欧洲智慧列车自组织模式研究项目介绍

欧洲智慧列车自组织模式研究项目SORTED MOBILITY介绍

徐纪康 整理

SORTED MOBILITY是“面向分布式出行演进的自组织轨道交通”（Self-Organized Rail Traffic for the Evolution of Decentralized MOBILITY）的缩写。该项目提出了一种整体性方法，用于城市及城际区域公共交通运营的自组织管理，尤其将轨道交通作为出行网络核心进行重点关注。在这一方法框架下，智能列车将以自组织模式运行。轨道交通系统将具备更强的韧性，能够根据出行需求变化及突发干扰情况，实现对动态环境的自适应调整。

1 铁路系统运营痛点

突发事件影响：铁路系统在运营中易因突发事件导致性能下降，进而引发冲突—即两列及以上列车若按计划速度运行，会同时占用同一轨道段。

冲突连锁反应：冲突发生时，列车需减速或停车以保证安全间距，最终导致延误传播。

2 传统管理模式局限

采用集中式，主要依赖调度员的个人经验进行重调度决策。

3 SORTED MOBILITY项目的研究目标

为解决铁路系统中突发事件引发的冲突及延误传播问题，欧洲SORTED MOBILITY项目提出了一种基于自组织的列车重调度与重路由范式，替代传统集中式铁路管理模式。

该模式以列车作为智能体，通过 邻域选择、假设生成、共识达成、合并 四个模块的生成实时交通计划，并以法国巴黎-勒阿弗尔线一段80公里线路为案例进行概念验证，结果显示共识解决方案能较好地逼近全局最优。

该项目提出“范式转移”—用多智能体（智慧列车）的自组织决策替代传统集中式管理。

自组织决策通过列车（智能体）序贯执行以下四个模块实现，最终生成实时交通计划（RTTP）（包含重路径与重调度决策），具体流程如下所示：

4 优缺点比较分析

SORTEDMOBILITY项目提出的自组织列车管理模式，相比传统集中式模式，在决策流程上的核心差异体现在决策主体、灵活性与响应性三个维度：

1.决策主体：传统集中式模式以“调度员”为核心，依赖个人经验及集中式算法制定决策；自组织模式以“列车”为智能体，列车自主参与邻域识别、决策假设生成、共识协商，具备独立决策能力；

2.灵活性：集中式模式受限于统一的优化目标（如最小化总延误），难以兼顾个体列车需求；自组织模式中列车可优先评估自身延误，邻域可动态重叠，决策更贴合局部交通实际；

3.响应性：集中式模式需汇总全系统信息后计算决策，可能存在延迟；自组织模式通过“每30分钟更新交通状态+分布式共识”实现近实时调整，对扰动的响应更迅速。

部分英文原文内容

Trains of the Future: How Self-Organising Railways Could Revolutionise Transportation

Railway traffic management is on the cusp of transformation. While self-driving cars and autonomous drones capture headlines, an international research project has been exploring whether trains could manage themselves. The Sorted Mobility project, funded by JPI Urban Europe, brings together rail operators and researchers from France, Italy, Denmark, and the Netherlands to investigate whether self-organised rail traffic could match or even outperform traditional centralised management.

By Sahfayet Choudhury

Published: 5 December 2024

“Today, trains work according to a timetable, and when something happens, an operator decides how to deal with traffic, so delays don’t propagate through the network,” explains Paola Pellegrini, the project’s coordinator. She adds, “but with networks becoming increasingly complex, we wanted to explore if trains could have their own ‘brain’ and negotiate with each other.”

Shifting Away from Central Control

In this self-organised system, trains would communicate directly, making local decisions about priorities and platforms without central control. The project developed a sophisticated testing environment combining mathematical optimisation with simulations. “Every detail of railway infrastructure and train movements is represented,” Paola says, including train weight, length, and acceleration curves.

Within Sorted Mobility’s autonomous system, trains use a consensus system inspired by artificial intelligence models when delays occur. Each train maintains a list of preferred options but doesn’t need to reveal why it prefers certain choices—crucial for maintaining commercial privacy. If trains can’t reach an agreement, they default to their original schedule, ensuring system stability.

Testing Across Diverse Railways

The project tested this concept in distinctly different scenarios. One was a busy Italian line between Pioltello and Rovato, which is 54km long and carries both freight and passenger traffic. Here, researchers examined how self-organisation could work in a competitive environment where multiple operators share a track. Running simulations with over 75 trains in a five-hour period, they found the system could maintain overall performance while protecting operators’ commercial interests.

“Most trains could be a bit happier without worsening system performance and keeping their private information,” Paola notes. This proved particularly valuable for freight operators, who might not want to reveal the importance of specific shipments but still need to negotiate priority.

In France, the team studied a rural “capillary” line between Guingamp and Paimpol, which is 37km long with a movement of 150 passengers and sees 21 trains every five and a half hours. Here, they found that self-organisation could maintain service quality while potentially reducing operational overhead. “These types of lines are very critical in France and other similar countries,” Paola explains. “There must be a way to operate them with minimum costs while maintaining service.”

The single-operator environment allowed researchers to focus on passenger benefits. They discovered that trains that could use their own passenger counting data to make decisions achieved better outcomes than centralised systems that lack access to this information due to privacy barriers.

Tackling Urban Complexity

The most ambitious test came in Copenhagen’s suburban rail network, comprised of 170km of a star-shaped urban network with multiple travel operators and over 16000 passengers using the system in the three hours of the morning peak. Here, researchers integrated passenger prediction into the decision-making process. Using a sophisticated population model incorporating socioeconomic data, work patterns, and travel habits, they created the first-ever system of this kind that considers how railway performance affects passenger route choices.

“We predict demand flows considering the impact of railway traffic on passenger choices,” Paola explains. “If I’m a passenger, I’m going to check my travel app and decide based on what lines are working. This had never been done in the literature before.”

While time constraints limited the full exploration of this approach, initial results suggested significant potential. The team observed that looking 20 minutes ahead in passenger predictions could inform better decisions, though they also identified challenges with “border effects” when significant events occurred just outside their prediction window.

Across all three contexts, self-organised systems performed comparably to traditional centralised management while offering distinct advantages. Beyond maintaining privacy and improving passenger service, self-organisation could help solve the scaling challenges that plague current systems.

“The problem of scaling up decisions is very real today,” Paola notes. While centralised systems struggle with more extensive networks, self-organisation’s local decision-making approach could naturally handle network expansion.

However, implementation faces significant institutional and legal hurdles. As Paola explains, “We probably won’t have a fully self-organised system soon, but we could start thinking of hybrid systems where trains make some decisions.” The project’s final round table, which included representatives from Deutsche Bahn, Network Rail, and other European railway infrastructure providers, endorsed this gradual approach.

Rural lines might see the first implementations. “Changing the system in these isolated lines may be easier, operated by a single operator where problems are less critical – we might see change there within five to ten years,” Paola suggests. Success in these environments could pave the way for broader adoption.

Building Better Assessment Tools

Perhaps equally valuable to the self-organisation concept itself, the project developed sophisticated assessment tools combining optimisation, simulation, and passenger modelling. This framework allows for a realistic evaluation of any traffic management approach, whether centralised or self-organised.

“What we started doing was a very original contribution,” Paola notes. “We predicted demand flows considering the impact of railway traffic on passenger choices. This opens up crucial questions about how people would react to smarter systems. Would they use them more?”

A Model for Future Research

Beyond its technical findings, the project demonstrated the value of allowing truly exploratory research in railway systems. “This was amazing,” Paola reflects. “We had actual collaboration between academia and industry without needing to end up with a commercialised product. We were really doing pure research.”

This freedom allowed the team to investigate a concept that could reshape railway management thoroughly. While full implementation remains distant, the project proved self-organisation’s viability and identified promising paths forward through hybrid systems and targeted deployment.

素材来源：读者投稿

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