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Opportunities for energy and the environment

In 2017, light rail systems accounted for an estimated 34 billion passenger-kilometres, providing a significantly smaller proportion of global urban rail activity than metro rail, which accounted for almost 500 billion passenger-kilometres. This is partly explained by the much lower vehicle capacity and operational speed of light rail services compared with metro rail, and partly by the concentration of the light rail network in a relatively small number of countries. While metro rail

is more heavily represented in Asia (where the world’s megacities are concentrated), light rail is Page | 37 most abundant in the European Union, which is home to the world’s ten largest light rail

systems operating over half of the total 12 000 kilometres of light rail tracks (Table 1.1).

Globally, urban rail accounts for a relatively small share of urban passenger transport activity (Figure 1.12). Japan, Korea and Russia, which host several of the busiest metro systems in the world, have a share of urban rail that is higher than the world average. The share of light rail activity in urban rail activity is 23% in the European Union compared with 9% globally.

Figure 1.12 Modal shares of urban transport activity in passenger-kilometres (left) and as a share of urban rail in total urban passenger activity by country (right), 2017

Note: The figures include metro and light rail systems within city limits and do not include suburban and commuter rail networks. Source: IEA (2018a).

Key message • On a global scale, rail accounts for a minor share of urban passenger transport. On a country basis, Japan and Korea have the highest shares of rail in urban transport.

Conventional and high-speed rail

On a global basis, conventional and high-speed rail have consistently accounted for around 15% of the passenger-kilometres travelled by all transport modes outside of city limits since 2000 and around 90% of all travel by rail (even with extensive metro construction in many countries). There has been a net increase of conventional rail and high-speed rail activity from 2.1 trillion passenger-kilometres in 2000 to 3.9 trillion passenger-kilometres in 2016, an average annual growth rate of 4% (Figure 1.13, left).

This pace for overall conventional and high-speed rail expansion, in part, is driven by the increase of conventional rail in India, where activity nearly tripled between 2000 and 2016. This has been a principal influence to sustain the global average annual growth rate of conventional rail at 3.3%. By 2016, India represented 37% of conventional passenger rail activity worldwide, ahead of China, with 29%, and Japan, with 11%.8

8 The UITP estimates that 90% of rail passengers in the European Union use regional or suburban commuter rail (likely to be reported under conventional rail services) (UITP, 2016). According to the UITP, the countries that transport the most

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Opportunities for energy and the environment

Figure 1.13 Non-urban transport activity by mode, 2000-2017 (left) and the share of high-speed rail in non-urban rail, 2000-2016 (right)

Sources: IEA (2018a); UIC (2018b).

Key message • Non-urban road passenger transport and aviation have grown at the same rate as nonurban rail since 2000.

Box 1.3 Usage patterns of conventional passenger rail services

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There are key differences between countries and regions which rely on high shares of conventional rail for passenger transport. Two parameters are especially interesting: the average distance covered in a single trip (Figure 1.14, left) and the passenger train occupancy factor (Figure 1.14, right).

Figure 1.14 Conventional rail average passenger trip distance and train occupancy, 2016

Note: GDP = gross domestic product; PPP = purchasing power parity.

Sources: IEA assessment, based on UIC (2018a); National Bureau of Statistics of China (2018); Indian Railways (2018a); Japan Ministry of Land, Infrastructure and Tourism (2018); AAR (2017) and Russian Federation State Statistics Service (2018).

Key message • Unsurprisingly, average trip distances are longest in the biggest countries. Train occupancy rates are positively correlated with relatively low per-capita income.

100 - 200 kilometres. Average trip distances in more densely populated or smaller countries or

passengers by commuter rail are Japan (about 16 billion trips per year), India (4.5 billion trips per year) and Brazil (1 billion trips per year) (UITP, 2018e).

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regions, such as Japan and the European Union, are close to 50 kilometres. This reflects the importance of suburban/commuter rail (typically characterised by high passenger throughput and relatively short trip distances) in the total of conventional rail travel in countries with higher densities.

High-speed rail developments in the European Union and several Asian countries (mostly China) have shifted some demand away from conventional rail and also generated new travel demand. Despite its limited geographical spread, high-speed rail activity grew by more than 11% per year between 2000 and 2016, nearly three-times faster than growth in any other non-urban transport mode, attaining nearly 600 billion passenger-kilometres in 2016. Most growth occurred after 2007, mainly driven by the surge in China (Figure 1.15) (UIC, 2018b). By 2016, high-speed rail activity represented 16% of overall activity on conventional and high-speed networks, up from 6% in 2000.

Figure 1.15 High-speed rail activity for key regions, 2000-2016

Sources: IEA assessment based on UIC (2018a).

Key message • High-speed rail activity worldwide expanded fivefold in less than ten years, predominantly in China.

Box 1.4 Digital technologies: autonomous trains and advanced rail traffic management and control systems

Rapid advances in data, analytics and connectivity are driving a wave of digitalisation including in the transport sector (IEA, 2017). As demand for rail mobility increases faster than new infrastructure construction, digital technologies can facilitate more intensive use of tracks by reducing the time and distance between trains, to increase capacity and boost returns on investment while improving user convenience and maintaining high safety standards.9

Advanced traffic management and control systems help ensure the safe and efficient operation of rail transport. These systems include Communication-Based Train Controls (CBTC),10 extensively used for urban rail, where they are combined with Driver Assistance Systems (DAS) to maximise the use of the network. They also include the European Railway

9High utilisation rates can make rail projects more economically viable and decrease the time necessary to offset life-cycle emissions from infrastructure construction (particularly underground or elevated segments), as discussed in the “additional emissions: looking at rail from a life-cycle perspective” section.

10CBTC include Automatic Train Operation (ATO), Protection (ATP) and Stop (ATS) technologies.

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Opportunities for energy and the environment

Traffic Management System (ERTMS), which uses control, command, signalling and communication systems to ensure the inter-operability of trains across the region, primarily on conventional and high-speed rail networks.11 All these technologies have the capacity to maximise network utilisation by reducing the headway between trains,12 and they have also demonstrated effectiveness in reducing energy consumption by up to 15% (Dunbar, Roberts and Zhao, 2017).13

Automated trains offer the promise of improved safety, lower costs and improved energy efficiency,14 beyond the level achieved by advanced traffic management and control systems. The rail sector defines “Grades of Automation” (GoA) under the International Electrotechnical Commission (IEC) 62267 standard, which range from fully manual operations, such as a tram operating in street traffic (GoA-0), to unattended, fully automated operations (GoA-4). The first fully automated metro (GoA-4) opened in 1981 in Kobe, Japan and there are now over 1 000 kilometres of GoA-4 lines in 42 cities worldwide, or around 7% of the total installed metro networks (UITP, 2018a; UITP, 2018f).

While the number of driverless metros is expanding rapidly on closed and secured lines, there are significant challenges in deploying fully autonomous trains on open, uncontrolled or unsecured lines (such as trams, intercity and freight lines). On this front, two examples of recent progress are the operation of a driverless freight train on a 280 kilometre heavy-haul line in Western Australia and the testing of the world’s first autonomous tram in Potsdam, Germany (Burroughs, 2018; Connolly, 2018). Efforts are underway to deploy autonomous driving technology in conventional rail services as well: to ensure compatibility between ERTMS and developments in automation. The European Union initiative “Shift2Rail” is working on potential standardisation and inter-operability of automated train operations with ERTMS (Siemens, 2016). In addition, the French rail operator, SNCF, has announced plans to deploy “semi-autonomous” trains by 2020 and fully automated trains by 2023 (Railway Gazette, 2018).

The use of other digital technologies such as big data analytics and artificial intelligence, also offer opportunities to significantly improve services for end-users through seamless integration across different modes and other measures.15 They can also contribute to improving energy efficiency16 and reducing costs for operators.17 Digitalisation is also

11The ERTMS has two primary components: GSM-R (Global System for Mobile Communications - Railways) and ETCS (European Train Control System), (European Commission, 2018). GSM-R is a dedicated radio communication system for voice and data services. ETCS is a signalling and train protection system used to monitor train speed and movements.

12The ERTMS can enable capacity on existing tracks to be increased by 40%. Simulations of higher levels of ERMTS signalling (Level 2 and 3) demonstrate that utilisation could be more than doubled (ERTMS, 2018).

13The combined use of CBTC systems and DAS can result in additional energy savings. CBTC, coupled with more reliance on coasting, has demonstrated average energy savings of 16% in the Paris metro system (Urien, 2013).

14For example, fully automated train operations have the potential to reduce energy consumption by up to 20% by optimising driving patterns while taking into account other variables such as real-time weight and timetables (Trentesaux et al., 2018; Siemens, 2016; González-Gil et al., 2014; Douglas et al., 2015).

15In the Netherlands for example, Nederlandse Spoorwegen (NS) is testing its Zitplaatszoeker app, which uses colour-coded occupancy visualisation (using weight sensors on tracks) to help customers to find a seat and ensure a more even distribution of passengers and weight (Vosman, 2018).

16Massive volumes of data collected by sensors on trains and tracks can be analysed to optimise operations at the train and system level in real-time. Such data can be used to educate drivers on the practices best designed to reduce energy consumption, including through DAS. The potential energy saving of DAS range from 5-20%, depending on connectivity to traffic management systems, use of system-level optimisation and type of route (González-Gil et al., 2014; Institute for Futures Studies and Technology Assessment - UIC, n.a.; Douglas et al., 2015). Digital technologies can also enable optimised timetabling and planning of rail operations, favouring energy savings. For example, energy-efficient train timetabling (EETT)

– i.e. developing an optimised timetable across a network to maximise energy-efficient driving – can lead to energy savings of up to 35% (Scheepmaker, Goverde and Kroon, 2017)

17The use of big data analytics and artificial intelligence can inform and improve maintenance regimes, helping to reduce costly repairs and downtime, improving safety outcomes and extending asset lifetimes. With the help of digitalized assets and data, condition-based and predictive maintenance reduce maintenance costs by up to 25%, or nearly EUR 17 billion globally per year (McKinsey and Company, 2017). Big data analytics on smarter components, like wheel bearings, can also provide insights to improve the design of future components, improving performance, extending lifetimes and reducing waste.