Efficient public transport systems are crucial for sustainable urban development as cities face increasing mobility demands. Yet, many public transport networks struggle to meet diverse user needs due to historical development, urban constraints, and financial limitations. Traditionally, planning of transport network structure is often based on limited surveys, expert opinions, or partial usage statistics. This provides an incomplete basis for decision-making. We introduce an data-driven approach to public transport planning and optimization, calculating detailed accessibility measures at the individual housing level. Our visual analytics workflow combines population-group-based simulations with dynamic infrastructure analysis, utilizing a scenario-based model to simulate daily travel patterns of varied demographic groups, including schoolchildren, students, workers, and pensioners. These population groups, each with unique mobility requirements and routines, interact with the transport system under different scenarios traveling to and from Points of Interest (POI), assessed through travel time calculations. Results are visualized through heatmaps, density maps, and network overlays, as well as detailed statistics. Our system allows us to analyze both the underlying data and simulation results on multiple levels of granularity, delivering both broad insights and granular details. Case studies with the city of Konstanz, Germany reveal key areas where public transport does not meet specific needs, confirmed through a formative user study. Due to the high cost of changing legacy networks, our analysis facilitates the identification of strategic enhancements, such as optimized schedules or rerouting, and few targeted stop relocations, highlighting consequential variations in accessibility to pinpointing critical service gaps.

翻译：随着城市面临日益增长的出行需求，高效的公共交通系统对可持续城市发展至关重要。然而，由于历史发展、城市限制和财政约束，许多公共交通网络难以满足多样化的用户需求。传统的交通网络结构规划通常基于有限的调查、专家意见或部分使用统计数据，这为决策提供了不完整的基础。我们提出一种数据驱动的公共交通规划与优化方法，在单个住宅层面计算精细化的可达性指标。我们的可视化分析工作流程将基于人口分组的模拟与动态基础设施分析相结合，利用基于场景的模型模拟不同人口群体（包括学童、学生、工作者和退休人员）的日常出行模式。这些具有独特出行需求和习惯的人口群体在不同场景下与交通系统互动，通过往返兴趣点（POI）的行程时间计算进行评估。结果通过热力图、密度图、网络叠加图及详细统计数据实现可视化。我们的系统支持在多个粒度层级上分析基础数据与模拟结果，既能提供宏观洞察也能呈现微观细节。以德国康斯坦茨市为例的案例研究揭示了公共交通无法满足特定需求的关键区域，这一发现通过形成性用户研究得以验证。鉴于改造传统网络的高昂成本，我们的分析有助于识别战略性改进措施，例如优化时刻表或调整线路，以及少量有针对性的站点迁移，通过突出可达性的关键差异来精准定位服务缺口。