Safety, mobility, and environmental sustainability form the triad of challenges in modern transportation systems. To tackle these issues, there has been an increasing emphasis on intelligent transportation systems (ITS) technology, which employs interdisciplinary approaches to provide effective solutions. Transportation systems are characterized by their large scale, non-linearity, time-varying behavior, interconnectivity, heterogeneity, and distributed nature, with various participants engaging in intensive interactions. As the fields of sensing, communication, and control techniques advance, we expect a significant rise in connected automation applications in the transportation system. This development will likely lead to an increased presence of autonomous vehicles (AVs) and connected and automated vehicles (CAVs). However, these emerging automated systems will coexist with traditional human-driven traffic for an extended period, making cooperation in mixed traffic conditions a crucial yet challenging research topic. In this dissertation, a cooperative framework for mixed traffic is proposed at both macroscopic and microscopic levels. At the macroscopic level, shared automated mobility applications are explored, and dispatching and scheduling algorithms for battery electric truck fleets are investigated to improve operational efficiency. On a microscopic level, a corridor-wise ramp management framework is introduced to handle the unique challenges of mixed traffic. Moreover, with the inclusion of non-CAV agents such as traditional vehicles in mixed traffic, the research pursues two key directions. First, it examines infrastructure-side sensing to improve detection and monitoring capabilities, thereby enhancing decision-making and overall system management. Second, it undertakes the modeling of driver behavior to better understand and predict human actions in mixed traffic scenarios, which could lead to improved traffic flow management.