Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

Xu, Ningzhe; Nie, Qifan; Li, Jun; Jones, Steven

doi:10.1016/j.tbs.2023.100584

Cited by 9 publications

(3 citation statements)

References 83 publications

(137 reference statements)

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“…The correlation between infrastructure and active travel has been studied using regression analysis Clark et al 2014 and mixed-effect models (Haybatollahi, 2015;Siiba, 2020) to account for the clustering of data within neighborhoods. Decision tree analysis has been utilized in several studies to identify the most relevant predictors of active travel behavior, such as socio-demographic characteristics, safety concerns, and access to active travel infrastructure (Liu et al, 2021;Xu et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

Demographic Analysis of Active Transport Mode Users in Urban Context

Jaber,

Hamadneh

2024

Period. Polytech. Transp. Eng.

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Active transportation, such as walking, cycling, and micro-mobility modes, has received a lot of attention in recent years due to its potential benefits to urban residents, such as less traffic, better air quality, more opportunities to get exercise, and an overall higher quality of life. In this study, we used Classification and Regression Trees (CART) to compare and contrast three mobility options: shared micro-mobility, private micro-mobility, and walking. We surveyed 219 people living in Budapest, Hungary, to learn more about their travel habits and investigate the demographic elements that influence people's mode choice, such as age, gender, ownership of micro-mobility vehicles, education, job, and income. Results showed that ownership of personal micro-mobility vehicles, and age as important predictors of active travel mode choice. Men seem to consider cost and weather conditions when choosing shared micromobility modes, while women value safety and weather conditions. Our findings can guide policy decisions and urban planning initiatives by identifying the most significant predictors of mode choice and evaluating the possible benefits and drawbacks of each mode.

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Section: Literature Reviewmentioning

confidence: 99%

Demographic Analysis of Active Transport Mode Users in Urban Context

Jaber,

Hamadneh

2024

Period. Polytech. Transp. Eng.

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“…Shared mobility is a broad concept including many forms such as "carsharing", "ridesharing", "carpooling", "vanpooling", "on-demand ride services", "ride-hailing", "transportation network companies (TNCs)", and "ride-splitting" (Chen et Xu et al 2023). These concepts sometimes are used interchangeably and seem to be overlapping.…”

Section: Introductionmentioning

confidence: 99%

Investigating the continuous usage intention of app-based ride-sourcing services: An Extended Expectation–Confirmation Model

Elnadi

Gheith

2023

Preprint

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Despite the existence of several studies that focus on ride-sourcing services, most of them have concentrated on exploring users' pre-usage behaviour. This is one of the earliest studies that focused on exploring users’ post-usage behaviour. This paper aims to identify the significant factors that may affect existing consumers’ intentions to continue using of app-based ride-sourcing services in Egypt. Three robust models, namely the Expectation-Confirmation Model (ECM), the Technology Acceptance Model (TAM), and the Technology Readiness Model (TRM) were integrated and used as the theoretical framework for this study to accomplish this goal. An online questionnaire was used to collect the empirical data needed to test the hypotheses. The partial least squares structural equation modelling (PLS-SEM) technique has been used to evaluate the postulated relationships using data from 381 respondents. The results demonstrated the essential role that optimism, innovativeness, discomfort, and insecurity play in shaping users’ perceptions of ride-sourcing apps’ usefulness. The findings also showed that perceived ease of use was determined only by innovativeness and discomfort. Additionally, the findings confirmed that the continuance usage intention of ride-sourcing apps is significantly associated with users’ satisfaction level, perceived usefulness, and perceived ease of use. The results of this study will aid service providers in comprehending the variables influencing users' decisions to continue using ride-sourcing apps. Therefore, service providers can discover how to retain and satisfy their current users.

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“…The correlation between infrastructure and active travel has been studied using regression analysis [5] and mixed-effect models [6], [7] to account for the clustering of data within neighborhoods. Decision tree analysis has been utilized in several studies to identify the most relevant predictors of active travel behavior, such as socio-demographic characteristics, safety concerns, and access to active travel infrastructure [8], [9].…”

mentioning

confidence: 99%

Demographic Analysis of Active Travel Users in Urban Context

Jaber,

Hamadneh

2023

Preprint

View full text Add to dashboard Cite

Active transportation, such as walking, cycling, and micro-mobility modes, has received a lot of attention in recent years due to its potential benefits to urban residents, such as less traffic, better air quality, more opportunities to get exercise, and an overall higher quality of life. In this study, we used Classification and Regression Trees (CART) to compare and contrast three mobility options: shared micro-mobility, individual micro-mobility, and walking. We surveyed 219 people living in Budapest, Hungary, to learn more about their travel habits and investigate the demographic elements that influence people's mode choice, such as age, gender, ownership of micro-mobility modes, education, job, and income. Results showed that ownership of personal micro-mobility modes, and age as important predictors of active travel mode choice. Males seem to prioritize cost and weather conditions when choosing shared micromobility modes, while females value safety and weather conditions. Our findings can guide policy decisions and urban planning initiatives by identifying the most significant predictors of mode choice and evaluating the possible benefits and drawbacks of each mode.

show abstract

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

Cited by 9 publications

References 83 publications

Demographic Analysis of Active Transport Mode Users in Urban Context

Demographic Analysis of Active Transport Mode Users in Urban Context

Investigating the continuous usage intention of app-based ride-sourcing services: An Extended Expectation–Confirmation Model

Demographic Analysis of Active Travel Users in Urban Context

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