PART 3 : Leveraging Real-Time Data and Insights for Optimised Fleet Management in Public Transport

Public transport doesn’t pause for surprises, so why should your operations? In this episode, we dive into how fleet managers can turn unpredictable challenges into manageable moments using real-time data, digital twins, and a heavy dose of operational common sense.

From burst water mains to escaped sheep and overloaded match day traffic, the secret to seamless service lies in being able to see, simulate, and respond, instantly.

Here’s what we explore:

• How operators are using live telematics, weather forecasts, and match day schedules to dynamically adjust service
• The value of API-integrated insights from across vehicles, chargers, and routes
• Why data sanitization is crucial—because no, your vehicle didn’t drive -60km in reverse
• Using real-time alerts and route maps to respond to unexpected disruptions (from traffic jams to cows on the road)
• Range anxiety mitigation and power consumption forecasting—especially during seasonal spikes like Christmas
• What-if simulations that let you forecast future problems before they derail your service
• The power of pairing 15+ years of EV experience with intelligent systems to validate data and make the right call faster

This isn’t just about data—it’s about delivering reliable, efficient service your community can count on.

Whether you’re managing buses, energy, or charging logistics, this episode helps you harness the full potential of your digital infrastructure.

#FleetManagement #PublicTransport #EVOperations #RealTimeData #DigitalTwin #Telematics #PowerForecasting #SmartMobility #OperationalExcellence #MatchDayLogistics #ZeroEmissionTransport #ZeroMission

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Connect with our team Alan Crowley, Kevin Christopher, Brenda Shanahan, Eduardo Espinal, Stephen Breen, Liam Nolan, Callum Hennessy Cian Kavanagh, Niamh Quinn

Transcript

 I heard that some of the lads in the office talk about how they give it the match day. So if the client is a bus company, they would plug in the match fixtures and then say, "Okay, buses are working to the max on this day, carrying that load," if they needed to increase the schedule by 10% or 20%. So I suppose it's constantly taking all these permutations of what could happen, so you probably want telematics, weather, route, um, vehicle battery, ice vehicles.

And I understand as well we're also integrating in some data from Fuelcard. Uh, and I had that conversation with somebody else saying, "You know, the Fuelcard data combined with telematics data..." 'Cause in certain circumstances as well, uh, the guys just go into the petrol station and give a nominal number rather than the actual outdoor reading. So, I suppose that's one of the things that's unique in our organization.

The team from an EV perspective have been doing this for 15 years, so they're not just relying on digital twin outcomes. They can overlay a layer of common sense, uh, and when the system produces some weird and wonderful data, as can happen with all, they can, you know, trace back, but I think there's been some occurrences like that from telematics or something, is there? Yeah. I- again, be- because I get...

For, for digital twin and for the, the big data, uh, problem that can arise in these industries, your first step is to do data sanitization and to kind of, um, make sure you are getting rid of those extreme points. You know, uh, a vehicle that says it's traveled negative 60 kilometers in a day, it means something has gone awfully wrong, and you know, you can't drive the car in reverse the whole day and expect it to show up like that.

Um, so yeah, we, we do need to take those, um, scenarios in place. Uh, again, not to pick on anything too specific, but for example, if you've a charger and you know that there's some issue on that side of thing or with that brand, with this car, that allows us... Or, you know, different vehicle brands having compatibility issues with, um, cert- charging, we can add that in and we can take that into account as well.

But it's that experience that lets us know in the first place that, oh, hang on now, you need to take this into consideration. Um, one thing I like about your example of match day is the idea of seeing it in real time. So if you have a lot of traffic building up and suddenly your, your vehicle is out on route, but isn't progressing as much as p- as it should, that can show up in, in central, in the hub and be like, "Okay, what's up?" And then you can either, either driver through whatever mechanism can report back, "Oh, by the way, you know, all the sheep have escaped," or, "There's cows on the road," you know, "There's on the road obs-" Um, yeah, you wouldn't see that on the news, but it's something that i- is needs to be known.

Um, but it just lets a- again... And then that route, if there's that segment affected by other routes, you can now pull out, pull out, okay, which routes also have that segment of road? Is that a persistent problem? O- oh, actually, sorry, a more real life example I suppose is the burst, uh, water main. Now suddenly that diversion is going to need to be taken into account and that's something that you could go, "Right, let me select that, uh, section. What routes are affected? Okay, well what does it look like if we navigate around this obstacle? Does that mean that, uh, another vehicle needs to be deployed or redirected to deal with that scenario?" And I suppose that's the real benefit of this kind of technology.

You can manage that live as opposed to dealing with it after the fact, or dealing with anecdotal evidence from the driver. Uh, and you're there to support because obviously the range anxiety is, is a big consideration for these guys as well. Mapping that process means we have access to the historical data and if w- if we l- take the historical data and go, "Well this is..." and forecast into the future, we can go, "Well this is what this process is likely to do." Um, and then you can add in components that, oh, "Okay, well what about the reduction of efficiency? What about if the battery is..." There's a lot of what if questions there.

What if the battery is, um, uh, has kind of battery strings that are loose or if it's degradating or if there's issues around percentage? You can kind of see what could happen and then how would that impact your business and then you can make mitigations to that. Right. Um, and that's just at the kind of the very low level. The level above that, again, is just around charging and, and power consumption and trying to figure out like...

A, a big hassle on the, the, the no emission side is figuring out your power consumption, like how much power is actually going to be drawn. Um, well if we have, if we have all the data around, well we've used this much power the day before, likely to use this the day after, but you can add in things like, well actually it's Christmas so our power consumption is, is not the norm. It's above the average.

So okay, let's, let's map that out. Let's see what that does look like. Um, let's look at our historical data as, as is, um, and then you can kind of make decisions based on that as well and kind of making decisions, making plans, um, trying to get the most out of your fleet, out of your vehicles is, is always the biggest challenge in this industry.