An integrated estimation approach to incorporate latent variables through SEM into discrete mode choice models to analyze mode choice attitude of a rider

Shah, Bharvi A.; Zala, L.B.; Desai, Nipa A.

doi:10.1016/j.trip.2023.100819

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…However, using only a single SEM analysis may lead to overlooking the correlations and interrelationships between latent variables, as well as between latent variables and other attribute variables. This oversight can result in misinterpreting potential categories and repeating factors unnecessarily 22 , 23 .To more accurately describe unobservable factors, scholars have integrated the Logit model with the SEM based on the theory of planned behavior (TPB) was developed. It was determined that commuters’ attitudes, cognition, norms, and service level of travel mode significantly affect individual travel mode choice intention and behavior 24 , 25 .…”

Section: Literature Reviewmentioning

confidence: 99%

A study on commuters’ public transportation mode choice behavior in river valley-type cities considering terrain spatial perception: evidence from Lanzhou, China

Fan,

Qi,

Zheng

et al. 2024

Sci Rep

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Existing research rarely examines the subjective and objective built environment of river valley-type cities in relation to travel mode choice, particularly overlooking the heterogeneity among travelers in these cities. In this paper, based on questionnaire survey data and built environment data, terrain spatial perception (TSP) is introduced to expand the theory of planned behavior (TPB), and a Structural Equation Model (SEM) is established. Factor analysis and path analysis are conducted using SPSS and AMOS to estimate latent variables. An integrated model of SEM and random parameter Logit model (RPLM), which can not only analyze the psychological perception factors of commuters in river valley-type cities but also consider the heterogeneity of psychological perception, was constructed to analyze the impact of personal attributes, objective built environment factors, and psychological latent variables on the commuting mode choice behavior of public transport users in river valley-type cities. The results indicate that the five observation indicators corresponding to the proposed terrain spatial perception latent variables can better explain the terrain spatial perception of commuters in river valley-type cities. Different from plain cities, the subjective and objective built environment of river valley-type cities notably influence the travel behavior of commuters. Moreover, the parameters of terrain spatial perception follow a normal distribution, indicating that the sensitivity of different commuters to the terrain spatial perception of river valley-type cities is heterogeneous. The results of our study can provide a reference for alleviating traffic issues in valley cities.

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Section: Literature Reviewmentioning

confidence: 99%

A study on commuters’ public transportation mode choice behavior in river valley-type cities considering terrain spatial perception: evidence from Lanzhou, China

Fan,

Qi,

Zheng

et al. 2024

Sci Rep

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show abstract

“…Before analyzing the structural equation model, the scale data obtained from the survey needed to be tested for reliability and validity. This statistical approach is used to understand the structure of the observed variables and their relationship to the underlying variables [62]. Confirmatory factor analysis (CFA) was conducted to test the fit of the vey needed to be tested for reliability and validity.…”

Section: Analysis Of Reliability and Validitymentioning

confidence: 99%

“…The corresponding test results are shown in Table 6. The chi-square/degrees of freedom (χ 2 /d f ), root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker-Lewis Index (TLI), and standardized root mean square residual (SRMR) indicators are within acceptable limits [62,75], indicating that the overall fitness of this structural equation model is good.…”

Section: Analysis Of Reliability and Validitymentioning

confidence: 99%

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

Cui

Zhu

et al. 2023

Sustainability

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The high-quality development of urban–rural buses is an important way to narrow the gap between urban and rural areas. However, studies on the quality of bus services and its impact on bus travel-mode choice have mostly focused on urban buses, with less attention paid to urban–rural buses. This paper aims to explore how urban–rural bus service quality affects rural residents’ travel-mode-choice behavior based on survey data from rural residents in Henan Province, China. A structural equation model (SEM) is applied to examine the critical factors affecting rural residents’ satisfaction with urban–rural bus service quality and the heterogeneity of satisfaction among rural residents with different attributes. Furthermore, an integrated approach of SEM and the multinomial logit (MNL) model is proposed to identify the key factors that affect rural residents’ bus travel-mode choice. The results indicate that reliability and convenience are the key factors affecting satisfaction with urban–rural bus service quality. There are significant differences in rural residents’ satisfaction by gender, age, income, education level, occupation, and travel-mode attributes. Urban–rural bus service quality has a significant positive impact on rural residents’ bus travel-choice behavior, and its degree of influence is greater than all explicit variables except private car ownership. The findings of this study can help operators and governments formulate policies to improve the service quality of urban–rural buses and ultimately promote the use of buses.

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“…These models often contain buses, trains, and planes [13] Urban travel demand and behavior are largely influenced by a number of variables, including land-use patterns, socioeconomic conditions, travel costs, means of transportation, and demographic traits. It can be seen in table 1 regarding the explanation of these factors [14] According to Van Wee and Banister, various factors have complex effects on demand or travel behavior. These complex causal relationships result in a variety of methods and interpretations of data analysis.…”

Section: Introductionmentioning

confidence: 99%

Study on the analysis of travel behavior: A review

Firdausi,

Ahyudanari,

Herijanto

2023

E3S Web Conf.

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Travel behavior analysis involves the utilization and examination of travel demand through the application of theories and analytical techniques derived from diverse scientific disciplines. There are many methods used by researchers in analyzing travel behavior. Mode selection is the stage where the transportation planning process is responsible for determining the travel load or knowing the number of people and goods, then selecting the available transportation models that serve as the starting point of the destination. Travel factors have a strong influence on a person’s travel behavior, (trip maker behavior), including income, vehicle ownership, vehicle condition, settlement density of one’s socioeconomic conditions. This paper describes the disadvantages and advantages of travel behavior modeling analysis and factors that influence the choice of modes based on travel behavior, so that in the future researchers can use it as an illustration in determining the right analysis.

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An integrated estimation approach to incorporate latent variables through SEM into discrete mode choice models to analyze mode choice attitude of a rider

Cited by 3 publications

References 21 publications

A study on commuters’ public transportation mode choice behavior in river valley-type cities considering terrain spatial perception: evidence from Lanzhou, China

A study on commuters’ public transportation mode choice behavior in river valley-type cities considering terrain spatial perception: evidence from Lanzhou, China

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

Study on the analysis of travel behavior: A review

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