The second project from which data for the analysis was taken is an FOT conducted by Loughborough University for an OEM company. The purpose of that study was to find more about visual behaviour in relation to the vehicle instrument cluster (e.g. speed, RPM, fuel-level and Heating, Ventilation and Air-Conditioning (HVAC) controls) during normal driving in different road environments.
In this PhD the data was used for a different purpose, i.e. to analyse the deceleration behaviour during normal driving in different scenarios (road environments) and therefore these data are adequate. The design of the study was different than the TeleFOT study. The sample consisted of 12 drivers (6 males and 6 females) from 23 to 65 years old. Information on the driver (subject number, age band, gender) was reported in the summary sheet of the project (see Table 3.7). Three different make/models of vehicle were used in this project. Therefore, the influence of the vehicle can be examined to some extent by including the model of the car in the analysis. All the cars were equipped with Race Technology Ltd equipment, which comprises a GPS and accelerometer package linked and synchronised to a four- channel video system (forward road view, driver face, backward road view, driver reaction from the passenger seat), and so they were capable of recording vehicle kinematics and driving environment features. The sampling frequency was 100 Hz and the data was processed by Race Technology V8.5 software.
signalised unsignalised Percentage of the deceleration events(<-2.5) 26.6 22.8 Percentage of the deceleration events(<-3) 11.7 4.3 0.0 5.0 10.0 15.0 20.0 25.0 30.0 Per ce n tage (% )
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The participants first spent a short period of dynamic familiarisation with all three cars; this step ensured that all drivers felt comfortable in what was likely to be an unfamiliar vehicle and then they were asked to drive along three specific routes in the Leicestershire county of England. Each route represented a different road type: motorway (“out and back” route using one junction of the M1), urban (Loughborough) and rural (around the forest area between Loughborough and Leicester) (see Figure 3.9). The routes were chosen carefully to include different scenarios, different road elements (e.g. high-speed roads, dual carriageways, roundabouts, cross-junction, traffic light, mid-block crossings) in order to be possible to analyse and compare the deceleration behaviour in all those different scenarios.
Table 3.7: Driver population by age and gender Age Band Male Female Total
17-30 1 1 2 31-40 1 1 2 41-50 1 3 4 51-60 2 1 3 60+ 1 0 1 Total 6 6 12
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Figure 3.9: The three routes of the field test
Each participant completed all three driving scenarios; the total trial driving time was approximately one hour, split between motorway (10-15 minutes), rural (20-25 minutes) and urban (20-25 minutes). Each participant began from the same start point and ended at the same endpoint, however, to control for order/learning effects the order in which the segments are administered was randomized. It is also introduced
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an extra trip, a fourth one which occurred during the night. So, each participant drove 4 different scenarios (different car and day/night) each in three different road types, which lead to 130 trips in total (a few trips were missing, since 3 drivers withdraw after executing some trips and did not complete the tests). The sampling frequency was 100 Hz for the duration of all trips so this yield over 15.3 million observations.
Using the algorithm, described in the Methodology chapter, to detect the deceleration events, 1,785 events were identified. As in the TeleFOT project, the variables that were essential for the analysis were extracted from the Race Technology V8.5 software or calculated from the data extraction algorithm or determined qualitatively by viewing the videos related to the deceleration events. Moreover, as with the TeleFOT dataset, the outliers were detected and excluded resulting in 95 outliers and 1690 remaining deceleration events and the correlation between the explanatory variables was examined identifying correlation only between the Driver_id and the Trip_id.
From the histogram of the deceleration rates (Figure 3.10), it can be observed that the deceleration values are relatively low since the data represent normal driving and did not include any collisions. More specifically the average deceleration value was found to be -2.57 m/s2 and the maximum value was -7.08 m/s2, while the average duration
was 6.2 sec and the maximum duration was 33.06 sec. Various descriptive statistics were generated for the different factors to obtain a general picture and understand how the deceleration change in relation to specific manoeuvres (Table 3.8).
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Figure 3.10: The distribution of the deceleration values (left) and of the duration(right) of the events
As far as the initial speed is concerned, its average value was 45 km/h (12.5m/s), with 60% of the events starting at speeds between 18 km/h (5 m/s) and 54 km/h (15 m/s). The mean duration and mean deceleration value for different initial speeds are presented in Figure 3.11 and it can be concluded that the higher the initial speed the longer and harder the deceleration event (see Table 3.8 too).
Figure 3.11: Diagram of the initial speed vs the mean deceleration (left) and the mean duration (right)
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Table 3.8: Deceleration features’ statistics based on different factors Mean values
Number of cases
Maximum deceleration
value (m/s2) Duration (sec)
Gender: male 888 -2.59 6.26 female 1216 -2.56 6.2 Initial speed: 0-5 169 -2.40 2.95 5-10 564 -2.46 4.62 11-15 743 -2.59 6.21 16-20 417 -2.65 8.22 21-25 166 -2.72 9.16 >25 47 -2.81 9.49 Traffic density: low 1175 -2.59 6.35 medium 395 -2.54 6.61 high 148 -2.53 6.17 Age: 17-30 322 -2.58 6.54 31-40 349 -2.55 5.76 41-50 697 -2.62 6.41 51-60 580 -2.5 6.24 60+ 156 -2.58 5.74 Traffic light: Signalised 277 -2.52 6.56 Unsignalised 1827 -2.58 6.17 Reason of braking: Roundabout 306 -2.54 7.31 T-junction 446 -2.52 6.12 Cross- junction 250 -2.58 7.65 Pedestrian crossing 40 -2.79 5.24 Mid-block crossing 51 -2.5 5.24 Dynamic-obstacle 918 -2.58 5.48 End of the trial 93 -2.68 7.25
Road Type: Motorway 275 -2.6 7.48 Rural 994 -2.63 6.6 Urban 835 -2.49 5.37 Car model: Vehicle A 573 -2.52 5.22 Vehicle B 845 -2.66 6.9 Vehicle C 686 -2.5 6.16
Most of the deceleration events (68.3%) occurred in low traffic density conditions, 23% in medium traffic conditions and only 8.7% in high-density conditions. The mean of the maximum deceleration values and of the duration for different traffic densities did not
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indicate that a relationship exists between the observed rates and the traffic density (see Table 3.8). Regarding the road type, it can be concluded from the Table 3.8 that the deceleration value is almost the same on motorways and rural roads but it is smaller at urban roads which indicate a softer deceleration and also a shorter one. Moving onto the driver factors it can be noted that age affects neither the deceleration value nor the deceleration duration. The gender seems to have some influence in the deceleration value as males seem to decelerate harder than females, but the deceleration duration is really similar.
In addition, the deceleration value and duration seem to differ depending on the vehicle model. More specifically the biggest deceleration value and the longest duration was observed to happen when the participants were driving Vehicle B and the smallest and shortest when they were driving Vehicle C.
Last but not least, the situational factors will be discussed. The deceleration events for each reason of braking are 306 for roundabouts, 1446 for T-junctions, 25 for cross- junctions, 40 for pedestrian crossings, 51 for stopping at car blocks, 918 for obstacles and 98 to stop because the trial is over. As can be seen from Table 3.8 the reason for braking affects the deceleration behaviour: hard braking due to the pedestrian crossing, both big deceleration value and short duration, can be observed. Also, the braking behaviour is quite similar for the roundabouts, the cross and T-junctions. Finally, some influence is noted between the deceleration event and the fact that a road element is signalised or not, which is that for non-signalised road elements the braking is harder since the deceleration value is greater than for signalised roads and the duration is shorter.