City-scale Vehicle Trajectory Data from Traffic Camera Videos

Abstract

Vehicle trajectory data underpins various applications in intelligent transportation systems, such as traffic surveillance, traffic prediction, and traffic control. Traditional vehicle trajectory datasets, recorded by GPS devices or single cameras, are often biased towards specific vehicles (e.g., taxis) or incomplete (typically < 1 km), limiting their reliability for downstream applications. With the widespread deployment of traffic cameras across the city road network, we have the opportunity to capture all vehicles passing by. By collecting city-scale traffic camera video data, we apply a trajectory recovery framework that identifies vehicles across all cameras and reconstructs their paths in between. Leveraging this approach, we are the first to release a comprehensive vehicle trajectory dataset that covers almost full-amount of city vehicle trajectories, with approximately 5 million trajectories recovered from over 3000 traffic cameras in two metropolises. To assess the quality and quantity of this dataset, we evaluate the recovery methods, visualize specific cases, and compare the results with external road speed and flow statistics. The results demonstrate the consistency and reliability of the released trajectories. This dataset holds great promise for research in areas such as unveiling traffic dynamics, traffic network resilience assessment, and traffic network planning.

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

“…Therefore, the data is continuous in space, but is lacking information on VRUs and the periods in between the ights. Recently, Yu et al (2023) published a stationary camera-based dataset. For two cities in China, the authors used cameras at intersections distributed across the city to extract motorized tra c. They developed an algorithm to recognize the vehicles so that the trajectories could be tracked over the entire area and recognized across several cameras.…”

“…However, these sources are either limited in their information content, e.g. only the vehicular ow and occupancy for single loop detectors, or they are restricted to certain modes of transport, such as bus or taxi GNSS data (Yu et al 2023). Using video analysis from static cameras, as well as equipped unmanned aerial vehicles has become a viable means for tra c monitoring (Barmpounakis et al 2016, Kim et al 2019).…”