## 4 Industry 4.0 Concept in Railway Transportation
### Autonomous Trains and Automated Railway Traffic  
Due to the demand to reduce automobile traffic in highly urbanised areas, new innovative transportation technologies are introduced that can facilitate high levels of urban mobility with less traffic congestion and improved energy efficiency. The Autonomous Rail Rapid Transit (ARRT) vehicles that were proposed recently have the advantages of rapid implementation, adaptation of the driving mission en route, autonomous management, and possible integration, coexistence, and cooperation with other urban modes of transport (so-called intermodality). Some examples of such hybrid transportation systems include an autonomous personal transit (shuttle) system for public transport [156], shown in Figure 10, and a tram-like shuttle vehicle using virtual corridors defined for roadways [157], wherein these vehicles use built-in sensors to drive along the virtual route, with the additional benefit of being able to modify their driving missions in real time. These vehicles can undergo real-time route adaptation according to current traffic conditions, so the flexibility of road vehicles can be combined with the simultaneous regulation of such traffic in accordance with the rules of conventional railway traffic. These advantages make ARRT systems good candidates for future transportation in medium-sized cities [158]. Figure 10: Example of autonomous vehicle using the concept of virtual tracks and equipped with 5G remote sensing and communication platform [150]. Figure 9: Industrial revolutions and their consequences to transportation and logistics [151]. Innovative autonomous transport systems can also be made compatible with the current metro railway infrastructure with the possibility of having significantly lower implementation costs if they are integrated into the existing traffic infrastructure [159]. With the introduction of IoT, artificial intelligence and Big Data analytics, and next generation 5G communications, the autonomous vehicle supervision and control combines sensors, communication, and control and navigation software [160]. Spatial and temporal information about traffic entities is crucial for the effective management of vehicles with increased autonomy, such as when multiple independent autonomous high-speed trains are driven on the same railway track with hard mutual distance constraints [161]. Alternative very close formations may be used for the purpose of implementing advanced train dynamics control features, such as the concept of seamless inter-changeability (trains coupling and uncoupling on the move), as presented in [162]. A Global Navigation Satellite System (GNSS) is typically used for geospatial positioning and vehicle speed estimation, such as for the predictive control of autonomous transportation systems [163]. Due to the GNSS limitations of positioning precision and signal availability, high-speed trains also require alternative positioning methods, such as the use of independent inertial measurement unit (IMU) sensor suites [164] or signals from 5G communication networks. The latter can indeed provide very accurate location information with much higher availability that GNSS [15], which can be used for the cooperative control of multiple trains, further augmented by train-to-train communications [165]. The Industry 4.0 concept can facilitate a more systematic utilisation of information flows, including Industrial IoT technologies for better interconnectedness of different traffic entities and big data analytics for the purpose of transportation optimisation. Thus, it represents a key enabling technology to facilitate automated rail traffic control and energy use optimisation and their integration in a straightforward fashion. This has been illustrated by previous examples in references [149; 150], wherein the advanced information and communication technologies, data from remote sensor networks, and artificial intelligence have been successfully used to improve railway transportation safety and energy efficiency.