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Day-long origin-destination (OD) demand estimation for transportation forecasting is advantageous in terms of accuracy and reliability because it is not affected by hourly variations in the OD distribution. In this paper, we propose a method to estimate the time coefficient of day-long OD demand to estimate hourly OD demand and to predict hourly traffic for urban transportation planning of a large-scale road network that lacks discrete-time rich traffic data. The model proposed estimates the time coefficients from observed link flows given a proven day-long OD demand based on a bilevel formulation of the generalized least square and semidynamic traffic assignment (OD-modification approach). The OD-modification approach is formulated as a static user-equilibrium assignment with elastic demand, based on the residual demand at the end of each period. Our model does not require setting many parameters regarding the OD demand matrices and the discrete-time dynamic traffic assignments. Applying the model to large-scale road network demonstrates that it efficiently improves estimation accuracy because the 24-hour time coefficients of survey data are slightly biased and may be modified properly. In addition, the methods that partially relax the assumption of OD-modification approach and transform the estimated demand into demand based on departure time are examined.

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.