Towards Hybrid Traffic Laws for Mixed Flow of Human-Driven Vehicles and Connected Autonomous Vehicles

TL;DR

The paper addresses how to manage mixed traffic composed of human-driven vehicles and connected autonomous vehicles by proposing hybrid traffic laws that grant a restricted lane to buses and CAVs with higher occupancy. It evaluates static and dynamic threshold-based access policies using SUMO simulations across diverse demand profiles and CAV shares, demonstrating that dynamic thresholds consistently reduce average passenger delay and promote CAV adoption and carpooling. Key findings show substantial gains at low CAV penetration, with optimal efficiency emerging when roughly 20% of vehicles access the restricted lane; results also reveal robustness limits and transfer challenges in more complex networks. The work contributes to policy design by showing enforceable, adaptive lane restrictions can improve efficiency and accelerate the transition to autonomous mobility in real-world settings.

Abstract

Hybrid traffic laws represent an innovative approach to managing mixed environments of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs) by introducing separate sets of regulations for each vehicle type. These laws are designed to leverage the unique capabilities of CAVs while ensuring both types of cars coexist effectively, ultimately aiming to enhance overall social welfare. This study uses the SUMO simulation platform to explore hybrid traffic laws in a restricted lane scenario. It evaluates static and dynamic lane access policies under varying traffic demands and CAV proportions. The policies aim to minimize average passenger delay and encourage the incorporation of autonomous vehicles with higher occupancy rates. Results demonstrate that dynamic policies significantly improve traffic flow, especially at low CAV proportions, compared to traditional dedicated bus lane strategies. These findings highlight the potential of hybrid traffic laws to enhance traffic efficiency and accelerate the transition to autonomous technology.

Figures (7)

• Figure 1: Illustration of the Configuration of the Road Network.
• Figure 2: Distribution of the Number of Passengers in a Car, Based on Real Data from NHTS.
• Figure 3: APD Change (in %) of CAVDynamic_24 Compared to DBL Across Different Demands and Vehicle Type Distributions.
• Figure 4: The VD under demand scenario Constant_3000 analyzed for varying proportions of vehicles permitted to access the restricted lane. The error bars are based on the standard deviation of the measured data.
• Figure 5: The average speed across each segment of the road under demand scenario Constant_3000, analyzed for varying proportions of vehicles permitted to access the restricted lane.