ENG5006 Engineering Project B
Technical Paper
Using Virtual Reality (VR) to Reveal the Impact of Work Zone
Disruptions Combined with Merging Sections on Driving Behaviour
Richard Dong, Ruixin Huo, Yichao Xu, Liyuan Li, Zhuo Pan, Chen Xiao
Discipline Coordinator: Nan Zheng，Zheng Xu
ABSTRACT
Existing roads require extensive maintenance and expansion work and, in addition to work zone,
access ramps on the highway are also a considerable risk area for collisions. The optimisation of
working areas close to ramps in accordance with driving behaviour is a necessary involved
prospect. This study looks at the effects of the combination of the two on driver behaviour. An
experimental simulation platform is built using a combination of virtual reality technology and
artificially intelligent traffic flow to simulate the possible dangerous effects of driver driving
behaviour in the absence and presence of traffic when the working zone is close to the on-ramp
and off-ramp, as well as the more extreme case when the working zone completely closes the lane.
Experiments are conducted to collect and analyse participant and subjective perception and
objective driving data. The results show that longer braking distances do not guarantee safer
driving behaviour when the work zone and the ramp are combined, that further restrictions on
driver speed are needed and that collisions are more likely to occur when driving at too low a speed
to leave the work zone and that blockages should be avoided.
INTRODUCTION
The effect of work zones on driving behaviour has garnered a wide interest with the increasing
expansion of the freeway system to improve road infrastructure. The necessity to build develop
the road infrastructure requires implementation of work zones, which can create disturbances such
as lane closures that compromise the safety and operational efficiency of the road network.
From 2 April 2019 to 30 June 2024, to accommodate Australia's growing population and the safety
of the national transport system, the Australian Government has carried out the agreement of Land
Transport Infrastructure Projects with $22,844.34 million funding (FFR, 2019). Road upgrading
and construction projects are a common disruption on the freeway, and work zones are considered
a contributing factor to traffic congestions and road accidents. In 2020, U.S.A. reported
approximately 102,000 car crashes and 4,400 injuries in work zones. Approximately 10% of
estimated national congestion is due to work zones (ARTBA, 2022). Consequently, the impact of
work zones on driving behaviour during maintenance is therefore of great concern.
Moreover, most of the hidden risks are associated with the merging sections of freeways, and they
are considered to be the primary cause of bottlenecks and traffic conflicts (Xu et al., 2021). In
addition, variations of traffic volumes and (lower) merging speeds of entering vehicles have been
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recognised to have significant associations on the higher collision rate on acceleration
lanes(Ahammed et al., 2008). On freeways, the major merging sections are the interchanges of
ramp and main road of highway. In a sample of 1,150 crashes on ramps in Northern Virginia,
approximately half of the crashes occurred while the driver at fault was leaving the interstate, 36%
occurred while the driver was entering the interstate, and 16% occurred at the midpoint of the
entrance road or on a ramp connecting two interstate highways (McCartt et al., 2004).
Furthermore, Singh indicated that drivers were identified as the key cause in 94% of road accident
reports, with driver identification errors accounting for 41% of crashes, decision errors accounting
for 33% and performance errors accounting for 11% (Singh, 2015). This reflects the uncertainty
of human driving behaviours as an extremely critical factor for road safety.
In order to determine if any safety implications are related to the combination of freeway disruption
and on-ramp/off-ramp, behavioural analysis must be conducted in order to determine how drivers
perceive and react to the disruption. To investigate this further, this study examines work zones
at the merging section of the freeway. Although there are extensive studies on the effect of
mainstream disruptions or ramp merging section on traffic flow, only a few have investigated the
behaviour of the individual driver. For example, previous studies have developed a time-varying
mix logit model to reflect the vehicle merging behaviour in the work area during the
implementation of the merging manoeuvre from the beginning to the completion of the merging
manoeuvre (Ahammed et al., 2008). Based on the driver workload theory, the safety evaluation of
the highway interchange merging area is carried out. The result showed that vehicles merging from
ramps can affect driving performance by adding additional mental load whilst on the road. This
generates a potential risk that cannot be ignored. Merging into the main traffic flow will increase
the workload of drivers to a higher level and even exceed the safety threshold (Hu et al., 2020).
Zhang et al. (2022) introduced a Nonhomogeneous Hidden Markov Model to decompose traffic
merging processes into 3 primitives which include motivation to perform a lane-changing
motivation, and then a process of the merging vehicle waiting for time to change lane, and the last
one is completes the merging manoeuvre. And the to identify the driving patterns during merging
processes and reveal the evolutionary mechanism in congested traffic flow at freeway on-ramps. .
Zhang et al. (2022) introduced a Nonhomogeneous Hidden Markov Model to decompose traffic
merging processes into 3 primitives which include motivation to perform a lane-changing
motivation, and then a process of the merging vehicle waiting for time to change lane, and the last
one is completing the merging manoeuvre. This was done to identify the driving patterns during
merging processes and reveal the evolutionary mechanism in congested traffic flow at freeway onramps.
In this study, the approach to this research is based on using microsimulation and virtual reality
(VR), in combination with driving behaviour analysis. Virtual reality is an emerging reliable
simulation technology that indicated similar results for 2D or 3D screens, and no significant
differences were observed regarding physiological responses or behavioural patterns (Weidner et
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al., 2017). The results suggest that a significant number (40%) of the surveyed simulator studies
may have been conducted via head-mounted displays. (Blissing and Bruzelius, 2018).
AIM
In this project, the impact of different types of work zones on highways based on diverse scenarios
will be collected and analysed. Driving data will be collected for analysis of driver’s behaviour,
such as overall speed changes, collision coefficients, acceleration changes, braking distances used
to decelerate before reaching the work area, and driving trajectories.
In order to make the result more generic, there are three basic work zones selected (Details would
be described in Methodology part):
•
•
•
The work zone near to on-ramp (scenario 1)
The work zone near to off-ramp (scenario 2)
The work zone near to the road closure (scenario 3)
Furthermore, since the environment of this project is VR, for realistic result, AI traffic flow is
necessary during the simulated driving. The AI vehicle should have normal driving logic and be
able to change lanes, shift gears, evade and cut in.
METHODOLOGY
The methodology for this research primarily resolves around three key stages, in which the first
stage involves building a simulation of a freeway environment with different types of traffic flow
and work zones on a VR platform; the second stage involves recruiting suitable participants to take
part in the experiment to collect data; and the third stage evaluates the data to create a conclusion
based on the correlation between different factors. Figure 1 illustrates the basic steps of this study.
Figure 1 The basic steps of the research
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Stage 1: Building the VR freeway environment on the simulation platform.
The environment was based to the Citylink and Westgate Freeway which is the road network that
links the Southbank and the Altona North in Melbourne. The type of junction is a Trumpet (tintersection of freeways) which is a simple conserve space way for freeway to normal way
interchanges. Due to a high-speed carriageway ends in a low-speed loop, it is a most critical
situation for the freeway road safety and capacity. The chosen ramp is a known blackspot where
51 crashes in total have occurred during the period 2014-2019; and 8.5 crashes occurred on average
per year during the period 2014-2019. In particular, 20 fatal car accidents and serious injury car
accidents occurred from 2014 to 2019, accounting for 39.2% of the total accident rate
(Vicroad.vic.gov.au, 2021).
According to the Australian "Guide to Temporary Traffic Management", there are several different
work zone configurations, and they can result in different collision rates. The three workspace
configuration types with the highest collision frequency are lane closure (39%), lane shift (22%)
and crossover.(Gu et al., 2019) Considering the interaction of work zone and merging section in
the freeway, this study will set up three major scenarios with different configurations laid on the
different merging sections (see Error! Reference source not found.2, Error! Reference source
not found.3, Error! Reference source not found.4), where the focus was on the combined impact
of the work zone and ramp on the freeway merging behaviours.
Microsimulation and virtual reality in combination with driving behaviour analysis was used, with
highly accurate vehicle models with features and specifications based on actual vehicles to produce
a simulation framework with naturalistic scenarios. Additionally, for the background traffic system,
the AI traffic logic was handled by AI Traffic Controller and created waypoint-based routes that
can be interconnected in a modular way in the scene. Moreover, to achieve the obstacle avoidance
function, all background vehicles had virtual sensors attached, which allowed it to make changes
accordingly when faced with construction-related lane closures.
From the traffic volume data (Goverment, 2019), the data shows two-way average annual vehicles
per day (AADT) is 200,000 veh/d, and trucks with 19,000 veh/d. The total number of average
annual vehicles per day was set as 8300 veh/h. Heavy vehicles represented 9.5% of the total flow
and was set at 791 veh/d. Likewise, the vehicles randomly spawn at a rate of 2 vehicles per second,
from 600 spawn points, with a distance of 1 metre separating each point.
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Figure 2 The 97 m long work zone encountered when a vehicle needs to travel from a ramp and merge onto
a freeway.
Figure 3 The 97 m long work zone that a vehicle encounters when it needs to pull away from freeway and
merge onto a ramp.
Figure 4 Vehicles leaving the freeway from the ramp next to the work zone when the freeway is closed
In this study, a driving simulator (DS) was used for the experiment as driving simulation is an
efficient, safe, and easy data collection method for examining driving behaviour in a controlled
traffic environment. Previous research that involved using driving simulations include when (Lang
et al., 2018) proposed a novel virtual reality-based driving training method in which participants
drove along a route using a driving controller and an eye-tracking virtual reality headset, and traffic
events on the road will help users improve their driving habits.
And compared to VR-based driving training with other training methods, the results showed that
participants using this training method performed better on average than those using other training
methods in terms of assessment scores and response times, which indicating that VR driving
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simulation has good working performance (Hussain et al., 2019). This study, based on the
participants' speed perception observations, the speed perception and actual speed of the driving
simulator were verified by experimentally recording their speed estimates, and then comparing
actual speed observations from the actual field and the simulator. The results of these experiments
show that the curves of the estimated and actual speeds have significantly similar trends, so that a
stationary driving simulator can be a useful tool to study actual speed and speed perception, so the
simulator is an effective tool for driving speed learning.
The equipment was a Logitech G923 steering wheel and pedals, pressure-sensitive brake system,
and proprietary TRUEFORCE feedback technology directly connect to in-game physics, as well
as nonlinear brake pedals equipped with anti-slip system. The virtual reality device uses a head
mounted Htc Vive Pro headset. The device has a built-in high-resolution AMOLED screen with a
binocular resolution of 3K (2880 x 1600) and a field of view angle of 110 degrees. It uses two
locators and supports spatial positioning tracking of 5m x 5m. The space is set according to the
seat height of the test site.
Stage 2: Acquiring participants and conducting the experiment
A total of 26 people were recruited (14 males, 12 females) for the experiment, where all of them
had varying degrees of driving experience. All participants were above 20 years old, hold valid
driving licenses and have at least one years of driving experience. However, due to various external
factors, 10 people were available to use the VR equipment and participated in the experiment.
Each participant was allowed approximately 15 minutes to get used to the simulation as well as
develop a “feel” for the control. The training scenario was a separate environment for participants
to get a basic understanding of the layout of a typical road as well as the movement of simulated
traffic flow. Once they reported adequate levels of comfort using the simulation device, each
participant would then drive each of the scenarios two times. As a result, participants completed a
total of 60 driving simulations.
Questionnaire content
Each participant filled out two questionnaires (see) – a prequestionnaire prior to engaging with the
VR equipment; and a post-questionnaire after completing the experiment. The pre-questionnaire
was done to sort the participants into different categories to see if there was any correlation with
the extent to which other factors influence driving behaviour when approaching work zones.
Participants were also instructed that should they experience any symptoms such as nausea,
dizziness, discomfort during the simulation, they should inform the researchers and the experiment
would be concluded and the results discarded. The post-questionnaire aimed to indicate if there
were any systematic variables that would be reasonably regarded to have influenced their
behaviour. The post questionnaires used a 5 Point Likert Scale with “strongly disagree (1)”,
“unsure (3)”, “strongly agree (5)”, and the types of data the questionnaire would collect is shown
in the table below:
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Table 1 Questionnaire Content
Questionnaire
Data type
Pre-Questionnaire Demographic
PostQuestionnaire
Content
Gender; age; driving experience (years); average hours
driven per day (hours/day); average hours per week
(hours/day) experience with VR
Realism of VR If the simulation was reflective of real life, if there was
simulation
any difficulty controlling the vehicle
Stage 3: Data collection and analysis
The collection of data was an automated process that was coded into the simulation software in
which the driving time, instantaneous vehicle position; and instantaneous vehicle velocity were
recorded at 0.1 second intervals before being exported into Microsoft excel for data analysis to be
conducted.
Based on the data collected, analysis was performed to assess vehicle performance in which
acceleration behaviour, vehicle trajectories, crash frequency, vehicle speed and braking distance,
were plotted and investigated to see what sort of behaviour changes occurred based on the different
types of work zone scenarios. Additionally, these trends were then compared to the feedback from
the questionnaires to identify if there were other factors outside of the nature of work zones that
would have caused any differences in behaviours.
RESULTS AND DISCUSSION
Acceleration Behaviour
As shown in Figure 5, the vehicle acceleration measured by the longitudinal and later acceleration
in two-directions for all data collected. The longitudinal acceleration behaviours are intense at
lower lateral acceleration ( ±2.5𝑚/𝑠 2 ) boundary with a 95% confidence interval, where the
longitudinal acceleration is relatively gentle (± 5𝑚/𝑠 2 ) at the tail ends of the lateral acceleration
(±10 𝑚/𝑠 2 ) with a confidence interval of 95%.
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Figure 5 the Cloud point plot of acceleration
Based on this observation, an increase in speed from an initial low velocity will lead to a high
probability for variations to occur in longitudinal acceleration, and lower probability for variations
in lateral acceleration. Conversely, higher vehicular speeds will have lower probabilities for
variations in longitudinal acceleration and higher lateral acceleration behaviour when the vehicle
speeds reduce over time.
It is notable that the probability of longitudinal vs. lateral acceleration match in Pareto distribution
model. This is reflective in common traffic scenarios where road users tend to driver a lower speed
under complex situations, and faster under more simplistic environments. However, complex road
environments also frequently have drivers performing rapid acceleration and emergency braking;
something that is rarer to occur in more simple environments.
Vehicle trajectories
The time-space diagram Figure 6, Figure 7 and Figure 8,shows the vehicle trajectories for different
work zone configurations to indicate the locations in which the participants changed lanes. The
slope of the line indicates the speed of the test vehicle, and the curved part of the line indicates that
the speed of the test vehicle has changed.
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Figure 6 The trajectory of Scenario 1
Figure 6 above shows the trajectory of the work zone that is near to on-ramp. When there is a
traffic flow in the work zone where is between 1150 m and 1350 m in the direction in which the
vehicle travels, 9 out of 10 participants' test vehicles can smoothly perform the merging operation
to the left lane based on the speed of the vehicles do not change significantly. However, 1
participant performed a sharp deceleration during the merge in the process of entering the work
zone and collided with the vehicle in front. This can be seen by the purple line that has a horizontal
inflection point at 1951 m, where the slope of this horizontal section is 0, showing that the
instantaneous speed at that moment is 0 km/h, indicating an event that forced the vehicle to be
stationary (in this case a crash). Compared with the condition of traffic flow, the test vehicle can
easily pass through the work zone at a steady speed when there is no traffic flow without any traffic
accidents. For the lateral lane change trajectories, refer to APPENDICE A. Lane change trajectory
of vehicle through Scenario 1.
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Figure 7 The trajectory of the Scenario 2
Figure 7 shows the trajectory of the work zone that is near to off-ramp. When there is a traffic flow
in the work zone where is between 530 m and 770 m in the direction of the vehicle travels, 8
participants' test vehicles successfully performed the merging operation to the left lane where the
speed of the vehicles do not sharply change. Only 2 out of 10 test vehicles reduced the speed
slowly during the merge and made a successful lane change based on the green line and purple
line. When there is no traffic flow, the test vehicles simply and steadily move through the work
zone without any accidents, as opposed to when there is traffic flow. For the lateral lane change
trajectories, please refer to APPENDICE B. Lane change trajectory of vehicle through Scenario 2.
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Figure 8 The trajectory of the Scenario 3
Figure 8 shows the trajectory of the work zone that is near to the road closure. When there is a
traffic flow in the work zone where is between 730 m and 770 m in the direction of travel, only 4
out of 10 participants' test vehicles could comfortably perform the merging operation to the offramp based on the speed of the vehicles do not change violently. 3 out of 10 participants' test
vehicles were forced into the off-ramp by the configuration of work zone and performed a severe
deceleration manoeuvre to avoid a crash before the test vehicles entered the off-ramp lane. It is
worth noting that 3 out of 10 participants' test vehicles performed a significantly deceleration
during the merge in the process of entering the off-ramp lane and collided with the vehicle in front.
In contrast to when there is traffic flow, the test vehicle may go through the work zone slowly and
methodically without any incidents. For the lateral lane change trajectories, please refer to
APPENDIX C. Lane change trajectory of vehicle through Scenario 3.
Crash Frequency
The frequency of collisions was analysed for the three experimental conditions and a total of four
collisions were observed. Three of these occurred in scenario 3 and one in scenario 1. In scenario
3, due to the full lane closure, all vehicles had to merge into the ramp, resulting in traffic congestion.
During this scenario, there were reports of a visual difference between the virtual reality simulation
and what was actually seen, resulting in the driver not being able to correctly judge the distance in
front of the car and a collision occurred.
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Figure 9 The observed collisions
Figure 10 The position of collisions occurred
In addition, all three collisions occurred after leaving the work zone insight of the very low speed
of the congested vehicle contrasted with a tendency to accelerate. The crash at scenario 1 occurred
just after entering the work zone, at a speed reduced to the speed limit requirement, due to avoiding
a merging vehicle on the right. In previous studies, the risk of collision increases when drivers
have low confidence in changing lanes, when merging vehicles are travelling at very high or very
low speeds, and when merging vehicles approach the end of the acceleration lane and are forced
to merge. The start of the acceleration lane is also an area of higher crash risk (Moradpour et al.,
2015). The results in this study also support this result.
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Vehicle Speed
The mean operation speed of the 10 participants passing through different work zone scenarios for
a 100km/h highway is shown in Figure 11. The average speeds of scenarios 1, 2, and 3 are
96.02±12.31, 82.89±9.7 and 40.6±21.1 km/h, respectively with p-value is 0.05. Comparisons
between the driving speeds of the different participants to reveal the effect of work zones on
vehicular speeds are done as described in the following:
In order to remind the representativeness of vehicle speed for each of the participants when they
engage in scenario 1, remove a minimum speed of 48km/h, and remove a maximum speed of
120km/h. Then the vehicle speed passing through scenario 1 work zone can be obtained as 80-120
km/h, so the average speed can be approximately considered as 96 km/h. Compared with the design
speed 100 km/h of this highway, the passing vehicle speed of scenario 1 work zone is basically
unchanged.
In a similar way, the representativeness of vehicle speeds when passing through scenario 2 can be
obtained by removing a minimum speed of 58km/h, and removing a maximum speed of 102km/h.
Consequently, the vehicle speed passing through scenario 2 work zone can be obtained as 70-100
km/h, so the average speed can be approximately considered as 83 km/h. Compared with the design
speed 100 km/h of this highway, the passing vehicle speed of scenario 2 work zone is reduced by
about 15%.
As for scenario 3 work zone, remove a minimum speed of 10km/h, and remove a maximum speed
of 120km/h, then the vehicle speed passing through scenario 3 work zone can be obtained as 1078 km/h, so the average speed can be approximately considered as 40 km/h. Compared with the
design speed 100 km/h of this highway, the passing vehicle speed of scenario 3 work zone is
reduced by about 60%.
According to the above logic in determining the vehicle speed when passing through these three
different types of working zones for the highway ramp traffic lane, the following conclusions can
be drawn about the relationship between vehicle speeds in work zones and the position of work
zones:
The work zone in Scenario 1 work zone has the lowest impact on vehicle speed – where it can be
considered that vehicles will predominantly travel at the design highway speed. From the work
zone layout of scenario 1 (shown in Figure 2), the reason for this situation may be that the higher
elevation at the exit ramp, the drivers before exit the highway ramp have a wider field of vision,
so they can perceive the work zone in advance, thus they have more time for route planning. Thus,
scenario 1 work zone does not affect the vehicles which are exit the ramp to change lanes, which
can lead to these vehicles quickly pass-through scenario 1 work zone, and then will not have a
great impact on the main road vehicles’ speed. The speed of vehicles traveling on the main road is
less affected by the ramp vehicles.
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The work zone in scenario 2 show approximately a 15% reduction in vehicle speed, which is a
stark contrast to scenario 3, where the reduction in the passing vehicle speed is approximately 60%.
The reason for the larger speed reduction can be found in the work zone layout of scenario 3
(shown in Figure 4), fully closed main road lanes will greatly increase the traffic flow on the ramp
traffic lane and reduce the area with which traffic can flow, a constant volume of traffic will
naturally cause greater reductions in the travelling speed of vehicles.
Mean speed when passing though workzone
Error Bars donate S.E
140
Mean speed/ km/h
120
100
80
60
40
20
0
PRTP 1
PRTP 2
PRTP 3
PRTP 4
Scenario 1
PRTP 5
Scenario 2
PRTP 6
PRTP 7
PRTP 8
PRTP 9
PRTP 10
Scenario 3
Figure 11: Mean speed when passing through the work zone
Braking distance
According to the velocity and acceleration data, the braking distance of different participants for
three different types of work zones can be determined, as shown in Figure 12. The method to
calculate braking distance result is to subtract the longitudinal coordinates when the vehicle starts
to decelerate from the longitudinal position of the work zone start point.
The braking distance of each participant under the road conditions of three different work zone
scenarios, subsequently lead to the following results:
1) The first situation is that the braking distance of scenario 1 work zone is bigger than the other
two types of work zone, for all participants except for participant 1,2 and 4.
2) Conversely the braking distance of scenario 2 work zone is bigger than the other two types of
work zone. This kind of situation happened 3 times out of 10 participants’ experiments, which
is 30% of the total experiment.
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3) Interestingly, there are no instances where the braking distance of scenario 3 work zone is
bigger than the other two types of work zone which seems to imply scenario 3 work zone will
have less effect on braking distance compared to the scenario 1 and scenario 2.
According to the above observations between the braking distance and different types of working
zones, the following conclusions can be drawn about the relationship between the vehicle braking
distance and the position of working zone:
The observation of scenario 1 having greater braking distances can be attributed to vehicles on the
main road and the ramp driving in the same direction which, reduces the need for lane changes.
This in turn simplifies decision making as there is less pressure to perform more dangerous
manoeuvres (such as merging into lanes with heavy traffic density). Additionally, greater braking
distances mean there is a greater allowance for road users to adjust their travelling speed, which
results in a more secure traffic flow, as well as more safe traffic conditions.
In contrast, in Scenario 2, some of vehicles on the main road need to enter the ramp, so the
frequency of lane changes will be more than in Scenario 1, which will have a certain impact on
the vehicles in the straight line on the main road, and then reduce the braking distance of the
vehicles tend to travel on the straight main road.
The smaller braking distances that occur in the scenario 3 work zone may imply that there is a
greater probability for more collisions to be encountered in this scenario. The reason may be that
when the work zone completely closes of the main road, all traffic flows will choose the same
diversion route. Combining the effect of the lateral road span as well as the forced diversion
increases the difficulty for drivers to change lanes under heavy (and in some instances more
complex) traffic conditions, as there is less room for error to avoid road accidents (Paolo and Sar,
2012). Therefore, it is easier to collide when changing lanes in scenario 3, which also corresponds
to the results of the ‘Crash Frequency’ data analysis section.
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Braking distance for 10 participants experiments
400
Error Bars donate S.E
Braking distance / m
350
300
250
200
150
100
50
0
PRTP 1
PRTP 2
PRTP 3
Scenario1
PRTP 4
PRTP 5
PRTP 6
Scenario2
PRTP 7
PRTP 8
PRTP 9
PRTP 10
Scenario3
Figure 12 The braking distance of work zones
Participant’s Feedback
Based on the responses in the pre-questionnaire, it was revealed that the demographics of all
participants were similar. The test group were primarily males, all within the age range of 24 years
old to 28 years old, having an average of 4.2 years of driving experience (with the fewest being 2
and the most being 8), and all participants averaged approximately 7 to 8 driving hours per week,
with about 1.5 hours per day. Additionally, all participants had minimal experience with VR
simulations and using VR equipment.
Given the similarities of all participants, it is difficult to ascertain the extent other external factors
contributed to the behavioural differences. Unlike in previous research where it was found that
there was correlation between the age group of the participants and how risky their driving
behaviour was. (Moradpour et al., 2015), the narrow age range within the group likely resulted in
there being a minimal correlation between the age differences within the participants and the
differences in driving behaviour.
The post-questionnaire revealed that some participants had issues controlling the vehicle, and as
such required to adjust their driving to exhibit “conservative” (where they would take longer to
arrive at freeway speed, and merge much earlier) behaviour than usual to avoid accidents.
One of the participants had notable issues acclimatizing to the driving simulator, stating the control
of the wheel felt “very un-intuitive” and the pedal sensitivity was very different to what they were
used to. This “un-intuitive” control over the wheel was reflected in other participants where it was
reported that they could not properly control the distance between the front and the next car, and
there were occasional issues with lateral instability, which forced earlier braking than what they
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would typically do. Another participant reported to have issues with the braking system as it lacked
“mechanical feel”, and as such, felt this issue in vehicle control caused the accident in scenario 3.
In terms of realism, all participants did not feel any sense of vertigo or nausea, and all thought that
the scene was a reasonable representation of real-life scenarios they have experienced.
The same participant mentioned that some of the simulated traffic movements appeared to be
unusual, although not “outside the realm of possibility based on some of the driving I have seen
other people do”. It was reported that this unusual, simulated vehicle pattern forced a change in
driving behaviour that was very different to what the participant naturally did. This could be
explained by the issues in the experimental setup where there were some compatibility issues
importing highly realistic vehicle physics into the simulation, which resulted in some participants
having issues responding appropriately to the changing dynamics of the traffic. As the traffic flow
was simulated, there were comments on how the flow of traffic, felt uneven, with multiple
instances of “jittery movement” causing confusion on how to properly respond, as they were not
used to this kind of environment.
This caused a change in driving behaviour where the participants proceeded to exhibit more
cautious driving behaviour (slower approach speeds and earlier merging) when approaching the
work zones in later trials. These responses contrasted the findings made in (Weidner et al., 2017),
where it was previously concluded that VR will not exhibit significant difference in physiological
responses or behavioural patterns in participants.
LIMITATIONS AND SUGGESTIONS FOR FURTHER IMPROVEMENT
The results from the research were pulled from a small scale of participants. Due to the time
restrictions imposed upon this study, the remaining pool of participants were unable to participate
in this study. Future study would involve a large sample size of participants with varying degrees
of road (and driving) experience to produce a more thorough assessment to cover a wider
distribution of driving behaviour for various reasons.
Substantial lines of C# code were used in the creation of the driving simulation, which may have
presented difficulties in the system’s rendering and processing capabilities. As reported in the
feedback in the questionnaires, some participants experienced delays during the simulation, which
in turn, may not be as reflective as real-life scenarios on the road.
The equipment used to conduct the experiment was reported to have less of a “mechanical feel”,
and as such required some participants to adjust their driving behaviour, which was atypical to
how they would usually act on freeways. Improvements to the equipment used in similar studies
where the participants are able to closely replicant their typical driving behavioural patterns (for
instance, location and time of merging) is expected to yield interesting results where they may
potentially exhibit less cautious driving behaviour in dangerous road scenarios. It is worth noting
that if more unsafe driving patterns are recorded and are implemented in autonomous vehicle
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coding, it may potentially increase the safety of autonomous vehicles, as the algorithm may take a
more cautious approach when processing real-time data of other people’s driving.
CONCLUSIONS
Based on the driving simulator, this study determined the influence on driving behaviour when the
location of work zone near to on-ramp and off-ramp, respectively, and caused road closure, under
the condition that all participants successfully completed the task. Drivers slowed down as they
approached the work zone and accelerated quickly after passing, which is in line with previous
research (Adeli, 2014). The longitudinal and lateral accelerations during vehicle travel were
consistent with the Bivariate Pareto distribution model, with the probability of longitudinal
acceleration being greater than lateral at low speeds. In the case of full lane closures, the vehicle
braking distance is shortest, the speed is forced to the lowest and the collision rate is highest.
Therefore, when more lanes are closed due to work zone, the traffic flow should be controlled to
avoid low speeds and congestion. The work zone near the on-ramp in this study provides a longer
braking distance than the work zone near the off-ramp, but as vehicles passing will be affected by
vehicles entering the high speed from the ramp, the probability of a side-on collision that is too
late to avoid is higher, so for such a work zone arrangement, it is even more important to ensure
that vehicles pass at a suitably low speed.
Previous research has shown that narrower lanes can encourage drivers to actively slow down, so
how to design for slowing down when ramps and work zones are in parallel could be an objective
for future research.
ACKNOWLEDGEMENTS
This paper is supported by unit coordinator Dr Shaun Gregory, discipline coordinator Dr Tom
Hughes, supervisor Nan Zheng and demonstrator Zheng Xu. The authors wish to acknowledge the
discussion and contribution of the team members and the free use of MONASH library.
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APPENDICES
APPENDICE A. Lane change trajectory of vehicle through Scenario 1
APPENDICE B. Lane change trajectory of vehicle through Scenario 2
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APPENDIX C. Lane change trajectory of vehicle through Scenario 3
APPENDIX D. Questionnaire (pre and post)
Questionnaire (pre and post)
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