The purpose of this doctoral study is to advance the state of the art of the simulation and modeling of transportation systems by addressing some of the limitations of state-of-the-practice models such as the inability to deal with large amounts of data in real-time, the aggregate nature of the majority of models, the lack of network realism in driving simulation and human behavior realism in traffic simulation, and finally the inability to evaluate new and emerging Intelligent Transportation Systems (ITS) applications. Within this broader research area, the work is divided into five tightly-connected research sub-topics. Subtopic 1 aims at developing and validating a novel forecasting paradigm named SPN to forecast traffic data, so as to provide an efficient and effective way for online traffic simulation and modeling. The SPN provides higher predictive accuracy and requires dramatically less processing time compared with existing approaches. Subtopic 2 develops a large-scale agent-based simulation model and validates it. The research demonstrates the feasibility of regional agent-based simulations while depending upon data readily available. Subtopic 3 proposes a semi-heuristic algorithm for estimating dynamic travel demand for large-scale simulation models. The algorithm manages to match the simulated traffic volumes to field counts while keeping the estimated Origin-Destination (OD) matrices close to the true ones. Subtopic 4 integrates a traffic simulator with a driving simulator to allow for human-in-the-loop simulation. The integrated simulator successfully shows differences in emissions levels among drivers with different years of driving experience. Finally, Subtopic 5 designs and implements an integrated IntelliDrive simulation testbed, and uses this testbed for the simulation and evaluation of an application named intelligent intersection. The research demonstrates the mobility enhancement and environmental benefits of the application, as well as the advantages of using the testbed to design and evaluate IntelliDrive applications. The aforementioned contributions of this dissertation advance the state of the art in transportation simulation and modeling, broaden the range of transportation issues for which those models and tools can be used to address, and create a vision for a human-oriented, integrated, and intelligent next-generation simulation and modeling system.