The Digital Transformation of Metros study reviewed the strategies, initiatives, and technologies used by metros to implement digital transformation for four key purposes: safety improvement, station operations and management, train operations, and depot management. In recent years there have been several digital trends observed in metros, including provision of real-time train loading information, centralised station management, customer-facing staff equipped with tablets, installation of passenger counting equipment, etc. Metros’ long-term digital transformation plans typically involve multi-phase programmes with strong support from management, employee expertise, and partnership with external parties. Ultimately, digital transformation is highly related to transforming employees. Therefore the study summarised metros’ good practices to create a digital culture, as well as ways to remove barriers along the journey to digital transformation.
This research project examined metros’ practices when making the decision of whether to replace or refurbish ageing rolling stock. As annualised expenditure on rolling stock is typically about 20-25% of total operating costs, fleet investment decisions have significant impacts on overall metro costs. The focus of the study was to identify key factors and criteria in deciding to replace or refurbish rolling stock at end of nominal life, including the risks and opportunities of life extension beyond initial design life; to identify best practices in design, specification and planning of refurbishments; and to advise metros on appraisal and business case development process, parameters and assumptions.
Metros have been gaining increasingly significant benefits through refurbishment, and many metros (especially newer ones) are now undertaking or planning refurbishments to ageing fleets that are approaching or past their initial design lives. These refurbishment programmes are designed to extend initial design lives by as much as 15-20 years.
A key guiding principle is that refurbishment prolongs ‘more of the same’, as reliability following refurbishment tends to remain fairly similar. Therefore, only highly reliable fleets are usually worth refurbishing. A second principle is that most metros limit the extent of technology change attempted through refurbishment. So if significant upgrade is required, for example to enable unattended train operations, generally a new vehicle is preferred.
This case study has successfully assisted CoMET and Nova members in their decision-making. An Asian member needed to buy new trains when their 15-year-old line was extended and re-signalled. Findings from this report assisted with their decision to replace all the trains on the lines, instead of converting the older trains to work with newer signalling and then operating a mixed fleet. Conversely, Montréal STM used this research in support of a decision to refurbish their 40-year-old MR-73 cars and extend their life to 60 years. This is projected to save Quebec taxpayers nearly $500 million over the next 20 years. More information on Montréal’s decision can be found here.
Public transport is essential to the success and feasibility of major events, and most major cities with metros are likely to host at least one large-scale event over a 15-year horizon. A 2014 Nova case study captured members’ experience with hosting a wide range of events and covered the entire timeline of hosting a major event, as illustrated below.
The study found that early and active involvement in major event planning – which can include major capital projects – is very beneficial for metros, as is conducting their own demand forecasting. The long lead-time for most major events also allows for metros to learn from each other and visit metros hosting the same or similar events. Despite the short-term nature of most major events, metros gain the most value from retaining longer-term improvements, whether transformational or incremental.
The study demonstrated that while major events can present challenges to metros, many metros are using them successfully as opportunities to showcase their existing good practices, experiment with new ones, identify needs, and leverage funding.
RTSC’s recent research into unattended train operations (UTO) has investigated the role of human operational support on UTO lines. In doing so, a key finding relates to the use of attendants or train captains – metro staff members who are based in the passenger car, rather than in a separate driver’s cab. Formal definitions of Grades of Automation as in IEC-62290 assume that if a staff member is onboard, they are fulfilling a necessary role, in operating the train, as in London’s Docklands Light Railway where attendants close the doors. The assumption is therefore that lines capable of being operated unattended (Grade of Automation 4) are operated unattended. This research study has found that in fact some metros with GoA4 lines actually use attendants on all trains for other reasons, for example to provide customer service. This has led RTSC to describe this type of line as ‘Attended GoA4,’ reflecting the fact that it fits the specification for a GoA4 line as described in IEC-62290, but is not being operated unattended. The diagram below illustrates how ‘Attended GoA4’ automation fits in with the grades of automation.
The case study explored in more detail the actual staffing levels used or planned on participating metros’ automatic lines. The total number of operational staff was compared with the number of assets (stations and trains) in service, providing metros with a useful benchmarking metric that normalised for differences in line length and service level. A typology of staffing models was also developed and linked with associated staffing levels. This work on staffing was complemented by investigation into the technologies required to enable automation; their costs; and metros experiences with their reliability.
This research was presented as a poster at the 94th Transportation Research Board of the National Academies Annual Meeting in Washington DC, in 2015.