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Highlights
- 45% reduction in late buses
- 7% reduction in passenger wait time at stops
- £18,400 in average additional profit per month
- 5x profit growth (on CitySwift routes compared with non-Cityswift routes)
- 9x average return on investment
National Express West Midlands (NXWM) is a leading public transport operator in the UK, and part of National Express Group, a leading transport provider delivering services in 11 countries with annual revenues of around £2bn.
NXWM works alongside CitySwift’s automated runtime generation software to help deliver the most efficient and reliable transport solutions on the market, through processes enabled by technology - a key part of National Express Group’s Evolve Strategy.
“CitySwift has an underlying knowledge of the public transport business. They are not just another IT company saying, ‘Oh, we know how to use big data and we’re sure we know how it can apply to you.’ They actually know how it can be applied effectively.”
- Andy Foster, Director of Network Planning, National Express Bus
URGENCY TO ADAPT TO A SHIFT IN PASSENGER MOBILITY AND DEMAND
The bus industry has been forced to be more agile and fast-paced than ever before. With different routines, working hours, and overall mobility patterns, there is uncertainty around passenger demand – and an urgent requirement to understand it.
To continue providing a world-class service to customers, NXWM needed to ensure that they were accurately tracking and matching vehicle supply with the new levels of demand. The network is more focused than ever on encouraging people to get back to buses more frequently, so it was important that the network could ensure a positive reputation for punctuality and reliability among passengers.
In order to optimise the service for passengers, network planners needed data. The data that network planners could retrieve was scattered across spreadsheets, and analysing this siloed data was leading to time and resource drain. This manual effort was slowing things down and ultimately hindering the effectiveness of NXWM’s network and the overall passenger experience.
FRICTION FREE: AUTOMATED RUNTIMES, FUELED BY DATA
Using CitySwift’s Evolve AI-powered technology, NXWM has been able to analyse its scattered data sources in one place. Evolve ingests this data and optimises timetables for punctuality and reliability, while still catering specifically to match vehicle supply with passenger demand.
As a crucial part of the planning process, the team uses Evolve to select an existing schedule, and run it through CitySwift’s data engine. The resulting output is a pre-formatted, visually appealing set of modifiable runtimes that the team uploads to their scheduling software.
For the NXWM team, a task that previously took days now takes minutes. In fact, the whole process takes less than 10 minutes, so the team at NXWM can adapt their schedules at speed, and to changing trends in demand, in just a few clicks. This team can reallocate this time to strategic, proactive planning and decision-making.
Built on a powerful data analysis platform, Evolve processes all of NXWM’s data for complete integrity, accuracy, and reliability, ensuring no data anomalies. The platform is connected to NXWM’s scheduling, AVL, and, crucially, ticketing data – so the team can trust that their optimised schedules are built using a comprehensive foundation of their own data, and taking into account passenger movements and needs.
DELIVERING A REPUTATION FOR THE MOST RELIABLE PASSENGER EXPERIENCE
Partnering with CitySwift, NXWM have been able to adapt its service quickly and in line with the new and unforeseen era of mobility. In NXWM’s Birmingham Central Depot alone, Evolve has helped NXWM provide a smooth and reliable service to all passengers with wait time at stops reduced by 7% on average – leading to a reduced journey time and more reliable service for passengers. Alongside this reduced wait time, there has been a 7.7% increase in service reliability (also known as runtime accuracy) for passengers.
This reliable service leads to an increase in patronage and a high net promoter score, which improves NXWM’s reputation as an attractive operator that focuses relentlessly on the customer. This positive sentiment, while encouraging people back on buses, will strengthen NXWM’s relationships with local authorities – growing successful partnerships that serve National Express Group’s propositions to reinvigorate public transport and fill the transit gap.
SUPERIOR OUTCOMES FOR THE BUSINESS THROUGH MAJOR ROI
In addition to improving the passenger experience, NXWM is also growing profits and delivering a higher return on investment – routes that have implemented Evolve have seen 5x profit growth compared with non-CitySwift routes.
Across a two-month period, the operator made an average additional profit of £18,400 per month on CitySwift routes – this consists of 105 buses – with an average 9x ROI. Scaled to the full fleet of 575 high-frequency buses, this is an average additional profit of £101,200 per month.
On the lower frequency fleet of 725 buses, the ROI ranges from 1.7x to 5x would generate an estimated additional profit of between £49,696 and £149,087 per month. The low-frequency calculations are based on approximately 725 buses, 2m PAX per week, and a minimum 1% generalised journey time improvement. The 3 different scenarios are based on 3 different levels of elasticity for journey time improvements vs passenger demand growth.
What’s next for CitySwift and National Express?
National Express plans to further increase the adoption of SwiftSchedule across their worldwide urban bus operations, deploying the performance optimisation platform across most service changes and multiplying the ROI they have already achieved. Using CitySwift, the team will predict punctuality and determine resource levels, as well as optimise service frequency – matching supply and demand across the entire network. This will support National Express Group’s focus on the customer, and delivering a reliable service – ultimately getting passengers back on board, and improving revenue.
