- freely available
Sensors 2017, 17(6), 1457; https://doi.org/10.3390/s17061457
- Increase efficiency and competitiveness: railways face ferocious competition from other modes (for example, the road sector provides attractive, cost-effective, reliable, flexible, and convenient door-to-door transport of freight and passengers across borders). In Europe, the challenge is further increased by a fragmented rail market, with numerous national systems for rail signaling and speed control. Thus, interoperability represents a key challenge for the free flow of rail traffic.
- Reduce rail noise and vibration, particularly in urban areas.
- Reduce greenhouse gas emissions. Although rail transport compares favorably to other transport means in terms of environmental impact, it can be further improved.
- Safety and security : rail safety in the European Union (EU) is among the highest in the world. Rail incidents (accidents, terrorism...) are not frequent and cause a relatively low toll of deaths, but often involve a substantial number of people. In order to maintain and enhance security, interoperable and harmonized safety standards are required.
- Reduce operation and maintenance costs, augment the capacity of the rail network.
2. Communication Systems in Railway Scenarios
2.1. Train-to-Infrastructure Connection
2.2. Inter-Car Connection
2.3. Intra-Car Communication Networks
- Direct transmission from the Base Station (BS). The problem in this mode is that the signal from the BS has to penetrate into the car, what derives in a loss of up to 24 dB that needs to be compensated by incrementing the transmission power and the receiver sensitivity.
- Use of in-car repeaters. The signals from the BS are received by an on-vehicle transceiver, which forwards them to a micro-base or to a Wi-Fi signal repeater. Note that this scheme increases the signal power through repeaters, but these additional devices increase the communications delay significantly. For this reason, a topic under research is the design and implementation of transmission schemes that offer good coverage for repeaters at high speeds.
- Two-hop access mode. In this mode the transmission requires first to travel from the BS to the antennas located on top of the train, and then to the receiver placed inside the train. This approach usually avoids the penetration losses related to a direct transmission from the BS. Nevertheless, it is worth noting that, since high frequency bands have large attenuations and path losses, its use may derive in a limited coverage.
Main Technologies for Intra-Car Communication Networks
- Satellite solutions. Distinct types are available (i.e., Geostationary Orbit (GEO), Medium Earth Orbit (MEO), Low Earth Orbit (LEO)) with different frequency bands and that may provide unidirectional or bidirectional communications. Satellites are used for both locating trains (aided by Global Navigation Satellite Systems (GNSS) systems , like GPS, the European GALILEO, the Russian GLONASS or the Chinese BEIDOU) and communicating with the wayside equipment.
- Terrestrial solutions. They can be grouped into two main categories: (a) technologies that rely on existing networks (i.e., public cellular networks), and (b) technologies that require ground infrastructure to be deployed: leaky coaxial cable, Wi-Fi, WiMAX, radio-over-Fiber, and optical solutions.
2.4. Inside the Railway Station
2.6. Wireless Sensor Networks
3. Overview on the Railway Applications Offered by GSM-R
3.1. GSM-R: The Solution Preferred
- ETCS: it allows for automating train control. It consists of a Radio Block Center (RBC) and a Lineside Electronic Unit (LEU). ETCS can be divided into three levels:
- ETCS level 1: the location of the train is determined by traditional means (i.e., no beacons are used for locating the train), whereas communications between fixed safety infrastructure and trains are performed by means of beacons (transponders placed between the rails of a railway track). GSM-R is only used for voice communications.
- ETCS level 2: the communications between trains and the railway infrastructure are continuous and supported by GSM-R technology. The location of the train is estimated by means of fixed beacons.
- ETCS level 3: the integrity of the train elements is checked at the train, thus no devices are required in the track. Fixed beacons are used to locate the train.
- EURORADIO GSM-R: radio infrastructure.
- EUROBALISE: beacons allowing for locating the trains accurately.
- EUROCAB: on-board management system that includes European Vital Computer (EVC), Driver-Machine Interface (DMI), and measurement devices such as odometers.
3.2. Railway-Specific Services and Requirements
- Services: voice, data, and call related features (Table 4).
- Voice Group Call Service (VGCS) conducts group calls between trains or Base Stations (BSs), or between station staff and trackside workers.
- Voice Broadcast Service (VBS) is used to broadcast recorded messages or announce operations to certain groups of trains or BSs. The call set-up required times are shown in Table 5, it shall be achieved in 95% of cases (MI). Furthermore, call set-up times for 99% of cases shall not be more than 1.5 times the required call setup time (MI).
- Functional addressing (FA): a train can be addressed by a number identifying its function.
- Location dependent addressing (LDA): calls from a train can be addressed based on its location.
- Shunting mode for communicating to a group involved in shunting operations.
- Railway specific features [43,48] include the set-up of urgent or frequent calls through single keystroke or similar; display of functional identity of calling/called party; fast and guaranteed call set-up; seamless communication support for train speeds up to 500 km/h; automatic and manual test modes with fault indications; control over mobile network selection; and control over system configuration.
4. Long Term Evolution (LTE): One Step Ahead of Broadband Communication Systems
4.1. Current Status of Standardization
- FBMC offers higher bandwidth efficiency, which is very beneficial since the simultaneous communications between different trains can be more efficiently allocated into the scarce spectrum available in railway environments.
- Coexistence between the current GSM-R and the new broadband systems is a major concern in the railway industry. OFDM-based systems usually exhibit a high co-channel interference, leading to a potential performance impact on current GSM-R systems. FBMC-based systems are much more efficient, thus allowing for better coexistence with current systems.
- Improved multiple-access facilities in the UL: due to the use of close-to-perfect subcarrier filters that ensure frequency localized subcarriers, FBMC does not require sophisticated synchronization methods for avoiding multiple-access interference. Nevertheless, while OFDMA is suitable for allocating efficiently a subset of subcarriers per user in the DL, the situation is different in the UL, because user signals must arrive at the Evolved NodeB (eNodeB) synchronously, both in terms of symbol timing and carrier frequency. For a practical deployment, a close-to-perfect carrier synchronization is necessary, which is affordable in a stationary network, but becomes a very difficult task in a network that includes mobile nodes.
- Suitability for doubly dispersive channels: the waveforms used in FBMC can be optimized for doubly dispersive channels like the ones present in high-speed train communications, hence allowing for a compromise between time and frequency channel response.
4.2. Migration Roadmap
5. The Rise of the Internet of Trains
- Telecommunications networks are becoming dedicated to IIoT applications and, as it was described in Section 2, broadband communications are getting inexpensive, faster, and ubiquitous. Train companies run fiber along their tracks and have relationships with mobile operators to use their networks to maintain continuous mobile connectivity. M2M technology can boost efficiency by using sensors embedded into different objects and systems to automate tasks and deliver real-time monitoring and analysis.
- Sensors for data acquisition are getting smaller, more affordable, and now consume less energy. In some cases, battery life can be extended to up to five years, which is important, because it is not always possible to be close to an electrical supply.
- Cloud-based services have become more pervasive, fueled both by fast connectivity and ever-smarter devices. They can be used to store sensor data and to provide the computation required for big data analytics.
- Big data and the Cyber-Physical System (CPS) enabled by Industrial IoT (IIoT) allow the different transportation modes to communicate with each other and with the surrounding environment, paving the way for truly integrated and intermodal solutions.
Industrial IoT Developments in the Rail Industry
6. IoT-Enabled Services: From More Efficient Operations to New Business Models
6.1. From Reactive to Predictive Maintenance
- Increased up-time through a significant reduction of unplanned downtime.
- Extension and flexibility of maintenance intervals because the risk is understood.
- Improved utilization of assets (e.g., more mileage with fewer cars).
- Enhanced planning, with streamlined Supply Chain Management (SCM).
- Maintenance can be performed at the least costly location. IIoT will have an important role in applications for dynamic maintenance as a provider of additional sources of data collected by sensors. In this way, in a Computer Integrated Manufacturing (CIM) context, an Enterprise Resource Planning (ERP) will act as an ad-hoc software extension that will manage the collected data.
- Uptime guarantees can be provided.
- Increased service contract capture rate, recurring revenues, and higher percentage of the total service revenue.
6.2. Smart Infrastructure
6.2.1. Advanced Monitoring of Assets
6.2.2. Video Surveillance Systems
6.2.4. Key Findings
6.3.1. Passenger Information System (PIS)
6.3.2. Freight Information System (FIS)
6.3.3. Key Findings
6.4. Train Control Systems
6.4.1. Autonomous Systems
6.4.2. Safety Assurance and Signaling Systems
6.4.3. Cyber Security for Railways
- Connecting physical infrastructure (e.g., tracks, tunnels, bridges/viaducts, switches/rail junctions).
- Mobile units (e.g., locomotives, rolling-stock system).
- Train stations (e.g., exterior, interior or restricted areas) and areas outside the train station.
- Control systems (e.g., signaling, central and local rail traffic management).
- Communication systems and communication network.
- Power supply (e.g., catenaries, power supply, national grid, diesel stations).
- Staff (e.g., driving personnel, handling personnel, maintenance personnel, information processing personnel).
- Freight (e.g., non-dangerous, explosive, toxic, flammable).
6.4.4. Key Findings
6.5. Energy Efficiency
Conflicts of Interest
|3GPP||3rd Generation Partnership Project|
|ASCI||Advanced Speech Call Items|
|BSC||Base Station Controller|
|CBTC||Communications Based Train Control|
|CCBG||Critical Communication Broadband Group|
|DSS||Decision Support System|
|EGPRS||Enhanced General Packet Radio Service|
|EIRENE||European Integrated Railway Radio Enhanced NEtwork|
|eLDA||enhanced Location Dependent Addressing|
|eMBMS||Evolved Multimedia Broadcast Multicast Service|
|eMLPP||enhanced Multi-Level Precedence and Pre-emption|
|EMU||Electric Multiple Unit|
|eREC||enhanced Railway Emergency Call|
|ERA||European Railway Agency|
|ERTMS||European Rail Traffic Management System|
|ETCS||European Train Control System|
|ETSI||European Telecommunications Standards Institute|
|FRS||Functional Requirements Specification|
|GCR||Group Call Register|
|GNSS||Global Navigation Satellite Systems|
|GSM-R||Global System for Mobile Communications-Railways|
|IMS||IP Multimedia Subsystem|
|IMT-Advanced||International Mobile Telecommunications - Advanced|
|IoT||Internet of Things|
|QoE||Quality of Experience|
|QoS||Quality of Service|
|LAS||Link Assurance Signal|
|LDA||Location Dependant Addressing|
|MAC||Medium Access Control|
|MBMS||Multimedia Broadcast Multicast Service|
|MBSFN||Multicast and Broadcast over Single Frequency Networks|
|MCPTT||Mission Critical Push To Talk over LTE|
|MRO||Maintenance, Repair and Operation|
|OFDM||Orthogonal Frequency Division Multiplexing|
|PoC||Push-to-Talk over Cellular|
|RAMS||Reliability, Availability, Maintainability and Safety|
|SIL||Safety Integrity Level|
|TCC||Train Control Center|
|TEDS||TETRA Enhanced Data Service|
|TETRA||Trans European Trunked RAdio|
|UIC||Union Internationale des Chemins de Fer|
|VBS||Voice Broadcast Service|
|VGCS||Voice Group Call Service|
|VoLTE||Voice over LTE|
|WiMAX||Worldwide Interoperability for Microwave Access|
|WLAN||Wireless Local Area Network|
|WSN||Wireless Sensor Networks|
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|Maximum speed (kph)||s ≤ 70||70 < s ≤ 160||160 < s < 250||≥250|
|Line length (km)||l ≤ 20||20 < l < 100||100 ≤ l < 250||l ≥ 250|
|Parallel tracks (units)||1||2||3||4|
|Rolling stock||Single||Similar||Mixed||Very Mixed|
|Passengers (per km of line)||n < 100,000||100,000 ≤ n < 200,000||200,000 ≤ n < 500,000||n ≥ 500,000|
|Range of services||Single||Small diversity||Multiple variances||Extremely varied|
|Frequency||DL: 921–925 MHz, |
UL: 876–880 MHz
|700 MHz||400 MHz||2.4/5.8 GHz||2.4/2.5/3.5 GHz||800/910 MHz, |
|450 MHz, 800 MHz, 1.4 GHz |
and 1.8 GHz
|Channel bandwidth||200 kHz||12.5 kHz||25 kHz||20–40 MHz||1.3–20 MHz||5 MHz||1.4–100 MHz||10–100 MHz||30–1000 MHz||>20 MHz||1.5–5 MHz|
|Peak data rate||172 Kbps||40–100 Kbps||5–10 Kbps||>10 Mbps||>30 Mbps||>2 Mbps (stationary) |
>384 kbps (mobile)
|DL: 50 Mbps, UL: 10 Mbps||1–10 Gbps||1–10 Mbps||>2 Mbps||DL: 5.3 Mbps, |
UL: 1.8 Mbps
|All-IP in native mode||Not standalone||No||No||Yes||Yes||Yes||Yes||Yes||Yes||Yes||Yes|
|Handover mechanism||Standard||Standard||Standard||Proprietary||Standard||Standard||Standard, soft (no data loss)||Standard||Standard||Variable||Proprietary|
|Modulation multiplexing||GMSK TDMA||4FSK||DPSK TDMA||QPSK, QAM||BPSK, QPSK, 16-QAM||PSK||QPSK, 16-QAM and |
64-QAM (OFDM, SCFDMA)
|Std. and OFDM||FSK-PSK||OFDM|
|Maturity||Mature||Mature in US||Mature||Widely adopted||Mature, lead to WiMAX 2||Mature||Emerging||Concepts like ’moving cell’||Mature (N700)||Mature but costly||Mature|
|Market support||Until 2025–2030||US||Almost obsolete||Yes||Decreasing support||Moving to LTE||Building standards||Mature||Japan, Europe||Europe (Thalys, SNCF)||Flarion|
|Wireless Technology||Robustness||Real-Time Performance||Range||Link Throughput||Network Scalability||Power Awareness|
|Service Group||Type of Service||Cab||ETCS Data Only||General Purpose||Operational||Shunting|
|General data applications||M||O||O||O||O|
|ETCS train control||NA||MI||NA||NA||NA|
|Specific features||Functional addressing (FA)||MI||NA||M||M||M|
|Location dependent addressing (LDA)||MI||M||O||O||O|
|Multiple driver communications within the same train||MI||NA||NA||NA||NA|
|Railway emergency calls||MI||NA||O||M||M|
|Call Type||Call Set-Up Time|
|Railway emergency call||<4 s (M)|
|High priority group calls||<5 s (M)|
|Group calls between drivers in the same area||<5 s (M)|
|All operational and high priority mobile-to-fixed calls not covered by the above||<5 s (O)|
|All operational and high priority fixed-to-mobile calls not covered by the above||<7 s (O)|
|All operational mobile-to-mobile calls not covered by the above||<10 s (O)|
|All other calls||<10 s (O)|
|Connection establishment delay of mobile originated calls||s (95%), ≤ 10 s (100%)|
|Connection establishment error ratio||(100%)|
|Connection loss rate||/h (100%)|
|Maximum end-to-end transfer delay (of 30 byte data block)||≤ 0.5 s (99%)|
|Transmission interference period||s (95%), s (99%)|
|Error-free period||s (95%), s (99%)|
|Network registration delay||≤ 30 s (95%), ≤ 35 s (99%), ≤ 40 s (100%)|
|Call-setup time||≤ 10 s (100%)|
|Emergency call-setup time||≤ 2 s (100%)|
|Duration of transmission failures||< 1 s (99%)|
|All-IP in native mode||No||Yes|
|Frequency||DL: 921–925 MHz, UL: 876–880 MHz||450 MHz, 800 MHz, 1.4 GHz and 1.8 GHz|
|Bandwidth||0.2 MHz||1.4–20 MHz|
|Modulation||GMSK||QPSK and 16-QAM|
|Peak data rate||DL/UL: 172 Kbps||DL: 50 Mbps, UL: 10 Mbps|
|Peak spectral efficiency||0.33 bps/Hz||2.55 bps/Hz|
|Cell range||8 Km||4–12 Km|
|Cell configuration||Single sector||Single sector|
|Data transmission||Requires voice call connection||Packet switching, UDP data|
|Packet retransmission||No (serial data)||Reduced (UDP packets)|
|MIMO||No||2 × 2|
|Mobility||500 Km/h||500 Km/h|
|Handover success rate||≥ 99.5%||≥ 99.9%|
|Handover type||Hard||Soft (no data loss)|
|Develop new-generation terminals|
|New-generation terminal trials|
|New-generation terminal roll-out|
|New-generation infrastructure trials|
|New-generation infrastructure transition|
|Parameter||Expected Evolution |
|Organizational model||In Europe, the scenario will not change substantially. Regulation for all member states will come from the EU, but overall responsibility will continue to be held at a national level.|
|Voice requirements||It may change over time. Some stakeholders have indicated some interest in making use of voice communications which are barely used today (e.g., for communications with train crew and/or passenger announcements independently of the communications between driver and controller). Some of the voice functions of GSM-R, such as the REC, may cease to be critical voice requirements if alternative solutions are available (e.g., if the emergency call and halt to train movement is handled through data/signaling).|
|ETCS||It currently uses GSM circuit-switched data and it is being evolved to allow the operations over IP packet networks. ETCS operation over GSM-R GPRS is ongoing.|
|Signaling requirements||It will not change substantially over the next 15+ years.|
|Communications||The technologies in use will continue to change rapidly with a major evolution in networks, services and devices over 3–5 year cycles.|
|Applications||The demand for more data applications will increase. Innovative services needed to increase profits.|
|Radio spectrum||In key bands, spectrum for mobile use will continue to be in high demand, becoming increasingly scarce and costly to acquire.|
|Predictive maintenance||Rabatel et al. ||Expert systems||Anomaly detection in complex maintenance operations. Precision is in all cases above 90% limiting both the number of false alarms and the number of undetected anomalies.|
|Thaduri et al. ||State-of-the-art, analytics, sensor fusion and Big Data||Precise location of a heavy freight train and its main parameters.|
|Firlik et al. ||Sensors, optimization procedures||Adjust the maintenance needs and track speed limits dynamically using embedded sensors. Experimental results of the implementation.|
|Soh et al. ||State-of-the-art||Different strategies for preventive maintenance scheduling problem: hybrid genetic algorithms, ontology-based modeling, heuristic approaches and strategic gang scheduling.|
|Nunez et al. ||Big Data||Maintenance decisions regarding railway tracks, all parts of the track can be monitored with appropriate intervals while maintaining the processing load within feasible limit.|
|Turner et al. [69,70]||Expert systems, DSS, ontologies||Knowledge based systems to develop a prototype for maintenance scheduling.|
|Canete et al. [71,72]||WSN, Zigbee||Monitoring system for slab track infrastructures using an energy consumption optimization strategy.|
|Xu et al. ||WSN, remote monitoring||Monitor the slope deformation, the variation in the internal stress and the PPV (Peak Particle Velocity) in an existing slope adjacent to a railway track.|
|Flammini et al. ||WSN||Early warning system for infrastructure surveillance and threat detection.|
|Sa et al. ||Shapelet algorithms||Detecting replacement of Railway Point Machines (RPMs) using an electric current sensor.|
|Ngigi et al. ||State-of-the-art||Applications of modern predictive control methods, analysis tools and techniques for condition monitoring systems.|
|Saa et al. ||Ontologies, knowledge rules-based system||Tool to design complex infrastructures.|
|Advanced monitoring||Ostachowicz et al. ||State-of-the-art||Trends in SHM|
|Kouroussis et al. ||State-of-the-art||Overview about the static and dynamic behaviour of ballasted railway tracks in SHM. Estimation of stress transfer from the train passage to the track using predictive numerical models.|
|Aygün et al. ||State-of-the-art, WSN||General applications, SHM network topology and deployments, hardware/software properties, communication protocols and standards; and energy harvesting solutions.|
|Wang et al. ||State-of-the-art, WSN||Integration of different types of sensors for SHM.|
|Giannoulis et al. ||State-of-the-art, WSN||Qualitative and quantitative analysis of WSN requirements, accurate timing and synchronized sensing for high sampling rate sensors.|
|Kolakowski et al. ||Sensors, ultrasonic probeheads, numerical models||Tests over a railway truss bridge.|
|Lai et al. ||Sensors||Development and experimental results of a liquid level sensor based on a fiber Bragg grating for monitoring differential settlement of railway track.|
|Berlin et al. ||WSN, feature extraction||Analysis of the vibration patterns caused by trains passing by.|
|Chen et al. ||Sensors, optical imaging, knowledge-based systems||Monitor rail damage in the turnout zone.|
|Hodge et al. ||State-of-the-art Sensors, WSN||Review of network design for condition monitoring.|
|Chen et al. ||High-level programming abstraction, WSN, middleware||Practical application for SHM, results obtained using the Cooja simulator.|
|Val et al. ||WSN||Time-synchronized network for SHM, the design includes channel measurements, network topology and architecture, physical and MAC layer design and network discovery. Performance evaluation show maximum sampling synchronization jitter values within 1 s for sensor nodes belonging the same base station, and 2 s for nodes of different base stations.|
|Li et al. ||Artificial intelligence, dynamic programming and genetic algorithms||Modeling the physical topology optimization for SHM.|
|Bischoff et al. ||WSN||Bridge structural monitoring based on events to achieve energy efficient operation.|
|Franceschinis et al. ||WSN||Predictive monitoring of train wagon conditions. Performance, based on ns-2 simulation results, suggests that the combined use of WSN and Wi-Fi in a hierarchical architecture is adequate for long trains (e.g., several coaches) and a large number of sensing nodes.|
|Anjali et al. ||WSN||Zigbee-based collision avoidance system that relies on vibration sensors.|
|Video security||Ambellouis et al. ||State-of-the-art||Analysis of surveillance systems, architectures, detection and analysis of complex events, onboard surveillance, applications to railway transport and review of the main worldwide projects.|
|Bochetti et al. ||Video analytics, artificial intelligence||Security management system integrating heterogeneous intrusion detection, access control, intelligent video-surveillance and sound detection devices. Probability of detection of at least the 80% for most alarms (including motion detection, unattended luggage, yellow line crossing) and a false alarm rate of less 10 nuisance alarms per day.|
|Li et al. ||System framework||Comprehensive video surveillance and management platform, successfully applied in the operation of Suzhou Subway Line 1.|
|Flammini et al. ||Bayesian networks||Framework with detection models for the evaluation of threat detection.|
|Operations||Zhang et al. ||IoT, complex event processing||Design of Electric Multiple Unit (EMU) IoT-system oriented to Maintenance, Repair and Operation (MRO) including holographic train visualization and alerts.|
|Briola et al. ||Ontology, natural language processing||Management of data collected from the centralized traffic control, improvement of the user interface through the exploitation of natural language queries.|
|Tutcher et al. ||Ontology, natural language processing||Asset Monitoring As A Service (AMaAS).|
|Fu et al. ||Decision support system, heuristics||Integrated hierarchical approach for creating line plans|
|Yang et al. ||Human-computer interaction, mathematical models||System for completing cyclic train timetables in high-speed railway scenarios|
|Wegele et al. ||Decision support systems, rescheduling algorithms||Dispatching support tools for re-ordering trains in case of delays.|
|Ho et al. ||Particle Swarm optimization (PSO)||The performance of PSO is evaluated by comparing the service quality of the resulting timetables obtained from a sequential timetable generation approach.|
|Albrecht et al. ||Heuristics||Space search to re-schedule timetable in case of infrastructure maintenance to minimize total delay and maximum train delay.|
|Tan et al. ||Discrete-event optimization model||Optimization algorithm for the real-time management of a complex rail network.|
|PIS||Ai et al. ||State-of-the-art||Combination of passenger loading information from trains with social networking.|
|Stelzer et al. ||Architecture design||Information exchange for connection dispatching, optimization of the interchange times for existing connections in intermodal transport.|
|Fingar et al. ||Sensors, RFID, QR and NFC||Solution that enables the use of phones for acquiring electronic public transport ticket.|
|Chiltern Railways ||Sensors, bluetooth||Application that open gates and determine the journeys taken.|
|FIS||Scholten et al. ||WSN||Monitoring integrity of cargo trains.|
|Zarri et al. ||Business rules, knowledge representation, W3C languages||Checking rail transport of hazardous materials.|
|Nan et al. ||WSN||Monitoring of rolling bearing in freight trains, comparison of different routing protocols and use of data compression and coding schemes based on lifting integer wavelet and Embedded Zerotree Wavelet (EZW) algorithms.|
|Casola et al. [113,114]||WSN, embedded systems, cryptography||Monitoring of freight trains transporting hazardous materials. Analysis on network performance by measuring the packet loss rate on different nodes in two working conditions: train standing in the station and train running.|
|Tumuler et al. ||Instrumentation, numerical analysis||Performance monitoring of track transitions under different loading environments. Identification of different factors contributing towards this differential movement, as well as development of design and maintenance strategies to mitigate the problem.|
|Crevier et al. ||Operations planning, bilevel optimization||Revenue management for rail freight using bilevel mathematical formulation which encompasses pricing decisions and network planning.|
|Bilegan et al. ||Multi-commodity flow problem, probabilistic mathematical model||Revenue management policy to dynamically accept/reject transportation requests in favor of forecasted demands with higher potential profit.|
|Sirikijpanichkul et al. ||Agent-based modelling, ontologies||Model for evaluating decisions on the positioning of road-rail inter-modal freight hubs.|
|Luo et al. ||Dynamic forecasting, stochastic comparison||Revenue management in intermodal transportation.|
|Wang et al. ||Stochastic resource allocation||Resource management for containerized cargo transportation.|
|Masoud et al. ||Mixed integer programming, heuristics||Scheduling optimization of the performance of sugarcane rail transport system.|
|Autonomous systems, safety assurance and signaling systems||Dominguez et al. ||ATO speed profile||A computer aided procedure for the design of optimal speed profiles for automatic subway and light rail systems. The newly designed profiles result in 20% of savings versus the one already in use. Taking into account the implementation of an on board storage device, up to 47.5% of savings could be expected.|
|Guo et al. ||ATP driver-machine interface, GUI model||Interface for controlling over-speeding automatically.|
|Salmane et al. ||Dempster–Shafer, hidden Markov model||Detecting hazard situations at level crossings with video analytics.|
|Govoni et al. ||State-of-the-art, fixed object scanner algorithm||Surveillance of railway crossing areas with UWB.|
|Goverde and Meng ||Data collection and processing||Detection of conflicts due to timetable flaws or capacity bottlenecks.|
|Kecman et al. ||Timed-event graph model, prediction algorithm||Model for predicting accurately the timing of certain train events.|
|Kecman et al. ||Process mining||Automatic identification of route conflicts with conflicting trains, arrival and departure times/delays at stations, and train paths on track section and blocking time level.|
|Corman et al. ||Advanced mathematical models, automatic tools for rescheduling traffic in real-time||Real-time control of railway traffic.|
|Sama et al. ||Alternative graph, disjunctive programming, metaheuristic algorithms||Fast scheduling and routing trains in complex and busy railway networks.|
|Marais et al. ||State-of-the-art||GNSS-based solutions for signaling applications.|
|Lu et al. ||Stochastic Petri net model||GNSS and sensor fusion in train localization.|
|Aboelela et al. ||WSN, fuzzy data aggregation||Multi-layered and multi-path routing architecture to predict inclinations in track.|
|Daliri et al. ||WSN, fuzzy logic, sensors||Image processing and electromagnetic detection of hazardous objects.|
|Wang et al. ||WSN||Monitoring system for early earthquake detection.|
|Wu et al. ||Key management protocols, cryptography||Secure train-to-train communication schemes: autonomous train-to-train channel with asymmetric cryptographic primitives and quasi-autonomous train-to-train channel with symmetric cryptographic primitives.|
|Chan et al. ||Key update scheme||Secure key establishment for train-to-infrastructure networking.|
|Bennetts et al. ||State-of-the-art||Securing railways: plans against the identified threats.|
|Greenberg et al. ||Simulation tools||Models that replicate rail passenger traffic flows, model to trace chemical plumes released by a slow-moving freight train, model that estimates the regional economic consequences of a variety of rail-related hazard events.|
|Energy efficiency||Xun et al. ||Analytical methods of coordinated train control||Fully automatic operation system by modifying the running time between adjacent stations.|
|Gruden et al. ||WSN, remote sensing, energy scavenging||Monitoring the wheel bearings, the number of successfully transmitted messages per day is in average about 92%, lost messages are caused by fading dips or mechanical damages of the sensors.|
|Hamid et al. ||Genetic algorithms||Design of an optimized train trajectory, energy by up to around 25% can be saved.|
|Bocharnikov et al. ||Genetic algorithms||Optimal train trajectories in electrically powered suburban railways. Energy savings of up to 40% may be achieved for a 10% increase in journey time.|
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