A Data-Fusion based Method for Identifying Individual Passenger Travel Dependence on Public Transportation

Hu, Song; Weng, Jiancheng; Zhou, Wei; Lin, Pengfei; Liu, Zhe

doi:10.1109/itsc.2019.8917147

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…car, motorcycle) and are dependent on public transportation (Pettersson & Khan, 2020). People in many different countries and cultural contexts rely on some method of public transport for getting food, clothing, education, shelter, healthcare, sanitation (Hu, Weng, Zhou, Lin, & Liu, 2019), such as transport within metropolitan areas to places of employment (Johnson, Ercolani, & Mackie, 2017). It is thus reasonable to theorize that anxious anticipatory thinking could emerge in people dependent on public transport.…”

Section: Psychological Medicinementioning

confidence: 99%

Investigating the effect of national government physical distancing measures on depression and anxiety during the COVID-19 pandemic through meta-analysis and meta-regression

et al. 2021

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Background COVID-19 physical distancing measures can potentially increase the likelihood of mental disorders. It is unknown whether these measures are associated with depression and anxiety. Objectives To investigate meta-analytic global levels of depression and anxiety during the COVID-19 pandemic and how the implementation of mitigation strategies (i.e. public transportation closures, stay-at-home orders, etc.) impacted such disorders. Data sources PubMed, MEDLINE, Web of Science, BIOSIS Citation Index, Current Content Connect, PsycINFO, CINAHL, medRxiv, and PsyArXiv databases for depression and anxiety prevalences; Oxford Covid-19 Government Response Tracker for the containment and closure policies indexes; Global Burden of Disease Study for previous levels of depression and anxiety. Study eligibility criteria Original studies conducted during COVID-19 pandemic, which assessed categorical depression and anxiety, using PHQ-9 and GAD-7 scales (cutoff ⩾10). Participants and interventions General population, healthcare providers, students, and patients. National physical distancing measures. Study appraisal and synthesis methods Meta-analysis and meta-regression. Results In total, 226 638 individuals were assessed within the 60 included studies. Global prevalence of both depression and anxiety during the COVID-19 pandemic was 24.0% and 21.3%, respectively. There were differences in the prevalence of both anxiety and depression reported across regions and countries. Asia (17.6% and 17.9%), and China (16.2% and 15.5%) especially, had the lowest prevalence of both disorders. Regarding the impact of mitigation strategies on mental health, only public transportation closures increased the prevalence of anxiety, especially in Europe. Limitations Country-level data on physical distancing measures and previous anxiety/depression may not necessarily reflect local (i.e. city-specific) contexts. Conclusions and implications of key findings Mental health concerns should not be viewed only as a delayed consequence of the COVID-19 pandemic, but also as a concurrent epidemic. Our data provide support for policy-makers to consider real-time enhanced mental health services, and increase initiatives to foster positive mental health outcomes.

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Section: Psychological Medicinementioning

confidence: 99%

Investigating the effect of national government physical distancing measures on depression and anxiety during the COVID-19 pandemic through meta-analysis and meta-regression

et al. 2021

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“…Depending on the setting (i.e., rural, small to large metropolitan areas), there is a significant number of individuals who do not have an alternative way of transport (i.e., car, motorcycle) and are dependent on public transportation. 102 People in many different countries and cultural contexts rely on some method of public transport for getting food, clothing, education, shelter, healthcare, sanitation, 103 such as transport within metropolitan areas to places of employment. 104 It is thus reasonable to theorize that anxious anticipatory thinking could emerge in people dependent on public transport.…”

Section: Discussionmentioning

confidence: 99%

Investigating the effect of national government physical distancing measures on depression and anxiety during the COVID-19 pandemic through meta-analysis and meta-regression

Castaldelli‐Maia

Marziali

et al. 2020

Preprint

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Background: COVID-19 physical distancing measures can potentially increase the likelihood of mental disorders. It is unknown whether these measures are associated with depression and anxiety. Objectives: To investigate meta-analytic global levels of depression and anxiety during the COVID-19 pandemic and how implementation of mitigation strategies (i.e. public transportation closures, stay-at-home orders, etc.) impacted such disorders. Data sources: Pubmed, MEDLINE, Web of Science, BIOSIS Citation Index, Current Content Connect, PsycINFO, CINAHL, medRxiv, and PsyArXiv databases for depression and anxiety prevalences; Oxford Covid-19 Government Response Tracker for the containment and closure policies indexes; Global Burden of Disease Study for previous levels of depression and anxiety. Study eligibility criteria: Original studies conducted during COVID-19 pandemic, which assessed categorical depression and anxiety, using PHQ-9 and GAD-7 scales (cutoff ≥ 10). Participants and interventions: General population, healthcare providers, students, and patients. National physical distancing measures. Study appraisal and synthesis methods: Meta-analysis and meta-regresssion. Results: In total, 226,638 individuals were assessed within the 60 included studies. Global prevalence of both depression and anxiety during COVID-19 pandemic were 24.0% and 21.3%, respectively. There was a wide variance in the prevalence of both anxiety and depression reported in different regions of the world and countries. Asia, and China particularly, had the lowest prevalence of both disorders. Regarding the impact of mitigation strategies on mental health, only public transportation closures increased anxiety prevalence. Limitations: Country-level data on physical distancing measures and previous anxiety/depression may not necessarily reflect local (i.e., city-specific) contexts. Conclusions and implications of key findings: Mental health concerns should not be viewed only as a delayed consequence of the COVID-19 pandemic, but also as a concurrent epidemic. Our data provides support for policy-makers to consider real-time enhanced mental health services, and increase initiatives to foster positive mental health outcomes.

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“…Taking residents who live in Beijing with an advanced PT system as the main research objective in this paper, the three indicators of the ratio of PT travel days (TD), the ratio of PT travel number (TN) and the ratio of PT roundtrip were considered representative indicators regarding RDPT [5].…”

Section: A Traveler Group Classificationmentioning

confidence: 99%

“…Furthermore, due to the diversity of urban traffic modes and the spatial imbalance of infrastructure construction, residents' mode selection intention and travel behavior are complex, based on heterogeneity characteristics and are difficult to predict. Thus, travelers with different individual attributes have different PT dependence levels, which are influenced by internal and external factors [5]. To better understand travel choice decision-making and improve PT service quality, it is essential to explore the causal relationship between the multidimensional influencing factors and residents' dependence on PT (RDPT).…”

Section: Introductionmentioning

confidence: 99%

Extending the Theory of Planned Behavior to Explore the Influence of Residents’ Dependence on Public Transport

Chau

Weng

et al. 2021

IEEE Access

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The accurate depiction and understanding of the influence mechanism of residents' dependence on public transport (RDPT) is an important foundation for increasing the proportion of green trips and alleviating urban traffic congestion. To explore the influence mechanism of RDPT, this paper extends the theory of planned behavior (TPB) by introducing objective factors of attributes, environment, and travel characteristics. The agglomerative nesting clustering algorithm and multiple structural equation modeling (SEM) are then developed to identify the RDPT levels and analyze the influence relationships between the integrated influencing factors and RDPT based on travel survey data. The results indicate that the objective variables have indirect impacts on RDPT by influencing psychological variable attitudes, subjective norms and perceived control and travel intention. The residents' self-selection (RSS) effects of different clusters are all detected under normal conditions, and the environment on RDPT is still significant after controlling for this effect. The findings reflect that the influence mechanisms of the three SEMs for different clusters are distinct, unlike those of the baseline model, and distinctive observation variables have dissimilar explanatory abilities for the travel intention of different residents. Therefore, some beneficial policy implications are proposed for residents, especially those who retain low and relatively low public transport dependence levels, to increase public transport usage while reduce the car usage based on the significant findings.

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A Data-Fusion based Method for Identifying Individual Passenger Travel Dependence on Public Transportation

Cited by 5 publications

References 11 publications

Investigating the effect of national government physical distancing measures on depression and anxiety during the COVID-19 pandemic through meta-analysis and meta-regression

Investigating the effect of national government physical distancing measures on depression and anxiety during the COVID-19 pandemic through meta-analysis and meta-regression

Investigating the effect of national government physical distancing measures on depression and anxiety during the COVID-19 pandemic through meta-analysis and meta-regression

Extending the Theory of Planned Behavior to Explore the Influence of Residents’ Dependence on Public Transport

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