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Weather conditions affect road traffic in many ways such as traffic demand, traffic safety, and traffic flow. The influence of the weather events might cause extra cost and time if they are not considered during travel plan. The efficiency of the travel activities under different road conditions can be improved in terms of safety, mobility, and travel time saving. The aim of this study is to improve travel planning performance measures under different weather conditions. Vehicle speed data from weigh in motion traffic recorder (WIM) along with weather data have been used to obtain the vehicle speed reduction during adverse weather events. Also, vehicle speed during the events were compared with the normal vehicle speed under regular weather condition for different locations in the State of Ohio, and time durations. Results showed that heavy snow associated with low visibility have the most impacts on driver speed behavior. Then, the vehicle speed performance during the events was used to calculate the expected time throughout each event. Using probability approach on two different networks show significant results in terms of departure times and route choices. Fuzzy mathematical model was conducted to determine if there are significant differences in the satisfaction levels compared to probability approach when the snow storm hits all the links in the network. Thus, the results showed that there is not remarkable difference between the fuzzy and probability. This methodology can be used to demonstrate a predictive tool to assess departure time and route choice under snow storm events. These approaches may help decision makers to obtain the optimal path before and during adverse weather events. This concept is presented in the context of the transportation network planning performance.

Construction projects are always expected to be delayed, but the likelihood of a delay varies between projects because of the particular circumstances and schedule for that project. It is usually left to the scheduler to estimate these future circumstances of the project when preparing the schedule and determining the duration of the project. However, the schedule of the Critical Path Method (CPM) does not indicate the factors that are assumed to participate in determining the likelihood for delay. These deterministic durations, apart from the relationship between the activities, are the dominant contributor to the critical path in the CPM calculation. Risk management focuses on the processes that are considered critical, although delay may emerge from non-critical paths.In this study, a new fuzzy model is proposed to provide a subjective assessment of the likelihood of delay for activities in different periods. Using this model, the scheduler’s assessment of the likelihood of delay for each activity can be combined to determine the likelihood of a project delay. This process is done utilizing fuzzy logic and fault-tree analysis and is then combined with the CPM schedule of the project. The result is a fuzzy fault-tree that shows the potential delay of the project and its contributing paths.Applying this method on a sample project, the results show that risk of delay comes not only from critical paths but also from non-critical paths. Consequently, the CPM schedule duration can be reevaluated such that the project can be rescheduled to account for the new findings and, at the very least, the risk of delay can be accounted for.

It is critical to provide an efficient and accurate information system in the post-disaster phase for individuals' in order to access and obtain the necessary resources in a timely manner; but current map based post-disaster management systems provide all emergency resource lists without filtering them which usually leads to high levels of energy consumed in calculation. Also an effective post-disaster management system (PDMS) will result in distribution of all emergency resources such as, hospital, storage and transportation much more reasonably and be more beneficial to the individuals in the post disaster period. In this Dissertation, firstly, semi-supervised learning (SSL) based graph systems was constructed for PDMS. A Graph-based PDMS' resource map was converted to a directed graph that presented by adjacent matrix and then the decision information will be conducted from the PDMS by two ways, one is clustering operation, and another is graph-based semi-supervised optimization process. In this study, PDMS was applied for emergency resource distribution in post-disaster (responses phase), a path optimization algorithm based ant colony optimization (ACO) was used for minimizing the cost in post-disaster, simulation results show the effectiveness of the proposed methodology. This analysis was done by comparing it with clustering based algorithms under improvement ACO of tour improvement algorithm (TIA) and Min-Max Ant System (MMAS) and the results also show that the SSL based graph will be more effective for calculating the optimization path in PDMS. This research improved the map by combining the disaster map with the initial GIS based map which located the target area considering the influence of disaster. First, all initial map and disaster map will be under Gaussian transformation while we acquired the histogram of all map pictures. And then all pictures will be under discrete wavelet transform (DWT), a Gaussian fusion algorithm was applied in the DWT pictures. Second, inverse DWT (iDWT) was applied to generate a new map for a post-disaster management system. Finally, simulation works were proposed and the results showed the effectiveness of the proposed method by comparing it to other fusion algorithms, such as mean-mean fusion and max-UD fusion through the evaluation indices including entropy, spatial frequency (SF) and image quality index (IQI). Fuzzy set model were proposed to improve the presentation capacity of nodes in this GIS based PDMS.