Technology Category
Computer Vision: Motion & TrackingApplication Domains: Mobility, Driving & FlightPlanning, Routing, and Scheduling: Activity and Plan Recognition
Microscopic Vehicle Trajectory Datasets from UAV-collected Video for Heterogeneous, Area-Based Urban Traffic
🤖 AI Summary
To address the challenge of extracting high-fidelity microscopic vehicle trajectories from roadside video in dense, heterogeneous traffic—where occlusion, limited field-of-view, and irregular vehicle motion severely degrade tracking accuracy—this work introduces MVT, the first open-source, UAV-based aerial trajectory dataset with centimeter-level precision. Captured over six representative urban corridors in India’s National Capital Region at 30 Hz, MVT provides spatiotemporal coordinates, velocity, acceleration, and fine-grained vehicle class labels. We propose Data from Sky (DFS), an automated trajectory extraction framework integrating manual verification, spatial average speed consistency checks, and probe-vehicle trajectory validation to ensure high confidence and reliability. As the inaugural UAV-derived microscopic dataset tailored to heterogeneous urban environments, MVT spans diverse traffic densities and compositional mixes, enabling empirical discovery of key behavioral patterns—including lane-keeping preferences, speed distributions, and lateral maneuvering characteristics—and supporting downstream research in heterogeneous traffic modeling, simulation, and safety analysis.
Technology Category
Application Category
📝 Abstract
This paper offers openly available microscopic vehicle trajectory (MVT) datasets collected using unmanned aerial vehicles (UAVs) in heterogeneous, area-based urban traffic conditions. Traditional roadside video collection often fails in dense mixed traffic due to occlusion, limited viewing angles, and irregular vehicle movements. UAV-based recording provides a top-down perspective that reduces these issues and captures rich spatial and temporal dynamics. The datasets described here were extracted using the Data from Sky (DFS) platform and validated against manual counts, space mean speeds, and probe trajectories in earlier work. Each dataset contains time-stamped vehicle positions, speeds, longitudinal and lateral accelerations, and vehicle classifications at a resolution of 30 frames per second. Data were collected at six mid-block locations in the national capital region of India, covering diverse traffic compositions and density levels. Exploratory analyses highlight key behavioural patterns, including lane-keeping preferences, speed distributions, and lateral manoeuvres typical of heterogeneous and area-based traffic settings. These datasets are intended as a resource for the global research community to support simulation modelling, safety assessment, and behavioural studies under area-based traffic conditions. By making these empirical datasets openly available, this work offers researchers a unique opportunity to develop, test, and validate models that more accurately represent complex urban traffic environments.
Problem
Research questions and friction points this paper is trying to address.
Provides UAV-collected microscopic vehicle trajectory datasets for urban traffic analysis
Addresses occlusion and limited viewing issues in dense mixed traffic conditions
Supports simulation modeling and behavioral studies in heterogeneous traffic environments
Innovation
Methods, ideas, or system contributions that make the work stand out.
UAV top-down video collection reduces occlusion issues
Data from Sky platform extracts high-resolution vehicle trajectories
Datasets include diverse traffic compositions and behavioral patterns
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
