The ageing population is accelerating around the world. There exists the increasing need for transportation policies and strategies focusing on the mobility of the elderly. This paper analyzed mobility characteristics of the elderly using the 2010 Household Travel Survey data in Seoul Metropolitan Area. In particular, ordered probit models for trip frequencies are developed to identify key factors influencing elderly’s travel, considering socio-demographic as well as land use variables. Then, the model results were compared between the elderly and non-elderly groups, and among the distinct three elderly groups (65-69, 70-74, and 75+). Overall, we found distinctively different socio-demographic variables such as household size, car ownership, age, gender, employment status, and occupation affecting total trips, work trips, and non-work trips between the elderly and non-elderly groups. In addition, land use variables showed significant impacts on elderly’s travel. Among them, social welfare facilities, sports facilities, and medical facilities were positively related to non-work trips of the elderly.

In recent years, with the Chinese government’s emphasis on the development of the cold chain logistics market for fresh agricultural products, the rapid development of agricultural cold chain logistics has been promoted in many aspects. However, in the circulation of fresh agricultural products, there is still a serious problem of “broken chain” leading to a high corrosion rate. Therefore, this research has analyzed the uncertain factors affecting the cold chain distribution system based on fault tree model, and then transform it into Bayesian network to evaluate the reliability of the cold chain distribution system for fresh agricultural product, and identify the key factors affecting the reliability of the cold chain distribution system through calculated probability importance of each node. Then we have constructed nonlinear equations with the limit of the cost, based on reliability allocation method to improve the system reliability. Numerical examples are given to validate the proposed models. The optimization result shows that higher reliability value assigned to the factors with high probability importance is more conducive to the improvement of system reliability.

The spatial mismatch between warehouse locations and urban freight demand mainly driven by logistics sprawl can have negative environmental impacts, due to the increase in average trucking distances. This study investigated the spatial dimension of warehouse rent determinants identifying the regional specifics of supply and demand of warehouse facilities and services. Based on the case of the Seoul Metropolitan Area in South Korea, spatial autoregressive regression (SAR) and mixed geographically weighted regression (MGWR) models were developed to explain the spatial stationary and non-stationary relationship between warehouse rent and the explanatory variables, including the transactional characteristics of the rental contracts, physical characteristics of the buildings, location factors, and various warehousing services. The MGWR results identified the distance to the nearest highway interchange, repackaging service, and built-in ramps as globally fixed variables and contract floor space, total building floor space, building age, and land price as locally varying variables. The results of this study allowed us to provide meaningful insights into the sustainable development of urban logistics facilities through a better understanding of the interaction between logistics activities, transportation infrastructure, and land use.

Purpose: The purpose of this study is to quantify port efficiency assessment indicators to analyze the impact of COVID-19 on Chinese One Belt One Road (OBOR) ports.Design/methodology/approach: This study utilized a grey prediction model GM(1,1) to forecast five relevant indicators for each of the 17 OBOR ports both with and without COVID-19 background conditions. Additionally, the data envelopment analysis (DEA) efficiency assessment approach was used to analyze the impact of COVID-19 on port efficiency.Findings: The results indicate that cargo and container throughput growth rates during the COVID-19 pandemic are reduced by 1.7 and 2.1%, respectively. There was also a noticeable reduction in technological efficiency (TE) as well as pure technological efficiency (PTE), while scale efficiency (SE) remained largely unaffected. Furthermore, the dynamic efficiency MI was mainly negatively impacted by changes in overall efficiency change (EFFCH), where pure efficiency change (PECH) less than one contributed significantly towards overall regression of port efficiencies during this period.Originality/value: This paper is unique in its use of a combination of the grey prediction model and DEA efficiency assessment to quantify changes in important indicators during pandemic periods. This approach not only provides a quantitative understanding of the impact on port-level efficiency through numerical quantification but also offers readers an intuitive understanding.

The advancement of Information Technology (IT) has efficiently and systematically changed most of all industry activities. With the increased implementation of IT in logistics, the implementation of Radio Frequency Identification (RFID) in logistics could enable the improvement of supply Chain Capacity (SC) and play an important role in the procurement of resources, production, transport, loading and unloading, warehouse, etc., consisting of Supply Chain Management (SCM). This study identifies how composition factors have an effect on SCM efficiency and the intention of implementing RFID according to the implementation of RFID into the food-beverage industry. In addition, this study attempts to reveal the contributing factors affecting the intention of implementing RFID into food-beverage companies by using the intention determination model for implementing RFID and analyzing data characterizing those companies. Based on nine hypotheses produced according to the implementation of RFID into the food-beverage industry, Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used for achieving the goals of this study. As the analysis results of this study indicate, the implementation effects of RFID into SCM showed not a unidirectional influence relationship but a multidirectional and complicated influence relationship.

This article proposes a mathematical model for integrating terminal operation strategies with shipment scheduling. We are motivated by findings from the literature on the integrated design of logistic systems. The objective of this research is to efficiently utilize the existing hub terminals and transport network by considering the minimization of costs related to terminal congestion, transport, and carbon dioxide (CO₂) emissions. Cooperative behaviors of terminal managers and fleet assignment managers are modeled in a bi-level problem framework. The total cost includes a processing cost and transport cost, and CO₂ related to the assigned fleets. We introduce a terminal cost function to capture the relationship between unit processing cost with respect to hub delay, which allows us to find the minimum cost path and efficiently distribute shipments to hub terminals. The case study shows that the collaborative logistics outperforms a single routing strategy and capacity expansions in minimization of both total cost and CO₂ emissions.