Why we need Big Data Approaches for Relocation of Shared Mobility Fleet

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Jared Zimmerman / Flickr

Maximum Flexibility is every operator’s nightmare

Free-floating sharing systems offer the ultimate flexibility and freedom because you can drop off a shared bike or vehicle anywhere. It’s the one carsharing feature that convinces people to give it a try even if they are strong believers in vehicle ownership. All of a sudden they have the flexibility of their own car at a fraction of the cost.

Yet the ultimate freedom for members is the biggest challenges for operators. Their focus is on maximum utilization of their assets. Yet, bikes are left next to stations that are full already. Cars are parked in neighborhoods where they sit idle for hours or even days. And then there are areas that are constantly emptied out, no matter how many bikes or cars are dropped off. If there are electric vehicles added, there is the additional challenge of having them parked miles away from a charging stations when they should really be plugged in.

What is currently done?

All shared vehicle providers have teams of people that relocate bikes or cars. In the case of bikes, they are often moved on trucks. In the case of vehicles, relocations are more labour intense because each vehicle has to be driven individually to the new location. Providers generally hire Fleet Managers with strong local knowledge who become relocation dispatchers. They are  responsible of assigning relocation requests to a third party which is a time-consuming Sisyphus task. It can easily take up half of a morning depending on the software an operator is using.

The majority of software solutions for shared mobility systems offer some real-time overview of all the vehicles, sometimes in a visual way on a map other times as a table with GPS positions. But no Fleet Manager watches vehicle movements 24/7 so insights into best relocation strategies are limited.

Other software systems allow data to be extracted. After a few months of operations, a reasonably large data set can be exported for analysis. Visualization is an option and there is an abundance of software in the market that allows this. The visualisation of data helps an operator to see patterns yet, it does not come up with a redistribution strategy or even issue tasks to a relocation team.

How relocation could be improved

What is needed is a tool that integrates the latest trends in big data analysis, artificial intelligence and machine learning yet still allows the operator the ability to set parameters. Data analysis is great to evaluate where most rentals are started and ended but it utterly lacks local knowledge. The operator should be able to arrange the city into smaller areas that make sense from a traffic perspective. Also, if there is for instance a big event in the downtown core on a weekend, the operator should be able to block an entire area for redistribution. Once the parameters are set, the system would highlight areas that are in need of vehicles and neighbourhoods that have too many using different colors and then send the relocation tasks automatically to the relocation team.

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