This research is focused on the optimization of transportation system management by developing statistical models involving dynamic network modeling, spatiotemporal analysis, and theories of AI. Currently, massive historical data have been generated through ubiquitous sensing technologies, leading to the spring up of data-driven strategies for transportation optimization. However, transportation modeling is still challenging due to the complexspace-time dependencies, the directed network topology, and the high computational cost for large-scale networks. The integrated management of urban transportation systems also remains unfulfilled. More efficient cyber-technologies well tackling these issues, if successfully achieved, would significantly improve the urban transportation efficiency and assist better urban governance.
