My ultimate research goal is to help build an efficient, reliable and sustainable transportation system. One of the major obstacles to such a system is its inherent uncertainty, due to for example, accidents, inclement weather, work zone construction, and fluctuating demand. The rapid development of sensor, information and communication technologies has made real-time traffic information increasingly available for travelers and system operators to improve decision making in uncertain situations. My research thus focuses on understanding travelers' learning and choice behavior in an uncertain, dynamic, connected, information-rich network and incorporating advanced behavioral theories in transportation network optimization models to improve the performance of transportation systems. I examine the problem of travel choice in an uncertain system through a series of theoretical and empirical studies using data from both laboratory experiments and in-vehicle tracking and monitoring devices in real-life urban networks. I design algorithms for the optimal adaptive routing problem with realistic information accessiblities, including delayed, pre-trip, radio and trajectory information, as well as the optimal non-adaptive path problem where the link-wise and time-wise correlations of link travel times are considered. A current project sponsored by ARPA-E aims to design an incentivizing scheme to award travelers for good choices so that the system-wide congestion, energy consumption and emissions are minimized.