In order to analyse characteristics and travel habits of car sharing members, interviewees who reported to own a car sharing subscription were selected; thus, 189 answers (about 4.3% of the whole sample) are retained. Attributes of car sharing users, both at individual and household level, and use frequencies of different travel means are compared with those of the overall sample and with those of the fraction of the sample living within the service area of at least one car sharing operator. In fact, service areas are not including some of the more peripheral areas of the city, thus the socioeconomic characteristics of the people living there may differ from those of the whole population. Table 15 displays the characteristics of these three samples at individual level. The sample of respondents living in the operating area has similar characteristics of those of the whole sample. The majority of car sharing users (about 62%) became a member of the system from 6 months up to 2 years before the interview time, since several car sharing operators were introduced in Turin from 2015 to 2016.
Figure 19 shows the spatial distribution of locations of residences of interviewed car sharing members. In particular, the thick red line delimits the union of the service areas of all the three car sharing operators, whereas Traffic Analysis Zones belonging to the study area are marked using a black line. On the other hand, green points represent the home locations of car sharing members, whereas the colour intensity of each zone represents the density of interviewed members living in that zone. Observing Figure 19, one can note that most of the car sharing members (around 86%) lives in the service area of at least one car sharing operator. The distribution of males and females differs among the three samples; in particular, the proportion of male car sharing members is greater than the corresponding proportions of the whole population (χ2 = 6.4804, p-value < 0.05) and the sample living in the operating area (χ2 = 4.5159, p-value < 0.05). Unlike the other two samples, the majority (77%) of car sharing members has an age between 25 to 54 years, and half of them is aged more than 29 and less than 48 years (Figure 18). Both the whole sample and the sample in the operating area show an interquartile range of age which is wider than the one of car sharing members (Figure 18). Furthermore, the median of members’ age is 36, which is significantly lower than that of the entire population (50, Mann-Whitney-Wilcoxon = 267’470, p-value < 0.01) and that of the sample living in the operating area (48, Mann-Whitney-Wilcoxon = 152’367, p-value < 0.01), like in previous studies. Moreover, the proportion of retired people among members is lower than the one of the entire population (5% against 28%). Like in previous works, car sharing members tend to have a higher education; in particular the number of members owning a Master’s Degree or a Ph.D. is twice the corresponding numbers of the whole sample (χ2 = 94.044, p-value < 0.01) and the subsample in the operating area (χ2 = 69.422, p-value < 0.01). Lastly, a higher percentage of car sharing users own a public transport subscription; in particular the proportion of public transport subscribers is about 39%
among car sharing members, 27% among the total number of interviews sample (χ2 = 33.583, p-value
< 0.01) and 21% among residents in the operating area sample (χ2 = 10.147, p-value < 0.01). Similar results are obtained for bike sharing subscription; specifically, 28% of car sharing members are also bike sharers, whereas only 3% of the entire population (χ2 = 351.81, p-value < 0.01) and 4% of those living in the operating area (χ2 = 178.91, p-value < 0.01) have a bike sharing subscription.
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Table 15. Socio-economic at individual level of the whole sample, the portion living in the operating area and car sharing members
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Figure 18. Box plots of distribution of age of the whole sample, the portion living in the operating area and car sharing members
Figure 19. Operating area and residence locations of interviewed car sharing members
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Like in the previous case, Table 16 indicates that socio-economic characteristics at household level do not significantly change between the whole population and the sample living in the operating area. Moreover, the distribution of household members, workers and underage children is similar in the three datasets. On the contrary, the average number of licensed drivers in each household is slightly greater for households with a car sharing member; in particular, members live in households with about 2.02 drivers, whereas households in the study area and in the operating area have about 1.83 (Mann-Whitney-Wilcoxon = 462’910, p-value < 0.01) and 1.79 (Mann-Whitney-Wilcoxon = 251’700, p-value < 0.01) drivers, respectively. Unlike previous studies, the variation of the average number of owned cars is not statistically significant among the three datasets. Furthermore, Table 16 shows that households of car sharing members have more dispersed income level, with a greater median (about 2’250€) rather than those of the whole population (1’900€, Mann-Whitney-Wilcoxon
= 489’342, p-value < 0.01) and the subsample living in the operating area (1’900€, Mann-Whitney-Wilcoxon = 258’507, p-value < 0.01).
72 datasets. Observing these two elements, one can note that the majority of car sharing members (about 64%) use shared vehicles only occasionally; moreover, about 33% of them indicated to have never use the service. This suggests that car sharing is not adopted for systematic trips. Furthermore, car sharing members drive a private car more often rather than the whole sample (χ2 = 4.3531, p-value <
0.05) and than those living in the operating area (χ2 = 18.183, p-value < 0.01). Similarly, car sharers show more frequent use of urban bus respect to the whole population (χ2 = 5.9186, p-value < 0.01).
This might be caused by the different travel habits of the two samples, i.e. interviews living in the service area usually perform shorter urban trips which are suitable for urban buses, conversely, respondents belonging to the whole sample carry out longer trips since some of them lives outside the central area, therefore, using urban buses less. Overall, car sharing members use public transport more often than the other two groups. In particular, the proportion of the whole population that never adopts metro is three times the corresponding value of car sharing members (59% against 30%).
Moreover, the proportion of car sharer using frequently bike is significantly higher rather than the entire sample (χ2 = 30.502, p-value < 0.01) and respondents living in the operating area (χ2 = 18.967, p-value < 0.01). Similarly, car sharing members show a higher usage frequency rather than the two other datasets, even if only for occasional trips; in particular, about 30% of car sharing subscribers use bike sharing three times or less a week, whereas the corresponding value for all the respondents is about 4%.
Table 17. Usage frequencies of each travel mode for the whole sample, the portion living in the operating area and car sharing members
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Figure 20. Percentages of use reported use frequencies of each travel mode for the whole sample, the portion living in the operating area (Sample_OA) and car sharing members. [Car_D. car as driver, Car_P:
car as passenger; Motorb: motorbike; U_bus: urban bus; SC_bus: school/company bus; Metro: metro;
S_bus: suburban bus; Train: train; Bike: bike; B_shar: bike sharing; C_shar: car sharing]
In conclusion, comparing socio-economic characteristics and travel habits of car sharing members to those of the whole population and a subsample living in at least one operating area, car sharing members tend to be male, younger, with a higher level of education and multimodal.
Moreover, they live in household with a higher number of licensed drivers and a higher income level.
Concerning travel habits of subscribers, they use private car, public transport, bike and bike sharing more often, suggesting a multimodal behaviour.
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