sumo3Dviz: A three dimensional traffic visualisation

2026-04-21

AI Summary

This study addresses the limited photorealistic three-dimensional visualization capabilities of existing traffic microsimulation tools—such as SUMO—which hinder their applicability in psychological experiments and immersive user studies. To bridge this gap, the authors propose a lightweight, open-source 3D visualization pipeline that operates solely on standard SUMO output data, eliminating the need for game engines or proprietary software. The approach is cross-platform, scriptable, and implemented in Python, with distribution via pip. By refining discrete trajectory interpolation and smoothing vehicle orientation, the method significantly enhances visual continuity and supports high-quality batch rendering of videos from both first-person and external viewpoints. The solution is openly available and well-suited for educational demonstrations, automated experimentation, and virtual stated-preference surveys.

Technology Category

• Computer Vision: 3D Computer Vision
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Traffic microsimulation software such as SUMO generate rich spatio-temporal data describing individual vehicle movements, interactions, and support the development of control strategies. While numerical outputs and 2D visualisations are sufficient for many technical analyses, they are often inadequate for applications that require intuitive interpretation, effective communication, or human-centred evaluation. In particular, user studies in mobility psychology, acceptance research, and virtual experience stated-preference experiments require realistic visualisations that reflect how traffic scenarios are perceived from a human perspective. This paper introduces sumo3Dviz, a lightweight, open-source 3D visualisation pipeline for SUMO traffic simulations. It converts standard SUMO simulation outputs, such as vehicle trajectories and signal states, into high-quality 3D renderings using a Python-based framework. In contrast to heavyweight game-engine-based approaches or tightly coupled co-simulation frameworks, sumo3Dviz is designed to be simple, scriptable, and reproducible. The tool is installable through the pip package manager, runs across operating systems, and works independently of any proprietary software or licenses. sumo3Dviz supports both external camera views and first-person perspectives, enabling cinematic overviews as well as driver-level experiences. The rendering process is optimized for batch video generation, making it suitable for large-scale scenario visualisation, educational demonstrations, and automated experiment pipelines. A key technical challenge addressed by the tool is trajectory interpolation and orientation smoothing, enabling visually coherent motion from discrete simulation outputs. Source Code on project's GitHub page: https://github.com/DerKevinRiehl/sumo3dviz/.

Problem

• 3D visualisation
• traffic simulation
• human-centred evaluation
• SUMO
• virtual experience

Innovation

• 3D visualization
• traffic microsimulation
• trajectory interpolation
• open-source tool
• SUMO