Estimating Mode of Transport in Daily Mobility during the COVID-19 Pandemic Using a Multinomial Logistic Regression Model

Mazanec, Jaroslav; Harantová, Veronika; Štefancová, Vladimíra; Foltýnová, Hana Brůhová

doi:10.3390/ijerph20054600

Cited by 7 publications

(3 citation statements)

References 68 publications

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“…Concerning the sustainability of supply chain firms, as expressed by the decision of customer firms to maintain the usage of 3PL services, our findings are aligned with the studies of Ji et al [54] and Nila and Roy [55], which refer to logistic services provider selection based on sustainability characteristics. Moreover, our research comes in terms of the increasing need for supply chain firms to predict their customers' demand for their services [56], as well as the prioritization and promotion of their sustainability through long-term relationships with their customers [57] and operational efficiency [58].…”

Section: Discussionmentioning

confidence: 99%

Determining Factors for Supply Chain Services Provider Selection and Long-Term Relationship Maintenance: Evidence from Greece

Sakas,

Giannakopoulos,

Kanellos

et al. 2023

Logistics

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Background: Due to increased globalization and its subsequent rise in competitiveness, the role of supply chain services (3PL) in managing logistics, reducing operational and non-operational costs, and managing customer and supplier relationships, have become of utmost importance. Customer-centric production has led to the development of a close relationship between production processes. Amidst all this, the demand for logistic services has dramatically increased, thus putting more pressure on firms for enhanced operational results, and leading to the outsourcing of their internal and external logistic activities. On the other hand, supply chain firms that provide 3PL services seek to enhance their sustainability and predict their customers’ demand. Methods: The authors collected quantitative data from 81 firms that operate in various industrial sectors in Greece. A questionnaire was sent for completion, in which firms could rate and evaluate various aspects that were discerned as important for deciding to cooperate with a 3PL service provider and maintain this cooperation in the long run. To extract the required outcomes, statistical analyses like categorical regression (CATREG) and MANOVA were utilized. Results: The demand for 3PL services was affected by 3PL service providers’ operational performance based on accuracy, reputation, and IT capabilities, while the customer firms’ intention for maintaining cooperation with 3PL service providers was affected by their reliability level, improved service efficiency, and trustworthiness. Conclusions: 3PL service providers should seek to improve the reputation, IT infrastructure, and accuracy of their firm's operations to have a continuous demand for their services. Apart from that, 3PL service providers to maintain the cooperation with their customers, need to enhance the levels of their services reliability and efficiency, while also creating a bond of trust with their existing customers.

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Section: Discussionmentioning

confidence: 99%

Determining Factors for Supply Chain Services Provider Selection and Long-Term Relationship Maintenance: Evidence from Greece

Sakas,

Giannakopoulos,

Kanellos

et al. 2023

Logistics

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“…The predictive model will employ the Logistic Regression algorithm to estimate the mobility gradient of hospitals in 2021, categorizing them into three classes: LOW, MEDIUM, and HIGH mobility, based on the distance patients travel to reach the hospital. This choice is supported by studies demonstrating the effectiveness of Logistic Regression in predicting mobility behaviors and health risks, such as the analysis of travel behavior during the COVID-19 pandemic [19] and the assessment of cardiovascular risk [32], highlighting its applicability in complex healthcare contexts.…”

Section: Prediction Modelmentioning

confidence: 95%

Machine Learning for Evaluating Hospital Mobility: An Italian Case Study

Santamato,

Tricase,

Faccilongo

et al. 2024

Preprint

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This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions of Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, and clinical variables impact patient perceptions of service quality, influencing their healthcare choices. Through the analysis of mobility trends with machine learning, significant differences between regions were uncovered, highlighting the influence of regional context on perceived quality. The integration of SHAP (SHapley Additive exPlanations) values into our analysis provided deeper insights into the logistic regression model, elucidating the specific contribution of each variable to perceived healthcare quality. This incorporation of SHAP values underscores the study's commitment to employing advanced, explainable AI techniques to enhance the interpretability and fairness of healthcare service evaluations. The choice of logistic regression elucidated the impact of specific variables on quality perception, offering essential insights for optimizing healthcare resource distribution and underscoring the importance of data-driven strategies to foster more equitable, efficient, and patient-centred healthcare systems. Contributing to the understanding of perceived quality dynamics within the healthcare context, the research paves the way for further investigations into enhancing accessibility and service quality, leveraging machine learning as a tool for improving healthcare services efficiency in diverse regional settings.

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“…As the transportation system is complex and consists of numerous links, the failure of any link can lead to disruption of the entire transportation system. In the post-COVID-19 era, promoting the recovery of international multimodal transport can be a feasible solution to mitigate the risk of logistics disruption [1,2]. It is crucial to conduct research on the resilience and risk management of multimodal transport logistics in this area [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Resilience Improvement and Risk Management of Multimodal Transport Logistics in the Post–COVID-19 Era: The Case of TIR-Based Sea–Road Multimodal Transport Logistics

2023

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The COVID-19 pandemic has severely impacted international economics and trade, including cargo transportation. As a result, enhancing the resilience of transport and logistics in the post–COVID-19 era has become a general trend. Multimodal transport, with its advantages of speed, large volume and multiple modes, has increasingly gained attention from countries worldwide. However, multimodal transport logistics is a complex and systematic process. Its smooth flow depends not only on the transport itself, but also on the efficient supervision of customs and other government departments at ports. This study employs the theory and method of a super-network to establish a model of multimodal transport logistics, which includes TIR-based sea–road multimodal transport and customs supervision relationships. Structural and resilience-related characteristics of the super-network are analyzed, and performance parameters of the super-network are proposed. A simulation analysis is conducted, and based on the results, countermeasures to improve the resilience and promote risk management of multimodal transport logistics in the post–COVID-19 era are suggested. The findings of this study provide an exploration of more effective ways to ensure the smoothness of multimodal transport logistics and improve system resilience. The study concludes with theoretical and managerial implications.

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Estimating Mode of Transport in Daily Mobility during the COVID-19 Pandemic Using a Multinomial Logistic Regression Model

Cited by 7 publications

References 68 publications

Determining Factors for Supply Chain Services Provider Selection and Long-Term Relationship Maintenance: Evidence from Greece

Determining Factors for Supply Chain Services Provider Selection and Long-Term Relationship Maintenance: Evidence from Greece

Machine Learning for Evaluating Hospital Mobility: An Italian Case Study

Resilience Improvement and Risk Management of Multimodal Transport Logistics in the Post–COVID-19 Era: The Case of TIR-Based Sea–Road Multimodal Transport Logistics

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