Non-commuting intentions during COVID-19 in Nanjing, China: A hybrid latent class modeling approach

Ma, Xinwei; Zhang, Shuai; Zhu, Minqing; Wu, Tom; He, Ming; Cui, Hongjun

doi:10.1016/j.cities.2023.104341

Cited by 4 publications

(4 citation statements)

References 107 publications

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“…According to data from the Turkish Statistical Institute (TSI) in 2023 [34], Istanbul boasts a population of 15,907,951 and witnesses approximately 33 million daily trips. Being the economic hub of Turkey, Istanbul faced significant impacts from the COVID-19 pandemic, aligning with similar studies conducted during this period [7,33,[35][36][37]. Consequently, an online survey was employed as a data collection method to comply with the pandemic-related measures, mirroring the approach taken by numerous other studies.…”

Section: Surveymentioning

confidence: 73%

“…LCA can be employed to categorise households into latent groups with similar responses and travel patterns, aiming to examine the variations in the impacts of COVID-19 countermeasures and travel behaviour within the community [39]. It is worth noting that the choice of transportation mode is not solely determined by individual characteristics but can also be influenced by psychological factors [33]. Therefore, the preference statements can provide valuable insights for model identification [41].…”

Section: Methodsmentioning

confidence: 99%

“…The research revealed that attitudes, prior experience with working from home, and specific enabling or constraining factors such as job type, employer support and household size all played a role in influencing the frequency of remote work. Ma et al (2023) conducted an analysis of non-commuting intentions during the COVID-19 pandemic using online survey data from Nanjing [33]. They employed a hybrid LCM approach.…”

Section: Literature Reviewmentioning

confidence: 99%

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The Effects of the COVID-19 Pandemic on the Modal Shifting Utilising a Latent Class Choice Model with Covariates

ERGIN

2024

PROMTT

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The COVID-19 pandemic has posed significant challenges to global public health organisations and governments, leading to countermeasures like hand sanitizer availability, social distancing, and mandatory face mask wearing, which have disrupted the public transportation sector and impacted the virus spread. Anticipating the effects of circumstances like a pandemic on mobility is essential for operators and managers of public transportation systems to effectively and safely manage the system. In this study, the measures taken during the pandemic, such as those mentioned above, were considered as indicators in the latent class model (LCM) for modal shifting. The model incorporates sociodemographic variables as covariates to understand their impact on modal shifting from public transport to private cars. An online survey with 53,973 valid responses was conducted in Istanbul, Turkiye. As a result of the LCM with covariates, two-latent-class model, the best fit among models ranging from two to six latent classes, emerged. Class-1 participants show increased sensitivity to the pandemic, shifting to private mode, while Class-2 participants are less concerned and tend to maintain their existing mode. The model suggests using LCM with covariates to estimate the modal shift from public transportation to private cars in any given situation.

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Section: Surveymentioning

confidence: 73%

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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The Effects of the COVID-19 Pandemic on the Modal Shifting Utilising a Latent Class Choice Model with Covariates

ERGIN

2024

PROMTT

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“…They preferred life-experiential, artificial elements such as bird nesting, artificial streetscapes, gym equipment, and bulletin boards. Their apathetic response towards UGS changes reflects a reduced sensitivity to alterations, as Ma X et al suggested, positing that such individuals are usually unwilling to follow the rules [76], and even with enforced quarantine policies, their daily habits remain unaffected. Pipitone J M et al argue that this is attributed to a lower sense of belonging, causing lower-income groups to view UGS merely as infrastructure rather than a necessity, resulting in less prominent changes in their UGS preferences [77].…”

Section: Green Space Preferences Across Diverse Social Groups Amidst ...mentioning

confidence: 99%

Resident Preferences for Urban Green Spaces in Response to Pandemic Public Health Emergency: A Case Study of Shanghai

Xiong,

Xu,

Zhao

2024

Sustainability

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The COVID-19 pandemic represents a quintessential public health crisis, profoundly impacting the utilization patterns of urban green spaces through stringent quarantine and lockdown measures. However, existing research inadequately addresses specific concerns regarding future urban green spaces and tends to oversimplify population divisions. This study delves into the needs and preferences of Shanghai residents affected by the pandemic and quarantine measures, focusing on various aspects such as specific types of green spaces, facilities, landscape elements, and landscape and spatial types. Multifactorial population clustering was also performed. This study delineates the following conclusions: (1) It is imperative to afford residents access to green spaces at least once a week, even during quarantine periods. (2) Residents exhibited a preference for accessible green spaces equipped with essential amenities, favoring unobstructed vistas and plant-centric ecological landscapes during the pandemic. Additionally, there is a notable preference for private green spaces among residents. (3) Post-pandemic, the “affluent” group displays a heightened overall demand for green spaces, the “middle-class” group shows a conspicuous inclination towards specific green space landscape elements, while the “low-income” group consistently exhibits a low preference for green spaces during and after the pandemic. This study underscores the necessity of developing human-centric green spaces to promote equity and resilience in the face of future emergencies, rooted in residents’ preferences amidst public health crises.

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Commuting Behavior Changes at Different Stages of Localized COVID-19 Outbreak: Evidence from Nanjing, China

Chen,

Wu,

Yin

et al. 2024

Systems

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Commuting behaviors have been changed by the COVID-19 pandemic. To investigate the impacts at different stages of sudden and localized COVID-19 outbreak, this paper carries out an online survey to obtain data, targeting the residents in Nanjing China, where there had been COVID-19 outbreaks and proposes a sequential analysis method to calculate the complexity of commuting behavior changes. The Tobit model is used to explore the factors that influence the complexity of commuting behavior changes. Results show that commuters using public transportation drop significantly when sudden outbreaks occur, with 43.5% of them switching to private cars or working from home. The number of residents working from home increases by 14 times. While an outbreak gradually subsides, commuting modes tend to recover, but does not immediately return to the state before the outbreak. Regression model results indicate that commuters aged 40–60 tend to maintain their commuting habits, while younger workers are more flexible on their commuting options. Middle-income commuters, or those living in low-risk areas or near a subway within 800 m prefer to change commuting modes, opting for what they perceive to be safer ways to commute. For commuters living in medium- or high-risk areas and those who are living with people who have non-green health codes, they tend to adjust their commuting modes in real time based on the color change in the health codes and the risk level of the areas they live. The research findings contribute to our understanding of commuting behaviors and targeted management needs during local outbreaks, and can help the government formulate a comprehensive and more effective pandemic prevention policy.

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Non-commuting intentions during COVID-19 in Nanjing, China: A hybrid latent class modeling approach

Cited by 4 publications

References 107 publications

The Effects of the COVID-19 Pandemic on the Modal Shifting Utilising a Latent Class Choice Model with Covariates

The Effects of the COVID-19 Pandemic on the Modal Shifting Utilising a Latent Class Choice Model with Covariates

Resident Preferences for Urban Green Spaces in Response to Pandemic Public Health Emergency: A Case Study of Shanghai

Commuting Behavior Changes at Different Stages of Localized COVID-19 Outbreak: Evidence from Nanjing, China

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