We were able to combine the previous elements to try to improve sustainable mobility at UniFG. Using the previous results, we developed a system through which the university can determine the amount of CO2 equivalent for a certain semester when registering new subjects and for those already enrolled, based on small amounts of data entered in the registration system, as follows.
Information on age, gender, department, role, and residence for each subject can be acquired through the UniFG member management system (ESSE3 platform). However, the city of residence is not a functional variable since a person residing in a certain city could decide to move to Foggia. One solution could be to periodically update the ESSE3 section relating to the domicile, in order to track how the issue quantity can vary by subject over a certain time period. In this way, because we are aware of the department to which each subject belongs and his or her domicile, the distance between these two points represents the distance in km that the subject will travel with a certain vehicle. With regard to the means of transport used, if the person uses a means other than a car, the subject has chosen (whether for reasons of necessity or as his or her own choice) a sustainable mobility system. On the other hand, if the subject uses a car, then we must calculate the expected emissions.
To obtain information on the use of the car, it is possible to enter a very short questionnaire to ESSE3 with a single question, such as, “Do you use a car to go to the university? Yes/No.” This question may be mandatory for new subjects wishing to enrol at the university but voluntary for those already enrolled, thus minimizing requests to guarantee a large number of answers. After obtaining information on car use, we could use the ML algorithms defined above to predict the vehicle’s type of power supply and year of registration with good accuracy. In this way, we could determine the CO2-equivalent emissions produced by subjects who use a car simply as the product of the distance travelled and the emission value returned by the GaBi for each of the EURO classes, to which we would include the subjects according the vehicle’s year of registration and type of power supply. The only variable on which we must hypothesize (always with a view to avoid burdening the questionnaire to be submitted through ESSE3) is the number of days when the subject goes to the university. However, on the basis of the training data set, we can assume that subjects who live in Foggia tend to go to the university four times a week, on average, while for those not who do not in Foggia, the number of trips tends to decrease as the distance increases (we can assume that for a distance of up to 50 km, the number of trips is three times a week, versus twice a week for a distance over 50 km). We can determine the trips per semester as the product of the weekly trips and the number of weeks in a semester (known a priori) and multiply this time value by the previous equivalent CO2 value, to obtain the emissions expected in a semester.
Through this calculation, the university could exploit the ESSE3 platform to sensitize those belonging to the academic community to the use of alternative means to cars, where possible. In particular, after setting an upper bound of emissions, we could display a sticker upon access to ESSE3 that expresses the user’s expected emissions value for the following semester, with a green sticker if the emissions are below the upper bound (as shown in Figure 2), yellow if they are higher than the upper bound but within a certain deviation from this limit, and red if the expected emissions are much higher. This calculation of prospective emissions, albeit probabilistic, could allow the UniFG to sensitize its members to replacing their cars with more sustainable transportation means. Furthermore, through the latest ML model, it also would be possible to predict the transportation means used in the following semester (the summer semester, if the first use occurs in the winter one), so that these probabilistic forecasts can be improved by asking users seasonally whether they still use a car.