Performance measures are calculated by applying each model both to the calibration and to the validation subset of macro-trips; in this way, it was possible to compare prediction performances and transferability of both logit models and Decision Trees.
In particular, the following indexes were adopted: Accuracy, Error rate, Recall, Precision, F-measure, Sensitivity and Specificity. Considering the prediction of the switching decision as a binary classification task, all these measures can be derived from a confusion matrix (Table 51), i.e. a matrix containing counts of observations belonging to predicted (in columns) and actual classes (in rows) (Cios et al., 2007; Han et al., 2012).
Table 51. Generic structure of a confusion matrix
Predicted
Car sharing Base
Actual Car sharing True Positive (TP) False Negative (FN) Base False Positive (FP) True Negative (TN)
The accuracy represents the percentage of correctly classified objects and it is defined as (Cios et al., 2007; Han et al., 2012; Larose and Larose, 2015):
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁
Conversely, the error rate is the percentage of misclassified observations (Cios et al., 2007; Han et al., 2012; Larose and Larose, 2015):
𝐸𝑟𝑟𝑜𝑟 𝑟𝑎𝑡𝑒 = 𝐹𝑃 + 𝐹𝑁
𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁= 1 − 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦
However, these two measures are misleading, since a high accuracy (and a corresponding low error rate) is obtained if the model detects only elements belonging to one single class. Therefore, other measures should be adopted to consider predictive powers for each class separately (Han et al., 2012). Considering car sharing as the reference class, Precision is an index of exactness, i.e. it represents the percentage of predicted car sharing trips among actual car sharing trips; whereas, Recall is a measure of completeness, i.e. what percentage of actual car sharing trips are predicted as such (Han et al., 2012). Precision and Recall are defined as follows (Cios et al., 2007; Han et al., 2012):
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑃
𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁
Both Precision and Recall are combined into a single index, namely F-measure, which is calculated as the harmonic mean between these two measures. Therefore, for the car sharing class, F-measure is estimated as (Han et al., 2012):
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𝐹 − 𝑚𝑒𝑎𝑠𝑢𝑟𝑒 =2 ∗ 𝑅𝑒𝑐𝑎𝑙𝑙 ∗ 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑅𝑒𝑐𝑎𝑙𝑙 + 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛
Similarly, Sensitivity and Specificity are adopted to evaluate prediction performances for every single class. The former represents the true positive recognition rate, which is equal to class Recall, whereas the latter is a true negative rate. These two measures are calculated as follows (Cios et al., 2007; Han et al., 2012):
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁
𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 = 𝑇𝑁 𝐹𝑃 + 𝑇𝑁
All these measures were adopted to evaluate the performances of both logit models and Decision Trees, in terms of prediction powers of the potential trips that might be carried out on car sharing, rather than the currently reported travel mode. In particular, Table 52 and Table 53 report parameters for the five logit models, whereas Table 54 and Table 55 exhibit values for the two group of Decision Trees: the first group considers absolute values of trip attributes as exogenous variables, on the contrary, the second group adopts relative values. Table 52 and Table 54 contain results obtained from the validation datasets, on the other hand, Table 53 and Table 55 show results derived from the models applied to the calibration datasets. In all the four tables, Precision, Recall, F-measure, Sensitivity and Specificity are referred to car sharing alternative.
Observing the two tables of logit models and the two ones related to Decision Trees, one can note that, accuracies of Decision Trees are lower than those of logit models. However, respect to logit models, Decision Trees show higher values of Precision, Recall, F-measure, Sensitivity and Specificity of car sharing alternative. This indicates that this method has a greater prediction capability for car sharing, rather than for the base mode, therefore lower values of accuracies were obtained.
As regards mode-specific versions of the models, among the four logit models (Table 52 and Table 53), the one related to bike mode has the greatest predictive performances for car sharing trips, however the dataset to calibrate and validate this mode is the smallest one. On the other hand, public transport logit model shows the second highest value of F-measure, suggesting a good predictive capability. Overall, performance values calculated using the validation subsample (Table 52) are similar to those obtained from the calibration subsample (Table 53), suggesting that the models can be applied to other datasets without changing prediction performances; therefore results are stable and generalizable.
On the other hand, results reported in Table 54 and Table 55 are slightly different. In particular, considering the validation subset (Table 54) switching trips on car sharing are best predicted from private car, whereas model performances calculated using the calibration dataset (Table 55) indicate that car sharing potential trips are predicted more correctly for bike mode. Moreover, Decision Tree predicting shifting intentions of walking trips shows the maximum value of Recall (and Sensitivity), indicating that this model is able to recognize all the switching trip towards car sharing. Furthermore the comparison of the two tables suggest that all the performance measures have different values,
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therefore results of Decision Tree seems less transferable to different datasets, rather than logit models.
Table 53. Values of performance measures for logit models, calculated using the calibration dataset (percentage values)
Absolute values of trip attributes Relative values of trip attributes All Car Public
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Table 55. Values of performances measures for the two groups of Decision Trees, calculated using the calibration dataset (percentage values)
Absolute values of trip attributes Relative values of trip attributes All Car Public
transport Bike Walking Car Public
transport Bike Walking Accuracy 70.35 73.31 80.91 84.93 90.08 72.30 83.74 90.41 92.01 Classification
error
29.65 26.69 19.09 15.07 9.92 27.70 16.26 9.59 7.99 Precision 40.46 51.40 47.78 65.52 41.94 50.23 52.07 78.26 42.27 Recall 80.45 78.81 92.47 95.00 100.00 71.19 94.62 90.00 100.00 F-measure 53.84 62.22 63.00 77.55 59.09 58.90 67.18 83.72 64.20 Sensitivity 80.45 78.81 92.47 95.00 100.00 71.19 94.62 90.00 100.00 Specificity 67.58 71.19 78.44 81.13 89.32 72.73 81.42 90.57 91.39
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Results and conclusions
Each of the three approaches provided different considerations, from different perspectives, contributing to enrich the global view on the relationship between car sharing and traditional transport means. Since each adopted method has a different basis, some results are complementary and others are common. However, the aim of all the methods was to evaluate the variables affecting the intentions to switch from the reported mode (car, public transport, bike or walking) towards car sharing, in order to perform a specific macro-trip in the future. In this way, mode-specific factors to promote or avoid the shift can be identified. Exogenous variables are attributes of the macro-trip and characteristics of travellers (only for logit models and Decision Trees).
All the three approaches pointed out the general inertia of users to maintain their travel mode, rather than to switch to car sharing, suggesting that car sharing potential switches might be hindered by travel habits. This result was indicated by the higher number of registered non switch answers, rather than switch answers, in the Stated-preferences experiments (Section 4.5). Moreover, the current car sharing system, and, in particular, the related advantages, are not well-known among Turin inhabitants, probability due to its recent introduction in the city. However, current car sharing members are likely to adopt the service in future, pointing out that they are satisfied with the service.
This might suggest that once a person becomes a member, she frequently replaces her travel mode with car sharing.
As regards socio-economic characteristic of potential car sharing members, results of both logit models and Decision Trees highlighted that car sharers tend to be young and living in households with high income, as obtained from statistical analysis of the sample of car sharing members (Section 4.4). Moreover, they have multimodal travel habits and use frequently active modes, as confirmed by the high percentages of use of these travel means of descriptive statistics in (Section 4.4). In addition, potential car sharing users seem to have a Value Of Time lower than car drivers and higher than public transport users (visual approach). On the other hand, some variables are mode-specific. For instance, car was found to play a negative role for public transport users, bikers and walkers.
Furthermore, females are more willing to switch from public transport, whereas they tend not to switch from bike mode.
The analysis of trip attributes that can promote or avoid the shift is helpful to outline the relationship of car sharing with traditional travel means, defining the best ambit of use of each mode.
All three models pointed out that reducing the cost of car sharing could induce the shift from private cars. Moreover, the same effect can be strengthened by increasing the cost of driving a private vehicle (logit model), reducing the duration of the trips by at least 3 minutes (Decision Tree) or decreasing the walking time to reach the shared car (Decision Tree). Car sharing can substitute private car for trips shorter than 14 kilometres (Decision Tree), even starting from outside the city and with a destination within the city (logit model). However, potential members are willing to walk up to 6 minutes to reach the shared vehicle (Decision Tree). Logit model suggested that car sharing might replace private car in non-working days, however Decision Tree predicted positive switches even in weekdays, for both systematic and non systematic trip purposes, since the outcome of this model is related to specific segment of users, i.e. with particular socio-economic characteristics; on the other hand, this segmentation could not be inferred from logit models.
As regards public transport, in general, low potential substitution rates were found for urban trips, i.e. with short distance and long duration (Decision Tree and visual approach), in particular for trips
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shorter than 10-18 kilometres. Furthermore, in order to avoid the shift towards car sharing, the cost of public transport trips should be lower (logit model, Decision Tree and visual approach). On the other hand, waiting time at the transit stop is a factor that affects switching intentions (logit model and Decision Tree), in particular, positive switches were predicted if the waiting time was greater than 3 minutes (Decision Tree), moreover potential car sharers are willing to pay up to 0.8 € to avoid 4 minutes of waiting time (Decision Tree). Furthermore, shifts might occur if the in-vehicle travel time on car sharing was lower than the one on public transport (Decision Tree and visual approach).
Therefore, in order to prevent the switch from public transport towards car sharing, policies to maintain low fares and short waiting time (e.g. by increasing transit frequencies) should be carried out; in addition the travel speed of public transit means should be increased to compete with that of car sharing, in order to reduce potential switches. Car sharing might replace trips performed in non working days (logit model) and during weekdays, by employees and students (Decision Tree).
Car sharing was found not to be suitable for very short trips, in particular for travel distances shorter than 2 kilometres and with a duration lower than 30 minutes, since these type of trips are usually performed by bike or walking (visual approach). In particular, trips up to 300 meters long are carried out on foot, whereas the maximum distance by bike turned out to be 1.4 kilometres (Decision Tree). Both logit models and Decision Trees highlighted that reducing the cost of car sharing and the walking distance to reach a vehicle might induce the shift, not only from private car, but also from bike and walking. However, bikers are willing to walk up to 9 minutes and they might decide to switch if they could reduce this time of at least 5 minutes, if compared to the walking time to reach their bikes.
In conclusion, the three adopted approaches were useful to analyse the relationship between car sharing and traditional travel means and to study the effect of particular factors on the switching intentions. Each model provides different information, which could lead to similar or different conclusions. In particular, differences might be understood considering that logit models identify positive or negative relationships between dependent and independent variables, whereas Decision Trees estimate different effects for the same variables, according to the values of other variables. For instance, the effect of a trip attribute might depend on the characteristics of users. Therefore, the three methods gave light to the problem from different angles. To sum up, the visual approach provided preliminary descriptive analysis on the effect of trip attributes. Moreover, logit models were helpful to understand the effect of different exogenous variables and to derive further information to forecast the consequences of the introduction and diffusion of car sharing on future scenarios, e.g. by using trip switching probabilities. On the other hand, results from Decision Trees were used to identify the non-continuous effects of different variables, by estimating specific thresholds for each factor.
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Chapter 6
Scenarios based on estimated switching models
6.1. Introduction
In this section, alternative mobility scenarios are generated using the previously estimated switching models (Section 5.3), in order to maximize the number of trips switching from private car towards car sharing and minimize those from public transport and active modes. The results of each scenario are used to analyse the modal split and the effect of car sharing in the use of public space.
Indeed, as reported in the state of the art section (Chapter 2), one of the advantages of this transport mode is to reduce the on-street parking space that private cars usually occupy. However, in many previous works, the reduction of the demand for parking spaces is often associated with a reduction of car ownership due to car-sharing, so that several papers (Martin et al., 2010; Mishra et al., 2015) quantify how many private cars could be replaced by a shared vehicle. Yet positive benefits on public spaces might be observed, albeit to a lesser extent, even if the private car is not given up, since the parking pressure near the main mobility attractors might be reduced.
Therefore, without considering changes in car ownership levels, which are related to long term effects, in the present work, trip-level impacts on parking spaces, which might occur in short-medium periods, are estimated, by analysing variations in the spatial configuration of the demand for car parking spaces after the introduction of car sharing. Following this perspective, a “parking event” is defined as a parking space that is occupied by a vehicle whose trip was switched to car-sharing. Even if the number of parking events is not equivalent to the number of parking slots, this conceptual measurement unit can be useful to assess the potentially saved parking space. Moreover, this quantification can be used as an input to GIS-based analysis, in order to provide some guidance to local authorities in evaluating car-sharing impacts on public spaces. In particular policy makers can properly address financial resources, since concessions to car-sharing operators are often based on parking permissions (Ceccato and Diana, 2018); on the other hand, sound basis are provided to define reallocation strategies of saved spaces in urban planning.
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