Real-Time Traffic State Measurement Using Autonomous Vehicles Open Data

Abstract

Autonomous vehicle (AV) technologies are expected to disrupt the existing urban transportation systems. AVs’ multi-sensor system can generate large amount of data, often used for localization and safety purposes. This study proposes and demonstrates a practical framework for real-time measurement of local traffic states using LiDAR data from AVs. Fundamental traffic flow variables including volume, density, and speed are computed along with the traffic time-space diagrams. The framework is tested using the Waymo Open dataset. Results provide insights into the possibility of real-time traffic state estimation using AVs’ data for traffic operations and management applications.

Keywords

• Autonomous vehicles
• traffic flow
• real-time state measurement
• Lagrangian sensing