is a personal trip planner for employees which allows to easily plan, book and pay for multiple types of mobility services in one app.
In fact, this concept describes a shift away from personally-owned modes of transportation and towards a MaaS solution. Mobility as a Service (MaaS) is the integration of various forms of transport services into a single mobility service accessible on demand.
The challenge for road traffic and transport in the coming years is to make smarter use of existing infrastructure. Information and operating systems play a decisive role in this. A smart approach is needed where organizations, transportation, technology and IT meet.
Turnn connects all kinds of transport operators to their platform so that travelers have a broad spectrum of transport modes to choose from. In addition, they provide an entire mobility administration for employers so that their employees can focus on getting to their desired destination without any paperwork when declaring these business trips.
Turnn uses all kinds of data about routes, including public transport, distances and travel times, prices, points of interest, number of transfers and connections to different means of transport. One of the components for their calculations is the data from the Distance Matrix API, which can predict the time required for a trip in real-time traffic conditions. This data is used in the travel planner of Turnn so that travellers can choose the best mode of transport at that time. For instance, when a part of their usual route is congested, they can choose to go by public transport and then get a shared car for the remainder of their route, thereby circumventing the congestion and getting on time at their destination.
The Distance Matrix enables to estimate the travel times and distances between the points. You can utilize the API for walking, cycling, driving and public transport. API accepts a list of points and returns a matrix with rows representing origins, rows representing destinations and cell values representing travel time and distances between the locations. The data below are required to implement a basic set of scenarios: driving distances from origin/to destination for each public transport stop, walking distances from/to public transport stops, distances to the closest car-sharing vehicle, parking station or bike-sharing stations, walking distances from origin/to destination for each bike-sharing station and each car-sharing vehicle.
The user can then choose their preferred trip based on cost, time, and convenience.
MaaS is contributing to a new type of future mobility and DistanceMatrix.ai is glad to be a part of it.