Electric drive vehicles (plug-in electric vehicle or hydrogen fuel cell vehicles) have been promoted by governments to foster a more sustainable transportation future. Wider adoption of these vehicles, however, depends on the availability of a convenient and reliable refueling/recharging infrastructure. This paper introduces a path-based, multi-scale, scenarioplanning modeling framework for locating a system of alternative-fuel stations. The approach builds on 1) the Flow Refueling Location Model (FRLM), which assumes that drivers stop along their origin-destination routes to refuel, and checks explicitly whether round trips can be completed without running out of fuel, and 2) the Freeway Traffic Capture Method (FTCM), which assesses the degree to which drivers can conveniently reach sites on the local street network near freeway intersections. This paper extends the FTCM to handle cases involving clusters of nearby freeway intersections, which is a limitation of its previous specification. Then, the cluster-based FTCM (CFTCM) is integrated with the FRLM and the DFRLM (FRLM with Deviations) to better conduct detailed geographic optimization of this multi-scale location planning problem. The main contribution of this research is the introduction of a framework that combines multi-scale planning methods to more effectively inform the early development stage of hydrogen refueling infrastructure planning. The proposed multi-scale modeling framework is applied to the Hartford, Connecticut region, which is one of the next areas targeted for fuel-cell vehicle (FCV) market and infrastructure expansion in the United States. This method is generalizable to other regions or other types of fast-fueling alternative fuel vehicles.

The thesis explained the construction and development path of the application engineering of big data technology for global marine environment forecasting: opening up a big data collection source global marine environment forecasting, developing the comprehensive data lake of marine environment forecasting, deeply digging the big data of marine environment forecasting, and applying multi-factor composite and multi-model coupled numerical forecasting method and combining with international scientific and technological cooperation and polar scientific expedition practice, to create the application engineering groups of big data technology for global marine environment forecasting. Secondly, the thesis introduced the overview, social and economic benefits of these application engineering groups of big data technology. Then, the current status, business technology level, and guarantee service capabilities of China’s data application of marine environment forecasting were shown in the big data level. Finally, from the perspective of forecasting experience and professional thinking, this study looked forward to the application prospects of big data in the global marine environment forecasting field as well as its technological changes and product (service) innovation.

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