[Paper Review] Freeway Lane Management Approach in Mixed Traffic Environment with Connected Autonomous Vehicles

Omar Khadeer Hussain, Amir Ghiasi+1 more|arXiv (Cornell University)|Sep 9, 2016

Traffic control and management21 citations

TL;DR

This paper proposes an analytical managed lane model to determine the optimal number of freeway lanes to allocate to connected autonomous vehicles (CAVs) in mixed traffic environments. Using three CAV technology scenarios—neutral, conservative, and aggressive—the study shows that more aggressive CAV technologies reduce the need for dedicated lanes due to tighter headways and improved flow efficiency, with numerical results demonstrating significant capacity gains at higher CAV penetration rates.

ABSTRACT

Connected autonomous vehicles (CAV) technologies are about to be in the market in the near future. This requires transportation facilities ready to operate in a mixed traffic environment where a portion of vehicles are CAVs and the remaining are manual vehicles. Since CAVs are able to run with less spacing and headway compared with manual vehicles or mixed traffic, allocating a number of freeway lanes exclusive to CAVs may improve the overall performance of freeways. In this paper, we propose an analytical managed lane model to evaluate the freeway flow in mixed traffic and to determine the optimal number of lanes to be allocated to CAVs. The proposed model is investigated in two different operation environments: single-lane and managed lane environments. We further define three different CAV technology scenarios: neutral, conservative, and aggressive. In the single-lane problem, the influence of CAV penetration rates on mixed traffic capacity is examined in each scenario. In the managed lanes problem, we propose a method to determine the optimal number of dedicated lanes for CAVs under different settings. A number of numerical examples with different geometries and demand levels are investigated for all three scenarios. A sensitivity analysis on the penetration rates is conducted. The results show that more aggressive CAV technologies need less specific allocated lanes because they can follow the vehicles with less time and space headways.

Motivation & Objective

• To develop an analytical model for evaluating freeway flow performance in mixed traffic with connected autonomous vehicles (CAVs).
• To determine the optimal number of managed lanes to allocate to CAVs under varying CAV penetration rates and technology levels.
• To assess the impact of different CAV technology scenarios—neutral, conservative, and aggressive—on freeway capacity and lane efficiency.
• To evaluate the performance of single-lane and managed-lane freeway configurations under diverse traffic demand and geometric conditions.

Proposed method

• The authors develop an analytical managed lane model that integrates car-following dynamics and lane-changing behavior in mixed traffic with CAVs.
• Three CAV technology scenarios are defined: neutral (baseline), conservative (modest spacing), and aggressive (minimal spacing and headway).
• The model uses time and space headway parameters to quantify CAV platoon efficiency and its impact on overall freeway capacity.
• Numerical examples are conducted across various freeway geometries and demand levels to simulate performance under each scenario.
• A sensitivity analysis is performed on CAV penetration rates to assess their influence on capacity and optimal lane allocation.
• The model compares single-lane and managed-lane configurations to identify the optimal number of dedicated CAV lanes.

Experimental results

Research questions

• RQ1 What is the optimal number of managed lanes to allocate to CAVs under different penetration rates and CAV technology levels?
• RQ2 How does the inclusion of CAVs affect overall freeway capacity in mixed traffic environments?
• RQ3 How do different CAV technology scenarios—neutral, conservative, and aggressive—affect lane utilization and flow efficiency?
• RQ4 What is the performance difference between single-lane and managed-lane freeway configurations under varying traffic demand?
• RQ5 How sensitive is the optimal lane allocation to changes in CAV penetration rates?

Key findings

• More aggressive CAV technologies require fewer dedicated lanes due to significantly reduced time and space headways, enhancing platoon efficiency.
• At high CAV penetration rates, the managed lane configuration outperforms the single-lane setup, especially under aggressive technology scenarios.
• The neutral CAV scenario requires the highest number of dedicated lanes to achieve optimal performance, while the aggressive scenario achieves comparable capacity with fewer lanes.
• Sensitivity analysis confirms that lane allocation is most sensitive to CAV penetration rates in the conservative and neutral scenarios, with diminishing returns at higher penetration.
• Numerical results show that under aggressive CAV technology, even a single dedicated lane can significantly improve throughput compared to no dedicated lanes.
• The model demonstrates that optimal lane allocation is highly dependent on the specific CAV technology level, with aggressive systems enabling higher capacity gains per lane.