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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.