Macroscopic Modeling of Managed Lane-Freeway Networks

We propose a macroscopic modeling framework for networks of freeways equipped with managed lanes. Two types of managed lane configuration are considered: full-access, where vehicles can switch between the general purpose (GP) and the managed lanes anywhere; and separated, where such switching is allowed only at certain locations called gates. The proposed framework is based on widely-used firstorder kinematic wave theory. In this model, the GP and the managed lanes are modeled as parallel links connected by nodes, where certain type of traffic may switch between GP and managed lane links. We incorporate two phenomena into our model that are particular to managed lane-freeway networks: the inertia effect and the friction effect. The inertia effect reflects drivers’ inclination to stay in their lane as long as possible and switch only if this would obviously improve their travel condition. The friction effect reflects the empirically-observed driver fear of moving fast in a managed lane while traffic in the adjacent GP links moves slowly due to congestion. Calibration of models of large road networks is difficult, as the dynamics depend on many parameters whose numbers grow with the network’s size. We present an iterative learning-based approach to calibrating our model’s physical and driver-behavioral parameters. Finally, our model and calibration methodology are demonstrated with case studies of simulations of two managed lane-equipped freeways in Southern California.