Spatiotemporal Travel Speed Estimation in Mixed Traffic Conditions: A Probe Vehicle-Based Approach with Autonomous Vehicle Sensor Integration

The increasing adoption of autonomous vehicles (AVs) presents new opportunities for traffic monitoring and spatiotemporal travel speed estimation. Traditional traffic detection methods, relying on fixed-point sensors, face inherent limitations, including high installation costs, limited spatial coverage, and accuracy degradation in congested conditions. To address these issues, this study introduces a novel approach utilizing AVs as mobile detectors to estimate travel speeds in mixed traffic conditions. The proposed methodology examines the impact of varying sampling rates and spatiotemporal intervals on estimation accuracy, using probe vehicle sensor data. Three distinct scenarios are evaluated, each incorporating progressively advanced sensor configurations, ranging from GPS-only data to forward- and rear-facing sensor integration. The results indicate that estimation accuracy significantly improves with higher sampling rates, with a minimum threshold of 30% required for reliable performance. Additionally, larger spatial intervals enhance estimation stability by mitigating fluctuations in speed data. The findings demonstrate that AV-based mobile detection systems provide a scalable and cost-effective alternative to traditional fixed detection methods. This research contributes to the evolving field of intelligent transportation systems by offering insights into optimizing data collection strategies for real-time traffic management. Future research directions include integrating vehicle-to-everything (V2X) communication and machine learning models to further refine estimation accuracy and predictive capabilities in dynamic urban mobility environments.