We are working with insurance companies to attract, through a mobility app that rewards users for sustainable mobility, low risk users to buy discounted mobility insurance coverages. This is based on the assumption that a low frequent driver has less probability to cause a car accident, and, similarly, that a micromobility user has less probability to cause a car accident when driving because they have a better perception of the risks and they are sensible to the "weak" movers on the road .
It may seem obvious for common sense that "if you drive less you cause less accidents", but insurance managers using conservative actuarial methods do not consider this a strong theory.
We expect that an extensive experiment to verify those assumptions is going to generate useful knowledge for insurance companies. They will therefore push their clients to drive less, in favor of micromobility.
Demonstrate that unfrequent car drivers cause less accidents while driving, and demonstrate that micromobility users cause less accidents while driving; insurers can convert them into clients offering discounts (also to reward them for their green impact).
The analytical effort toward improving mobility has always been focused on cars: where, when, how, ... cars are driven. Why not take the opposite perspective and focus on the non-driving behavior? And how to make this research useful and profitable for large players like insurance companies that can generate an extensive impact on society while combining sustainability with safety and with their business goals?
We positively impact the society, therefore the citizens, according to some Sustainable Development Goals.
On average 0,18kgCO2/km are avoided by sustainable mobility as an alternative to the traditional car. Therefore engaging 1 million citizens moving sustainably per 2000km/year, we get: 360ktonsCO2 avoided per year + health + urban livability.
Supported by a positive outcome of experiment objectives we can develop our business with additional insurance partners since we are able to demonstrate with actuarial data the basis of our proposal, which is to provide advantages to users that drive less therefore they have lower risk to cause accidents when driving.
Exploitation of involved datasets to learn how to enrich our own dataset.