Transportation agencies all over the country are facing fiscal shortages due to the increasing costs of management and maintenance of facilities. The political reluctance to increase gas taxes, the primary source of revenue for many government transportation agencies, along with the improving fuel efficiency of automobiles sold to consumers, only exacerbate the financial dire straits. The adoption of electric vehicles threatens to completely stop the inflow of money into federal, state and regional agencies. Consequently, expansion of the network and infrastructure is slowly being replaced by a more proactive approach to managing the use of existing facilities. The required insights to manage the network more efficiently is also partly due to a massive increase in the type and volume of available data. These data are paving the way for network-wide Intelligent Transportation Systems (ITS), which promises to maximize utilization of current facilities. The waves of revolutions overtaking the usual business affairs of transportation agencies have prompted the development and application of various analytical tools, models and and procedures to transportation. Contributions to this growth of analysis techniques are documented in this dissertation.

There are two main domains of transportation: demand and supply, which need to be simultaneously managed to effectively push towards optimal use of resources, facilities, and to minimize negative impacts like time wasted in delays, environmental pollution, and greenhouse gas emissions. The two domains are quite distinct and require specialized solutions to the problems. This dissertation documents the developed techniques in two sections, addressing the two domains of demand and supply. In the first section, a copula based approach is demonstrated to produce a reliable and accurate synthetic population which is essential to estimate the demand correctly. The second section deals with big data analytics using simple models and fast algorithms to produce results in real-time. The techniques developed target short-term traffic forecasting, linking of multiple disparate datasets to power niche analytics, and quickly computing accurate measures of highway network performance to inform decisions made by facility operators in real-time.

The analyses presented in this dissertation target many core aspects of transportation science, and enable the shared goal of providing safe, efficient and equitable service to travelers. Synthetic population in transportation is used primarily to estimate transportation demand from Activity Based Modeling (ABM) framework containing well-fitted behavioral and choice models. It allows accurate verification of the impacts of policies on the travel behavior of people, enabling confident implementation of policies, like setting transit fares or tolls, designed for the common benefit of many. Further accurate demand models allow for resilient and resourceful planning of new or repurposing existing infrastructure and assets. On the other hand, short-term traffic speed predictions and speed based reliable performance measures are key in providing advanced ITS, like real-time route guidance, traveler awareness, and others, geared towards minimizing time, energy and resource wastage, and maximizing user satisfaction. Merging of datasets allow transfer of data such as traffic volumes and speeds between them, allowing computation of the global and network-wide impacts and externalities of transportation, like greenhouse gas emissions, time, energy and resources consumed and wasted in traffic jams, etc.