and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.展开更多
基金funding support from the JST SPRING(Grant No.JPMJSP2128).
文摘Researchers are increasingly interested in the impact of the built environment on urban walkability.Pedestrian satisfaction is a key indicator of walkability and can elucidate latent factors to improve walking environment.This study develops an assessment framework for evaluating walking satisfaction on sidewalks in commercial districts in Japan according to the built environment and personal attributes.Questionnaire surveys were used to collect data from 963 Japanese residents’impact ratings of built environment variables.Six factors were extracted following exploratory factor analysis.Second-order confirmatory factor analysis was then applied to further explore the relationship between observed variables and latent factors.Latent class analysis was employed to classify the population,and personal attributes served as covariates in the multinomial logistic regression analysis to explore how these attributes affected the latent classes.The results indicated that visual impression,spatial richness,accessibility,comfort,diversity,and security influence pedestrian walking satisfaction on sidewalks in commercial districts.Travel purpose and travel method are important indicators that influence the latent classes of the population.The result presented herein can guide policy makers to optimize the construction of walkable urban environments and enact policies based on the factors and populations that are best suited to urban development.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.62176174 and 61806137.
文摘There has been a recent line of work to automatically detect the emotions of posts in social media.In literature,studies treat posts independently and detect their emotions separately.Different from previous studies,we explore the dependence among relevant posts via authors'backgrounds,since the authors with similar backgrounds,e.g.,"gender","location",tend to express similar emotions.However,personal attributes are not easy to obtain in most social media websites.Accordingly,we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection:the Joint Model with Personal Attention Mechanism(JPA)model is used to detect emotion and personal attributes jointly,and capture the attributes-aware words to connect similar people;the Neural Personal Discrimination(NPD)model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection.Experimental results show the usefulness of personal attributes in emotion detection,and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.
