Trajectory Reconstruction at Signalized Intersections in a Vehicle-to-Infrastructure Environment Department of Road Traffic Management, Intelligent Policing

Format:

Book

Conference/Event

Author/Creator:

Bai, Wei, author.

Contributor:

Fu, Chengxin

Yao, Zhihong

Conference Name:

2025 8th International Conference on Traffic Transportation and Civil Architecture (ICTTCA 2025) (2025-04-18 : Tianjin, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Vehicle trajectories encapsulate critical spatial-temporal information essential for traffic state estimation, congestion analysis, and operational parameter optimization. In a Vehicle-to-Infrastructure (V2I) environment, connected automated vehicles (CAVs) not only continuously transmit their own real-time trajectory data but also utilize onboard sensors to perceive and estimate the motion states of surrounding regular vehicles (RVs) within a defined communication range. These multi-source data streams, when integrated with fixed infrastructure-based detectors such as speed cameras at intersections, create a robust foundation for reconstructing full-sample vehicle trajectories, thereby addressing data sparsity issues caused by incomplete CAV penetration. Building upon classical car-following (CF) theory, this study introduces a novel trajectory reconstruction framework that fuses CAV-generated trajectories and infrastructure-based speed detection data. The proposed method specifically aims to reconstruct the unobserved trajectories of RVs located between successive CAVs within the same lane, ensuring continuity and accuracy in trajectory estimation. To validate the framework's effectiveness, extensive SUMO simulations were conducted under different CAV penetration rates (PRs: 5%, 10%, 15%, and 20%) with a controlled traffic flow rate of 1000 veh/h. Key findings indicate that the proposed method maintains stable reconstruction accuracy across all tested penetration rates, with errors remaining within acceptable thresholds. Furthermore, comparative analysis against state-of-the-art CF-based reconstruction approaches reveals substantial improvements in accuracy, achieving reductions of 84.51% (LE), 97.07% (QLE) and 95.55% (TE), respectively. The result highlights the proposed method potential for enhancing real-time traffic state estimation, optimizing signal control strategies, and improving overall traffic management in V2I-enabled urban networks

Notes:

Vendor supplied data

Publisher Number:

2025-99-0217

Access Restriction:

Restricted for use by site license