Transport resilience is an important area of research in the global effort to adapt to climate change. This paper introduces and applies a stochastic modeling methodology to assess the impact of multihazard events. Most cities are exposed to multiple types of extreme events, sometimes simultaneously, and focusing on single events may lead to inadequate design recommendations. By assigning failure probabilities of road segments and by estimating road failure through Monte Carlo simulation, roads and areas that are particularly vulnerable to multihazard events can be detected. The performance of the large-scale road network of the Tokai region in Japan (prone to both typhoons and earthquakes) is analyzed by considering three scenarios of hazards: flash flood, earthquake, and the combination of both hazards. The model considers two key traffic performance characteristics: postdisaster reduced road capacity and hourly variations in travel demand. Overall, several areas in the region are found to be currently severely at risk, thus providing direct information that can help authorities test the effectiveness of future road infrastructure projects.

Heat stress during grain filling has been documented to decrease wheat grain yield and quality in arid regions worldwide. We studied the effect of heat stress on wheat flour quality in heat tolerant cultivars to define the effects of heat stress on flour quality and to identify germplasm combining traits for heat tolerance and good flour quality. We studied the kernel phenotypic traits, the expression of seed storage proteins (SSPs), and the resulting flour quality under heat and normal conditions. Under heat stress, all cultivars yielded narrow-shaped seeds, and increased protein contents as compared to the control plants grown under normal conditions. The specific sedimentation values used to estimate the gluten quality varied between cultivars. We identified cultivars that could maintain good flour quality under heat stress conditions: ‘Imam’, which possessed the Glu-D1d allele responsible for the suitable bread-making; ‘Bohaine’, which displayed high expression level of SSPs; and ‘Condor’, which possessed slight variations in the ratio of each SSP under heat stress conditions. Combining the desirable traits from these cultivars could yield a wheat cultivar with heat tolerance and good flour quality.

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.

To make more efficient use of the expanded freeway and urban expressway networks, various measures such as bottleneck management and wide-area congestion pricing based on traffic data obtained from traffic detectors, including traffic volume and travel time, have been considered. Generally, the congestion status of the data varies from day to day. This study proposes a method for analyzing a graph of traffic volume and travel time to visually and intuitively grasp the change in the daily traffic situation using continuous one-hour values. These values are continuously generated hourly values obtained by shifting data every minute. Twenty-four hours 1 minute data for 128 days on 32 segments with detectors in the Nagoya Expressway Network in Japan were used to draw a continuous one-hour value graph. A number of graphs showed loops of continuous one-hour values with congestion and a smooth variation characteristic of values over time. These graphs provide an accurate estimate of the daily maximum one-hour traffic volumes and facilitate a sequential understanding of the congestion pattern changes on successive route segments. Hourly travel-time prediction models were constructed to macroscopically examine congestion measures over a range of several hours. These models were fabricated with high accuracy using multiple regression analysis based on the characteristics of continuous one-hour values. Exploratory predictive analysis of hourly travel-time models has allowed us to study and discuss various congestion factors in road structures and traffic flows, and it has been found to be easy to grasp the phenomenon and ensure accuracy and operability.