Sequenced Ordered Logit Model Considering Latent Variables for Determining Trip Satisfaction of Metro Passengers

Abstract

<jats:p> Improving the public transportation system to compete with the private modes requires an understanding of passenger perceptions of the service quality (SQ). In the literature, various models have been developed to identify effective SQ attributes and to assess their relationship with passenger satisfaction. However, most of them either ignore the socioeconomic and trip characteristics or consider them by a market segmentation approach. Since these variables can affect passenger perceptions, it is important to include them in the model. This paper aims to capture the effect of socioeconomic and trip variables by combining them with SQ attributes in a satisfaction analysis. An ordered logit model considering SQ latent variables is calibrated to model passenger satisfaction. The measurement part of a Structural Equation Model (SEM) is applied to construct latent variable structures. The case study was on the Tehran metro. The SQ attributes were used to form five SQ latent variables: “comfort,”“information,”“cleanliness,”“service,” and “safety/security.” The results indicate that socioeconomic and trip characteristics, as well as the SQ latent variables, had a significant effect on passenger satisfaction. From the results of this study, “service” and “comfort” were found to be the most effective contributors to satisfaction levels among the SQ latent variables. Among socioeconomic and trip characteristics, gender, education, driving license, egress mode, access time, and trip origin type (i.e., work, education, etc.) were also important in passenger satisfaction. </jats:p>

Impact of Illuminated Road Signs on Driver’s Perception

“…Notably, previous studies have examined the impact factors of road traffic accidents [53][54][55][56] and the impact of traffic accidents considering climate factors [6,57,58]. Other studies have analyzed the impact factors of a new commute railroad system, subway, and electric bicycles through OLR [59][60][61]. As such, in transportation sectors, OLR is utilized when the impact factor is analyzed based on the dependent variables composed of ordinal scales such as satisfaction, severity, and importance.…”