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Usually, when you want to make a trip, the first question you have to ask yourself is which means of transportation you want to take. In big cities, there is a wide range of choices: self-service scooters, free floating cars, VTC applications, buses, taxis, subways, etc. Then, all you have to do is take your smartphone or subway ticket and concoct your own itinerary.
The MaaS is characterized by a radical paradigm shift. This is a new way of understanding mobility. As its name suggests, MaaS aims to see mobility, no longer as a means of transport, but as a service. Thus, in order to travel from point A to point B, MaaS will propose an optimal route via a combination of different means of transport. This new logic of displacement is not yet free of challenges and new issues.
Here are 4 issues and challenges for MaaS by 2021.
The service or application in MaaS mode will offer, for example, to walk to a self-service scooter, to ride it to a certain metro station, and finally reach an electric vehicle whose parking may be rented automatically in advance.
One of the key features of successful MaaS will be its ability to provide pricing from A to Z. Thus, for any MaaS service, reliable and accurate pricing could become a decisive factor in user satisfaction.
Predicting travel time is already a playground for VTC apps. They are doubling their efforts to refine their travel time forecasting algorithms on a daily basis. In large cities where all activity is tracked by time, and time becomes a precious commodity; offering a travel time forecast is a competitive advantage.
Whether it is to accurately predict a travel time, a route, or a fare, it is necessary to use data, whether static or in real time. This may include, for example, the location of different self-service bicycles, road traffic, or public transit traffic. Indeed, if we take the example of travel time, it is directly linked to an definitive factor, namely, the distance to be traveled, but also to a factor that changes in real time, such as traffic.
In order to facilitate traffic flow, especially self-service vehicles, many spaces must be available, which is not always the case in large cities. Thus, it is certainly important to offer users the ability to forecast their costs and travel times. But when traveling by self-service car, the parking aspect is an uncertainty that is difficult to predict.
As you will have understood, the challenges and issues related to MaaS revolve around the precious commodity of information, both in the way it is obtained and in the way it is used. This will enable each new player in the MaaS ecosystem to meet users' expectations for transparency, but also to contribute to more fluid and smarter mobility. In order to support the future success of MaaS, each link in the mobility chain must be solid.
As such, it is an opportunity to rethink the way we manage parking in major cities. This includes the emergence of smart parking systems.