One of the many profoundly transformative aspects of the Covid-19 pandemic is the way that it has accelerated change in society. Existing trends such as working from home and digitalisation of everyday lives have been turbo-charged, effectively condensing more than a decade’s worth of anticipated change into a few short months.
With standard behaviour patterns thrown in the air and confidence in public transport severely undermined, we face a difficult and uncertain future. Suffering reduced bus revenues, bus service providers must find ways to continue delivering safe and reliable mobility.
The challenge here is that in ‘normal’ circumstances a sensible response to falling demand might be to reduce services to match ridership. But of course, that model won’t work well today because a) passenger numbers are restricted for social distancing, and b) a reduction in services will reduce overall mobility options, thereby eroding fragile faith in bus travel. And if all this were not worrying enough, consider also the wider implications of a world where more people shift away from public transport in favour of private cars, Uber and taxis.
Today many cities have almost as many cars on the streets as before Covid-19. We could well be standing on the precipice of a congestion and air quality disaster – one that will introduce more traffic, more pollution, and a climate crisis moving faster than ever. We must take a step back.
Navigating the New Normal
Bus ridership has taken a huge hit everywhere. At the time of writing, in London passenger numbers have recovered somewhat, but only to around 40% of pre-Covid levels. DfT data suggests similar levels across the UK outside of the capital – and this is of course consistent with stories emerging from all over the world.
If we want public transport to remain relevant and reliable, with strong bus ridership (within current social distance-defined levels), we must offer passengers the same quality and reliability of experience as before Covid-19 – but with fewer vehicles.
This, of course, is a phenomenal challenge. However, it is perfectly possible, but it will require us to completely reimagine our networks – and the way to do that is with data.
So, we know that we need to redefine our networks to meet future demand. This kind of forward prediction necessarily requires adequate management of disruption, and here there are three time frames that must be considered:
- Near short term: In the uncertain post-Covid (and beyond) world, networks must be adaptable for a few hours at a time. This would typically be in response to police incidents, emergency road closures, accidents or terror threats.
- Short term: We need to consider the ways in which we can adapt the network for slightly longer periods, of perhaps a few days at a time. This might be to manage severe weather events, which are an increasingly likely consequence of climate change.
- Long term: Networks must be adaptable for longer periods, measured in weeks or months. This kind of disruption may include the kinds of changing ridership patterns we have experienced with Covid-19.
Wanted: Unavailable Data
The one major problem with this requirement is that for this kind of forecasting the data we need isn’t historic; it’s future. The events we need to model haven’t happened yet.
Fortunately, this actually isn’t the insurmountable problem some might expect: this is where machine learning and dynamic forecasting can help, by creating a dynamic ‘forward simulator’ that can predict what will happen to a network in terms of the three time frames outlined.
This simulator would require additional input from external sources that may directly affect the network, for example an interface with the police to implement any road closures or similar issues.
We also need to be able to forecast travel times, enabling us to understand where and when people will be traveling, and making it possible to flex resources to ensure the network meets actual demand.
New Ways of Thinking
While the public transport sector has done a good job of utilising available data – think how journey planners have transformed mobility in urban areas for example – the data we will need to build dynamic networks and schedules simply doesn’t exist at the moment.
Recognising that genuine expertise and new ways of thinking are required, my R&D team is currently working with a range of external partners on three major research projects that will make machine learning and dynamic forecasting a reality.
- Trip Times Predictions: We are working with a top British university on a trip times predictions algorithm. Already we are seeing 98% accuracy for ‘here and now’ predictions – an encouraging outcome, though further research is needed into how we use this in the long term.
- Interventions: This theoretical simulator shows possible events in the network, and is currently being used to train machine learning algorithms to recommend the best strategy in managing disruption.
- Demand Prediction: We are in the process of building another theoretical simulator which will enable us to build bus networks and see what algorithm works best.
There are vast challenges ahead of us. However, in machine learning and dynamic forecasting we have the tools to build a dynamic new future for bus networks.
By harnessing these tools we will not only ensure the flexibility that is essential as we emerge from Covid-19; we will also restore faith in buses and public transport in general, creating a cleaner, greener and healthier future.
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