SHARE NOW’s artificial intelligence makes urban mobility predictable.
Milan, November 17, 2021 – How can promising business areas for car-sharing be identified? How is it possible to ensure that car-sharing vehicles are always available exactly where they are needed? How is it defined when cleaning must take place? And how can the service be further developed to meet demand? The answer to all these questions is simple, at least at SHARE NOW: Artificial intelligence (AI).
Artificial Intelligence, along with its accurate predictions, is in fact an integral part of the SHARE NOW’s corporate strategy to be able to manage a fleet of about 11,000 shared vehicles always more efficiently. The ultimate objective is to identify even better the needs of customers (what, where and when), to adapt the offer flexibly to these parameters and, consequently, to their individual needs.
“SHARE NOW’s artificial intelligence, with its reliable data and algorithms based on 13 years of experience, will take sustainable mobility to the next level, contributing at the same time to lighten urban traffic, to improve user satisfaction and to help operators to further reduce management costs." - explain Slavko Bevanda, CPO and CTO of SHARE NOW – “Moreover, it will play an even more important role in the future development of autonomous car*-*sharing: even before the customer starts looking for a car, we will have to be able to send the car itself to the customer, predicting the time and the place in which there will be the request."
In the White Paper published today, the company provides an overview of the main challenges car-sharing is able to face and overcome, thanks to the use of artificial intelligence:
1. Identifying the Ideal Business Territory
Car-sharing services only make sense where they are used. Therefore, the analysis of local conditions is a fundamental prerequisite for successful car-sharing. After a first study by the business development team, the company’s technology comes in. It is fully developed in-house and defines a set of parameters for predicting demand by using artificial intelligence, such as socio-demographic data, number of points of interest in the city, efficiency of public transport. In this way, the ideal operating area of the service is established, and, over time, decisions are made regarding the revision of the area itself, such as expansion or reduction.
2. Ensuring the Availability of Vehicles in the Right Place at the Right Time
Once the business area is properly defined, the fleet can only be used efficiently if the vehicles are in the right place at the right time. Based on artificial intelligence, SHARE NOW can predict when, where and how many cars are needed and calculate the demand accordingly. In addition, the algorithm continuously assigns to each vehicle a score that estimates how long it takes a car to be rented again. In this way, the company is able to predict whether it is cheaper to wait for the start of a new rental, assign a discount on the fare to the vehicle, or have it repositioned by the service into an area with higher demand. The aim is to minimize service intervention and to ensure that vehicles are always moved by users.
3. Optimization of Vehicle Cleaning and Maintenance
The user experience inside the vehicle is decisive for the success of car-sharing: no one wants to drive a dirty vehicle. SHARE NOW has therefore developed software solutions that, using artificial intelligence which assigns to each car a “dirtiness score”, predict when it is necessary to intervene. Predictive maintenance involves data on usage, such as: the time of the last cleaning, the vehicle model, and most important the feedback customers give in the app about the vehicle status.
4. Development of the Offer Based on Demand
Car-sharing thrives on being able to adapt quickly to changing customer needs. To ensure demand-driven further development, SHARE NOW must be able to make data-driven decisions daily. Thanks to the constant in-depth analysis of these data, it is possible to make predictive improvements to the product. This happened for example with the implementation of the pre-booking of the vehicle up to 100 days in advance.
The whitepaper is available for download here.