Influence of Public Transport Quality Attributes on User Satisfaction of Different Age Cohorts

Cited 11|Views7

Abstract

This study developed a multi-group structural equation model to determine the relationship between general satisfaction and quality attributes of public bus transportation, based on the users' perception, and compare the effects of these attributes for different age cohorts. The structural equation model approach allows the analysis of interrelations between quality attributes evaluated in the survey (observed variables) and the interrelations between unobservable variables (latent variables). The three latent variables evaluated in this study were Operational characteristics, Comfort, and User exposure to externalities. The results attest the theory that Operational characteristics have a direct influence on General satisfaction, while Comfort and User exposure to externalities have an indirect influence on satisfaction. The analysis for different age cohorts showed that the importance of indicators within each latent variable is the same, but the impact of Operational characteristics on General satisfaction is higher for Young adults.

Translated text

Key words

Public bus transportation satisfaction,Structural equation modeling,Age cohort

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper