Analysis of Road Safety in NSW: Mobile Phone Detection Camera Program

This report provides a comprehensive analysis of road safety in New South Wales, focusing on the Mobile Phone Detection Camera Program as a key initiative to reduce accidents. The report uses data from Transport NSW and Acusensus, highlighting the alarming statistics of mobile phone use while driving and its impact on road fatalities and serious injuries. It examines various factors contributing to crashes, including road surface conditions, alcohol consumption, fatigue, speeding, and driver behavior, using multiple graphs to illustrate trends and causalities. The report also reviews the SMART objective of the project and the impact of automated camera enforcement. The analysis emphasizes the need for education and enforcement to curb dangerous driving behaviors and achieve the government's road safety targets, providing valuable insights into the effectiveness of the Mobile Phone Detection Camera Program and potential areas for improvement.

SMART objective:
To roll out the project in atleast five suburbs of NSW by the end of April within a budget of $500,000 and to
reduce the number of serious road accidents to zero by 2024.
Data from Transport NSW
https://roadsafety.transport.nsw.gov.au/stayingsafe/mobilephones/technology.html
 The Mobile Phone Detection Camera Program is a component of the overall strategy to achieve the
Government’s target of reducing road fatalities and serious injuries by 30 per cent by 2021 (compared to
2008-2010 levels), and to zero by 2056.
 Automated, camera-based enforcement, coupled with police enforcement, has played a critical role in
addressing other high-risk behaviours on our roads such as speeding and red light running. These camera
programs have proven to help prevent crashes and reduce road trauma.
 The pilot program, which tested the camera technology from January to June 2019 in both fixed and
transportable (or trailer mounted) modes, proved the technology was able to operate with high reliability
in real world conditions. The cameras produced clear images in all weather and light conditions, and
exceeded expectations related to the handling, storage and security of data.
 During the pilot more than 100,000 drivers were found to be using a mobile phone illegally.
 Independent modelling by Monash University Accident Research Centre (MUARC) based on the reported
crash data estimates that the program will contribute to a reduction in road trauma of approximately 100
fatal and serious injury crashes over a five-year period.
 There is strong community support for using cameras to enforce illegal mobile phone use by motorists.
 A community survey commissioned by Transport for NSW was completed early April 2018, after laws
were tabled in NSW Parliament to permit use of the technology. Three quarters (74 per cent) of those
surveyed supported the use of cameras to enforce mobile phone offences.
 A further survey was undertaken in May 2019, during the pilot, and found the level of support had
increased to 80 per cent.
Data from Acusensus,https://www.acusensus.com/acusensus-headsup-jr-faq
Distracted driving is the new “drink driving” of our age. According to the Centre for Road Safety
approximately 25 per cent of drivers in NSW admit using their mobile phone whilst driving. Other
studies put this rate as high as 70%. An alarming reality.
In NSW alone, last financial year over 40,000 people were fined for using their phones while driving.
From offi cial statistics, between 2012 to 2017 in NSW there were 175 crashes involving mobile
phones resulting in 9 deaths and 50 serious injuries. This is harrowing enough however the category
is under-reported and the true number caused by phone use is likely much higher.
Average rates of road fatalities across developed nations like Australia, NZ, USA, UK and France have
been rising steadily for the past 5 years, and are now over 15% higher than their 2013 minimum. This
alarming increase has occurred after decades of year on year improvements and despite four of the
five road safety pillars improving. Vehicle safety systems, trauma care, road infrastructure and
management systems have generally improved, leaving only road user behaviour. In Acusensus’ view,
road user behaviour has worsened, and most of the problem is attributable to the increased
prevalence of mobile phone use.

These are all frightening and harrowing statistics. Our best chance to halt these painful figures is
through education and enforcement.
Fig 1: Crashes by type and region
Interpretation:
This graph evaluates the rate of crashes by type and region. The pink portion is denoting the crashes by
metropolitan and the red portion is defining the crashes by country. It had been observed that the rate of
crashes in rear end is extremely high, while in the off path, the rate of accident is 6,077. The overtaking in the
metropolitan area is less vulnerable towards crashes. The rate of crashes in this area is only 293. One of the
most significant areas, where the rate of crashes is extremely low is miscellaneous. In this area, the rate of
crash is only 340 and 182 respectively.

Fig 2: Road traffic crashes in NSW, comparison chart for 2018 to 2017
Interpretation:
This graph represents the rate of road traffic crashes in New South Wales in between 2017 to 2018. It had
been observed that the rate of serious injuries in the year 2018 is 5230, which is 6.9% lower than 2017. In
additional the statistics of unmatched serious injury in 2018 is 5673, which is 3.6% higher than 2017. The rate
of serious injuries in registered vehicles is 5,571,500. The rate of this kind of accident in New South Wales has
been increased by 2.2% in between 2017 to 2018. The rate of serious injuries by the license holders is only
19.72, the rate of which has been decreased by 2.2% in between 2017 to 2018.

Fig 3: Causalities from crashes by local government.
Interpretation:
The aim of this graph is to present the causalities of crashes by the local government. From the
graph, it is evident that the crash in Canterbury Bankstown is extremely severe, the rate of which is
6,750. On the contrary, the rate of crash in inner West is less severe, the rate of which is 2,700. On
the other hand, the rate of causalities of crash in the central cost is 4,559. From the graph, it can be
said that Junee is one of the most significant regions, where the causality of crash is less severe.

Fig 4: Crashes by road surface conditions
Interpretation:
The aim of this graoh is to present the crashes, caused by road traffic condition. It had been
observed that in the dry areas, the rate of crash, through road surface condition is lower than that of
the wet areas. In wet areas, the motor vehicels can lose their traction and thereby experience crash
easily. This can result in dangerous situation.However, it is to be noted in this context that the rate
of crash in wet area has been decreased in between 2014 to 2018. From the graph, it can also be
said that in the snow or ice areas, the rate of crashes is relatively high.

Fig 5: Injuries by alchohol consumption
Interpretation:
This graph is about the rate of imjury by alcohol consumption. With the incraesing rate of alcohol
consumption, the rate of inury, caused by alcohol consumption is rapidly increasing. In the year
2014, the rate of injury, caused by alcohol consumption was 7.3%, which was increased by .1%
during 2015. The rate of alcohol involvement by the unknown factor in the year 2014 was 92.7%,
which was decreased by .1% in the year 2015. The rate of injury by alcohol during 2016 was 6.5%,
which was drastically increased by .5% in the year 2017. One of the most stricking factor, ti be noted
here that the rate of inury by alcohol during 2018 was relatively low, which was 6.8%.

%
Fig 6: Crashes due to fatigue
Interpretation:
This graph is based on the nuber of crashes, which has bene caused due to faiture. In this statistical
representation, a comparatively anaysis from 2014 to 2018 has been carried out. It has been
observed that the rate of injury dueto fatigue in the year 2014 was 11.8%, which was increased by
0.9% in the year 2015. However, the rate of road injiry due to fatigute was comparatively lower in
the year 2016, which was further increased by 0.6% during 2017. One of the most important to be
considered here that the rate of% was comparatively higher than any other year.

Fig 7: Crashes due to speeding
Interpretation:
The purpose of graph is to evaluate the rate of roadside users, who are seriously injiured due to high
speed. Rush drivig is one of the alarming issues, from which the rate of accident and even death is
rapidly increasing. From the graph, it is evident that te rate of road side accident due to excessive
speed was 24.5% in the year 2014, which was increased by 0.3% in the year 2015. However, in the
year 2016, the rate of road side user, injured by excessive speed was relatively low, which was
23.5%. In the year 2017 also, the rate of road side user, injured by excessive speed was 24.1%. One
of the most important factors whuch has been found in the graph is that, gthe the rate of road side
user, injured by excessive speed in the year 2018, was relatively low, which was 23.0%.

Fig 8: Driver serious injuries due to type of collision
Interpretatio:
This graph deals with the rate of drivers, who have been seriously injured due to coalition. These are
the cases of hospitalised injured. From the graph, it is evident that the rate of coalition with car and
pick-up van that match with police report is 86% and the injuries that are not matching with police
report is 3,187. However, the rate of coalition with heavy vehiles that are matching with police cases
is 90% and the cases, that are not matching with police record is only 259. However, the case of
injury that are not mathing with police cases is comparatiovely high which is 1,175.

Fig 9: Road user by gender
Interpretation:
This graph deals with the number of road users on the basis of gender. From the graph, it is evident
that the rate of female driver driver than that of male drivers is low. The rate of male driver is
38,522, while the rate of female drivers, 32,529. In the same way, the rate of male passangers is
higher than that of the male passangers. One of the stricking aspect of this graph is that the rate of
pedal cyclists is extremely low.

Fig 10: Fatality by gender
Interpretation:
This graph is all about the rate of fatality by gender. From the graph, it is evident that the rate of
fataity among female is higher than that of male. Here, a comparison between 1996 to 2018 has
been presented. It had been ovserved that rate of fataility among male in the year 1996 was 413,
which was decreased by 150 in the year 2018. In the same way, the rate of fatality among female
during 1996 was 168, which was lowered by 84 in 2018.

Fig 11: Fatalities by behavioural factors NSW 2010
Interpretation:
This graph deals with the rate of fatalities by behavioural factors. It had been observed that the rate
of fatality due to excessive speed is extremely high, which is 42%, 36% and 45% in NSW,
Metropolitan and country respectively. On the other hand, the rate of fatality by illegal alcohol is
comparatively low, which is 20%, 15% and 23% in NSW, Metropolitan and country respectively. In
the same way, the rate of fatality due to restraint non-usage is lower than any other causes of
fatality, which is 17%, 14% and 18% in NSW, Metropolitan and country respectively.

Fig 15: Fatalities by crash type by urbanisation NSW 2008 to 2010
Interpretation:
This is the last graph, which deals with the fatalities by crash, due to urbanisation in New South
Wales in between 2008 and 2010. From the graph, it is evident that the rate of fatality by crush in
run off road especially in the road is extremely high, while in the area of head on, it is relatively low.
NASCAR-NSW Advanced saftey cameras and automated road complaiance.
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