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Shared mobility and big data

Platforms involved in shared mobility (ranging from bicycle sharing to ridesourcing) have access to massive datasets on both the transport network (such as the current levels of speed and congestion), and on their clients. These data are used for the internal optimisation of the shared services, but are also increasingly shared for commercial motives with third parties. Moreover, sharing platforms can set up partnerships with cities and transport authorities. Data integration and sharing is also key for Mobility as a Service. Finally, even privately owned cars are becoming providers of extremely valuable data.

• Data from sharing platforms are also increasingly shared for commercial motives.

• These data are used for the internal optimisation of the shared services.

• Privately owned cars are also becoming providers of valuable data.

• Sharing platforms can set up partnerships with cities and transport authorities.

• Platforms involved in shared mobility have access to massive datasets.

The ever increasing connectivity of all transport modes, including cycling and walking, is moving us towards a situation where real-time data will become an essential input in mobility management, and where sharing and obtaining data will be integral part of the strategic decisions of all actors involved in mobility.

Let us first consider the current situation. Platforms involved in shared mobility (ranging from bicycle sharing to ridesourcing companies) have access to massive datasets on both the transport network (such as the current levels of speed and congestion), and on their clients.
These data are of crucial importance to the internal optimisation of the shared services: if the vehicles (and docking stations) are not connected, then it is not possible to forecast demand or to manage the complex logistics of systems such as bicycle sharing (Romanillos et al., 2016; Fishman, 2016) or one-way carsharing (Le Vine et al., 2014; Shaheen et al., 2015b). One key insight in this era of ‘big data’ is that their forecasting power is maximised when different data sources are integrated. It therefore does not come as a surprise that one car manufacturer recently decided to purchase a bikesharing platform together with a crowdsourced shuttle-bus service. The main value added from the bikesharing platform is that the data collected from the bikes by the bikesharing platform help detect new routes that are fed into the shuttle-bus’s routing algorithms 

  • Technological developments such as improved interoperability among ridesharing databases and standards for sharing open source data will lead to further integration (Chan and Shaheen, 2013). The sharing of data by platforms with a trusted ‘third party’ can lead to the development of Mobility as a Service.
    But the information owned by the sharing platforms is also valuable for third parties, such as retailers, restaurants and hotels (Le Vine et al., 2014). Sharing platforms can therefore set up partnerships to give access to data describing the mobility behaviour of their clients. In turn, the information provided by the partners increases the accuracy of the geo-localised information of the sharing  platforms  . This type of data sharing will become increasingly critical in their earning models and will also raise important privacy issues .

Moreover, sharing platforms can set up partnerships with cities and transport authorities. In such partnerships, the platforms provide cities with data on traffic conditions in real time and on origin-destination patterns, while the cities provide structural information (such as planned road works and other anticipated sources of traffic disruption) and can integrate the platform in their own inter-modal travel planners . However, cities can also obtain ‘real time’ information from ‘traditional’ public transport and from ‘connected’ cyclists and pedestrians, who can be incentivised through the gamification of certain behaviours and on-line social networks (Shaheen et al., 2015a). This could lead to improvements in the transportation network, to the development of apps showing all available transportation options, and the identification of areas that are poorly served by transport services in general (Rainwater et al., 2015).

The integration of these two sources of data can also be used to provide the travellers with financial incentives to take alternative modes or routes than their usual ones if these routes are overcrowded  .

Interestingly, in the case of ridesourcing, the TNCs collect (at the rider’s expense!) detailed data on the routes chosen by their drivers. These data can be fed into the learning algorithms for self-driving cars, which may give TNCs an important head start in the race for automated mobility .

 

Of course, it is not just vehicles owned by sharing platforms that currently collect and share data. Cars are becoming increasingly connected, and can provide valuable information, such as on air quality, road conditions, vehicle speeds, and weather conditions. They also carry information on the drivers’ habits, some of which may be commercially valuable for entertainment providers.  One study estimates that the worth of Big Data in cars could reach $750 billion by 2030 – this raises the question of who this data belongs to: the driver, or the manufacturer?
This topic is currently the subject of a legislative proposal from the European Commission .

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