Model-based traffic state estimation for link traffic using moving cameras

Traffic State Estimation (TSE) is the process of inferring traffic conditions based on partially observed data using prior knowledge of traffic patterns. The type of input data used has a significant impact on the accuracy and methodology of TSE. Traditional TSE methods have relied on data from either stationary sensors like loop detectors or mobile sensors such as GPS-equipped floating cars. However, both approaches have their limitations. This paper proposes a method for estimating traffic states on a road link using vehicle trajectories obtained from cameras mounted on moving vehicles. It involves combining data from multiple moving cameras to construct time-space diagrams and using them to estimate parameters for the link's fundamental diagram (FD) and densities in unobserved regions of space-time. The Cell Transmission Model (CTM) is utilized in conjunction with a Genetic Algorithm (GA) to optimize the FD parameters and boundary conditions necessary for accurate estimation. To evaluate the effectiveness of the proposed methodology, simulated traffic data generated by the SUMO traffic simulator was employed incorporating 140 different space-time diagrams with varying lane density and speed. The evaluation of the simulated data demonstrates the effectiveness of the proposed approach, as it achieves a low root mean square error (RMSE) value of 0.0079 veh/m and is comparable to other CTM-based methods. In conclusion, the proposed TSE method opens new avenues for the estimation of traffic state using an innovative data collection method that uses vehicle trajectories collected from on-board cameras.

Automated Construction of Time-Space Diagrams for Traffic Analysis Using Street-View Video Sequence

Time-space diagrams are essential tools for analyzing traffic patterns and optimizing transportation infrastructure and traffic management strategies. Traditional data collection methods for these diagrams have limitations in terms of temporal and spatial coverage. Recent advancements in camera technology have overcome these limitations and provided extensive urban data. In this study, we propose an innovative approach to constructing time-space diagrams by utilizing street-view video sequences captured by cameras mounted on moving vehicles. Using the state-of-the-art YOLOv5, StrongSORT, and photogrammetry techniques for distance calculation, we can infer vehicle trajectories from the video data and generate time-space diagrams. To evaluate the effectiveness of our proposed method, we utilized datasets from the KITTI computer vision benchmark suite. The evaluation results demonstrate that our approach can generate trajectories from video data, although there are some errors that can be mitigated by improving the performance of the detector, tracker, and distance calculation components. In conclusion, the utilization of street-view video sequences captured by cameras mounted on moving vehicles, combined with state-of-the-art computer vision techniques, has immense potential for constructing comprehensive time-space diagrams. These diagrams offer valuable insights into traffic patterns and contribute to the design of transportation infrastructure and traffic management strategies.