Understanding the effects of travel demand management on metro commuters’ behavioural loyalty: a hybrid choice modelling approach

Huan, Ning; Hess, Stephane; Yao, Enjian

doi:10.1007/s11116-021-10179-3

Cited by 16 publications

(5 citation statements)

References 74 publications

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“…the probability of the respondent choosing one brand from three presented) using latent multinomial regression (Malone and Lusk, 2018). These two types of latent regressions (linear or multinomial) set the measurement errors to zero and distinctly distinguish each utility’s unique contribution beyond the others (Huan et al , 2021). Our three latent dimensions in both choice contexts are functional, social and hedonic utilities, measured by two indicators each.…”

Section: Discussionmentioning

confidence: 99%

Do Americans seek pleasure while Chinese care about others’ approval? Examining stereotypical utilities positioning in brand choice contexts

Friedmann

Weiss-Sidi

Vescovi

2022

JPBM

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Purpose Past marketing research has found that hedonic utility is more important for Western cultures, whereas social utility is more important for Eastern cultures, suggesting differential positioning in each culture. However, the research has so far focused on a single choice context of one brand. This paper aims to examine cultural differences in utility importance using two brand choice contexts: single choice and brand selection. Design/methodology/approach Four studies (n = 1268) were conducted. Study 1 focused on a single choice context by asking directly about utility importance when choosing a cellphone. Study 2 focused on a brand selection context using conjoint analysis for the same cellphone category used in Study 1. To validate the results of Studies 1 and 2 with the categories of perfume, sports shoes and computers, Study 3 analyzed single and selection contexts using latent regression methods. Finally, Study 4 explored the role of cognitive load in explaining the differences between the two choice contexts using the laptop category. Findings The analyses of the brand selection context, which simulates real-life choice, revealed that the importance ascribed to utilities was not idiosyncratic for each culture. In contrast, single-choice contexts demonstrated stereotypical cultural differences. Originality/value Positioning a specific utility message to fit the culture stereotype might not be necessary, as it does not always affect brand choice in a competitive environment.

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Section: Discussionmentioning

confidence: 99%

Do Americans seek pleasure while Chinese care about others’ approval? Examining stereotypical utilities positioning in brand choice contexts

Friedmann

Weiss-Sidi

Vescovi

2022

JPBM

View full text Add to dashboard Cite

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“…The structural model explains the relationship between latent and explicit variables [65]. The measurement model quantifies latent variables by exploring the relationship between each directly perceived observed variable and its corresponding latent variable [71]. SEM can be expressed as:…”

Section: Solution Methods Of the Sem-mnl Modelmentioning

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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“…Demand models have been used extensively to analyze human decision-making on travel modes [18], [20], [21], adoption of new technologies [22], [23], willingness to pay [24], and behavioral loyalty [25], [26]. The most common demand models are the class of discrete choice models (DCM) based on the theory of random utility maximization.…”

Section: Demand Modellingmentioning

confidence: 99%

Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?

Wang¹,

Wang²,

Zheng³

et al. 2023

Preprint

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Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. However, travel behavior depends on the factors represented by both numeric data and urban imagery, thus necessitating a synergetic framework to combine them. This study creates a theoretical framework of deep hybrid models with a crossing structure consisting of a mixing operator and a behavioral predictor, thus integrating the numeric and imagery data into a latent space. Empirically, this framework is applied to analyze travel mode choice using the MyDailyTravel Survey from Chicago as the numeric inputs and the satellite images as the imagery inputs. We found that deep hybrid models outperform both the traditional demand models and the recent deep learning in predicting the aggregate and disaggregate travel behavior with our supervision-as-mixing design. The latent space in deep hybrid models can be interpreted, because it reveals meaningful spatial and social patterns. The deep hybrid models can also generate new urban images that do not exist in reality and interpret them with economic theory, such as computing substitution patterns and social welfare changes. Overall, the deep hybrid models demonstrate the complementarity between the low-dimensional numeric and high-dimensional imagery data and between the traditional demand modeling and recent deep learning. It generalizes the latent classes and variables in classical hybrid demand models to a latent space, and leverages the computational power of deep learning for imagery while retaining the economic interpretability on the microeconomics foundation.

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Understanding the effects of travel demand management on metro commuters’ behavioural loyalty: a hybrid choice modelling approach

Cited by 16 publications

References 74 publications

Do Americans seek pleasure while Chinese care about others’ approval? Examining stereotypical utilities positioning in brand choice contexts

Do Americans seek pleasure while Chinese care about others’ approval? Examining stereotypical utilities positioning in brand choice contexts

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

Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?

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