A Survey of Vehicle-Enabled Control of Traffic for Freeway Flow in Partially Connected Environments

Efficient freeway traffic control can alleviate congestion, improve operational efficiency, and enhance traffic safety. With the rapid development of Intelligent Transportation Systems (ITS) and connectivity technologies, Connected Vehicles (CVs), including Connected Automated Vehicles (CAVs) and Connected Human-Driven Vehicles (C-HDVs), are expected to coexist with conventional Human-Driven Vehicles (HDVs) on freeways for a considerable period. In partially connected traffic environments, CVs can provide historical and real-time Vehicle Trajectory Data (VTD) as mobile sensing sources and can also influence surrounding traffic as mobile actuators through vehicle guidance and cooperative control, thereby enabling Vehicle-Enabled Control of Traffic (VECT) for freeway flow. This paper systematically reviews recent advances in VECT under partially connected environments. The literature is organized into four categories according to the functional role of CVs and the level at which information, decisions, or control actions are generated: (1) vehicle-trajectory-enabled traffic state analysis, (2) vehicle-trajectory-enabled traffic control, (3) vehicle-guidance-enabled traffic control, and (4) collaborative vehicle–flow control. The review covers traffic state estimation, traffic state prediction, traffic anomaly detection, Variable Speed Limit (VSL) control, Ramp Metering (RM), vehicle trajectory planning, moving bottleneck control, and cross-scale coordination between macroscopic traffic regulation and microscopic vehicle guidance. Finally, key methodological challenges, deployment issues, and future research directions are discussed. This review provides a systematic framework for understanding how CVs enable freeway traffic analysis and control from sensing to actuation and coordinated vehicle–flow regulation.