鐵路信號技術(shù)的未來：2040年及更遠(yuǎn)期的戰(zhàn)略技術(shù)路線圖

軌道世界 RailWorld

深耕產(chǎn)業(yè)·聚變生態(tài)·驅(qū)動未來

軌道交通行業(yè)動態(tài)

The Future of Railway Signalling

作者：Dr. Vinod Kumar Shah

徐紀(jì)康 提供素材

隨著全球鐵路網(wǎng)絡(luò)為順應(yīng)快速城市化進(jìn)程、氣候目標(biāo)要求與數(shù)字化變革趨勢不斷發(fā)展，鐵路信號領(lǐng)域正經(jīng)歷一場深刻的轉(zhuǎn)型。從繼電器控制系統(tǒng)，到人工智能驅(qū)動的自修復(fù)網(wǎng)絡(luò)，未來數(shù)十年，鐵路在列車運(yùn)行調(diào)度、安全保障與效率提升方面，有望實現(xiàn)質(zhì)的飛躍。本文梳理了這一技術(shù)演進(jìn)的關(guān)鍵階段—當(dāng)下的發(fā)展現(xiàn)狀、即將到來的技術(shù)突破，以及遠(yuǎn)期未來的可能圖景。

發(fā)展現(xiàn)狀（21世紀(jì)20年代）：數(shù)字化轉(zhuǎn)型全面推進(jìn)

如今，鐵路行業(yè)正完成從傳統(tǒng)繼電器信號系統(tǒng)向全數(shù)字化、電子化解決方案的轉(zhuǎn)型。在歐洲，歐洲列車控制系統(tǒng)（ETCS）2級的部署范圍持續(xù)擴(kuò)大，成為“歐洲單一鐵路區(qū)”的數(shù)字化核心支柱。德國、瑞士等國已取得顯著進(jìn)展，德國鐵路公司計劃到2035年，在其核心路網(wǎng)全面完成ETCS系統(tǒng)的鋪設(shè)。

在其他地區(qū)，印度、中國等鐵路大國的大型路網(wǎng)系統(tǒng)，正逐步采用自主研發(fā)的列車控制系統(tǒng)。例如印度的列車防碰撞系統(tǒng)—后更名為“卡瓦奇”（Kavach），截至2024年，該系統(tǒng)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(wǎng)絡(luò)正逐步取代老舊的銅纜基礎(chǔ)設(shè)施，實現(xiàn)高速、低延遲的數(shù)據(jù)傳輸。基于物聯(lián)網(wǎng)傳感器的預(yù)測性維護(hù)技術(shù)，能夠幫助運(yùn)營商在軌道和設(shè)備故障發(fā)生前檢測出異常問題，從而減少設(shè)備停機(jī)時間，提升運(yùn)行安全性。

近期發(fā)展（2025-2030年）：為智能化與自主化筑牢基礎(chǔ)

1. 5G-R技術(shù)落地：新一代鐵路通信標(biāo)準(zhǔn)

作為鐵路全球移動通信系統(tǒng)（GSM-R）的升級替代技術(shù)，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(yīng)用：

-對列車車廂內(nèi)部及站臺區(qū)域開展實時高清視頻監(jiān)控，保障運(yùn)營安全與乘客安全；

-為現(xiàn)場技術(shù)人員的檢修作業(yè)提供增強(qiáng)現(xiàn)實（AR）技術(shù)支持；

-優(yōu)化旅客服務(wù)體驗，包括車載流媒體服務(wù)、實時行程動態(tài)更新及全路網(wǎng)網(wǎng)絡(luò)連接。

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

2. 人工智能技術(shù)融合：實現(xiàn)運(yùn)營智慧化升級

基于人工智能的數(shù)據(jù)分析技術(shù)，將被廣泛應(yīng)用于優(yōu)化車流調(diào)度與預(yù)測系統(tǒng)故障。西門子交通、阿爾斯通等企業(yè)已開始利用機(jī)器學(xué)習(xí)技術(shù)，優(yōu)化列車運(yùn)行時刻表與設(shè)備維護(hù)方案。未來可期的應(yīng)用包括：

-基于實時路況動態(tài)調(diào)整列車運(yùn)行計劃；

-整合歷史數(shù)據(jù)與實時數(shù)據(jù)流，實現(xiàn)設(shè)備故障預(yù)測；

-通過智能牽引供電管理，優(yōu)化能源使用效率。

3. 列車自主運(yùn)行（ATO）：邁向4級自動化等級

完全自主運(yùn)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(tǒng)中成功落地。在近期規(guī)劃中，干線鐵路網(wǎng)將依托性能升級的列車控制系統(tǒng)與傳感器融合技術(shù)，率先在貨運(yùn)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(shù)賦能的信號系統(tǒng)

1. 衛(wèi)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(tǒng)將愈發(fā)依賴伽利略、全球定位系統(tǒng)（GPS）、北斗等全球?qū)Ш叫l(wèi)星系統(tǒng)（GNSS），實現(xiàn)列車的精準(zhǔn)實時定位。這一技術(shù)變革將帶來以下優(yōu)勢：

-降低對昂貴軌旁設(shè)備的依賴；

-實現(xiàn)移動閉塞行車組織，使列車在安全前提下進(jìn)一步縮小行車間隔；

-為傳統(tǒng)信號系統(tǒng)難以覆蓋的低密度線路或鄉(xiāng)村鐵路提供技術(shù)支持。

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

2. 量子通信技術(shù)：構(gòu)建“堅不可摧”的安全防線

隨著網(wǎng)絡(luò)威脅日趨復(fù)雜，信號系統(tǒng)將引入量子密鑰分發(fā)（QKD）技術(shù)，構(gòu)建即便是量子計算機(jī)也無法破解的加密防護(hù)體系。鑒于鐵路系統(tǒng)的國家關(guān)鍵基礎(chǔ)設(shè)施屬性，中、歐兩國已率先在交通基礎(chǔ)設(shè)施領(lǐng)域開展量子密鑰分發(fā)技術(shù)的早期試驗，鐵路行業(yè)成為重點(diǎn)應(yīng)用場景。

3. 邊緣計算技術(shù)：實現(xiàn)算力“本地化”

相較于將所有數(shù)據(jù)傳輸至中央控制中心處理的傳統(tǒng)模式，邊緣計算技術(shù)可支持軌旁系統(tǒng)實現(xiàn)本地自主決策。應(yīng)用場景包括：

-針對局部列車晚點(diǎn)、速度異常等情況，自動調(diào)整信號顯示；

-設(shè)備故障發(fā)生時，快速實現(xiàn)故障隔離與行車路徑重規(guī)劃；

-降低安全關(guān)鍵功能的響應(yīng)延遲。

4. 區(qū)塊鏈技術(shù)賦能鐵路資產(chǎn)管理

區(qū)塊鏈技術(shù)可提供防篡改的組件認(rèn)證記錄、設(shè)備維護(hù)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(biāo)準(zhǔn)場景下，這項技術(shù)的價值尤為突出。歐洲、日本的試點(diǎn)項目已驗證，區(qū)塊鏈技術(shù)在保障信號數(shù)據(jù)安全、構(gòu)建機(jī)車車輛部件數(shù)字孿生體等方面具備應(yīng)用潛力。

遠(yuǎn)期愿景（2040年后）：邁向自主化、認(rèn)知型鐵路生態(tài)系統(tǒng)

1. 實現(xiàn)交通網(wǎng)絡(luò)的深度融合

信號系統(tǒng)將不再孤立運(yùn)行。在“出行即服務(wù)”（MaaS）的發(fā)展模式下，鐵路將與公交、地鐵、網(wǎng)約車及自動駕駛擺渡車等交通方式深度整合，納入統(tǒng)一的交通網(wǎng)絡(luò)。交通管理系統(tǒng)將實現(xiàn)全出行鏈條的優(yōu)化調(diào)度：

-為晚點(diǎn)的接續(xù)列車提供信號優(yōu)先通行權(quán)；

-根據(jù)干線列車晚點(diǎn)情況，動態(tài)調(diào)整地鐵發(fā)車間隔；

-交通網(wǎng)絡(luò)發(fā)生突發(fā)中斷時，實時引導(dǎo)乘客換乘其他交通方式。

2. 構(gòu)建自修復(fù)信號網(wǎng)絡(luò)

由人工智能驅(qū)動的信號系統(tǒng)，將具備實時監(jiān)測自身運(yùn)行狀態(tài)的能力，并能參照現(xiàn)代云數(shù)據(jù)網(wǎng)絡(luò)的運(yùn)行模式，在故障發(fā)生時自動重構(gòu)系統(tǒng)。這類系統(tǒng)的核心能力包括：

-通過持續(xù)學(xué)習(xí)模型，實時檢測系統(tǒng)異常；

-實現(xiàn)列車運(yùn)行的實時動態(tài)路徑重規(guī)劃；

-根據(jù)車流變化與基礎(chǔ)設(shè)施健康狀態(tài)，靈活調(diào)整信號優(yōu)先級。

3. 研發(fā)認(rèn)知型信號系統(tǒng)

最具前瞻性的發(fā)展階段，是研發(fā)具備理解、預(yù)測與自主學(xué)習(xí)能力的認(rèn)知型信號系統(tǒng)。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(shù)據(jù)進(jìn)行綜合分析；

-預(yù)判潛在突發(fā)事件，并主動調(diào)整網(wǎng)絡(luò)運(yùn)行策略；

-從險性事件與運(yùn)營中斷事故中自主學(xué)習(xí)優(yōu)化，無需人工編程干預(yù)即可實現(xiàn)系統(tǒng)迭代升級。

結(jié)語：擘畫未來發(fā)展藍(lán)圖

鐵路信號系統(tǒng)正逐步完成從機(jī)電邏輯控制，向智能、場景感知型數(shù)字系統(tǒng)的跨越。這場變革不僅將提升鐵路運(yùn)輸?shù)陌踩耘c運(yùn)能，更將重新定義鐵路在綜合交通生態(tài)系統(tǒng)中的定位。

鐵路運(yùn)營商、技術(shù)供應(yīng)商與行業(yè)監(jiān)管機(jī)構(gòu)需共同做好準(zhǔn)備，迎接一個全新的時代—在這個時代，數(shù)字基礎(chǔ)設(shè)施與物理軌道同等重要；列車不僅能準(zhǔn)點(diǎn)運(yùn)行，更能實現(xiàn)自主思考、持續(xù)學(xué)習(xí)與動態(tài)自適應(yīng)。

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·