Microscopic Estimation of Arterial Vehicle Positions in a Low-Penetration-Rate Connected Vehicle Environment

Abstract

Wireless communication among vehicles and roadside infrastructure, known as connected vehicles, is expected to provide higher-resolution real-time vehicle data, which will allow more effective traffic monitoring and control. Availability of connected vehicle technology among the vehicle fleet will likely grow gradually, and possibly remain limited, with many drivers potentially being unwilling to transmit their locations. This is problematic given that research has indicated that the effectiveness of many connected vehicle mobility applications will be dependent on the availability of location data from a minimum of 20-30% of roadway vehicles. In an effort to improve the performance of connected vehicle applications at low connected vehicle technology penetration rates, we propose a novel technique to estimate the positions of non-communicating (unequipped) vehicles based on the behaviors of communicating (equipped) vehicles along a signalized arterial. Unequipped vehicle positions are estimated based on observed gaps in a stopped queue, and the forward movement of these estimated vehicles are simulated microscopically using a commercial traffic simulation software package. In simulations, the algorithm made more correct than incorrect estimates of unequipped vehicle positions in the same lane and within 7 meters longitudinally. When applied to a previously-developed connected vehicle traffic signal control strategy in simulation, the location estimation algorithm produced small improvements in delays, speeds, and stopped delay when compared to an equipped vehicle-only scenario at penetration rates of 25% or less. The location estimation algorithm is generic, and could be applied to other connected vehicle applications to improve performance at low penetration rates.