UDRIVE Comfort Modelling

As it was described in the Methodology Chapter, the UDRIVE dataset will be used for the comfort modelling analysis. First, following the described procedure for the detection of the deceleration events for the comfort analysis, 21,600 deceleration events (deceleration limit -1 m/s2_{) were identified and will be used for the modelling.}

The cumulative frequency of both the deceleration value and the jerk, which are the variables that set the limits for the comfort categorisation are displayed in Figure 3.18. It can be observed that 99% of the deceleration events have maximum deceleration value smaller than 3.9m/s2 _{in absolute value and jerk bigger than -3m/s}3_.

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Figure 3.18: Cumulative Frequency of the deceleration value and the jerk

For the modelling, the dependent variable is the comfort level, which is a categorical variable. As it was described in Chapter 3, different classifications of the deceleration events regarding comfort level took place. The first classification had four categories, the second has three and the third has only two categories (i.e. binary). The frequency of the deceleration events that belong to each of the four comfort categories is presented in Table 3.15. It can be noticed that only 4.4% of the events were perceived as very uncomfortable whereas 45.2% of the events were slightly comfortable.

Table 3.15: Frequency of the deceleration events of classification A

Frequency Per cent Cumulative Percent

Very comfortable 8094 33.8 33.8 Slightly comfortable 10813 45.2 79.0 Slightly uncomfortable 3966 16.6 95.6 Very uncomfortable 1060 4.4 100.0 Total 23933 100.0

The frequency of the deceleration events to each category for Classification B and C are presented in Table 3.16 and Table 3.17 respectively.

Table 3.16: Frequency of the deceleration events of classification B

Frequency Per cent Cumulative Percent

Comfortable 8094 33.8 33.8

Neutral 11882 49.6 83.4

Uncomfortable 3957 16.6 100.0

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Table 3.17: Frequency of the deceleration events of classification C

Frequency Per cent Cumulative Percent

Comfortable 11908 49.8 49.8

Uncomfortable 12025 50.2 100.0

Total 23933 100.0

Moreover, the explanatory variables that were examined are presented in Table 3.18. It can be observed that the variables can be categorised at the event level variables and the driver level ones.

Table 3.18: Explanatory variables used in the logit modelling

Code name Explanation Variable type

Initial speed The speed that the vehicle has at the beginning of the deceleration event.

Continuous Variable TTC The time to collision (TTC) from the

leading car at the beginning of the event.

Continuous Variable

THW The THW at the moment that the deceleration event starts.

Continuous Variable HW The space headway at the moment

that the deceleration event starts.

Continuous Variable Traffic congestion If there is traffic congestion when

the deceleration event is taking place.

Categorical Variable (0-> no traffic congestion)

Motorway If the event is happening in a motorway.

Categorical Variable (0-> the event is not happening in a motorway)

Rural (reference variable)

If the event is happening in a rural area (single carriageway roads or dual carriageways).

Categorical Variable (0-> the event is not happening in a rural area)

Urban If the event is happening in an urban area.

Categorical Variable (0-> the event is not happening in an urban area)

Intersection If the reason for braking is approaching an intersection.

Categorical Variable (0->there is no intersection)

Pedestrian If the reason for braking is a pedestrian.

Categorical Variable (0->there is no pedestrian)

PTW If there is braking because of a PTW. Categorical Variable (0->there is no ptw)

Cyclist If the reason for braking is a cyclist. Categorical Variable (0->there is no cyclist)

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One_lane If the deceleration event is happening on a one-lane road.

Categorical Variable (1->one lane road)

Male Driver’s gender Categorical Variable (0->if the driver is a woman)

Age 18-30 Driver’s age Categorical Variable (1->if the driver belongs to the specific age category)

Age 31-50

Age >50 (Reference variable)

AISS_total Arnett Inventory of Sensation seeking

Continuous Variable DBQ_all_violations Driver behaviour Questionnaire Continuous Variable

Some non-parametric tests were performed to depict if there are differences for the independent variables in each comfort category. In Figure 3.19, two examples of boxplots of two variables (initial TTC and initial THW) against comfort categories are presented. It can be seen that THW has some extreme values for every category. Also, for each comfort category, both TTC and THW seem to have different values and that might indicate that they have a significant effect on the comfort level of the deceleration event.

Figure 3.19: Boxplot of initial TTC and the initial THW for each comfort category

Last but not least, it should be noted that two statistical analyses will be undertaken; If all the variables are included, then fewer observations can be considered at the models since some explanatory variables are not available for all the observations (e.g. the TTC, THW and space headway are available only if there is a vehicle in front of the examined car when it is braking). Therefore, in the first one (Statistical Analysis

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I), all the explanatory variables from Table 3.18 were included, leading to fewer observations. Specifically, from 23,933 deceleration events that were identified, 5,843 events were included in the model. Many events happened without the existence of a leading vehicle and so, the variables TTC, THW, headway do not exist. Also, not all drivers have completed the questionnaire. In the second analyses (Statistical Analysis II) all the observations were included by taking out the variables that were mentioned before.