The advent of technology has led to substantial progress in the field of autonomous vehicles (AVs), indicating that AVs will become a generic mode of transport in the near future. However, there is a long journey ahead before we fully transition into an era dominated by AVs. Consequently, it is anticipated that traditional vehicles (TVs) and AVs will share the roadways in the coming years. AVs and TVs exhibit marked differences in aspects such as response time to signals and capability to share destination information. These differences suggest that traffic control policies designed exclusively for TVs may be substantially different from those suitable for a blend of AVs and TVs. To reconcile these disparities, the separation of lanes or roads for AVs and TVs could present a more effective strategy for traffic management. Moreover, the optimal traffic signal pattern at each intersection could depend on the AV-to-TV ratio. Through the implementation of an intelligent transportation system, the traffic control system can better manage mixed-flow scenarios for multi-intersection positions. This study defines a multi-intersection mixed traffic control problem (MIMTCP), in which both AVs and TVs pass through multiple signalized intersections. In addressing the MIMTCP, three primary subproblems were identified: traffic signal control, lane allocation, and trajectory planning. This paper offers a comprehensive review of the scholarly efforts directed at resolving these subproblems through the use of optimization algorithms, data-driven learning-based methodologies, and simulation-based approaches. Finally, we delve into promising research opportunities within each subproblem, with an emphasis on the development of strategic solutions and methodological approaches.