Efficacy of traffic management measures: The influence of complexity of driving conditions

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Challenge the future

Delft University of Technology

Efficacy of Traffic Management

Measures: The Influence of Complexity

of Driving Conditions

Dr. R. (Raymond) G. Hoogendoorn, J. (Jaap) Vreeswijk, MSc & Prof. dr. ir. B. (Bart) van Arem and Prof. dr. K. (Karel) A. Brookhuis

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The influence of complexity on longitudinal driving behavior

Outline

• Introduction;

• Introducing a theoretical framework of adaptation effects in

relation to complexity;

• Method;

• Results;

• Conclusion;

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Introduction

• Complexity of driving conditions has

been shown to have a substantial

impact on driving behavior;

• E.g. Brookhuis et al. (1991), Horrey et

al. (2009);

• Therefore an impact on the efficacy of

traffic management measures may be

assumed (see for instance

Hoogendoorn et al. (2011);

• However: how is complexity of driving

conditions actually related to these

adaptation effects in driving behavior?

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Introducing a

theoretical

framework

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• Governed by an interaction between

driver capability and task demands;

• Driver capability:

• Driver characteristics;

• Activation level;

• Distraction;

• Task demands: difficulty of the

driving task;

• Adverse condition: an imbalance

between task demands and driver

capability occurs;

External circumstances Road design Weather Environ-ment Interactions vehicles Roadside trafﬁc management In-car technology Complexity Static Dyna-mic Driver characteristics

Driver capability Task demands

Mental workload Situational awareness Compensation effects Performance effects Driving behavior

Figure 1: Theoretical framework of adaptation effects in longitudinal driving behavior in relation to complexity

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Introducing a

theoretical

framework

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• To resolve this imbalance:

compensation effects;

• E.g. Speed reductions, increase in

spacing

• When insufficient, performance

effects;

• E.g. perceptual narrowing, longer

inter-decision times, etc;

External circumstances Road design Weather Environ-ment Interactions vehicles Roadside trafﬁc management In-car technology Complexity Static Dyna-mic Driver characteristics

Driver capability Task demands

Mental workload Situational awareness Compensation effects Performance effects Driving behavior

Figure 1: Theoretical framework of adaptation effects in longitudinal driving behavior in relation to complexity

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Introducing a

theoretical

framework

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Longitudinal

Lateral

microscopic

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Research questions

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• However:

• To what extent does complexity of driving conditions influence compensation effects in longitudinal driving behavior, represented by changes in speed and spacing?

• To what extent does complexity of driving conditions influence perceptual thresholds with regard to relative speed and spacing?

• To what extent does complexity of driving conditions influence the sensitivity of accelerations towards relative speed and spacing?

• To what extent does complexity of driving conditions influence inter-decision times?

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Method

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• Driving simulator experiment

with a repeated measures

design;

• Virtual motorway with three

lanes in the same direction;

• Control condition (normal driving

conditions);

• Experimental condition (concrete

barriers and narrow lanes);

• 25 participants (mean age:

29.68, SD=6.93, mean driv,

experience: 9.6, SD=7.50);

Figure 2: Driving environment developed for the purpose of the experiment. On the left the control condition is displayed, while on the right the experimental condition is displayed.

3.3 Par ticipants

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The research population consisted of 25 employees and students of Delft University of Technol-2

ogy (16 male and 9 female participants). The age of he participants varied from 22 to 54 years 3

with a mean age of 29.68 years (SD = 6.93). Driving experience varied from 1 to 35 years with 4

a mean of 9.6 years (SD = 7.50). 5

3.4 Data analysis methods

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Compensation effects were analyzed through a comparison of the indicators of longitudinal 7

driving behavior (i.e., speed v and spacing s) between the control and the experimental condition 8

using a paired samples t-test with asigniﬁcance level of 0.05. 9

In order to determine performance effects represented by changes in the perceptual thresh-10

olds we started with estimating action points in the (Dv, s) plane in a psycho-spacing model 11

using the dataﬁltering technique described in Hoogendoorn et al. [11]. The basic assumption of 12

the applied method is that a trajectory can be represented by non- equidistant periods in which 13

acceleration is constant. This implies that speed v(t) is a continuous piecewise linear function 14

of time. For instance, let t_j for j = 0, ..., M denote the time instants at which the acceleration 15

changes (i.e., the action points). Given these time instants, weaim toﬁnd thepoints y_j describing 16

the value of the piecewise linear function at the time instants t_j. 17

This provides us with a distribution of action points in the relative speed-spacing (Dv, s) 18

plane. These distributions were compared using a Kolmogorov-Smirnov test with asigniﬁcance 19

level of 0.05. Also, in order to be able to compare changes in performance effects in longitudinal 20

driving behavior we estimated the perceptual thresholds through ﬁnding the coefﬁcients of the 21

polynomials p(x) in the third degree that ﬁtted the action points p(x(i)) to y(i) in a least squares 22

sense: 23

p(x) = p₁x3+ p₂x2+ p₃x+ p₄ (1)

This analysis was performed separately for acceleration reductions and acceleration in-24

creases at the action points. The goodness of ﬁt, which is regarded as an indication for the 25

7 Figure 2: Driving environment developed for the purpose of the experiment. On the left the control condition is displayed, while on the right the experimental condition is displayed.