Investigating Car Users’ Driving Behaviour through Speed Analysis

Abstract

Speed has been identified for a long time as a key risk factor in road traffic

Assessing the information flows and established their effects on the results of driver’s activity

“…The study used a monopolar recordthe activity of one tap at an electrically neutral point (Desimone & Duncan, 1995). In the Cardiosens system, a digital 3-channel microprocessor-based cardiograph is used as a portable ECG monitor recorder, recording an electronic Archives of Transport, 45(1), [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]2018 signal to a removable SD/MMC flash card (Zhuk et. al,, 2017).…”

“…Dependence of the change in the time of distraction of the tester from the performance of the main activity (  ), taking into account the specific gravity of the Delta-rhythm (  ) ( Figure 6) and thetarhythm (  ) (Figure 7), which reflect the inhibitory processes in the cerebral cortex from full power signal, to which the signals of , , , ,      -Archives of Transport, 45(1), [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]2018 rhythms are related, are linear in nature and indicate that as the percentage of signals from these rhythms increases, the time of abstraction from performing the main activity increases. Further, statistical equations and regression methods were used to derive linear equations (5)(6)(7)(8) and build the dependences ( Figures 6-9) of the time of abstraction from the performance of the main activity, taking into account the quantitative characteristics of the prevalence of excitatory and inhibitory processes in the cerebral cortex: fast (β + γ) and slow (δ + θ) rhythms of the EEG, as well as the forces of exciting and inhibitory processes in the cerebral cortex: fast (β -γ) and slow (δ -θ) EEG rhythms.…”

“…The given regression equations have statistical significance, while the value of the linear correlation tends to 1, which for equations (3)(4)(7)(8) indicates the presence of a direct linear relationship between the variables considered, and for equations (1-2, 5-6, 9-10) of the inverse linear connection. The calculated value of Fisher's criterion for models of changing the time of abstraction from the Archives of Transport, 45(1), [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]2018 performance of the main activity, taking into account the EEG indices (1-10) reflects the informative nature of the model, while the average approximation error is large enough; however, in the framework of the study, the construction of these models is necessary for determining the character of the dependence in constructing a general mathematical model of influence of information flows, which is displayed by EEG indicators, for the time of distraction from the performance of the driver's primary activity.…”

“…Compared with the model (11), the value of the average error of approximation which is 10.86 %, we see that by incorporating ECG in the model (12), managed to get a more accurate model of the impact of information flows at the time of driver distraction from performing its core activities. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]2018 The results of the assessment of the overall impact of the model information flow at the time of driver distraction from the main activity, considering the performance of EEG and ECG showed next indicators: the Multiple correlation coefficient -99,11 %; The average error of approximation -9,32%; Fisher's exact test: settlement -913,1; tabular-2,46. Based on the data shown in Table 4, it's calculated Fisher criterion more table index.…”

Truck Driver Behavior and Travel Time Effectiveness Using Smart GPS

“…Familar et al (2011) analyzed speeding behaviour with a multilevel modelling approach [11]; Gehlert et al (2012) introduced an evaluation of the different types of dynamic speed display signs [12]. Besides that, Eboli et al (2017) investigated car users' driving behaviour through speed analysis, and Galkin et al (2018) produced modelling trucks that speed on the route considering the driver's state [13,14]. Likewise, research on travel time from various perspectives such as that conducted by Rahmani et al (2015) with a non-parametric research for estimation of route travel time distributions [15].…”