Mobility system of the future

Both the world and our mobility are changing, and with this comes a change in our modes of transportation, our needs of transportation, as well as the relation between them. Because Limburg has its own unique position in the Netherlands, the Euregion and in Europe, we are looking for answers to the question: What will our mobility system look like in the future?

Although this implies finding a vision for the future of mobility in Limburg, the main focus of your solution should be on how to get from A to B in a Dutch area (Limburg) by leveraging its unique geographical location: the Euregio. The cross-border networks should be especially considered. What networks and background systems are needed to balance the demand and supply?

Infrastructure future proof

Our world is changing. We travel on demand and in a more efficient way. The vehicles we will be using are preferably not ours. We want to pay for a service when we need it. Because we care for a sustainable world. At least, that’s what forecasts tell us.

But what do we need to do now, to keep 2040’s infrastructure in Limburg accessible? Do we need to adapt current roads, airports, railways and waterways, to facilitate our needs now and in the future? Or do we want to separate them from existing facilities and build from scratch?

Thinking of that we need to keep in mind that there will be new mobility features, like drones, speed-pedelecs and hyperloops. Besides that our community ages; in 2040 over 50% of the total population in Limburg is over 60 years old. The question now is: how do we (re)organize our infrastructure, and network systems for all these different kind of needs?

The focus of your solution should take into account the modes of transport. Both now and those of the future.

Personal Mobility of the future

As humans, motives are what drives us to move from point A to point B, and to get to our destinations we use different forms of modaltities―foot, car, bike, or public transport. These modalities all have specific characteristics regarding safety, capacity, environmental impact and accessibility, and by learning how these characteristics affect the needs or motives for personal mobility would allow us to make more precise estimates on the impact these have on mobility infrastructure.

On a larger scale, more exact estimates will enable us to make changes in policy, economic changes and population characteristics.

• How can we make use of publicly available data on basis services (hospitals, shopping areas, entertainment areas) and population characteristics to create origin destination matrices?
• Can we establish some basic assumptions on how often people use these services and what their preferred mode of transport would be?
• Is it possible to utilize these matrices and models to establish the pressure put on the transportation infrastructure on a daily basis?

Our challenge for you: Find a solution that is able to predict crowd behavior using public transportation data and make use of those predictions in one way or another.

To create this predictive algorithm, you will need to use data from any public area such as hospitals, malls, transport, etc. and cross it with external data such as weather, traffic, vacations, sports events, etc.

The final objective will be to be able to anticipate traffic growth in public transportation.

Cars as a service

In the MaaS (Mobility as a Service) concept, transport will be customized according to the needs of the individual traveller, thus changing 3 key components: time, money and convenience, ultimately influencing our decisions on how to travel. With MaaS, the last mile will be easier to overcome, cost of ‘shared’ transport is expected to drop dramatically and the transport chain will gain time due to seamless exchange between different transport modes and less traffic congestion.

How will this impact the use of cars? Will everybody turn to car sharing or will there still be individual car ownership. And what will be the ratio between sharing, individual rides and even individual ownership? Knowing the answer to the last question, can we estimate the potential impact on parking demand, parking locations and parking layouts? We challenge you to the present us with a business model for parking which will be feasible in the full-grown MaaS concept.