Mixed flow of autonomous and human-driven vehicles: Analytical headway modeling and optimal lane management

Abstract

Presence of autonomous vehicles (AVs) affects traffic flow characteristics of a mixed traffic stream comprising human-driven vehicles. To model the impact of AVs on the saturation flow of arterials and highways, we propose analytical models to derive the expected value and variance of headway of a traffic stream with mixed AVs and conventional human-driven (or normal) vehicles (NVs), given the expected penetration rate of AVs. The proposed model considers the arrangement (order) of AVs and NVs in the mixed stream and the expected, the lowest, and the highest achievable headways and their variability are analytically derived. Moreover, the total delay of a two-lane road with interrupted traffic flow is analytically modeled for various possible lane allocation policies: (a) dedicated lanes, (b) mixed-mixed lanes, (c) mixed-NV lanes, and (d) mixed-AV lanes. Microsimulation experiments demonstrate the validity of the developed models for the average headway and its variability, as well as the delay formulas.