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What does a transport model that analyses the potential of Mobility as a Service and autonomous vehicles in the Norwegian capital look like? With the COWI Group and Oslo’s public transport authority, Ruter, we have analysed in four main scenarios, how on-demand shared mobility can make travelling around Oslo smarter and more efficient. We talked to our Director, Paul Speirs, about the right tool for the study and the potential of MaaS for other cities.
Why is PTV MaaS Modeller such a strong tool to analyse MaaS systems?
There are many themes that are becoming quite difficult to predict in traditional transport model forecasting. For instance, will car ownership today be the same in the future? We know it will change because we are already witnessing fewer young people owning cars. This is one reason why MaaS has emerged as a new travel option. People are switching from cars as an owned asset to something that they just use when they need it. But here is the crux. Measuring the adoption rate, how many people will switch and when, is difficult to predict.
We also don’t know enough about the impact of autonomous cars. Will they increase our desire to travel because travel is easier and more convenient? Will we choose autonomous cars where previously we would walk or cycle? Also, let’s not forget the positives of autonomous cars increasing inclusivity for society because it will provide transport choices to people who don’t have access to a car or public transport. But increasing inclusivity adds traffic to the roads – a double edged sword.
It’s quite hard to plan the right infrastructure investment with the uncertainty that autonomous cars bring. Assuming the technology is there, how do you measure any business model with certainty? PTV MaaS Modeller allows us to test, in parallel, many combinations of shared mobility operational assumptions. These could include, amongst many variables, the total shared mobility traveller demand, the vehicle fleet size, the maximum wait time for the traveller and the acceptable journey time detour for ride sharing. The combination of these variables, in turn, produce a range of possible future outcomes measuring results from one extreme to another and everything in between. This allows us to understand the worst and most optimistic forecasts and to find confidence in the more likely outcomes. It also allows us to identify variables that have little or no impact on the business model and those variables that can be quite sensitive to change. If you test enough combinations and measure the performance through a smartly designed KPI framework, you will find an optimal solution to support your business model. The Oslo study simulated almost 300 forecasts and it is this ability to prepare and simulate numerous forecasts that will lead to and support confident decision making. PTV MaaS Modeller allows you to do that.
How does PTV MaaS Modeller work?
Our algorithm within PTV MaaS Modeller produces an optimal solution by adhering to these three rules and ideals: Firstly, it minimises the unserved trip requests to make sure everyone gets a ride. Secondly, it minimises the fleet size required to serve the trip requests. Finally, it minimises the objective function, which in the case of PTV MaaS Modeller, focuses on three elements: the operator performance (cost), the passenger level of service (convenience) and the city-wide social benefits (congestion/environment).
They are all connected and finding the balance between the most lucrative operator business model whilst providing a high level of service to the customer and also demonstrating positive effects on congestion is the challenge. Generally, we begin with the operational focus and what service an operator will provide. For example making sure nobody waits longer than 15 minutes for a ride, or capping the detour at two-times the shortest trip. These are two variables that can be fine-tuned to minimise the operators costs (fleet size, vehicle kilometres travelled, dead-miles…), bearing in mind that if these go too far you will end up offering a lower level of service to the customer. We compile the simulation results with PTV MaaS Modeller’s KPI dashboard which is wholly customisable to report the results pertinent to the customer, be that the city, a public transport agency or an OEM. Minimising the objective function makes it an outcome-driven task and remembering we simulated almost 300 combinations of variable assumptions for The Oslo Study, drawing conclusions and decisions from the analysis can be done with increased confidence.
How will MaaS systems change infrastructure requirements?
It’s a challenge and an opportunity. For example, in front of Oslo’s central rail station are many tram and bus stops acting as a public transport mobility hub. The space also serves as a public plaza and it’s difficult to imagine how that space would function if suddenly it was full of shared mobility pick up and drop off activity. We have produced pick up and drop off maps that you can find in the report to illustrate the intensity of activity highlighting where the busiest areas are. The reality is, the city will have to think carefully how to organise this space. The 2018 ITF report The Shared Use City: Managing the Curb, which PTV contributed to, really sets the scene on this topic highlighting the opportunity and challenges. To enlighten a little, the report focuses on Lisbon and how a single on-street parking space, which typically is occupied by one vehicle for an average of 5 hours, could be turned over 90 times in a shared mobility system during the same time period. That’s 18 cars an hour or one every three to four minutes. Enough time for a pick up / drop off activity without disrupting the flow through the space. But that’s in Lisbon, which is making use of existing curb-side parking availability. In the centre of Oslo and other cities this may not be the case, so, first you have to identify the need, find and re-designate the space, and manage it. That’s going to be a challenge for any city.
Based on the results of the study for the entire city of Oslo, what would be best to focus on next to develop a MaaS system and get the service on the road?
As a public transport agency, if I were Ruter I would want to explore first/last mile feeder services to increase the suburban/rural catchment on to rail and metro. You can imagine that the existing catchment, certainly for suburban rail, is based on how far people are prepared to walk and cycle to the railway station. If you can increase that catchment by offering a feeder on-demand service, then you achieve many things: you retain people on public transport, you increase public transport patronage and you reduce people driving all the way in to Oslo because they only drive a short distance on the shared ride, then take the train/metro. PTV MaaS Modeller can enable Ruter to measure the impacts at certain stations or along certain corridors and provide the evidence to support small on-the-ground pilot studies. This approach lowers the risk and improves the chances of commercial and reputational success. For Ruter, and public transport agencies in general, it’s imperative they do this because rather than see ride share as a threat to their existing customer base, they have the opportunity to make it part of their overall service offer.
When we look at other cities, what opportunities do MaaS systems offer?
I took part in a workshop hosted by MaaS Scotland in March to discuss how best to look at three areas where MaaS can contribute to society. One was to better serve rural areas, and people who don’t have a very good public transport service or don’t have access to a car. It’s about including those people in the transport mix. The second target area is tourism. There’s plenty to see in Scotland, but it’s difficult to get around if you don’t have a car. The seasonality of tourism in Scotland makes it difficult to invest in infrastructure and service provision when for several months of the year the tourism market is not there to support it. You therefore need an adaptable system. The third topic seeks to address inclusivity, and in particular supporting social care and how people who need to visit clinics or maybe school children who need to get to school when they’re living remotely. MaaS can help mobilise these people in an efficient and customer-focussed way. With these types of opportunities in mind, MaaS Scotland is creating an ecosystem of experts who, together, can provide real life-enhancing on-the-ground services. Without doubt PTV MaaS Modeller and PTV MaaS Operator, our real-time dispatching solution, has a clear role to play in this exciting and evolving arena.
Are you looking for a tool to analyse the potential of MaaS for your city? No matter if you are an automotive, a city or a public transport provider – we provide the software that solves your individual MaaS use case.