In this section, answers to questions about the selected macro-trip are analysed (see Appendix A for a definition of the macro-trip). Since data are contained in sections C (Focus on a specific trip chain), D (Attitudinal survey) and E (Stated-preferences experiments), only respondents who fill these parts are considered; therefore, 3’454 interviews are retained for the analysis.
As reported in Table 18, the majority of respondents, whose macro-trip was selected, performed that trip chain more than once a week; in particular, about 44% reported a weekly frequency of more than three times and around 34% declared a frequency ranging from one to three times. Consequently, these interviews did not carry out the macro-trip occasionally, therefore their answers can be considered reliable, since they are based on a usually experienced trip.
Table 18. Reported frequency of the macro-trip
Moreover, Table 19 shows the number of transport modes on which respondents declared to have travelled for the majority of the trip chain duration. Comparing Table 19 with the corresponding results for the whole representative sample reported in Table 13, one can note that the two distributions of adopted transport modes are very similar, strengthening the basis for an analysis of modal share considering only the selected macro-trips.
Table 19. Number of adopted travel means by respondents for each selected macro-trip
I wave II wave III wave Total
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Considering the macro-trip under investigation, interviewees were asked to list all transport means they had used in the past to complete it in different occasions, as well as all those means they are considering to use in the future. The following tables present the related cross tabulation of the answers to those two questions, where a selection of the most frequently used means is listed in rows and future means are in columns. These tables consider only respondents who declared to have already carried out the selected trip chain in the past; for this reason, 167 interviews belonging to the whole sample are excluded (see Table 18) and, therefore, 3’287 interviews are retained. In particular, Table 20, Table 21, Table 23 and Table 24 show absolute and percentage values, respectively, of modal diversion patterns for the chained trip. Table 22 displays the number of respondents who reported to have used the travel mode in rows at least one time to perform the selected trip chain.
Percentages reported in each cell of Table 23 and Table 24 indicate the fraction of individuals that might use the mode indicated in the column label, among all individuals having already used the mode indicated in the row label to complete at least part of the trip (i.e. values in Table 22). Less commonly selected modes to make the same chained trip in the future (such as boats crossing the Po river in Turin) were not included in columns. Furthermore, since respondents could indicate more than one past or future transport mode, row and column sums are greater than 100%.
Values in the diagonal of Table 20, Table 21, Table 23 and Table 24 are related to those interviewees who would adopt the same mode in the future. Observing the overall table, one can note that these cells have the highest values in the matrix, highlighting the strong behavioural inertia of users. Among diagonal cells, travellers by train have a low value, suggesting their dissatisfaction with such mode. On the other hand, other values show the substitution patterns across different modes for the random trip. Car as a driver has the lowest values beyond the one in the diagonal compared to other rows, thus indicating that drivers tend to stick more to their means than users of other means.
On the contrary, car and bike sharers show the highest values on average, pointing out their multimodality and their attitude to share travel means, as in the case of public transport. Car passengers would ideally become drivers, but the reverse relationship is not observed. Similarly, car sharing members would use private car or urban public transport means, but the opposite relationship has not the same strength. Conversely, substitution relationships can be identified between urban public transport modes (such as urban bus and metro), and between walking and bike, to a smaller extent.
Considering columns rather than rows, private car is the most attractive mode. On the contrary, car sharing values are low if compared with those of traditional transport modes. This could be due to the fact that car sharing has been only recently introduced in Turin. Overall, car sharing seems to be a more appealing substitute of public transport, taxi and bike sharing than of car as a driver, car as a passenger, train and active means. As expected, car sharing can compete with the former modes since it can offer more flexibility rather than public transport and a privacy similar to the one of taxi, but with a lower cost. On the other hand, private car drivers exhibit a low willingness to switch to other means. Moreover, characteristics of trips performed with train and active modes are usually not suitable for car usage; in particular, train is often adopted for long inter-city trips, whereas walking and bike are used for short trips.
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Table 20.Modal diversion patterns for the chained trip under analysis (absolute values) (1)
Car as
Table 21. Modal diversion patterns for the chained trip under analysis (absolute values) (2)
Suburban bus Train Taxi Walking Bike Bike sharing Car sharing
Car as driver 105 57 76 356 334 100 155
Table 22. Total number of respondents reporting to have used the travel mode in rows for the chained trip at least one time
School/company bus 36 Bike sharing 51
Metro 379 Car sharing 46
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Table 23. Modal diversion patterns for the chained trip under analysis (respondent percentages) (1)
Car as
Table 24. Modal diversion patterns for the chained trip under analysis (respondent percentages) (2)
Suburban bus Train Taxi Walking Bike Bike sharing Car sharing
Car as driver 5.2 2.8 3.8 17.6 16.5 4.9 7.7 expressed their propensity to perform the same macro-trip in the future with an alternative mode.
Each interviewee had to answer to six experiments, one for each alternative, which are private car, public transport, taxi sharing, bike, bike sharing and car sharing. Respondent had to indicate their switching propensity through a 5-points ordinal scale. Table 25 shows the answers of these experiments, which are aggregated in three groups (positive, neutral and negative). In particular, rows indicate the modes currently adopted by the interviewed to complete the chained trip, and columns report the six alternative modes of the Stated-preferences experiments. Percentage values are calculated considering the total number of users currently adopting the mode reported in rows. In the original version of the survey, the modes declared by the respondents in the Travel diary section of the survey were more disaggregated, both for private (e.g. car as driver, car as passenger) and public
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modes (e.g. urban bus, suburban bus, metro, tram, train). However, in the six experiments, they were merged in order to match the diversification of the means proposed as alternative.
In order to get a visual representation of results in Table 25, Sankey diagrams were adopted. This type of diagram shows the flows from the elements on the left side to those on the right side, using flow bands with a width which is proportional to the flow rate. In the present case, the flows are the switching intentions from the current mode to the alternative one. Therefore, two diagrams were developed: the former considers the positive switching propensities (Figure 21), and the latter focuses on the negative answers (Figure 22).
Observing the results reported in Table 25, Figure 21 and Figure 22, one can note that car, public transport and walking are the most used transport means, while car sharing and bike sharing are not common. As highlighted by previous analysis, this reflects and confirms that both these means are not widespread among Turin residents, due to their recent introduction. Moreover, this justifies the adoption of Stated-preference experiments to understand the factors affecting the potential switching to these two modes. Positive frequencies higher than 50% are found only for the same mode already in use, suggesting that the majority of respondents chose to keep on using their means. In particular, car drivers show the strongest behavioural inertia given the low willingness to try different modes, followed by public transport users and walkers. The notable exception is the switch from bike to bike sharing, given the similarity of the two means, whereas the switch from car to car sharing is much less popular.
Comparing different columns of Table 25 allows assessing the potential attraction of different travel means towards passengers using different modes. The proportion of positive switches from private car to public transport is greater than the one describing the reverse relationship, indicating a potential attractiveness of the public transit system. Bike and bike sharing seems not very attractive to people using car and public transport. Moreover taxi sharing is even less attractive for all other means. Focusing on car sharing, overall, it is much less attractive than public transport and car.
Specifically, the relationship with both private car and public transport is ambiguous, since positive and negative intentions are reported for both the two modes. However, it seems more attractive for public transport users rather than for car drivers (χ2 = 32.141, p-value < 0.01). Furthermore, positive switches are reported also from bike, but not from walking. The analysis of the attractiveness of each transport modes is important to outline how the distribution of the actual travel demand could potentially change considering the introduction of an innovative transport mode, such as car sharing.
In conclusion, car drivers show the strongest behavioural inertia to change their mode. Car sharing attractiveness is low if compared with those of traditional transport modes. This could be due to the fact that car sharing has been only recently introduced in Turin. Overall, car sharing seems to be a potential substitute of private car, public transport and taxi rather than train and walking. These preliminary results can give an overview of the substitution and complementarity patterns among different means, but it is important to deepen our understanding of the underlying factors that can explain such trends.
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Table 25. Switching intentions from the current mode (in rows) to the alternative one (in columns) To…
Car Public
transport Taxi
sharing Bike Bike
sharing Car sharing Switching intention
from… N % N % N % N % N % N %
Car Yes 1’256 67.0 839 44.7 149 7.9 214 11.4 200 10.7 464 24.7 Neutral 260 13.9 332 17.7 178 9.5 162 8.6 150 8.0 248 13.2 N = 1’875 No 359 19.1 704 37.5 1’548 82.6 1’499 79.9 1’525 81.3 1’163 62.0 Public Yes 227 25.9 525 59.9 79 9.0 98 11.2 100 11.4 133 15.2 transport Neutral 210 24.0 146 16.7 77 8.8 85 9.7 61 7.0 119 13.6 N = 876 No 439 50.1 205 23.4 720 82.2 693 79.1 715 81.6 624 71.2 Taxi Yes 1 11.1 1 11.1 1 11.1 1 11.1 1 11.1 4 44.4 Neutral 2 22.2 1 11.1 1 11.1 2 22.2 3 33.3 1 11.1
N = 9 No 6 66.7 7 77.8 7 77.8 6 66.7 5 55.6 4 44.4
Walking Yes 66 11.7 153 27.2 22 3.9 155 27.5 122 21.7 36 6.4 Neutral 72 12.8 92 16.3 42 7.5 75 13.3 64 11.4 45 8.0 N = 563 No 425 75.5 318 56.5 499 88.6 333 59.1 377 67.0 482 85.6 Bike Yes 31 27.2 52 45.6 12 10.5 90 78.9 71 62.3 27 23.7 Neutral 19 16.7 19 16.7 9 7.9 10 8.8 19 16.7 11 9.6 N = 114 No 64 56.1 43 37.7 93 81.6 14 12.3 24 21.1 76 66.7
Bike Yes 4 33.3 5 41.7 1 8.3 7 58.3 7 58.3 7 58.3
sharing Neutral 5 41.7 5 41.7 2 16.7 5 41.7 3 25.0 2 16.7
N = 12 No 3 25.0 2 16.7 9 75.0 0 0.0 2 16.7 3 25.0
Car Yes 2 40.0 4 80.0 1 20.0 3 60.0 4 80.0 2 40.0
sharing Neutral 1 20.0 0 0.0 1 20.0 1 20.0 0 0.0 1 20.0
N = 5 No 2 40.0 1 20.0 3 60.0 1 20.0 1 20.0 2 40.0
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Figure 21. Sankey diagram for positive switches from the current mode (left side) to the alternative one (right side)
Figure 22. Sankey diagram for negative switches from the current mode (left side) to the alternative one (right side)
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Chapter 5 Models
In this chapter, data collected through the travel survey were used as input to different models, each of which had a specific aim. Specifically, in the first model, a logistic regression was adopted to identify variables affecting the decision to join the car sharing service. The same technique was implemented for the second model, the aim of which was to understand how the characteristics of potential users of car sharing interact with both trip attributes and past and future multimodality behaviours, in order to adopt car sharing to perform the macro-trip under analysis in the future.
Moreover, the third group of models had multiple aims. The first one was to define factors that influence the decision to switch from the travel mode effectively adopted to carry out the macro-trip towards car sharing. The second one was to analyse the relationship of car sharing with existing travel means (in particular, private car, public transport, bike and walking), in order to identify potential relationship of substitution or complementarity. The third aim was to define the best domain (or ambit of use) of each travel modes, i.e. the characteristics of the trips which are more likely to be performed with each mode. In order to reach these targets, three methods were applied: logit models based on Random Utility Maximization theory, Decision Trees and a visual approach. The obtained results were compared to assess the potentiality and limitations of each technique.
The first model was named “car sharing membership model” because of its aim; the second model was labelled as “propensity model”, whereas the third groups of models were defined as “choice” or
“switching models”. The rationale behind the two last names was based on the difference between the two models. Indeed, through the propensity model, generic attitudes of respondents towards the potential use of car sharing were analysed, without considering the characteristics of possible future trips which they might carry out on that travel mode, but rather focusing on multimodality behaviours.
It is worth noting that the reported propensities might not come true, since they were evaluated by generic preferences. On the other hand, choice models were adopted to test the effective decision to use car sharing to perform a specific real trip, thereby forecasting which trips might be diverted to car sharing. In particular, Stated-preferences experiments, which were used to calibrate these models, considered changes in the Level Of Service of car sharing, in order to generate realistic characteristics of travelling on this mode. For these reasons, the second and third models can be considered as
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complementary, contributing to generate a framework for the analysis of potential car sharing adoption.
Each of the following subsections contains the specification and calibration framework of each model, in addition to a discussion of modelling results. It is worth noting that independent variables adopted to calibrate the models include socio-economic and contextual factors of interviews and their households, as well as travel habits of individuals and characteristics of performed and potential trips.
However, attitudinal and affective factors were not considered, even if they can play a significant role in the choice to adopt car sharing (Efthymiou et al., 2013; Kim et al., 2017b; Ramos et al., 2020), since questions in the travel survey do not allow to an exhaustive definition of such variables.