How tech and AI can help rail win back riders
By Peter Knudsen, GM Passenger Predictability, Veovo
As more people across the globe roll up their sleeves for the COVID-19 vaccination, the prospect of returning to the office is finally becoming a reality. For rail and transit operators experiencing decreased ridership, the improving situation brings hope that passengers will return – if they are confident enough to do so.
As offices re-open, many companies are offering hybrid working arrangements. Along with this will come entirely new travel habits – travelling outside of peak times or on different days. There’s also the question of how people will now travel to the office, with trends showing many former transit riders now electing for travel in a single-occupancy vehicle.
For operators, the return to commuting means competing for choice riders and adjusting to these evolving usage patterns.
And compete they must. Although ridership levels have been creeping up, they remain well below pre-pandemic levels. According to the American Public Transportation Association, just 45 per cent of ridership is back nationally in the U.S. Meanwhile, New York’s Metropolitan Transportation Authority says ridership on its two commuter rail lines is still down by as much as 70 per cent on weekdays.
There may only be a narrow window to encourage travellers to return to the rails before new habits are formed. However, whether they travel by train may well come down to how safe they feel and how accessible is the service.
Understanding rider preferences is key to improving patronage
With heightened sensitivity, passengers want greater control over every possible element of their journey to avoid crowds. The more information that is available to them, the safer and more comfortable they will feel.
First, operators can respond to new passenger preferences by monitoring live occupancy and predicting in near real-time any changes based on people flows and the train schedule. Then, translate that data into meaningful information for riders via apps, passenger-facing public displays, or third-party digital services.
Secondly, operators can dive into understanding and delivering service to individual rider preferences – what service elements will delight customers or enforce the idea of safety in public transit whenever they want to ride.
In a recent survey of transport providers by consulting firm Arthur D. Little, the “availability, relevance, reliability, timeliness and personalisation” of passenger communications was cited as a key game changer required to rebuild customer relevance and trust.
So instead of only providing crowd data, communications could recommend actions to improve the transit experience. From the best time to leave home, route suggestions that avoid transfer congestion, or guidance on what entrance to use or what carriage to board.
Harnessing real-time insights for commuter crowd management
Riders want to feel safe travelling by train, but how can operators deliver on the promise of safety when returning passenger numbers are so fluid? The answer is to combine real-time insight with intelligent automation.
Understanding actual people flows and occupancy hot spots, as they happen, will allow operators to take action to reduce crowding and proactively respond to unexpected outages.
For example, if the arrival of a train is predicted to breach platform density levels, displays can automatically direct alighting passengers away from those entering the platform. Alternatively, the next train could be held back for a minute until the platform is cleared.
Another example is to control station congestion points where there are multiple underground access points. For predicted peaks, operators can adjust signage in real-time to create one-way flows, with some station entrances turned into exit-only through remote turnstile deactivation. Turning platforms from bidirectional to single direction thoroughfares greatly improves boarding and alighting times for more efficient platform occupancy management. Passengers may have to walk longer, but far better they do that above ground in the fresh air than navigating through a crowd below ground.
Changing flows will be a key strategy for reducing congestion in stations. However, to regain the loyalty of commuters, it will be equally important to keep them informed of any changes or improvements. In addition to the “what” of platform operations, passengers will need the “why” of safety and service improvement, conveyed through media campaigns, staff engagement, on-train announcements and in-station displays.
At the recent IRS Webinar, ’Restoring passenger confidence in rail: the power of demand management’, Transport for London (TFL) spoke about its joint £6 million campaign with the City of London, ‘#letsdolondon’. The initiative aims to halting the car-led recovery and getting people back into the city on public transport. To back its words with actions, the operator has enhanced the TFL Go app with real-time crowd data and bolstered its demand management resource to smooth peaks and create great experiences.
Success in returning passenger confidence will be built on action, information and improved perception, – Frank Ibe, TFL Head of Line Operations.
Planning services around a diverse commuter demographic and behaviours
As the return to work picks up, operators serving a diverse demographic may see a large difference in load factors, even within the same network.
Looking beyond commuter crowd management, a wider benefit of understanding people occupancy and flow across journeys is better planning and adapting services to changing demand profiles.
Forecasting and simulation should be centred around machine learning models that use real data – how people arrive, move and wait, including where they board, alight, transfer and terminate their journey. Using passenger behaviour analytics and layering it with network punctuality metrics, operators can accurately forecast rider journeys and train and platform occupancy. This allows them to make better decisions to optimise schedules and staffing, maintenance planning and cleaning, ensuring plans reflect changing travel patterns.
And, as passenger numbers rise, the same data around people movement can help improve station design and retail planning.
Looking down the track for regaining choice riders
Commuter numbers may take some time to return to pre-pandemic levels. However, by focusing on giving travellers better control over their journey, operators have a real opportunity to draw them back to rail sooner.
With more workers returning to offices, rail operators need to act now to rebuild patronage by:
1. Instilling trust by addressing the most crowded services first.
Drill into what data you need to tackle the issues and understand where you should prioritise your digitisation efforts. Not only for the improvement of steady-state operations but to also quickly assess and react to unplanned issues in real-time through response automation that intelligently guides passengers.
In systems that move millions of passengers, detecting unplanned issues in real-time and automating your response is vital to proactively address problems and intelligently guiding passengers. – Peter Knudsen, Veovo
2. Giving passengers the best information to make informed decisions.
Continually inform your passengers how you’re making their journeys safer. Envisioning a roadmap that delivers a better customer experience can’t be done in a vacuum or by simply relying on word of mouth. Riders need to know what steps operators are taking, when, and how passengers benefit. Use all possible digital channels to engage and empower preference.
With actionable insight and effective communications, operators can make the right decisions for their station, network and passengers — to encourage riders back now and earn their loyalty for the future.
Veovo recently joined Transport for London and Metro Madrid, McKinsey and UIC in an IRITS Webinar to discuss how rail operators can reduce uncertainty and increase confidence in travel. Watch the webinar.
If you are keen to discover more, please get in touch.