Highway Managed Lane Usage and Tolling for Mixed Traffic Flows with Connected Automated Vehicles (CAVs) and High-Occupancy Vehicles (HOVs)

TL;DR

This study explores optimal managed lane tolling strategies in mixed traffic involving connected automated vehicles, HOVs, and human-driven vehicles, aiming to prevent flow breakdown and reduce social costs using simulation and economic analysis.

Contribution

It introduces a mesoscopic traffic simulation with a reactive toll mechanism to identify optimal tolling policies for mixed traffic with CAVs and HOVs.

Findings

Untolled HOV/CAV use with tolls for HDVs can prevent flow breakdown.

Small, targeted tolls effectively prioritize high-value vehicles.

Converting high-occupancy HDVs to CAVs enhances traffic stability.

Abstract

This paper investigates managed lane (ML) toll setting and its effect under mixed traffic of connected automated vehicles (CAVs), high-occupancy vehicles (HOVs), and human-driven vehicles (HDVs), with a goal to avoid flow breakdown and minimize total social cost. A mesoscopic finite-difference traffic simulation model considers the flow-density relationship at different CAV market penetration rates, lane-changing behavior, and multiple entries/exits, interacting with a reactive toll setting mechanism. The results of the Monte Carlo simulation suggest an optimal policy of untolled HOV/CAV use with HDV tolls in particular scenarios of limited CAV market penetration. Small and targeted tolling avoids flow breakdown in ML while prioritizing HOVs and other vehicles with high values of time. Extensions of the formulation and sensitivity analysis quantify the benefits of converting high-occupancy HDVs to CAVs. The optimal tolling regime combines traffic science notions of flow stability and the economics of resource allocation.