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

2025-02-18

AI Summary

This paper addresses the low coordination efficiency between human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs) in mixed-traffic environments. To this end, we propose a classification-aware, dynamic traffic regulation framework that departs from conventional dedicated-lane strategies by introducing differentiated lane-access mechanisms. These mechanisms dynamically allocate lanes based on real-time traffic conditions and CAV penetration rates, optimizing for minimal passenger delay. Evaluated via SUMO-based microscopic simulation, the framework reduces average passenger delay by 37% under low CAV penetration (<20%), significantly improves network throughput, and incentivizes deployment of high-occupancy CAVs. Our key contributions are: (i) the first scalable regulatory modeling methodology tailored to mixed traffic, and (ii) a lightweight dynamic lane-access algorithm. Collectively, these advances establish a new paradigm for incremental, intelligent, and connected road governance.

Technology Category

• Application Domains: Transportation
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• Hybrid traffic laws for mixed vehicle types
• Simulation of static and dynamic lane policies
• Enhancing traffic efficiency with autonomous technology

Innovation

• Hybrid traffic laws for CAVs and HDVs
• SUMO simulation for traffic policy evaluation
• Dynamic lane access enhances traffic flow