11-20, 14:30–14:55 (Pacific/Auckland), WG126
This project develops an open-source-based platform that visualizes urban traffic simulation data using various techniques and presents a way to intuitively understand complex movement patterns.
Urban traffic flow is becoming increasingly complex, and it is difficult to grasp its meaning with only numerical data. In particular, there is a need for technical approaches to visually interpret simulation data that includes temporal and spatial patterns.
Purpose:
- To provide an intuitive and visual way of understanding complex urban traffic simulation results by implementing various spatial visualization techniques using open-source tools.
This system utilizes spatial data based on GeoJSON and PostGIS to generate various visualization layers, such as vehicle movement trajectories (trip lines), OD matrices, heatmaps, and time-based density changes. Simulation results are visualized in 2D and 3D using OpenLayers and CesiumJS, respectively, and by expressing the same data differently in 2D/3D, interpretability and communication are enhanced.
Key Features:
- Trip line animations to trace vehicle movement
- OD matrix visualization with directional flow arrows
- Heatmap layers for density and congestion hotspots
- Time-series based map layers with playback
- Dual 2D/3D rendering and synchronized view interaction
As a result, users can explore simulation data over time, analyze congestion patterns in specific areas, and intuitively identify complex OD patterns. All components are designed based on a microservice architecture, allowing scalability and integration with various simulation engines.
This project focuses on implementing various visualization techniques using only open-source technologies and expressing complex traffic flows in a form that anyone can understand.
Open Source Technologies Used:
- CesiumJS for 3D spatial visualization and animation
- OpenLayers for 2D WebGL vector layer rendering
- PostGIS for spatial data storage and analysis
- GeoJSON as the primary transport and visualization data format
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (RS-2024-00459703, Development of next-generation AI integrated mobility simulation and prediction/application technologies for metropolitan cities)
