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

TL;DR

This paper explores hybrid traffic laws for mixed traffic environments with human-driven and autonomous vehicles, using simulations to evaluate policies that improve flow and reduce delays, especially at low CAV proportions.

Contribution

It introduces and evaluates hybrid traffic laws with static and dynamic lane policies for mixed vehicle traffic using SUMO simulations.

Findings

Dynamic policies outperform traditional lane strategies at low CAV proportions.

Hybrid laws can significantly improve traffic flow and reduce passenger delays.

Results support the adoption of hybrid traffic laws to facilitate autonomous vehicle integration.

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.