MaaS or the New Mobility Paradigm!
Tags: Mobility, Machine Learning, Transportation, Predictions, Maas

MaaS, intermodality and active mobility: the future of mobility?

45 hours on average in traffic jams per year for Parisians, 100 hours for Londoners,… urban growth implies drastic changes in mobility for more pleasant cities.

Mobility of the future is based on a unification of transport services. MaaS, for Mobility as a Service, is a concept that aims to combine ticketing and multimodal information tools to allow users to move from a point A to point B. Centralizing all transport services helps development of alternatives to the private car and fosters indeed the emergence of new forms of mobility such as bicycles, trams and car-sharing.

Information is one of the keys to the success of intermodality. A motorist will agree to stop using his personal car for public and/or shared transport only if he has a reliable way to reach his destinations. How to ensure a pleasant user experience, without frustration and with complete transparency?

Multimodal applications ( Google Maps , Citymapper , , …) are flourishing but mainly provide real-time information on the availability of each mode of transport. To go further and transform multimodality into intermodality, these applications require the integration of a predictive layer to anticipate the availability of each mode, and guide the user towards the most relevant mode of transport, at the moment he requests it.

Data and predictive algorithms will play a crucial part in making mobility ever more active, smooth and connected!

Bike: a decisive link in the new mobility chain

12.5% of trips by bike: this is the ambitious objective of the French mobility plan for 2030! Through incentives measures, major cities are working to gradually replace cars with “active” transport alternatives, such as tramway, bus and shared bicycle. Shared bicycles users have two fears: finding an empty station at the start, and worse, not being able to drop off their bikes because the station is full at the finish! Predictive information available up to 12 hours in advance, encourages cycling in the city by reducing the uncertainty of not finding a dock or bike in a station.

Use case with Julie, from Ile-de-France

Julie works in Neuilly-sur-Seine. She wants to join the Place de la République. The weather is clear, Julie wants to enjoy the beauty of the Parisian streets.

Before leaving her office, she gets information about her journey by launching the Vianavigo application. The young woman’s phone indicates that she has a 100% chance of finding a bike at Madeleine Michelis station. Once at the station, she releases the bike and begins her journey towards the Sainte-Elisabeth station. It is the closest station, with free docks available, to its destination. BikePredict predicts that she will not find any free docks at Place de la République station. 35 minutes later, she drops off the bike and walks to her friends at Place de la République to enjoy her evening.

Simplify motorists’ lives with predictive data

Beyond peri-urban areas, the car remains privileged for its advantages and by the lack of alternatives, but the stress and lost time accumulated by this mode of transport are harmful. In addition to slowdowns (sometimes difficult to predict), motorists can spend a lot of time finding a place to park their vehicles in the city.

Park and Ride facilities allow motorists to leave their private cars on the suburbs and complete their journey by public transport. In addition to effective signalization during the journey, a motorist will use this option if he knows in advance or in real time that he is sure to find a parking spot in P+R.

Use case with Marc, from Bordeaux

Marc lives in a peri-urban area around Bordeaux: in Macau. He works on Allées Tourny in the historic heart of Bordeaux. Marc tries to avoid congestion in Bordeaux city center. He knows that it will be very difficult for him to find a parking spot. To join the office, he choses the Park and Ride solution. To avoid being frustrated when he arrives and not finding a parking spot, he uses infotbm. ParkPredict‘s predictive information directs him, up to 1 day in advance, to the Park and Ride facility where he can find available parking spots by the time he gets there.

From the Park and Ride, Julien then gets on the tram C to reach Les Quinconces, from where he will then walk to his final destination: Allées Tourny.

At Qucit, we believe that good information, provided in advance to users and via a channel he knows, will allow him to better choose his daily mobility. This information makes shared and sustainable modes of transport more reliable!

There are also other challenges for local authorities and the mobility sector in general — notably on a harmonized ticketing system. In order to make the MaaS promise a reality, it is necessary to be able to provide door-to-door solutions to users.