鐵路信號技術的未來：2040年及更遠期的戰略技術路線圖

軌道世界 RailWorld

深耕產業·聚變生態·驅動未來

軌道交通行業動態

The Future of Railway Signalling

作者：Dr. Vinod Kumar Shah

徐紀康 提供素材

隨著全球鐵路網絡為順應快速城市化進程、氣候目標要求與數字化變革趨勢不斷發展，鐵路信號領域正經歷一場深刻的轉型。從繼電器控制系統，到人工智能驅動的自修復網絡，未來數十年，鐵路在列車運行調度、安全保障與效率提升方面，有望實現質的飛躍。本文梳理了這一技術演進的關鍵階段—當下的發展現狀、即將到來的技術突破，以及遠期未來的可能圖景。

發展現狀（21世紀20年代）：數字化轉型全面推進

如今，鐵路行業正完成從傳統繼電器信號系統向全數字化、電子化解決方案的轉型。在歐洲，歐洲列車控制系統（ETCS）2級的部署范圍持續擴大，成為“歐洲單一鐵路區”的數字化核心支柱。德國、瑞士等國已取得顯著進展，德國鐵路公司計劃到2035年，在其核心路網全面完成ETCS系統的鋪設。

在其他地區，印度、中國等鐵路大國的大型路網系統，正逐步采用自主研發的列車控制系統。例如印度的列車防碰撞系統—后更名為“卡瓦奇”（Kavach），截至2024年，該系統已在超過3000公里的鐵路線上投入使用。

與此同時，光纖通信網絡正逐步取代老舊的銅纜基礎設施，實現高速、低延遲的數據傳輸。基于物聯網傳感器的預測性維護技術，能夠幫助運營商在軌道和設備故障發生前檢測出異常問題，從而減少設備停機時間，提升運行安全性。

近期發展（2025-2030年）：為智能化與自主化筑牢基礎

1. 5G-R技術落地：新一代鐵路通信標準

作為鐵路全球移動通信系統（GSM-R）的升級替代技術，5G-R有望在2030年前，在主要鐵路干線完成部署。憑借大幅提升的帶寬與超低延遲特性，5G-R將賦能多項前沿應用：

-對列車車廂內部及站臺區域開展實時高清視頻監控，保障運營安全與乘客安全；

-為現場技術人員的檢修作業提供增強現實（AR）技術支持；

-優化旅客服務體驗，包括車載流媒體服務、實時行程動態更新及全路網網絡連接。

中國已率先在高鐵線路啟動5G-R試點項目測試，歐洲也已啟動“未來鐵路移動通信系統”（FRMCS）項目，為其鐵路通信技術的轉型提供指導框架。

2. 人工智能技術融合：實現運營智慧化升級

基于人工智能的數據分析技術，將被廣泛應用于優化車流調度與預測系統故障。西門子交通、阿爾斯通等企業已開始利用機器學習技術，優化列車運行時刻表與設備維護方案。未來可期的應用包括：

-基于實時路況動態調整列車運行計劃；

-整合歷史數據與實時數據流，實現設備故障預測；

-通過智能牽引供電管理，優化能源使用效率。

3. 列車自主運行（ATO）：邁向4級自動化等級

完全自主運行列車—即4級自動化等級（GoA4），已在巴黎地鐵14號線、新加坡地鐵等封閉地鐵系統中成功落地。在近期規劃中，干線鐵路網將依托性能升級的列車控制系統與傳感器融合技術，率先在貨運線路與低客流量線路中引入4級自動化等級。

中期變革（2030-2040年）：分布式、高安全性、空間技術賦能的信號系統

1. 衛星定位列車控制：突破軌道電路的局限

下一代鐵路信號系統將愈發依賴伽利略、全球定位系統（GPS）、北斗等全球導航衛星系統（GNSS），實現列車的精準實時定位。這一技術變革將帶來以下優勢：

-降低對昂貴軌旁設備的依賴；

-實現移動閉塞行車組織，使列車在安全前提下進一步縮小行車間隔；

-為傳統信號系統難以覆蓋的低密度線路或鄉村鐵路提供技術支持。

澳大利亞鐵路軌道公司（ARTC）已在其“先進列車管理系統”（ATMS）項目中部署衛星列車控制系統，為其他國家提供了可借鑒的范本。

2. 量子通信技術：構建“堅不可摧”的安全防線

隨著網絡威脅日趨復雜，信號系統將引入量子密鑰分發（QKD）技術，構建即便是量子計算機也無法破解的加密防護體系。鑒于鐵路系統的國家關鍵基礎設施屬性，中、歐兩國已率先在交通基礎設施領域開展量子密鑰分發技術的早期試驗，鐵路行業成為重點應用場景。

3. 邊緣計算技術：實現算力“本地化”

相較于將所有數據傳輸至中央控制中心處理的傳統模式，邊緣計算技術可支持軌旁系統實現本地自主決策。應用場景包括：

-針對局部列車晚點、速度異常等情況，自動調整信號顯示；

-設備故障發生時，快速實現故障隔離與行車路徑重規劃；

-降低安全關鍵功能的響應延遲。

4. 區塊鏈技術賦能鐵路資產管理

區塊鏈技術可提供防篡改的組件認證記錄、設備維護歷史及事故日志。在對可追溯性與透明度要求極高的高安全標準場景下，這項技術的價值尤為突出。歐洲、日本的試點項目已驗證，區塊鏈技術在保障信號數據安全、構建機車車輛部件數字孿生體等方面具備應用潛力。

遠期愿景（2040年后）：邁向自主化、認知型鐵路生態系統

1. 實現交通網絡的深度融合

信號系統將不再孤立運行。在“出行即服務”（MaaS）的發展模式下，鐵路將與公交、地鐵、網約車及自動駕駛擺渡車等交通方式深度整合，納入統一的交通網絡。交通管理系統將實現全出行鏈條的優化調度：

-為晚點的接續列車提供信號優先通行權；

-根據干線列車晚點情況，動態調整地鐵發車間隔；

-交通網絡發生突發中斷時，實時引導乘客換乘其他交通方式。

2. 構建自修復信號網絡

由人工智能驅動的信號系統，將具備實時監測自身運行狀態的能力，并能參照現代云數據網絡的運行模式，在故障發生時自動重構系統。這類系統的核心能力包括：

-通過持續學習模型，實時檢測系統異常；

-實現列車運行的實時動態路徑重規劃；

-根據車流變化與基礎設施健康狀態，靈活調整信號優先級。

3. 研發認知型信號系統

最具前瞻性的發展階段，是研發具備理解、預測與自主學習能力的認知型信號系統。其核心功能包括：

-整合天氣狀況、線路擁堵程度、列車載客量等多維度場景數據進行綜合分析；

-預判潛在突發事件，并主動調整網絡運行策略；

-從險性事件與運營中斷事故中自主學習優化，無需人工編程干預即可實現系統迭代升級。

結語：擘畫未來發展藍圖

鐵路信號系統正逐步完成從機電邏輯控制，向智能、場景感知型數字系統的跨越。這場變革不僅將提升鐵路運輸的安全性與運能，更將重新定義鐵路在綜合交通生態系統中的定位。

鐵路運營商、技術供應商與行業監管機構需共同做好準備，迎接一個全新的時代—在這個時代，數字基礎設施與物理軌道同等重要；列車不僅能準點運行，更能實現自主思考、持續學習與動態自適應。

原文：

The Future of Railway Signalling: A Strategic Technology Roadmap Through 2040 and Beyond

Dr. Vinod Kumar Shah

As global rail networks evolve in response to rapid urbanization, climate imperatives, and digital disruption, the railway signalling landscape is undergoing a profound transformation. From relay-based systems to AI-powered, self-healing networks, the coming decades promise a quantum leap in how railways manage train movement, safety, and efficiency. This article maps the key phases of this evolution—what’s happening now, what’s next, and what the long-term future may hold.

Current State (2020s): Digitalization in Full Swing

Today, the railway industry is completing its transition from legacy relay-based signalling systems to fully digital and electronic solutions. In Europe, the deployment of European Train Control System (ETCS) Level 2 continues to expand, forming the digital backbone of the Single European Railway Area. Countries like Germany and Switzerland have made significant progress, with Deutsche Bahn aiming for complete ETCS rollout on its core network by 2035.

Elsewhere, large rail systems in India and China are adopting indigenous train control systems such as India’s Train Collision Avoidance System (TCAS)—renamed Kavach—which has already been implemented on over 3,000 km of track as of 2024.

Simultaneously, fiber optic communication networks are replacing aging copper infrastructure, enabling high-speed, low-latency data transmission. Predictive maintenance powered by IoT sensors is helping operators detect track and equipment anomalies before they cause failures, reducing downtime and boosting safety.

Near-Term Evolution (2025–2030): Laying the Foundations for Intelligence and Autonomy

1. 5G-R Implementation: The Next-Gen Rail Communication Standard

The successor to GSM-R (Global System for Mobile Communications – Railway), 5G-R is expected to roll out across major rail corridors by 2030. With significantly greater bandwidth and ultra-low latency, 5G-R will power a range of advanced applications:

Real-time HD video monitoring of train interiors and platforms for security and passenger safety.

Augmented reality (AR) support for field technicians performing maintenance tasks.

Enhanced passenger services, including streaming, real-time journey updates, and connectivity.

China has begun testing 5G-R pilot projects on high-speed rail lines, and Europe has initiated the Future Railway Mobile Communication System (FRMCS) project to guide its transition.

2. Artificial Intelligence Integration: Smarter Operations

AI-powered analytics will increasingly be used to optimize traffic flow and predict system failures. Companies like Siemens Mobility and Alstom are already leveraging machine learning to fine-tune timetables and maintenance regimes. Expect:

Dynamic train rescheduling based on real-time conditions.

Predictive failure detection using historic and real-time data streams.

Optimized energy use via smarter traction power management.

3. Autonomous Train Operations (ATO): Towards GoA4

Fully autonomous trains—Grade of Automation 4 (GoA4)—have already seen success in closed metro systems like Paris Metro Line 14 and Singapore’s MRT. In the near term, mainline networks will begin adopting GoA4 for freight and low-traffic routes, supported by improved train control and sensor fusion.

Medium-Term Transformation (2030–2040): Distributed, Secure, and Space-Enabled Systems

1. Satellite-Based Train Control: Beyond the Track Circuit

Next-generation signalling will rely increasingly on GNSS (Global Navigation Satellite Systems) such as Galileo, GPS, and BeiDou for precise, real-time train positioning. This shift will:

Reduce dependence on expensive trackside equipment.

Enable moving block operations that allow trains to run closer together safely.

Support low-density or rural lines that are hard to wire for traditional systems.

Australia’s ARTC is already deploying satellite-based train control in the Advanced Train Management System (ATMS) project, a model for other countries.

2. Quantum Communication: Unbreakable Security

As cyber threats grow more sophisticated, signalling systems will adopt quantum key distribution (QKD) to ensure encryption that even quantum computers cannot break. Early trials of QKD in transport infrastructure are already underway in China and Europe, with rail as a key application due to its critical national importance.

3. Edge Computing: Processing Power at the Source

Instead of sending all data to a central control center, edge computing will enable trackside systems to make decisions locally. For example:

Automatic signal adjustments in response to local train delays or speed anomalies.

Rapid fault isolation and rerouting in case of equipment failure.

Reduced latency for safety-critical functions.

4. Blockchain for Railway Asset Management

Blockchain technology will offer tamper-proof records of component certification, maintenance history, and incident logs. This is vital in high-safety environments where auditability and transparency are crucial. Pilot projects in Europe and Japan have shown promise in securing signalling data and creating digital twins of rolling stock components.

Long-Term Vision (2040+): Toward an Autonomous, Cognitive Rail Ecosystem

1. Fully Integrated Mobility Networks

Signalling will no longer operate in isolation. In a Mobility-as-a-Service (MaaS) environment, rail will be part of a unified network with buses, metro, ride-shares, and autonomous shuttles. Traffic management systems will optimize entire journeys:

Coordinating signal priority for late-running connecting services.

Adjusting metro headways based on long-distance train delays.

Real-time rerouting of passengers across modes in case of disruptions.

2. Self-Healing Signalling Networks

AI-driven systems will monitor their own performance and automatically reconfigure around failures—much like modern cloud data networks. These systems will:

Detect anomalies via continuous learning models.

Reroute traffic in real time.

Adapt signalling priorities to changing traffic conditions and infrastructure health.

3. Cognitive Signalling Systems

The most futuristic phase involves cognitive signalling systems that understand, predict, and learn. These systems could:

Interpret contextual data such as weather, congestion, and passenger load.

Anticipate emergencies and adjust network behavior proactively.

Learn from near-misses and disruptions, evolving over time without explicit reprogramming.

Conclusion: Charting the Path Ahead

The railway signalling landscape is set to transform from electromechanical logic to intelligent, context-aware digital systems. This transformation will not only enhance safety and capacity but also redefine how rail integrates into broader mobility ecosystems.

Railway operators, technology providers, and regulators must now prepare for a future where digital infrastructure is as critical as physical rails, and where trains don’t just move on time—they think, learn, and adapt.

素材來源：投稿

軌道世界視頻號

·軌道交通，通向美好生活·