Operational models and traffic control in intelligent vehicle/highway systems

The goal of this dissertation is to study several aspects of an Intelligent Vehicle/Highway System (IVHS) that would benefit the reduction of highway congestion.; An automated highway system (AHS) is envisioned to provide substantial increase in highway capacity and safety margin and hence reduce traffic congestion. This thesis investigates the operational models and traffic control in an AHS. We consider the disturbances created in the exit, entrance, speed transition, and lane change process and study operational constraints that can prevent the propagation of these disturbances on the highway. We also examine the relationship between the capacity and the complexity of operational protocols and parameters. Our analysis reveals that stable and high-capacity traffic flow is feasible in an AHS.; Regulating traffic demand on the highway by an advanced traffic management system is another way to reduce traffic congestion. Traffic destination information can be easily accessed using advanced vehicle identification technology in an IVHS. This thesis develops a new transient traffic flow model, called the component flow model that employs destination information. We study numerical schemes to solve the resulting hyperbolic system of the partial differential equations. We develop a feedback on-ramp controller based on the component flow model. The resulting nonlinear optimal control problem is solved using the method of successive linear programming. The on-ramp controller is implemented in a bottleneck congestion control example. Traffic simulation results show that the controller developed in this thesis has great promise in reducing traffic congestion and increasing system throughput.