Lamanna Fabio, Prisma Michele, Medeossi Giorgio
Freelance Civil Engineer, Treviso, Italy.
Trenolab, Gorizia, Italy.
PLoS One. 2025 Jul 18;20(7):e0328681. doi: 10.1371/journal.pone.0328681. eCollection 2025.
This study introduces a novel framework for analysing railway timetable connectivity through complex network theory and the Infomap clustering algorithm. By transforming timetable data into network representations, we systematically assess the connectivity and efficiency of the Norwegian railway system for the 2024 and projected 2033 scenarios. We define and implement the Timetable Connectivity Index (Tc), a comprehensive metric integrating service frequency, travel times, and the hierarchical network structure. The analysis is conducted across three distinct network spaces: Stops, Stations, and Changes, with both unweighted and weighted networks. Our results reveal key insights into how infrastructural developments, service frequencies, and travel time adjustments influence network connectivity. Comparative analysis of the two scenarios highlights improvements in physical infrastructure and travel time efficiency by 2033, while also identifying areas where service frequency distribution and transfer effectiveness may require further optimization. The findings provide valuable insights for railway planners and operators, aiming to improve the efficiency and reliability of train networks.
本研究引入了一种新颖的框架，通过复杂网络理论和Infomap聚类算法来分析铁路时刻表的连通性。通过将时刻表数据转换为网络表示形式，我们系统地评估了2024年以及预计的2033年情景下挪威铁路系统的连通性和效率。我们定义并实施了时刻表连通性指数（Tc），这是一个综合指标，整合了服务频率、旅行时间和分层网络结构。分析在三个不同的网络空间中进行：站点、车站和换乘点，包括无加权网络和加权网络。我们的结果揭示了基础设施发展、服务频率和旅行时间调整如何影响网络连通性的关键见解。对这两种情景的比较分析突出了到2033年物理基础设施和旅行时间效率的改善，同时也确定了服务频率分布和换乘有效性可能需要进一步优化的领域。这些发现为铁路规划者和运营商提供了有价值的见解，旨在提高列车网络的效率和可靠性。
