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/.

翻译：交通微观仿真软件（如SUMO）可生成描述个体车辆运动、交互的丰富时空数据，并支持控制策略的开发。尽管数值输出和二维可视化足以满足许多技术分析需求，但在需要直观解读、有效沟通或以人为本评估的应用场景中，这些方法往往存在不足。特别是在出行心理学、接受度研究以及虚拟体验陈述偏好实验等用户研究中，需要能够反映人类视角下交通场景感知的真实可视化效果。本文介绍sumo3Dviz——一个轻量级、开源的SUMO交通仿真三维可视化管线。该工具基于Python框架，可将车辆轨迹、信号状态等标准SUMO仿真输出转换为高质量三维渲染。与基于游戏引擎的重型方案或紧密耦合的联合仿真框架不同，sumo3Dviz的设计宗旨是简单、可脚本化且可复现。该工具可通过pip包管理器安装，支持跨操作系统运行，且无需依赖任何专有软件或许可证。sumo3Dviz既支持外部摄像头视角，也支持第一人称视角，可同时实现电影级全景概览与驾驶员级别体验。其渲染过程针对批量视频生成进行了优化，适用于大规模场景可视化、教学演示及自动化实验管线。该工具解决的核心技术挑战是轨迹插值与方向平滑，能够从离散仿真输出中生成视觉连贯的运动。项目源代码位于GitHub页面：https://github.com/DerKevinRiehl/sumo3dviz/。