Simulators and traffic counters

Technology has been playing an increasingly important role not only in enforcement through the use of red light cameras and speed cameras but also in transport planning and research. They help reduce some of the disadvantages of the traditional measures of driver behaviour discussed in Section 2.4.2 although they also introduce a number of their own disadvantages.

Traffic counters/classifiers and inductive loop detectors are primarily used for monitoring congestion and providing vehicle counts in particular locations to road departments. They work by detecting when a vehicle passes over it and can be configured as one or more loops (Soriguera and Robusté, 2011). The installation can be either permanently installed (Soriguera and Robusté, 2011) – as is frequently the case on motorways – or installed temporarily (Radalj, 2000). These devices can collect and/or calculate a number of different measures20 _{including number of vehicles, date,}

time, vehicle speed, vehicle type and number of axles (Radalj, 2000). A diagram of a portable traffic classifier and a photograph of a loop detector are shown in Figure 2-4. Although these devices can collect a lot of data for the location where one is installed, they are incapable of uniquely identifying each vehicle and therefore they cannot track the same vehicle across time. This means that although these devices can determine the average speed of vehicles on a particular stretch of road during different time periods of the day, researchers are unable to examine the influence of driver and most vehicle characteristics on speed. On the other hand, since the locations are known by researchers detailed spatial information can be employed if multiple locations are measured.

Loop Detector

(Colorado Department of Transportation, 2005) Figure 2-4: Portable traffic classifier and loop detector

Due to the nature of the data collected using traffic classifiers and loop detectors, most studies of driver behaviour using these sources focus on drivers‘ speed or speeding

behaviour. They are particularly suited for before and after studies of infrastructure or speed limit changes. For example, Kweon and Kockelman (2005) used crash data and data collected from loop detectors installed on high speed roads in Washington State (United States) to study the effect of speed limit changes on the numbers of fatal and non-fatal crashes. The results show that for roads with speed limits up to 55 miles/h (88 km/h) non-fatal crash rates are reduced, but fatal crash rates remain unchanged while the findings are sensitive to differences in traffic levels. In Finland, the impact of variable message signs (VMS) on vehicle speed and headways on 80 km/h roads was investigated by collecting data from three locations with traffic counters along the same stretches of road. Traffic counters were located 536 to 1,800 metres before the VMS, 360 to 1,100 m after the VMS and lastly 7,670 to 13,000

metres after the VMS. In general the researchers found vehicle speeds are reduced by 1 to 2 km/h for distances up to approximately 1 km from the VMS but changes after a longer distance are not statistically significant (Rämä and Kulmala, 2000). The effect of a change in the default speed limit21 _{in Western Australia from 60 km/h to 50 km/h}

was studied using traffic classifiers installed in 138 locations of which 23 roads maintained a 60 km/h speed limit after the change. On average, after 12 months the 85th _{percentile speed}22 _{was reduced by 2 km/h from 64.4 km/h to 62.4 km/h on the}

roads where a 50 km/h speed limit was now in effect. In comparison, the roads which remained at 60 km/h (but were now signposted to this effect) experienced a reduction in 85th _{percentile speed of 1.2 km/h after 12 months from 69 km/h to 67.8 km/h (Kidd}

and Radalj, 2003).

Overall these devices are useful for collecting large amounts of data from a given set of locations over a period of time. The conclusions that can be drawn from the collected data however are based on the behaviour of the population of drivers rather than the behaviour of individual drivers. This needs to be considered when determining if this is the optimal source of data to answer a particular research question. In comparison, simulators allow researchers to examine detailed aspects of driver behaviour in a controlled environment. Since study participants need to be present in-person to

21 _{The default speed limit is the speed limit that applies when there is no posted speed limit.}

complete simulator experiments these studies are also frequently able to make use of some of the traditional sources of driver behaviour data discussed in Section 2.4.1. Simulator experiments use virtual reality to create a simulated vehicle environment. The characteristics change from simulator to simulator but the most advanced

simulators, such as the simulator shown in Figure 2-5 from The University of Leeds, include movement, sound and genuine vehicle controls. The primary benefit of

simulator experiments is that while they recreate the experience of driving on the road (albeit imperfectly) researchers are able to control all aspects of the road environment.

Figure 2-5: Inside and outside the University of Leeds driving simulator (Jamson et al., 2010)

Simulator studies have been used to study the impact of road treatments on driver speed choice (Jamson et al., 2010), eating and drinking on driver performance (Young et al., 2008) and road width on vehicle speed and lateral displacement (Lewis-Evans and Charlton, 2006). They have also been used to test the application of psychological theories such as the theory of planned behaviour (TPB) to predict driver behaviour (Elliott et al., 2007) and to determine the speed differential before drivers pass another vehicle (Bar-Gera and Shinar, 2005). The range of driver behaviour studies using simulators is shown in Table 2-4.

Simulator studies have been shown to have relative validity for the purposes of examining crash risk (Yan et al., 2008) but an individual‘s behaviour in a simulator may not be reflective of their behaviour on a real road. Tests of differences in intra- driver variability of reaction time between simulator driving and on-road driving have shown that variability is higher in on-road experiments than in a simulator (Riener,

2010). In addition, it is not feasible to monitor drivers for weeks or months using a simulator environment and as such although the data may be valid it does not necessarily reflect the same individual‘s driving across time and space.

Table 2-4: Selection of simulator studies of driver behaviour

Citation Behaviours and Factors Studied Sample _Size (De Winter and

Happee, 2011) Motivational models of driver behaviour 10 to 804 (Conner et al.,

2007) Testing theory of planned behaviour on intention to speed 83 to 303 (Elliott et al.,

2007) Testing theory of planned behaviour on speeding behaviour 150 (Farah et al., 2009) Risk associated with passing behaviour 100 (Yan et al., 2007) Left-turn gap acceptance 63

(Thiffault and

Bergeron, 2003) Impact on driving performance of monotony-induced fatigue 56 (Lewis-Evans et

al., 2011) Impact of cognitive load on speed maintenance 53 (Lewis-Evans and

Charlton, 2006) Impact of road width on speed and lateral displacement 49 (Stephens and

Groeger, 2009) Impact of anger and anxiety traits on driver behaviour 48 (Lenné et al., 2010) Driving on arterial roads under the influence of alcohol and _cannabis 47

(Hatfield et al.,

2008) Reliability of implicit association test (IAT) in predicting speeding behaviour 45 (Mesken et al.,

2007) Impact of emotions on speeding behaviour 44 (Jamson et al.,

2010) Impact of road treatments on speed 40 (Strayer et al.,

2006) Comparison of driver performance between drink driving and mobile telephone usage 40 (Donmez et al.,

2007) Driver distraction from in-vehicle information systems 29 (Riener, 2010) Reaction time in simulator versus on-road experiments 18 (Lenné et al., 1997) Time of day variation in driving performance 11 (Jamson, 2006) Impact of ISA on speeding behaviour 10