No abstract

No abstract

The commodity distribution model proposed in this paper is developed in such a way that the movement of commodities is explained as an outcome of their flow through several freight agents in a supply chain. As commodity flow is fundamentally determined by demand, the proposed model was developed from a discrete choice model that considered the individual behavior of a customer to decide the suppliers from which to purchase and the amount of commodity to acquire from each of them. The model not only takes into account the interplay between shipper and customer in a supply chain but also captures the spatial interactions among alternatives and among customers, because spatial effects generally affect customer preference. In this study, several model specifications were developed and compared, with and without incorporating spatial interactions. The empirical results of the model, which were applied to analyze the urban commodity distribution in the Tokyo metropolitan area, indicate that integrating both spatial interactions among alternatives and among customers statistically improves the model's performance.

The commodity distribution model proposed in this paper is developed in such a way that the movement of commodities is explained as an outcome of its flow through several freight agents in a supply chain. As commodity flow is fundamentally determined by the demand, the proposed model is developed using a discrete choice model considering the individual behavior of a customer to decide the suppliers from which to purchase and the amount of commodity acquired from each of the suppliers. The model not only takes into account the interplay between shipper and customer in a supply chain but also captures the spatial interactions among alternatives and among customers since spatial effects generally impact customer preference. In this study, we developed and compared several model specifications, that is, with and without incorporating spatial interactions. The empirical results of the model applied to analyze the urban commodity distribution in the Tokyo Metropolitan Area indicate that integrating both spatial interactions among alternatives and among customers statistically improves the model performance.

Many commuters find themselves stranded during natural disasters like typhoons. In the Tokai region in Japan, many road sections become heavily congested during typhoons, with some commuters reporting homebound trips taking more than four times longer than usual because of road flooding at several locations. Although large typhoons are considered extreme events (in terms of magnitude), they occur frequently (i.e., several times per year), substantiating the need for better preparedness. Nonetheless, it is impossible to predict exactly which roads are going to be flooded during a typhoon. As a result, in this study, a stochastic modeling approach was used that assigns a failure probability to each road segment based on climate model outputs for the region. Using this stochastic model, the travel time reliability between any given origin–destination pair can be determined. By applying this model to the road network of the Tokai region, two major measures were identified that could be implemented to reduce severe congestion during a typhoon. First, targeted infrastructure management measures can be implemented to strengthen heavily used roads, thus reducing the failure probability of major roads. Second, travel demand management measures can be implemented, such as asking commuters to leave their workplace or school one or two hours after their normal departure time. Overall, it was found that strengthening heavily used roads has a bigger impact in relieving congestion than delaying departure time, but that combining both strategies achieves the best results.

Disaster relief operations are complex and can benefit greatly from a high level of preparedness. One of the main sources of complexity in disaster operations is uncertainty. An analysis of a disaster relief operation in Aichi Prefecture, Japan, preparing for the periodic Tokai–Tonankai earthquake is presented. In this study, the possible degradation of the road network is considered by including a stochastic element to represent the possibility of link failure dependent on earthquake intensity in each subregion. Also, a strategy to fix the roads is integrated into the analysis to evaluate its impact on the disaster logistics operations. The analysis is performed with the current road network of Aichi Prefecture. The results suggest the best preparation of resources and identify vulnerable destinations that are most likely to be cut off by the disaster. Also analyzed is a relocation of hubs that can reduce the total response time and take into account the possibility that some links will be destroyed. This analysis is important to help planners to evaluate their strategies, to identify vulnerable locations, and to be able to prepare in advance the best methods to deal with the uncertainty of road failure.

No abstract

This study estimates origin–destination (O-D) matrices of light and heavy trucks on the basis of the flow of commodities in the Tokyo metropolitan area. The truck O-D matrix is generally determined by either vehicle trip-based or commodity-based approaches, although the former cannot distinguish between loaded and empty trips and does not characterize the shipments. Three major concepts are proposed in this study. First, the truck trip O-D is estimated on the basis of the commodity approach because it can utilize the characteristics of the shipments. Second, the main contribution of the model is its ability to estimate both loaded and empty trips by modeling the truck movements as round trips and trip chains. The O-D of truck movement, particularly the movement of loaded trips in round trips or zero-order trip chains, is similar to that of the commodity flows. However, both movements have relatively different O-D when the loaded trips travel from one origin to many destinations or are part of an nth-order trip chain. Finally, the trip chain is modeled on the basis of characteristics such as average payload, adjacent zones, and the commodity O-D providing the most attractive zones traveled by trucks. The performance of the model is demonstrated by using the mean square error between the estimated and observed truck O-D matrices. The model concept is then applied to lightweight products obtained from the food industry. The proposed concept enhances the trip chain behavior and provides better results than the model without trip chain behavior.