In this section, a visual and descriptive approach is adopted in order to visualize the domain of traditional travel modes and car sharing. Following this method, the combined effect of selected trip characteristics on the choice of each mode is represented through visual charts. Data visualization is an effective way to represent complex and large datasets, obtaining an accessible and easily understandable summary of results (Yu and He, 2017).
Visual techniques in transportation studies were previously used for several aims. First, heat maps were adopted to evaluate spatial relationships among variables. Essa et al. adopted heat maps to compare real and simulated results of conflicts locations along many road sections (Essa and Sayed, 2015). Similarly, Wang et al. used heat maps to better understand the changes in the number of conflicts at different locations in a bus stop area (Wang et al., 2018). Moreover, in a pedestrian environment, Li et al. combined heat and real maps in order to visualize areas with different Level Of Service (Li et al., 2019). Secondly, heat maps were used to represent complex spatiotemporal interactions, in particular, to estimate traffic conditions on roads. Yildirimoglu and Geroliminis reported travel speed registered by loop detectors in spatial-temporal heat maps, in order to identify bottlenecks and congested road sections (Yildirimoglu and Geroliminis, 2013). Likewise, Ahn et al.
define three-dimensional heat maps to visualize traffic conditions on roads, in particular to represent the correlation among space, time and traffic flow (Ahn et al., 2014). Heat maps were also introduced to visualize dynamic changes of some measures used in transport analysis. Stipancic et al. adopted heat maps in order to represent aggregate values of a proposed congestion index (Stipancic et al., 2017). Hu et al. visualized space-time job accessibility patterns using three-dimensional heat maps (Hu and Downs, 2019). Yang et al. adopted density maps to obtain spatiotemporal information about taxicab availabilities and travellers’ activities (Yang et al., 2017). Glick and Figliozzi used heat maps to represent transit performance measures and to identify critical cases (Glick and Figliozzi, 2017).
In addition to visualization purposes, heat maps were generated as the basis to apply several statistical and mathematical techniques to derive further information. Nguyen et al. applied clustering analysis on previously generated heat maps in order to extract and classify highway traffic congestion patterns (Nguyen et al., 2019). Starting from heat maps, representing spatiotemporal characteristics of bus travel demand, Yu and He proposed an approach, based on Principal Component Analysis and clustering, to identify the distribution patterns of transit demand (Yu and He, 2017).
However, there are no previous works adopting a visual approach in mode choice analysis. The proposed methodology, unlike classical mode choice models, does not need any statistical hypothesis about input data, therefore it can be applied to any dataset, without high computational effort.
Furthermore, this approach allows analysing the combined and non-linear effect of multiple trip attributes, complementing quantitative analysis developed with mode choice models. Results thus obtained are significant for the study of the best ambit of use of different transport modes, in order to understand which kinds of trips are more conducive to be performed by car sharing. To reach this aim two kinds of visualization charts were generated: modal switch heat maps and modal switch density maps. The methods used to build these maps are described in the following.
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Modal switch heat maps
To derive the charts of the first kind, two attributes of the macro-trip, namely total travel time and total travel distance, are respectively discretized into n travel time bins whose fixed range is 5 minutes and m travel distance bins whose fixed range is 2 kilometres. The total travel time was computed as the sum of in-vehicle, waiting and walking time. All trip chains having the same combination of travel time and travel distance bin are then jointly considered in a unique set, irrespective of the travel means that was used, and therefore n * m different sets are generated.
Four heat maps of this first kind were derived, where each map shows the fraction of respondents willing to shift to one of the four switching modes presented in the Stated-preferences choice tasks (private car, public transport, bike and car sharing) (see appendix A for further details on Stated-preference experiments), for all sets that contain at least 10 trips. Three additional heat maps of the second kind were derived, where total travel time and total travel cost were rather considered, thus excluding the bike switching mode which has no associated cost. The range of cost bins is fixed at 0.5 euros.
Different sizes of bins for both kinds of heat maps were evaluated. Clearly, the larger the size the larger is the number of observations there contained and therefore related results are more reliable.
On the other hand, too large bins are originating oversimplified plots that might lead to poor interpretation. The final size of the bins was set in order to strike the right balance between these two contrasting factors. Consequently, heat maps thus generated can be easily read showing enough and reliable information about trip characteristics.
Cold regions in all maps show characteristics of the chained trip that are associated with lower mode shift propensities, while hot regions point out larger shifting propensities. Clearly, the shifting propensity is directly affected by the Stated-preferences attributes levels, but here the focus is not on comparing the used mode and the shifting mode, which was rather done in a previous section. The main interest of these heat maps rather lies in comparing charts for different travel modes. This leads to a visualization of the preferred ambits of use of each switching mode, particularly in case traditional travel modes (car, public transport and bike) and car sharing are considered.
Modal switch density maps
As a complement to the above representations, a second group of charts were created in order to better understand the relationship between a switching mode and the current one (declared in the Revealed-preferences part of the survey). In particular, first, only chained trips with positive values of switch were considered. The same attributes of the above mentioned second kind of heat maps are considered here, namely travel times and travel costs. However, in this case, the differences between Stated-preferences attributes of the alternative mode and the corresponding attributes of the current mode were computed for each chained trip and grouped into pre-determined bins. Each bin is therefore containing those trips that could be performed with the switching mode in the future. The corresponding cardinality is plotted in a graph on a grey scale, where the darker colour represents a larger number of trips.
Then the same procedure was repeated for trips with negative switching intentions. Therefore two graphs were generated for each couple of switching mode and the current one. In particular, the first group of graphs plots positive switching intention answers and the second one plots negative switching intention answers. Positive values on the horizontal and vertical axis respectively mean
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larger travel times and larger travel costs for Stated-preferences modes over current modes, therefore making the switching option not convenient. Thus, in each of the four quadrants of the graphs, either the switching mode or the current one is advantageous for one or both of the two Stated-preferences attributes. The bike mode was not considered since the associated trip cost is null.
Adopting this graphical approach rather than using a mode choice model with the same variables, allows obtaining more qualitative but rich information. Even if the proposed visual approach does not provide classical parameters derived through an econometric approach, such as the Value Of Time and demand elasticities, it allows to immediately understand the combined and non-linear effect of two variables on the choice of each of the four switching modes. Identifying non-linear trends in variables is useful to introduce these effects in mode choice models (Pinjari and Bhat, 2006).
Therefore this method can complement more quantitative analyses, which were previously described.
Analysis of results of the visual approach
Observing the cold areas of Figure 26 and Figure 27 one can note that the switching intention percentage towards car sharing is generally low, compared to the one towards car (Figure 28 and Figure 29) and public transport (Figure 30 and Figure 31), possibly because of its relatively recent introduction in the study area. In particular, even if in Turin the number of vehicles of car sharing operators is quite high with about 8 cars per square kilometre (Ciuffini et al., 2018), Figure 26 and Figure 27 confirm that the knowledge of car sharing system is not common among Turin inhabitants.
In the graphs plotting distance versus time, the slope of a hypothetical straight line passing through the origin is the velocity of the reported trip chain. Therefore, Figure 28 shows that the majority of people choose the car for quicker trips, whereas the choice to switch towards car sharing seems to be less dependent on the speed of the trip (Figure 26). This might in part point to the current limitations of car sharing systems on the spatial localization of trip origins and destinations related to their service operational area, which make them a less viable option for trips in suburban areas whose speed is generally higher. Even if the Stated-preferences experiment did not make specific reference to the service area limitation of current services, it is likely that respondents have considered the characteristics of the existing car sharing offer in Turin.
Furthermore, the comparison among Figure 26, Figure 28 and Figure 30 indicates that car, car sharing and public transport are not suitable for short trips (up to 2 kilometres) with long duration (up to 30 minutes), since these trips are usually performed walking or by bike. Moreover car and car sharing are not chosen for trips shorter than 10 kilometres and lasting around 60 minutes (Figure 26 and Figure 28); however this kind of trips might be performed on public transport (Figure 30). Those trips might be performed by “transit captives”, or in general by people that are reluctant in using either cars or car sharing.
The distribution of hot areas of costs of car (Figure 29), car sharing (Figure 27) and public transport (Figure 31), suggests that public transport prospective users are willing to accept more travel time in order to save money. Furthermore, in these graphs the slope of a hypothetical straight line passing through the origin represents a sort of value of time. Therefore one can note that the highest value is reported for car, a medium value for car sharing and the lowest for public transport, as expected. From this visual analysis, car sharing is found to be a mix of car and public transport.
Observing Figure 26, Figure 28 and Figure 30, it is possible to overlap the areas with the highest values of switching intention percentages for each modal switch heat map of motorised travel modes, which represent the best ambit of use of each of them. Following this virtual procedure, the car sharing
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area lies in the intersection between car and public transport covering trips with short distances and times, which are typical of an urban contest.
Coming to the consideration of density maps, Figure 32 and Figure 34 were analysed. In the first quadrant (north-east), the majority of points have a negative switch, as expected (Figure 34), since the current mode car is more advantageous than car sharing in terms of both time and cost. However, there are some points with a positive value of switch and, in particular, only 8% of these respondents have a car sharing subscription. This suggests that this mode might attract some people even if it is disadvantageous on travel time and travel cost grounds. On the other hand, comparing the third quadrant (south-west) of both figures shows that the majority of points have negative values of switch (Figure 34), even if car sharing trips are both shorter and cheaper than car trips. Therefore, reducing the cost and the duration of trips on car sharing does not seem to be the main way to attract the majority of people away from private cars to achieve a radical overhaul of mode shares. This suggests that subjective determinants of the use of private car rather than car sharing currently exist. In the north-west quadrant there are more non-switch (Figure 34) than switch (Figure 32) points, while the opposite is found in the south-west quadrant. The overall conclusion is that car sharing should be more competitive on cost rather than on travel time grounds to lure people away from their cars, as also found when considering the above introduced mode switch models.
Concerning public transport, observing Figure 33 and Figure 35, one can note that most of the points are concentrated in the second and third quadrant in both cases, where car sharing travel time is less than the one of public transport. In the adopted Stated-preferences settings, waiting and walking time of the latter mode make in fact the overall travel time of car sharing shorter in virtually all circumstances. Positive switching densities are in any case smaller than those from private cars to car sharing. In the second quadrant (north-west), the majority of points have a negative value of switch (Figure 35): these respondents keep on using public transport when it is cheaper than car sharing, even if the latter has a shorter travel time. Complementing the above results related to substitution patterns between car and car sharing, it seems that car sharing services which are competitive with public transport on travel time grounds might induce an undesired diversion from public transport to car sharing itself, whereas the competition on travel costs is more detrimental to the use of cars. As a final note, there are a lot of observations with negative value of switch (Figure 35), even if car sharing is advantageous both for travel time and cost. Therefore, the analysis suggests that, like for car users, travel time and cost are not the only determinants of the switch from public transport and car sharing.
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Figure 26. Switching intention percent towards car sharing for each class of RP attributes (distance and duration)
Figure 27. Switching intention percent towards car sharing for each class of RP attributes (cost and duration)
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Figure 28. Switching intention percent towards car for each class of RP attributes (distance and duration)
Figure 29. Switching intention percent towards car for each class of RP attributes (cost and duration)
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Figure 30. Switching intention percent towards public transport for each class of RP attributes (distance and duration)
Figure 31. Switching intention percent towards public transport for each class of RP attributes (cost and duration)
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Figure 32. Densities of positive switching intentions from car to car sharing as a function of the difference between SP attributes (cost and duration) of car sharing and the corresponding RP attributes of car
Figure 33. Densities of positive switching intentions from public transport to car sharing as a function of the difference between SP attributes (cost and duration) of car sharing and the corresponding RP attributes of
public transport
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Figure 34. Densities of negative switching intentions from car to car sharing as a function of the difference between SP attributes (cost and duration) of car sharing and the corresponding RP attributes of car
Figure 35. Densities of negative switching intentions from public transport to car sharing as a function of the difference between SP attributes (cost and duration) of car sharing and the corresponding RP attributes of
public transport
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