Queue Length Estimation at Traffic Signals: Connected Vehicles with Range Measurement Sensors

TL;DR

This paper proposes simple queue length estimators using connected vehicles with range sensors, enabling real-time traffic state estimation without ground truth data, and demonstrates improved accuracy through simulation.

Contribution

It introduces plug-and-play queue length estimation models leveraging connected vehicles with range sensors, applicable to cyclic traffic signals without ground truth data.

Findings

Improved estimation accuracy with up to 25% variance-to-mean ratio reduction.

Effective at low market penetration rates below 20%.

Models validated with microscopic traffic simulation data.

Abstract

Today vehicles are becoming a rich source of data as they are equipped with localization or tracking and with wireless communications technologies. With the increasing interest in automated- or self- driving technologies, vehicles are also being equipped with range measuring sensors (e.g., LIDAR, stereo cameras, and ultrasonic) to detect other vehicles and objects in the surrounding environment. It is possible to envision that such vehicles could share their data with the transportation infrastructure elements (e.g., a traffic signal controller) to enable different mobility and safety applications. Data from these connected vehicles could then be used to estimate the system state in real-time. This paper develops queue length estimators from connected vehicles equipped with range measurement sensors. Simple plug-and-play models are developed for queue length estimations without needing ground truth queue lengths by extending the previous formulations. The proposed method is simple to implement and can be adopted to cyclic queues at traffic signals with known phase lengths. The derived models are evaluated with data from microscopic traffic simulations. From numerical experiments, the QLE model with range sensors improves the errors as much as 25% in variance-to-mean ratio and 5\% in coefficient of variation at low less than 20% market penetration rates.