INFLUENCE OF ROAD TRAFFIC ENVIRONMENT AND …eprints.qut.edu.au/90233/1/DDI 2015_(15325)_Influence of Road... · engineers and road safety experts in order to avoid disruptions in

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International Driver Distraction and Inattention Conference, Sydney, New South Wales, November 2015

© ARRB Group Ltd and Authors 2015 1

INFLUENCE OF ROAD TRAFFIC ENVIRONMENT AND MOBILE PHONE DISTRACTION ON THE SPEED SELECTION BEHAVIOUR OF YOUNG DRIVERS

Oscar Oviedo-Trespalacios, Queensland University of Technology (QUT), Centre for Accident Research and Road Safety – Queensland (CARRS-Q), Australia

Md. Mazharul Haque, Queensland University of Technology (QUT), Centre for Accident Research and Road Safety – Queensland (CARRS-Q), Australia

Mark J. King, Queensland University of Technology (QUT), Centre for Accident Research and Road Safety – Queensland (CARRS-Q), Australia

Simon Washington, Queensland University of Technology (QUT), Centre for Accident Research and Road Safety – Queensland (CARRS-Q), Australia

ABSTRACT

Travel speed is one of the most critical parameters for road safety; the evidence suggests that increased vehicle speed is associated with higher crash risk and injury severity. Both naturalistic and simulator studies have reported that drivers distracted by a mobile phone select a lower driving speed. Speed decrements have been argued to be a risk compensatory behaviour of distracted drivers. Nonetheless, the extent and circumstances of the speed change among distracted drivers are still not known very well. As such, the primary objective of this study was to investigate patterns of speed variation in relation to contextual factors and distraction. Using the CARRS-Q high-fidelity Advanced Driving Simulator, the speed selection behaviour of 32 drivers aged 18-26 years was examined in two phone conditions: baseline (no phone conversation) and handheld phone operation. The simulator driving route contained five different types of road traffic complexities, including one road section with a horizontal S curve, one horizontal S curve with adjacent traffic, one straight segment of suburban road without traffic, one straight segment of suburban road with traffic interactions, and one road segment in a city environment. Speed deviations from the posted speed limit were analysed using Ward’s Hierarchical Clustering method to identify the effects of road traffic environment and cognitive distraction. The speed deviations along curved road sections formed two different clusters for the two phone conditions, implying that distracted drivers adopt a different strategy for selecting driving speed in a complex driving situation. In particular, distracted drivers selected a lower speed while driving along a horizontal curve. The speed deviation along the city road segment and other straight road segments grouped into a different cluster, and the deviations were not significantly different across phone conditions, suggesting a negligible effect of distraction on speed selection along these road sections. Future research should focus on developing a risk compensation model to explain the relationship between road traffic complexity and distraction.

INTRODUCTION

In normal circumstances, drivers and passengers make a trip with the objective of arriving at a destination with a lower cost (O'neill, 1977).Drivers generally accomplish this objective by selecting a driving speed suitable to the contextual factors and vehicle type. The vehicle characteristics impose fixed restrictions that are usually related to the vehicle technology. On the other hand, the contextual factors are related to road traffic features that guide the driving speed usually by imposing speed limits. Depending on the contextual factors like road alignment, sight distance and adjacent land use, suitable speed limits are determined by traffic

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engineers and road safety experts in order to avoid disruptions in the traffic system (OECD, 2006), e.g. crashes, traffic congestion and conflicts between road users among many others. Nonetheless, safety is always the most critical issue in the speed management along any road segment (Draskóczy and Mocsári, 1997).

Amid the technical restrictions, speed selection results from a driver’s decision making process, and it could be above, below, or on the speed limit. Speed limit violations have been linked with severe safety consequences such as fatal and injury crashes. In a comprehensive literature review by Aarts and Van Schagen (2006), crashes were found to increase at an exponential rate with the speed increase of individual vehicles. In addition to speed violations, drivers' interactions with the road traffic environment also play an important role in determining crash risk (Elvik et al., 2004). The risk of crash due to a high driving speed has been reported to increase at a faster rate along roads designed for lower speeds than those designed for higher speeds. In contrast, a driving speed below the posted speed limit does not have any correlation with crash risk (Shinar, 1998, Aarts and Van Schagen, 2006), although speed differentials can potentially create conflicts with other road users.

A driver may select a lower speed depending on the road traffic characteristics and in-vehicle factors. Numerous road traffic characteristics and environmental factors encourage a driver to select a driving speed lower than the posted speed limit, including horizontal alignment (Bella, 2008), fog (Li et al., 2015), high-density traffic (Trick et al., 2010), changes in the traffic flow (Stavrinos et al., 2013), high speed limits (Liu and Ou, 2011), tunnels (Rudin-Brown et al., 2013), and wet weather (Edwards, 1999).The in-vehicle factors that may cause a driver to select a slower driving speed include passenger conversations (Charlton, 2009), infotainment system tasks (Platten et al., 2013), portable music player tasks (Young et al., 2012), and mobile phone tasks (Rakauskas et al., 2004, Saifuzzaman et al., 2015, Haque et al., 2015) among many others.

Researchers have interpreted the slower speed selection due to mobile phone tasks as a risk compensatory behaviour of distracted drivers (Törnros and Bolling, 2006, Saifuzzaman et al., 2015, Haque and Washington, 2014). However, there remain concerns about whether lower driving speeds will create conflicts with other road users and result in an increased risk of collision (Reimer et al., 2014).It is important to mention that speed decrements are not consistently observed across all studies on mobile phone distracted driving. Some studies have suggested a speed increase among mobile phone distracted drivers (Fitch et al., 2014).This background seems to indicate that the issue is by no means closed and more research is needed to establish the conditions for the emergence of risk compensatory behaviour.

So far, the speed selection behaviour of a driver has been linked independently with in-vehicle tasks and contextual factors. In particular, the road traffic environment has been associated with different features that could affect, independently or in combination, the speed selection behaviour of distracted drivers. As such, the objective of this paper was to determine the combined effects of contextual factors and mobile phone distraction on the speed selection behaviour of drivers. The research questions of this paper were:

Is there any influence of road traffic environmental features on the speed selection behaviour of distracted drivers? If so, what are the road traffic environmental features that lead to speed decrements in mobile phone distracted driving?

In this research the focus of interest is to examine the impact of cognitive and physical distraction, due to mobile phone conversations using a handheld device, on driving speed. Handheld phone conversation while driving is an activity still legal in many countries.

METHOD

Participants

A total of 32 young (18-26 years old) drivers including 16 males and 16 females were recruited for this study. The average age of the participants was 21.47 (SD 1.99) years. Mean ages for males and females were 21.8 (SD 1.80) and 21.1 (SD 2.19) years, respectively. The average driving experience was 4.2 (SD 1.89) years. About 44% of the drivers drove less than ten thousand kilometres in a typical year, about 47% drove about ten to twenty thousand kilometres,

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and the remainder drove more than twenty thousand kilometres. About 34% of the participants held provisional licences and the rest had open (non-restricted) licences. Note that a provisional licence in Queensland, Australia is issued to a newly licensed driver for a usual duration of 3 years before they receive an open licence. The average driving experience of provisional and open licence holders was respectively 2.64 (SD 0.75) and 5.01 (SD 1.79) years.

Apparatus

This experiment was conducted in the CARRS-Q Advanced Driving Simulator located at Queensland University of Technology (QUT).This high fidelity simulator was comprised of a complete car with working controls and instruments surrounded by three front-view projectors providing 180-degree high resolution field view to drivers. Wing mirrors and the rear view mirror were replaced by LCD monitors to simulate rear view mirror images. Road images and interactive traffic were generated at life size onto front-view projectors, wing mirrors and the rear view mirror at 60 Hz to provide a photorealistic virtual environment. The car used in this experiment was a complete Holden Commodore vehicle with automatic transmission. The full-bodied car was rested on a 6 degree-of-freedom motion base that could move and twist in three dimensions to accurately reproduce motion cues for sustained acceleration, braking manoeuvres, cornering and interaction with varying road surfaces. The simulator was also capable of producing realistic forces through the steering wheel to provide the realism of driving particularly during negotiating the horizontal curves. The simulator used SCANeR™ studio software with eight computers linking vehicle dynamics with the virtual road traffic environment. The audio system of the car was linked with the simulator software so that it could accurately simulate surround environment sounds for engine noise, external road noise and sounds for other traffic interactions, and thus further enhancing the realism of the driving experience. Driving performance data like position, speed, acceleration and braking were recorded at rates up to 20 Hz.

Simulated Driving Route

The designed driving route in the CARRS-Q Advanced Driving Simulator contained simulated routes on both urban and rural areas. The simulated route was about 7 km long and included a detailed simulation of the Brisbane CBD (central business district of Brisbane, Queensland, Australia) with a great deal of accuracy, and a hypothetical suburban area created to satisfy the purpose of this research. The speed limit in the CBD was mostly 40 kph, whereas the speed limit in suburban areas varied between 50 and 60 kph. Three route starting points were designed to reduce learning effects and allow driving under the three different phone conditions, i.e. baseline (no phone conversation), handheld and hands-free phone conversations. All three routes had the same geometry and road layout but the locations of traffic events were randomized across the routes. The driving conditions were counterbalanced across participants to control for carry-over effects. In this study, only data from the baseline and handheld conditions were analysed since this study aimed to examine the effects of cognitive distraction and contextual factors on the speed selection behaviour. In the handheld condition, drivers performed the phone conversation using a Nokia 500 phone. The phone conversation dialogues included verbal puzzles and simple arithmetic problems that required simultaneous memory storage and processing of information, and thus the conversation was cognitive in nature.

To achieve the objective of this study, five road sections with diverse road traffic features were selected. These were: (1) a road section in a city environment, (2) a road section with a horizontal S-curve (r = 100m and -225m) and stable traffic flow from the opposite direction, (3) a road section with a horizontal S-curve (r = -225m and 100m) with no traffic, (4) a straight road segment in a suburban environment with no traffic, and (5) a straight road segment in a suburban environment with traffic interactions. Speed data were collected and averaged from a 100m road section of each road traffic scenario.

Data Analysis

As explained in the introduction, given the lack of theoretical development in this area, cluster analysis was used because we were interested in empirically defining similar drivers’ speed selection patterns based on heterogeneous road traffic features, and presence or not of distraction. Ward’s cluster analysis is a powerful tool to identify recurrent patterns and makes

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few assumptions about relationships between samples (Mooi and Sarstedt, 2011). Rather than predefine the number of groups, the analysis dictates the number of groups that emerged from the data. Moreover, this tool has been suggested in the analysis of behavioural adaptation in road safety (Rudin-Brown and Jamson, 2013) and various examples exist in the literature. More particularly, authors have used this technique to group speed profiles over several different road segments in rural areas (Charlton et al., 2015) and motorways (Pascale et al., 2015), but to the best of our knowledge, this is the first time this technique is applied to study speed selection behaviour of distracted drivers.

Speed deviations from the speed limit were used for the cluster analysis to find a set of groups in the data. This was calculated using the mean speed over the 100m section and the posted speed limit. The applied cluster analysis method assigns a variable to a cluster or group based on the minimum information loss criteria measured by the sum of squares (Ward Jr, 1963). Overall, the analysis process consisted of the following major steps.

1. Selecting variables: The variables were selected to identify possible changes in speed selection behaviour due to mobile phone distracted driving. Data on the deviation from the speed limit—that is, the difference between the posted speed limit and actual driving speed—in each of the five road segments were extracted for baseline and handheld phone conditions. This made a total of 10 variables containing the speed deviations across road traffic features and phone conditions.

2. Applying cluster analysis to identify the patterns of speed deviation across road traffic complexities and phone conditions: Ward’s hierarchical clustering method was applied in this study, which involves assigning a set of variables to a cluster by optimizing the variables such that there is small within-cluster variance but large between-cluster variance. The optimal number of clusters was determined by plotting the possible number of clusters on the x-axis (starting with the one-cluster solution at the very left) and their proximity measures on the y-axis. Proximity is defined as the increase in the squared error that results when two clusters are merged and can be expressed as (Lu et al., 2006):

𝐸 =∑ ∑‖𝑥 − 𝑐𝑘‖2

𝑥∈𝑄𝑘

𝑐

𝑘=1 (1)

where E is proximity, x is the normalized data vector of distribution parameters, c is the number of clusters, Qk is the kth cluster and ck is the centre of cluster k. Using this plot, the total number of clusters was selected by the “elbow method” that determines the number of clusters based on a distinctive break in the changes of proximity measure (Mooi and Sarstedt, 2011).

3. Evaluating cluster validity: In this stage, the selected clusters were evaluated both from theoretical perspectives and applying statistical analyses. At first, the clusters were conceptualized according to the levels of speed deviation by the drivers across road traffic features and distraction conditions. Then, statistical differences were assessed amongst variables in each cluster in order to ensure internal consistency.

4. Analysing the differences between clusters: After identifying the clusters, statistical comparisons were conducted among the clusters to identify the effects of distraction and contextual factors on speed deviation.

RESULTS

Table 1 presents the speed deviation from the speed limit by road traffic features and phone conditions. Using t-tests for paired samples, the impact of distraction on speed selection was tested across different road segments. The speed deviations were significantly different across phone conditions for the S curve with adjacent traffic (t31=4.07; p<0.001), S curve with no traffic (t31=5.514; p<0.001), and straight road with traffic interactions (t28=3.898; p<0.00) segments. For all of these road traffic features, distracted drivers were found to drive at a slower speed than non-distracted drivers. Higher deviations from the speed limit occurred when distracted drivers drove along a road section with a horizontal S curve. In contrast, the difference between

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baseline and handheld driving in terms of speed deviation was not significant for the segments of city road without traffic (t31=-1.95; p=0.61) and straight road without traffic (t31=0.97; p=0.34).

Table 1: Descriptive statistics of speed deviations across road traffic features and phone conditions

Road traffic feature

Phone Condition

Sample

size*

Mean deviation from the

speed limit

S.D. Min Max

City - No traffic Baseline 32 -1.91 4.53 -11.91 9.22

City - No traffic Handheld 32 -.14 5.22 -11.52 11.67

S Curve – Adjacent traffic

Baseline 32 -6.65 4.92 -16.89 9.22

S Curve – Adjacent traffic

Handheld 32 -10.92 5.50 -24.97 -2.93

S Curve - No traffic

Baseline 32 -5.95 4.11 -14.43 1.98

S Curve - No traffic

Handheld 32 -10.98 5.46 -22.19 -2.27

Straight road - No traffic

Baseline 32 -1.28 3.74 -13.99 5.67

Straight road - No traffic

Handheld 32 -2.14 5.02 -17.43 6.66

Straight road–Traffic

interactions Baseline 29 -3.52 4.99 -18.33 2.21

Straight road–Traffic

interactions Handheld 32 -9.28 8.74 -34.99 7.49

*The sample size in some conditions is below 32 because of data recording failure in the simulator

Applying Ward’s hierarchical clustering method, speed deviations along five road segments in two phone conditions, totalling 10 driving conditions (baseline driving along a horizontal S curve, handheld driving along a straight road without traffic interactions, etc.), were clustered to identify sets of common characteristics. Figure 1 presents a plot between proximity measures (see eq. 1) and number of possible clusters. From this plot, it can be seen that there is a significant drop in proximity measures when the cluster number changes from 1 to 2 or 2 to 3; but the difference in proximity measures is marginal if the cluster number changes from 3 to 4. Therefore, following the "elbow method" described in the previous section, it was clear that driving conditions could be divided into three clusters based on deviations from the speed limit.

Figure 1: Variations in proximity measures by possible number of clusters

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Figure 2 describes each cluster by driving conditions and speed deviation from the speed limit. The driving conditions included in cluster 1 were handheld driving along a horizontal S curve with adjacent traffic, handheld driving along a horizontal S curve without traffic, and handheld driving along a straight road with traffic interactions. It appears that cluster-1 included driving conditions that represented driving scenarios that were challenging due to distracted driving and complex road traffic features. Cluster-2 included two driving conditions like baseline driving along a horizontal S curve with adjacent traffic and baseline driving along a horizontal S curve without traffic. Therefore, the members of cluster-2 included only relatively complex road traffic scenarios but not distracted driving. The driving conditions included in cluster-3 were baseline and handheld driving along city streets, baseline and handheld driving along a straight road without traffic, and baseline driving along a straight road with traffic interactions. The members of cluster-3 mainly represented distracted or non-distracted driving scenarios in relatively simple driving environments or non-distracted driving in a complex scenario. The internal consistencies of clusters with more than two members were assessed by Friedman's two-way Analysis of Variance by Ranks, while the internal consistency of the cluster with two members was assessed by a t-test for paired samples. The internal consistency was strong for each cluster as none of the clusters had significant differences among their associate members: Cluster-1 (ANOVA, df=2, p=0.37), Cluster-2 (t31=0.76; p=0.46), and Cluster-3 (ANOVA, df=4, p=0.25).

Figure 2: Cluster pertinence by segment/condition and speed deviation from the speed limit

Combining the data of members within each cluster, the mean speed deviation of each cluster was computed and compared across clusters. To test the statistical differences between the clusters, post-hoc t-test comparisons were conducted, and the results are presented in table 2. The difference between each pair of clusters was statistically significant. Cluster-1 had the highest speed deviation from the speed limit compared to cluster-2 and cluster-3. Among them, the largest difference was found between cluster-1 and cluster-3.

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Table 2: Differences in speed deviation between clusters

Comparison Estimated

(∆) t-statistic df p-value 95% CI

Cluster-1 versus Cluster-2 -4.046 -4.103 31 <0.001 (-6.06 -2.04)



Figure 3 presents the mean speed deviation of each cluster. Cluster-1 had the highest deviation from the speed limit compare to other clusters. While the mean speed deviation from the speed limit for cluster-1 was -10.35 kph (st. dev. = 5.13), the corresponding deviations for cluster-2 and cluster-3 were respectively -6.30 kph (st.dev. = 3.71) and -1.79 kph (st. dev. = 3.13). It appears that drivers driving under the conditions in cluster-1 adopted a much slower speed than the posted speed limit. The speed decrease of drivers was relatively lower for cluster-2 and almost negligible for cluster-3.Based on the levels of speed deviations, clusters 1, 2 and 3 were labelled as ‘high speed adaptation’, ‘low speed adaptation’, and ‘negligible speed adaptation’, respectively.

Figure 3: Mean speed deviations by clusters

DISCUSSION

The aim of this study was to investigate the influence of road traffic environmental features on the speed selection behaviour of distracted drivers. Due to a great variety of environmental features and combinations, a cluster analysis was implemented for recognizing sub-types of road traffic environment features based on the speed deviations of drivers in both distracted and non-distracted conditions. The results showed three clearly defined sets of segments that maximize the homogeneity within clusters or differentiation across them. The identified clusters of segments were distinguishable by the degree of change in the speed deviation from the speed limit, i.e. Cluster-1 “High speed adaptation”, Cluster-2 “Low speed adaptation”, and Cluster-3 “Negligible speed adaptation”. The relationship between road traffic environmental features and mobile phone distraction on the speed selection behaviour is discussed below.

Cluster-1 or “high speed adaptation” group included three driving conditions, including handheld driving along a horizontal S curve with adjacent traffic, handheld driving along a horizontal S

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curve without traffic, and handheld driving along a straight road with traffic interactions. In the high speed adaptation scenario, the average driving speed was 10.35 kph below the speed limit. Previous research showed that horizontal alignment and high traffic flow can separately influence the speed selection of distracted drivers. For instance, Tractinsky et al. (2013) found in a driving simulator study that drivers distracted by phone conversations and dialling selected a higher speed on straight roads (82.1kph) than winding roads (64.4 kph). Strayer et al. (2003) found that drivers distracted by mobile phone conversations spent more time at lower speed than the baseline in a high traffic density situation. Cognitively and physically distracted drivers with handheld conversations adapt their speed according to road environmental features such as horizontal curvature and traffic flow.

Cluster-2 or “low speed adaptation” included two driving conditions: baseline driving along a horizontal S curve with adjacent traffic and baseline driving along a horizontal S curve without traffic. This result also corroborated the findings of the previous work in the field. Schurr et al. (2002) found that drivers tend to significantly decrease their speed while driving along a segment with a horizontal curve (r <350m). Horberry et al. (2006) argued that, independent of distractions, drivers may slow down due to the effects of external complexity. In this experiment, the horizontal alignment was a horizontal S curve with radii of 100m and -225m.The main difference between cluster-1 and cluster-2 is the presence of mobile phone distraction. It appears that the effect of mobile phone distraction is additive to the road complexity. The speed deviation was much higher when drivers were distracted and exposed to a complex road traffic environment; and the speed deviation was less when the drivers were exposed to complex road traffic features but not distracted.

Cluster-3 or “Negligible speed adaptation” group included five driving conditions, including baseline and handheld driving along city streets, baseline and handheld driving along a straight road without traffic, and baseline driving along a straight road with traffic interactions. Unlike the previous findings, there is no difference in speed deviation across distracted and non-distracted conditions for the road traffic features clustered in this group. The city environment in this study included a detailed simulation of Brisbane CBD with buildings and urban furniture but there were no traffic interactions along the road sections selected for observing driving speed. The straight segments were located in a suburban environment but there was no traffic interaction for two out of three driving conditions. In general, these driving conditions, in combination of road traffic features and phone conditions, might not be complex enough for drivers to deviate the driving speed from the posted speed limit significantly. Although a speed reduction was expected in distracted driving conditions, it appears that the road traffic features and distraction conditions in combination determine the driving complexity and subsequent speed adaptation. Garrison and Williams (2013) also reported a negligible change in driving speed among the distracted drivers along road sections that were not complex.

In summary, it seems that that the speed selection behaviour of drivers depends on the interaction between road traffic environment and mobile phone distraction. It is therefore likely that a link exists between compensatory behaviour and complexity of the road traffic environment, as has been widely suggested in the literature (Manner et al., 2013). A timely research question here is what road environmental features demand low or high speed adaptation from the driver. The findings of this study clearly suggest some answers. From the cluster analysis, it is apparent that horizontal alignment and traffic interaction were the road environmental features that most affected the speed selection behaviour of distracted drivers. The influence of other complex road environment features on the speed adaptation of distracted drivers has also been reported in other studies. In a recent study, Rudin-Brown et al. (2013) studied the speed selection of distracted drivers in road tunnels and found that drivers engaged in texting selected a lower speed while driving in tunnels compared to driving along a freeway.

CONCLUSIONS

In this research, the influence of road traffic environmental features on the speed selection behaviour of distracted drivers was investigated. Based on cluster analysis, the segments were grouped into three clusters with different levels of speed adaptation measured in terms of speed deviation from the posted speed limit. Road traffic features like horizontal curve and traffic interaction were found to have a significant effect on speed selection. The effects of these complex features on speed deviation were accentuated in the presence of cognitive and physical distraction resulting from handheld mobile phone conversations. In contrast, handheld

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conversation did not result in significant speed deviation along road segments in simple driving environments. Therefore, it can be concluded that the speed adaptation of distracted drivers has a significant association with the complexity of road traffic features in that the cognitive and physical distraction task results in high speed deviation in complex road traffic environments but negligible speed deviation in simple environments.

The main contribution of this study is a better understanding of speed selection of cognitively and physically distracted drivers across different levels of complexity of road environment features. Future research should extend this research to include different levels of distraction such as only cognitive distraction so that the effects of interaction between distraction and road traffic features on speed adaptation can be determined at various interaction levels. This research provides insights into the speed adaptation behaviours of distracted drivers, but their implications for road safety still represent a significant research gap. Another extension of this research could be development of a risk compensation model including the interaction between road traffic complexity and distraction in order to measure residual crash risk.

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AUTHOR BIOGRAPHIES

Oscar Oviedo-Trespalacios is an Industrial and Systems Engineer with a Master of Science in Operation Research, Statistics and Stochastic processes. He has interest in safety in complex systems and experience as researcher and lecturer in quality, human factors and safety engineering. Currently, he is a Research Officer and Doctoral Researcher in human factors engineering at Queensland University of Technology (QUT). His research interests include: Traffic safety, statistical methods, human factors, cognitive engineering and industrial safety.

Dr. Md. Mazharul Haque is a civil engineer and specialist in the area of statistical modelling of traffic safety. He received his PhD degree from NUS. He won an award from the Mitsui Sumitomo Insurance Group for his work on Motorcycle Safety. Besides statistical modelling of traffic safety, his research interests include mobile phone distraction on traffic safety, engineering factors related to traffic safety, motorcycle safety, intelligent transport and sustainable transportation system.

Dr. Mark King is a psychologist and specialist in the area of road safety. He was awarded his PhD in road safety from the Queensland University of Technology (QUT). Besides road safety, his research interests include the interface between intelligent transport systems and human users, and translation of research into policy and practice.

Prof. Simon Washington is a leading researcher in the area of transportation safety and advanced statistical and econometric methods. He received his PhD in Civil Engineering from the University of California at Berkeley. His research interests span a variety of areas including research into the human and engineering causes and mechanisms associated with transport system related crashes, sustainable transport issues such as non-motorised travel, air quality and global warming impacts of transport, understanding system impacts of travel behaviour, and links between transport, land use, and urban planning.

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Documents

INFLUENCE OF ROAD TRAFFIC ENVIRONMENT AND …eprints.qut.edu.au/90233/1/DDI 2015_(15325)_Influence of Road... · engineers and road safety experts in order to avoid disruptions in