The purpose of this study is to identify the characteristics of freight mode choices made by shippers and carriers with the introduction of a new freight transport system. We set an area in which actual freight transport takes place as the analysis scope and performed a survey of the shippers and carriers that transport containers to identify their stated preference (SP) regarding the new freight mode. The SP survey was carried out through an experimental design and this study considered the three factors of transport time, transport cost, and service level. This study compared and analyzed the models by distance using an individual behavior model. The results of estimating the model showed that the explanatory power of the model classified by distance and the individual parameters have statistical significance. The hit ratio was also high, which confirms that the model was estimated properly. In addition, the range of elasticity and the value of travel time analyzed using the model were evaluated to be appropriate compared to previous studies. The findings of the elasticity analysis show that strategies for reducing the transport cost are effective to increase the demand for the new transport mode. The value of travel time of freight transport was found to be higher than the current value generally applied in Korea. Considering that the value of travel time currently used is based on road freight transport, further research is required to apply a new value of travel time that reflects the characteristics of the new transport mode in the future.

The shift in consumer behaviour from in‐person to online shopping has led to an increase in parcel delivery volume and its associated negative impacts, such as CO ₂ emissions in cities. With the emergence of drone‐delivery technologies, the authors analyzed a joint delivery method using drones and trucks, which is an emerging alternative solution for last‐mile delivery in terms of CO ₂ emissions and delivery time. The analysis verified the highest possible level of reduction in CO ₂ emissions for the simultaneous and strategic operation of drones and trucks compared to diesel‐ or electric‐only truck operations. Moreover, this approach leads to reduced delivery times. A sensitivity analysis was performed to optimize the delivery‐drone flight performance in a drone‐and‐truck delivery strategy. It was found that a 1.5‐km drone flight performance was sufficient when considering the reasonable assumptions adopted in this study. Furthermore, based on the analysis results, drone‐and‐truck cooperative delivery strategies may not provide a significant advantage in terms of CO ₂ emissions compared with alternative transportation modes, such as fuel‐cell trucks with sufficiently low emissions. It has been empirically verified that changes in CO ₂ emissions are proportional to the number of clusters. However, there is a risk of local optima due to microscopic fluctuations among neighbouring cluster numbers, which occurs during the search for the optimal number of clusters.

Last-mile deliveries using drones and robots have recently emerged as alternatives to deliveries using conventional trucks. Because of the newness of the topic, there has been no synthesized summary of the acceptance of the emerging delivery modes, although the importance of the topic has increased with the growth of home deliveries. To fill the knowledge gap, this research systematically reviewed the literature on the willingness to accept drone and robot deliveries using the two largest publication databases: Web of Science and SCOPUS. A total of 46 academic studies were reviewed with respect to their trends, study scope (i.e., type of mode, type of products, modeling framework, and background theories), and factors that positively and negatively affect the willingness. Based on the review, knowledge gaps and the corresponding future research directions were derived: (a) robot-related investigations have been understudied compared to drones; (b) research from the shipper perspective is necessary; (c) transportation and logistics planning-related factors (e.g., socio-economic factors, trip and shipment characteristics) should be investigated; (d) an investigation of users’ behaviors based on actual usage (rather than relying solely on hypothetical scenarios) is necessary; (e) regulatory aspects should be taken into account; and (f) the heterogeneity of products needs to be taken into consideration.

In this study, we have designed a new intermodal automated container transport system (ACTS) via a roll-on/roll-off method that connects a logistics hub between a port and inland. Further, we have presented the development of a simulation model and the results of the simulator development. A simulation program was developed using a general-purpose simulation system (GPSS)/H to predict the processing capacity of the ACTS system and derive the optimum input equipment size and operation method. Additionally, the PROOF5 program was used for the visual verification of the system algorithm and for users to intuitively view and monitor events occurring in the terminal. The simulation program developed in this study can be used in the future as a tool to compare and analyze the efficiency of the existing road-transport-based system and the proposed ACTS system when the latter is implemented in a specific region.

This paper examines behavioral change patterns of food consumption through three channels—dine-in at restaurant, food delivery, and food take-out—during and after the recent COVID-19 pandemic. The unique feature of the behaviors during the COVID-19 pandemic that set them apart from those during previous pandemics was the rise of online shopping. Thus, it is important to understand the new consumer behaviors for the three food consumption channels over time (before, during, and after the pandemic), for example, how they were affected by the pandemic, what dominant change patterns were, and how the intertwined relationship between the channels evolved. This study develops a latent class model using survey data collected by the Korea Rural Economic Institute in 2021 to jointly investigate the behavioral change patterns of dine-in at restaurant, food delivery, and food take-out. The results showed that there existed six latent groups, namely, 1) “status quo maintainers,” 2) “dine-in minimizers,” 3) “mild online adopters,” 4) “temporary shifters,” 5) “food expenditure cutters,” and 6) “strong online adopters.” The results highlighted that the behavioral change pattern varied by the persistence of the changes and the consumption channel choice. The socio-economic and demographic characteristics of each group were also discussed.