Evaluating a signalized intersection performance using unmanned aerial Data

ABSTRACT

This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions.

KEYWORDS:

• —Measures of effectiveness (MOEs)
• Signalized intersection
• Unmanned Aerial Vehicles (UAVs)
• Drones
• Traffic data

Acknowledgments

The authors would like to thank the pNEUMA project for providing the dataset that was used in this study.

Disclosure statement

No potential conflict of interest was reported by the authors.