A Dynamic Bus Lane Strategy for Integrated Management of Human-Driven and Autonomous Vehicles

TL;DR

This paper proposes a dynamic bus lane strategy that optimally manages mixed traffic with human-driven and autonomous vehicles, improving traffic flow by leveraging CAV capabilities and real-time optimization.

Contribution

It introduces a novel dynamic bus lane strategy that incorporates CAV trajectory planning and a MILP-based optimization for mixed traffic management.

Findings

Significant reduction in vehicle travel time at various CAV market penetration rates.

Effective coordination of CAVs and HDVs improves overall traffic efficiency.

The strategy adapts to real-time traffic conditions through a rolling horizon control.

Abstract

This study introduces a dynamic bus lane (DBL) strategy, referred to as the dynamic bus priority lane (DBPL) strategy, designed for mixed traffic environments featuring both manual and automated vehicles. Unlike previous DBL strategies, this approach accounts for partially connected and autonomous vehicles (CAVs) capable of autonomous trajectory planning. By leveraging this capability, the strategy grants certain CAVs Right of Way (ROW) in bus lanes while utilizing their leading effects in general lanes to guide vehicle platoons through intersections, thereby indirectly influencing the trajectories of other vehicles. The ROW allocation is optimized using a mixed-integer linear programming (MILP) model, aimed at minimizing total vehicle travel time. Since different CAVs entering the bus lane affect other vehicles travel times, the model incorporates lane change effects when estimating the states of CAVs, human-driven vehicles (HDVs), and connected autonomous buses (CABs) as they approach the stop bar. A dynamic control framework with a rolling horizon procedure is established to ensure precise execution of the ROW optimization under varying traffic conditions. Simulation experiments across two scenarios assess the performance of the proposed DBPL strategy at different CAV market penetration rates (MPRs).