Risk Factors and Intelligent Transport System answers
–
possible opportunities and shortcomings
By:
Ioanna Spyropoulou, Dr. (corresponding author)
Research Associate,
Department of Transportation Planning and Engineering,
National Technical University of Athens,
5, Iroon Polytechniou str, 15773 Zografou, Greece,
tel.: +302107722889, fax: +302107721454,
e-mail: iospyrop@central.ntua.gr
John Golias, Prof.
Professor,
Department of Transportation Planning and Engineering,
National Technical University of Athens,
5, Iroon Polytechniou str, 15773 Zografou, Greece,
tel.: +302107721276, fax: +302107721454,
e-mail: igolias@central.ntua.gr
Matthew Karlaftis, Dr.
Assistant Professor,
Department of Transportation Planning and Engineering,
National Technical University of Athens,
5, Iroon Polytechniou str, 15773 Zografou, Greece,
tel.: +302107721280, fax: +302107721454,
e-mail: mgk@central.ntua.gr
Merja Penttinen, MSc
Senior Research Scientist
VTT Transport and Logistics Systems,
P.O. Box 1000, FI-02044 VTT, Finland,
tel: +358405546826,
Email: Merja.Penttinen@vtt.fi
Truls Vaa
Research Psychologist
Institute of Transport Economics,
P.O.Box 6110 Etterstad, N-0602 OSLO, Norway,
tel: +4722573825,
Email: tva@toi.no
Abstract: Road accidents comprise one of the main causes of death in western
society and a new set of measures that are anticipated to improve road safety has
evolved – namely, the intelligent transport systems. This paper presents a structured
evaluation of intelligent transport systems. First, common causes of accidents are
identified and specific systems that could remove those causes or reduce their effects
are proposed. The systems are then assessed based on conclusions drawn from
research studies and expert opinions. For each of the suggested systems the potential
capabilities for improving driver behaviour and road safety but also the anticipated
shortcomings of their implementation are discussed.
1. Introduction
Road accidents comprise one of the major causes of death in western countries, and
the primary cause of death for specific groups, such as young people (OECD, 2006).
A great variety of measures aiming at the reduction of accident rates have been
discussed and applied worldwide, and have indeed improved the safety of the road
networks. With the new targets set for road safety levels in various countries,
however, there is a demand for more dynamic solutions. Intelligent transport systems
(ITS) could be a promising direction towards providing an efficient solution for the
improvement of road safety, thus setting new standards (Spyropoulou et al., 2005).
Intelligent transport systems comprise a quite recent development in the field of
transport and technology and as such it demonstrates rapid development (Peirce,
Lappin, 2003). ITS are anticipated to contribute to several issues that have been a
consequence of the mobility growth including improving road safety, and reducing
traffic congestion and environmental pollution. Other elements at which ITS are
anticipated to contribute are increase in mobility especially of the less ‘mobile’ groups
(disabled, elderly drivers etc.) and increase in driving comfort.
The majority of the research studies conducted on the impact of intelligent transport
systems on road safety follows a ‘system’ methodology. In particular, the intelligent
transport systems that are foreseen to have been implemented in the near future are
described and their general impact on road safety is discussed. The present study
adopts a more ‘humanistic’ and ‘safety-related’ approach by defining first the risk
factors that contribute to road accidents and then proposing targeted IT-solutions.
The procedure of application and evaluation of intelligent transport systems seems to
be a rather slow one. The quantification of the impact of such systems on accident
rates can only be established after they have been implemented for a long period so
that specific elements including technological features, penetration rates and driver
behaviour when using the systems have been stabilised. Still, specific elements of
their impact can be somewhat estimated indirectly with the use of surrogate measures.
Hence, although the implementation cost of intelligent transport systems is definable
and is quite high, their benefits are still abstract. After all this research, the question
remains: “Can intelligent transport systems be an efficient answer to road accidents?”.
The objective of this study is to determine the possibilities that intelligent transport
systems can provide on the improvement of road safety by targeting specific risk
factors. In addition, the shortcomings of these proposed solutions are also discussed to
indicate the needs for future research and system development. The capabilities and
shortcomings of ITS are extracted from three different types of analysis. The first is
the collection of research studies on the impact of intelligent transport systems on the
user and on road safety in general. The second type of analysis used involves applying
a common evaluation procedure to the investigated systems – namely, SWOT
(Strengths, Weaknesses, Opportunities and Threats) analysis. Finally, the third type of
analysis is the conduction of a Delphi study dealing with the impact of intelligent
transport systems, from which experts’ opinions were also extracted.
In the second section of the paper the risk factors contributing to accident occurrence
are discussed, and in the third section possible IT-solutions that could reduce the
defined risk are described. The fourth section deals with the impact of these systems
on road safety both in terms of positive and negative impact. Last, in the fifth section
a general overview of the potential impact of intelligent transport systems on road
safety is provided.
2. Risk factors
Traffic accidents are quite complex events and rarely have a single cause. To illustrate
this statement the following example is presented: A driver crosses a junction
(following a sharp curve) and hits a vehicle coming from the junction leg that had
‘right of way’. The result in this example is an accident. However, the cause cannot be
extracted from the above description. This driver, who was a novice driver who was
not familiar with this particular road network, was returning home after a night at the
bar, having drunk several beers, was speeding, and was listening to his favourite song
on the radio while talking on his mobile phone. The accident took place at night, and
the pavement was quite slippery as it had been raining. Possible isolated explanations
of what had caused the accident are:

the driver was not aware of the ‘STOP’ sign and he was driving too fast to
make a complete stop upstream of the junction approach, or

although he was aware of its existence he did not see the sign because he was
listening to music and talking on the mobile phone, or

he did see the sign but the pavement was too slippery and he did not manage
to stop, or

he did see the sign on time but his reaction time was too slow because of being
drunk, or

he did stop at the junction approach but did not judge well the speed and
distance of the other vehicle because of his inexperience and decided to cross.
Still, it would be difficult to identify a single cause for this accident. The cause of the
accident could be one of the above described ones, or a combination of them. A
general description of right or wrong observations, decisions and actions that may or
may not lead to an accident is illustrated in Figure1.
***** Please insert Figure 1 here ******
Figure1. Description of driving procedure and actions (Häkkinen and Luoma, 1990 )
There are multiple sub-causes and procedures that contribute to an accident, the
treatment of one, some or all of which may result in the avoidance of the accident or
the mitigation of its consequences. These sub-causes will be referred to as risk factors.
Risk factors can be classified into three main categories – namely the ones induced by
the human, the road environment and the vehicle. Human factors involve the
condition or the actions taken by the driver. Road environment factors involve the
roadway design and roadside hazard and conditions. Last, vehicle factors involve
vehicle-operation failures or vehicle design issues. Intelligent transport system
solutions only target at reducing or eliminating human and road environment factors,
hence only these two factors will be presented in this paper.
2.1 Human Factors
The most prevalent risk factors contributing to a road accident are human factors
(Treat et al., 1979). In addition, there is a great variety of different human factors in
relation to environmental factors and their treatment can be less costly and is more
effective on road safety, if achieved. For the reasons mentioned above research on
risk factors and countermeasures is more concentrated on the human factors category.
Human factors can be divided in several different categories, which will be dealt with
separately and are:

Alcohol and illicit substances

Speeding

Violations of the highway-code

Driver inattention

Driver decision errors
2.1.1 Alcohol and illicit substances
One of the most prevalent risk factors is driving under the influence (DUI) of alcohol,
illicit substances or a combination of legal drugs and alcohol. The main effects of
DUI are a reduction in driver perception, concentration and an increase in reaction
times. There is a clear relationship between DUI and accident rates; increase in blood
alcohol concentration (BAC) or other such substances results into higher accident risk
(Honkanen et al., 1980; Hurst et al., 1994; Movig et al., 2004). The relationship
between DUI and severity rates has not been established yet (Smink et al., 2005)
although in some studies there has been evidence that alcohol has been the prevailing
factor in most severe accidents (Finnish Motor Insurers’ centre, 2006).
Driving under the influence of such substances (and especially alcohol) is
encountered in many countries to a lesser or a greater degree. A survey that took place
in 23 European countries showed that the proportion of checked drivers with higher
BAC than the legal limit ranged from 4% to 64%, with the average being 16%
(SARTRE, 2004). Road accidents involving DUI are related to driver characteristics
such as age and gender. In particular, this type of accidents decreases with age
(Mason et al., 1992), and young male drivers are of the highest risk drivers (AbdelAty and Abdelwahab, 2000). Last, DUI is observed more frequently at the weekend
nights (Vanlaar, 2005) as the majority of leisure trips take place during these periods.
2.1.2 Speeding
Speeding is also one of the most significant factors contributing to road accidents. The
relationship between speeding and accident rates has been established and is twofold.
Higher speeds lead to greater risk rates (Kloeden et al., 2001) and higher speeds lead
to higher severity rates (Nilsson, 1982; Elvik et al., 2004; Evans, 2004). High speeds
increase the probability of making a perception (Hale, 1990) or a decision error as
there is less time to react to observations. They also reduce manoeuvrability and timeto-collision (TTC), and increase the crash impact. A consequence of the latter is that
speeding accidents involving vulnerable users such as pedestrians or cyclists have a
greater probability of being fatal (Anderson et al., 1997).
SARTRE 3 results (2004) showed that the proportion of drivers being penalised for
speeding ranges between 8% and 46% between the different countries, with the
average being 18%. This emphasises the need for measures that targeting at speeding
drivers to reduce this percentage, especially in countries where it has been observed to
be quite high.
2.1.3 Violations of the highway-code
Violations of the highway-code are actions involving the conscious ignorance of the
traffic rules and can be considered as conscious risk-taking. However, the majority of
drivers that violate the highway-code underestimate the risk and overestimate their
driving abilities, as thoughts such as ‘I am careful when crossing at the red signal
indication’ are usually in mind. Examples of violations of the highway-code are
crossing a junction when the traffic signal indication is red, not yielding way at ‘rightof-way’ movements, not stopping at ‘STOP’ signs etc. It must be emphasised that this
category deals with these actions when they are made consciously.
Young drivers are more prone to violations of the highway-code (Aberg and Rimmo,
1998) due to the ‘sensation seeking’ behaviour that characterises this particular driver
group (Jonah, 1997). Furthermore, violations of the highway-code are mainly
observed at experienced rather than novice drivers (Rimmo and Aberg, 1999).
2.1.4 Driver inattention
Driver inattention can be described as a lack of focus (visual or cognitive) on the
required field of view, typically the forward and the peripheral, that leads to a loss of
concentration on the driving task. It occurs when the driver performs a secondary task
(tuning the radio, talking on the phone) while driving, and part of his attention is
switched from driving, which is the primary task, to the secondary one. Driver
inattention is a significant risk factor and has also been linked with severe accidents
(NHTSA, 2001).
Within driver inattention, driver fatigue and drowsiness are also discussed, as the
main effect of fatigue and drowsiness is ‘a progressive withdrawal of attentions from
road and traffic demands’ (Brown, 1994). The most extreme form of this withdrawal
is the eyes closure for a prolonged period. Several studies have identified driver
drowsiness and fatigue as significant risk factors (Dingus et al., 1987; Fell, 1994), and
have emphasised their involvement in fatal accidents especially on rural roads.
2.1.5 Driver decision errors
Driver decision errors also contribute to road accidents. Such errors differ from driver
inattention errors as in the first case drivers perceive the traffic situation well but
make the wrong decision concerning the proper action to take (GAO, 2003). Driver
decisions involve three different levels – namely, strategic, tactical and operational
(Michon, 1985). Decisions on the strategic level include trip planning, route selection
etc. Tactical decisions include interaction with other users and road environment (gap
acceptance, yielding right of way etc) and are related with the drivers’ perception of
the ‘road scene’. Last, operational decisions mainly involve vehicle control, including
steering, braking etc. Strategic decisions operate on a conscious level, as conscious
appraisals of what to do, while operational decisions are highly automated (tactical
decisions are in-between conscious and unconscious level). Driver decision errors
leading to road accidents occur when driver decision errors are made in the tactical
and operational level.
Driver decision errors have been correlated with driver age and experience. In
particular, novice, young and elderly drivers are expected to make such errors (Pelz
and Schumann, 1971) more often. Young drivers and novice drivers tend to
overestimate their driving abilities (Edwards, 2001), which increases the probability
of making decision errors. Another factor contributing to high percentages of novice
drivers making decision errors is the complexity of multi-tasking in driving, as many
actions during driving have not become automated due to lack of experience (Milech
et al., 1989). Elderly drivers commit such errors mainly at intersections or other
complex traffic situations due to problems with divided attention (Keskinen et al.,
1998).
2.2 Environmental Factors
The most important environmental factors that may have an impact on road safety,
and are discussed in the context of this study are the following:

Geometrical characteristics

Pavement conditions

Weather conditions

Street light conditions
2.2.1 Geometrical characteristics
The geometric design of a road section or junction follows specific guidelines mainly
to allow for safer driving. The research on the various geometrical elements of a road
section and on their impact on road safety is extensive. In general, a bad design can
comprise a potential road hazard. Geometrical characteristics that affect road safety
include road type, type (and design) of junction, lane width, median width, road
gradient, curve design, shoulder width etc. (Perchonock et al., 1978; Matthews and
Barnes, 1988; FHWA, 1992; Kuciemba and Cirillo, 1992; Zegeer et. al, 1995; Milton
and Mannering, 1998; Lamm et al., 1999; SAFESTAR).
2.2.2 Pavement conditions
Adverse pavement conditions comprise another element of the road environment that
can contribute to road accidents. Elements including pavement geometric design,
paving materials and mix design, pavement surface properties, colour and visibility
can influence road safety (Tighe et al., 2000). The most prevalent element of
pavement conditions contributing to road accidents is wet pavements. Asi (2007)
noted that accident rates on highways increase in the rainy season due to the low
pavement skid resistance. Elvik and Vaa (2004) noted that accident risk at wet snow
surfaces and on snow and ice surfaces is 50% and 150% higher respectively than on
dry road surfaces.
2.2.3 Weather conditions
Several studies have identified adverse weather conditions to be contributing factors
in road accidents (Andrey and Olley, 1990). Conditions such as snow and ice (mainly
in Nordic countries), rain, fog and strong winds comprise potential accident hazards.
Edwards investigated the relationship between weather conditions including rain, fog
and strong winds on accident rates (1996) and accident severity (1998). In all cases,
weather conditions seemed to affect road safety.
2.2.4 Street light conditions
Accident rates during night-time increase, and although this is often a consequence of
general driver behaviour when driving at night (i.e. drowsiness or alcohol
consumption etc.) poor lighting conditions also play an important role on increased
risk rates. Accident rates were associated with darker environment conditions and
were found to be independent of time of day, day of week or alcohol consumption
(Ownes and Sivak, 1993). Darker environment is also identified as a significant risk
factor for accidents involving pedestrians (Sullivan and Flannagan, 2002).
3. Intelligent Transport solutions
In this section, a number of intelligent transport systems will be suggested as
countermeasures for the identified risk factors. Three different types of systems
targeting at improving road safety will be presented: systems which operate before the
accident happens (pre-crash) and their aim is accident avoidance or mitigation of its
impact, systems which operate during the accident (crash) and systems which operate
after the accident has occurred (post-crash). This approach is consistent with the
matrix proposed by Haddon (1970). However, only pre-crash systems can serve as
countermeasures for the specific risk factors. Crash and post-crash systems cannot be
related with the risk factors. However, a short discussion of their potential impact is
provided in this section as they are anticipated to improve road safety by mitigating
the severity of an accident.
3.1 Human factors – pre-crash systems
3.1.1 Alcohol and illicit substances
A range of devices referred to as alco-lock, or alcohol interlock which target
intoxicated drivers have been developed. The operation of this device is as follows:
the BAC of the driver is monitored before the start of a drive from breath samples and
if it is detected to be over a threshold, which is set to be the legal limit, the engine
does not start. The same procedure may also take place en-route, i.e. the driver is
required to blow while driving, and if his BAC is over the limit he has to make a stop
(ETSC, 2005). This device has been accepted quite well by public bodies and is
considered as a road safety measure into the national agendas for road safety in some
countries (ICADTS 2001; Mathijssen, 2005).
2.1.2 Speeding
A wide range of functions targeting at speeding drivers have been developed
(Almqvist, 1991) which are referred to as intelligent speed adaptation (ISA),
intelligent cruise control (ICC), speed limiter etc. These systems have different
functionalities, and can be informative, warning or intervening (ETSC, 2005). The
systems monitor the driving speed and if it is detected to be higher than a threshold
level they (a) provide information (visual or auditory) of the limit to the driver
(informative), (b) provide a warning to the driver (visual, auditory or haptic)
(warning) or (c) do not allow the driver to exceed the speed limit in the first place
(intervening). Depending on the technological capabilities of the system, the speed
threshold level can be fixed (posted), variable (lower permanent speed limits e.g. at
pedestrian crossings) or dynamic (adjustable depending on specific road environment
conditions e.g. weather, traffic etc.).
2.1.3 Violations of the highway-code
Until today, systems aiming at the reduction of highway-code violations have not
been developed at a sufficient level. Since these involve conscious driver actions,
systems providing information or warnings will be rather ineffective (unless the
warnings are quite irritating). Still, such systems are based on the coordination of data
received from various different actors (the vehicle of the user, other vehicles, road
environment (traffic lights) etc.). Cooperative intelligent transport systems (Tsugawa
et al., 2000) may comprise potential future solutions targeting at this specific risk
factor.
2.1.4 Driver inattention and driver decision errors
Although these two risk factors were discussed as separate risk factors, as they are of
distinct nature, the resulting driving errors are the same. For example, a driver may
change lane while another vehicle is fast approaching at his/hers target lane and cause
an accident, either because he/she has not seen the other vehicle (inattention error) or
because he/she thought the gap was sufficient enough (decision error). Still the action
is the same, i.e. changing lane whereas he/she shouldn’t have done so. There is a wide
range of intelligent transport systems targeting at reducing driver inattention and
driver decision errors. The way they operate is by monitoring driver behaviour and
providing warnings or less frequently intervene with the driving task when they detect
inappropriate behaviour or potential vehicle conflicts. Examples of such systems
include longitudinal vehicle control (forward collision warning), lateral vehicle
control (lane keeping and lane departure warning), intersection, pedestrian or obstacle
warning and driver monitoring systems.
3.2 Geometrical characteristics – pre-crash systems
The nature of environmental risk factors is completely different from that of human
factors. In particular, driver actions (resulting from the different human factors) can
be changed whereas environmental characteristics cannot. Intelligent transport
systems that reduce road accidents caused by the interaction of the driver with the
road environment, mainly aim at changing driver behaviour when driving under
hazardous road environment conditions. This is mainly achieved by raising driver
awareness on these conditions (Monsere et al., 2005) with the employment of systems
that inform or warn the driver of adverse road environment conditions. Such systems
can be in-built systems such as radio data systems (RDS) or enhanced navigation
systems or even based on nomadic devices such as messages to mobile phones or can
be part of the road infrastructure such as variable message signs (VMS).
A system aiming at improving visibility under low visibility conditions (night or fog)
is the vision enhancement system (VES) (CRA, 1998). This system improves the
visibility of the driving scene by presenting additional visual information through an
in-vehicle screen display.
3.3 Crash and post-crash systems
Systems belonging in these categories aim at mitigating accident severity. A crash
system that is still under development is smart restraints. This system links the
operation of the safety features of a vehicle (seat-belt, safety bag) with the
characteristics of the accident (impact size, direction etc) and the occupants (age, size,
pregnant women etc). Hence, the characteristics of the potential conflict are first
detected and the pre-deployment of the appropriate in-vehicle safety devices in the
most suitable way is achieved. Variables such as occupant weight, seating position,
safety-belt usage and vehicle deceleration to control belt forces and air-bag
deployment are considered (PRISM, 2005). Smart restraints are expected to reduce
accident severity rates (Rekveldt and Labibes, 2003).
Post-crash functions operate after the accident has occurred, and the most popular
such system is the emergency-call (eCall). Once the accident has occurred, the
emergency services are alerted automatically or manually and the necessary data
including the exact position of the accident, the driver and accident characteristics is
transmitted. The European eCall initiative is set to be launched in 2009 offering a
common telephone number (112) at which the call will be made (European
Commission, 2005). A Finnish in-depth accident study of the effect of eCall indicated
a potential reduction of fatalities of around 5% due to more efficient incident
management (Virtanen, 2005).
4. Opportunities and shortcomings
4.1 Methodology
In this section the proposed intelligent transport solutions will be evaluated. Three
tools contributing to this evaluation will be used. These are a literature review based
on an extensive search of related studies, the conduction of a SWOT analysis and the
results of a Delphi questionnaire. For the literature review, several national,
international and EU projects as well as research studies published in scientific
journals and conferences were collected and their main findings were processed. The
second tool, is the results from a SWOT (Strengths, Weaknesses, Opportunities and
Threats) analysis (Penttinen et al., 2006) , which was conducted by experts who were
asked to identify the potential S, W, O, T’s of specific ITS. SWOT has been
previously employed to assess the role of intelligent transport systems in the
transportation planning process (FHWA, 1998). Strengths and weaknesses involve the
technological possibilities and problems of the systems and comprise internal factors,
whereas opportunities and threats are external factors including user behaviour,
political, legislative and market issues. In this paper, only the external factors will be
presented as the internal ones are temporary and can change rapidly with system
technological development. Last, the results of a Delphi study will also be used in the
assessment of the systems. Experts in the field were asked to fill-in a questionnaire
discussing issues related to the impact of specific systems on road safety. The Delphi
study tool has been previously employed in the field of intelligent transport systems
(Marchau and van der Heijden, 1998).
4.2 Results
4.2.1 Human factors – pre-crash systems
(a) Alcohol and illicit substances: alco-lock
This system aims at preventing intoxicated drivers from driving, and as such it is
anticipated to improve road safety significantly (Beck et al., 1999; Bjerre 2005).
Several concerns however involving its operation, mainly for monitoring the BAC
level while en-route, have been raised. Beirness (2001) noted that it is not always
possible to stop the car, and in specific cases it might be riskier to stop the car than
continue driving under the influence. Hence, the possibility of allowing for a specific
number of system overruling emergencies was proposed.
An issue that needs to be considered involves the acceptability of this system. Drivers
who DUI are not expected to be willing to have their vehicle equipped with an alcolock device. Several studies have addressed the issue of acceptability and results vary
greatly with national characteristics. Acceptability of the mandatory system was
found to be quite high in Canada (Beirness et al., 2000), and some European countries
including Slovenia, Greece, France, Sweden and Ireland whereas other countries
including the Netherlands, Germany, Austria and the Czech Republic were found to
be rather low (SARTRE2, 1998). Acceptability rates of alco-lock devices are
contradicting in the US (ICADTS, 2005). Specific user groups that would benefit
most from alco-lock are drink-and-drive offenders and drivers responsible for the
transportation of other people or goods (bus, truck drivers etc.).
(b) Speeding: speed limiting systems
Speed limiting systems have different opportunities and shortcomings depending on
their functionality. In particular, the reduction of speeding increases with the increase
of system intervention. The intervening system will reduce speeding greatly, whereas
the informative system will have the smallest effect. In general, speed limiting
systems are expected to increase compliance with the legal speed limits (Vlassenroot
and De Mol, 2004). In addition, several studies have also identified a reduction in
speed variability (Varhelyi and Makinen, 2001, Regan et al., 2004). Speed limiting
devices have the potential of improving road safety by reducing accident rates and
accident severity (Biding and Lind, 2002).
On the other hand, the use of such systems also imposes specific threats. A negative
consequence of the system, depending on the human machine interaction (HMI)
design, is driver distraction (Varhelyi 2002) due to the information provided or the
warnings, or driver irritation which might lead to switching the system off. Driver
over-reliance on the system to receive information for the speed limits may reduce
concentration on the driving task (Nilsson, 1995). The intervening system may also
cause driver frustration leading to drivers compensating for their lost time by
employing higher speeds (Persson et al., 1993), keeping shorter headways (Comte,
2000) or performing more aggressive overtaking manoeuvres.
Acceptability is also an issue with speed limiting systems, as drivers who do not
accept the speed limits and are consciously speeding are the least likely to use them
(Jamson, 2006). Speeding offenders, young drivers and professional (truck) drivers
are those who are likely to benefit most from such systems, and schemes motivating
or enforcing their use should be designed.
(c) Driver inattention and driver decision errors: collision avoidance/lateral
control/driver monitoring
Collision avoidance systems alert the driver when the possibility of a potential
conflict with another vehicle, pedestrian or obstacle is detected. These systems raise
driver awareness on situations that have not been observed and reduce reaction times
of the subsequent driver actions (braking, changing lane etc.). Hence, collision
avoidance systems are expected to contribute to the reduction of accident and severity
rates by reducing driving speeds (Yamada, 2002) and harsher breaking (Wakasugi and
Yamada, 2000). Potential problems with the use of collision avoidance systems
include driver frustration caused by the high number of false warnings (Sakabe et al.,
2002) and loss of concentration towards the driving task when drivers over-rely on the
system to warn them of potential hazards. Novice and elderly drivers might benefit
most from collision avoidance systems.
Systems providing assistance at the lateral control of a vehicle alert drivers when
inappropriate vehicle lateral movement is detected which could be either unintentional
(lane keeping systems) or intentional (lane departure systems). The use of such
systems is anticipated to reduce both accident rates (Katteler, 2003) and accident
severity (Sala and Mussone, 2000). As with collision warning systems, shortcomings
of the system use include driver frustration and driver over-reliance on the system
leading in a reduced attention on the driving task. An additional threat of lane
departure systems is the increase in workload caused by the extra task that the drivers
are required to perform – namely, indicating their target lane whenever they intend to
change lanes (Tijerina et al., 1995). Systems providing assistance at the lateral control
of a vehicle are expected to be beneficial mainly for elderly and professional drivers.
Driver monitoring systems alert the driver when fatigue or drowsiness is detected
(CRA, 1998) and will force him/her to take a break before continuing the drive, and
are also projected as potential solutions towards improving road safety. Driver
frustration to the transmitted warnings is expected to be a shortcoming of driver
monitoring systems. A more worrying effect, however, is the possibility of drivers
over-relying on the system and deciding on driving although feeling drowsy or tired,
resulting in an increase of exposure at hazardous driving conditions and accident risk
(Vincent et al., 1998). Drivers who drive long hours (professional, bus drivers etc.)
are expected to benefit most from such systems.
4.2.2 Environmental factors – pre-crash systems
(a) Driver information systems
Systems that inform/warn the driver of hazardous driving conditions such as adverse
weather, bad surface, sharp curves etc. are expected to improve road safety (Rama,
2001). Drivers receiving such information are expected to drive more cautiously by
reducing driving speeds, increasing headways or to change to safer routes or transport
modes. The degree of the impact of the systems on driver behaviour depends on
whether drivers consider the information they receive reliable (Chatterjee et al., 2002)
and on whether they believe that they should adjust their behaviour based on this
information. An important factor influencing their decision is the actual message that
is transmitted, as the same information can be expressed in several ways and evoking
different reactions. The only threat that the use of such systems imposes is driver
distraction, which is greater for in-vehicle systems (Kantowitz et al., 1999).
(b) Vision enhancement system
Vision enhancement system will ‘see’ objects (or pedestrians) on the road that are not
visible by head lamps at night or in poor visibility conditions and transmit their image
to the driver via an in-vehicle screen display. Hence, obstacle (or pedestrian) detection
distances are increased (Staahl et al., 1995; Barham et al., 1999) allowing for more
time for the appropriate driving action (braking, changing lane etc.), and are expected
to improve road safety. Behavioural adaptation of users when using such systems is
feared, as drivers feel safer and employ higher driving speeds (Stanton and Pinto,
2000). Concerns including drivers dividing their attention between the external and
internal environment (CRA, 1998; Smiley, 2000) and the degradation of peripheral
target detection and identification performance outside of the head-up display screen
(Bossi et al., 1997) have also been raised.
5. Discussion
This study discusses potential intelligent transport system solutions that can improve
road safety by targeting specific risk factors. Hence, risk factors including both human
and road environment factors that have been found to contribute to road accidents are
identified. Prevalent human risk factors include intoxicated drivers, speeding, traffic
rules violations, and driver inattention and decision errors. Whereas, the most
important road environment risk factors are bad geometrical design of a road section
or junction, and adverse surface, weather or road lighting conditions.
For most of the identifiable risk factors intelligent transport systems that have the
potential to act as targeted countermeasures exist as fully developed systems that have
been introduced into the market or in a less developed form. Example of the proposed
systems include alco-lock, speed limiting systems, collision and lateral warning
systems, driver monitoring, driver information and vision enhancement systems.
Some of these systems prevent the driver from driving under hazardous conditions
(alco-lock, driver monitoring system, intervening speed limiter), others assist drivers
while driving by alerting the driver when detecting possible errors (collision
avoidance, lateral warning) or by enhancing his perception/vision (vision
enhancement system) and other systems inform or warn the driver of risky driving or
environment conditions (informative and warning speed limiters and driver
information systems).
The employment of the proposed intelligent transport systems provides the
opportunity to reduce risk rates and hence improve road safety. Nevertheless, it also
imposes specific threats that should not be neglected. These arise from a number of
factors including driver frustration because of the frequent warnings, driver overreliance on the systems and behavioural adaptation which may lead in an increase in
risk rates. The adverse effects of the use of intelligent transport systems should be
looked-at thoroughly and ways of overcoming them have to be proposed in order to
allow for their successful implementation.
6. References
Aberg, L. and Rimmo, P-A. (1998) ‘Dimensions of aberrant driver behaviour’,
Ergonomics, 41(1), pp. 39 – 56.
Abdel-Aty, M. A. and Abdelwahab, H. T. (2000) ‘Exploring the relationship between
alcohol and driver characteristics in motor vehicle accidents’, Accident Analysis and
Prevention, 32, pp. 473 – 482.
Almqvist, S., Hyden, C. and Risser, R. (1991) ‘Use of speed limiters in cars for
increased safety and a better environment’, Transportation Research Record, 1318,
pp. 34 – 39.
Anderson, R. W. G, McLean, A. J. Farmer, M. B. J., Lee, B. H. and Brooks, C. G.
(1997) ‘Vehicle travel speeds and the incidence of fatal pedestrian crashes’, Accident
Analysis and Prevention, 29(5), pp. 667-674.
Andrey, J. and Olley, R. (1990) ‘The relationship between weather and road safety:
Past and future directions’, Climatological Bulletin, 24(3), pp. 123 – 127.
Asi, M. I. (2007) ‘Evaluating skid resistance of different asphalt concrete mixes’,
Building and Environment, 42, pp. 325 – 329.
Barham, P., Oxley, P., Thompson, C., Fish, D. and Rio, A (1999) ‘Jaguar car’s near
infrared night vision system - overview of human factors research to date’. in Gale,
A.G. et al. (eds.), Vision in Vehicles VII, Elsevier Science Publishers, Amsterdam.
Beck, K. H., Rauch, W. J. and Baker, E. A. (1999) ‘Effects of alcohol ignition interlock license restrictions on multiple alcohol offenders: A randomized trial in
Maryland’, American Journal of Public Health, 89(11), pp. 1696 – 1700.
Beirness, D. J., Marques, P. M., Voas, R. B. and Tippetts, A. S. (2000). ‘The impact
of mandated versus voluntary participation in the Alberta ignition interlock program’.
Proceedings of the 15th International Conference on Alcohol, Drugs and Traffic
Safety, Stockholm, Sweden.
Beirness, D. J. (2001) Best practices for alcohol interlock programs. Traffic Injury
Research Foundation, Report ISBN: 0-920071-14-7.
Biding, T. and Lind, G. (2002) Intelligent Speed Adaptation (ISA), Results of largescale trials in Borlänge, Lidköping, Lund and Umeå during the period 1999 – 2000,
Vägverket, Publication 2002:89 E.
Bjerre, B. (2005) ‘Primary and secondary prevention of drink driving by the use of
alcolock device and program: Swedish experiences’, Accident Analysis and
Prevention, 37, pp. 1145 – 1152.
Bossi, L. L., Ward, N. J., Parkes, A. M., Howarth, P. A. (1997) ‘The effect of vision
enhancement systems on driver peripheral visual performance’, in Noy, Y. I. (ed.)
Ergonomics and safety of intelligent driver interfaces, Erlbaum, Lawrence,
Associates, Inc, Mahwah, NJ.
Brown, I. D. (1994) ‘Driver fatigue’, Human Factors¸ 36(2), pp. 298 – 314.
Chatterjee, K., Hounsell, N.B., Firmin, P.E., Bonsall P.W. (2002) ‘Driver response to
variable message sign information in London’, Transportation Research, 10C, pp.
149-169.
Comte, S. L. (2000) ‘New systems: new behaviour?’, Transportation Research, 3F,
pp. 95 –111.
CRA (Charles River Associates Incorporated), (1998) Consumer acceptance of
automotive crash avoidance devices: A report of qualitative research, USDoT, CRA
Interim Report (Project No. 852-05).
Dingus, T. A., Hardee, H. L. and Wierwille, W. W. (1987) ‘Development of models
for on-board detection of driver impairment’, Accident Analysis and Prevention, 19,
pp. 271– 283.
Edwards, J. B. (1996) ‘Weather-related road accidents in England and Wales: a
spatial analysis’, Journal of Transport Geography, 4(3), pp. 201 – 212.
Edwards, J. B. (1998) ‘The relationship between road accident severity and recorded
weather’, Journal of Safety Research, 29(4), pp. 249 – 262.
Edwards, M. (2001) ‘Standards for novice driver education and licensing’, in: Driver
Education at the Crossroads, Proceedings from the Committee on Operator Education
and Regulation, Report E-C024.
Elvik, R., Christensen, P., and Amundsen, A. (2004) Speed and road accidents: an
evaluation of the POWER model, TOI Report 740/2004.
Elvik, R. and Vaa, T. (2004) The handbook of road safety measures, Elsevier, Oxford.
ETSC (2005) In-car enforcement technologies today, ETSC Report (ISBN
9076024200).
European Commission (2005) Cars that can dial 112: Commission and industry target
2009, Press Release Report IP/05/134.
Evans, L. (2004) Traffic Safety, Science Serving Society, MI, USA.
FHWA (1998) Integrating intelligent transportation systems within the transportation
lanning process, An Interim handbook, Federal Highway Administration, Office of
Traffic Management and ITS Applications, Office of Environment and Planning.
http://www.fhwa.dot.gov/tfhrc/safety/pubs/its/planning/interimhb.pdf
Fell, D. (1994) Safety update: Problem definition and countermeasure summary:
fatigue, New South Wales Road Safety Bureau Report RUS-5.
General Accounting Office (GAO) (2003) Research Continues on a Variety of Factors
That Contribute to Motor Vehicle Crashes, GAO Report 03-436.
FHWA (1992) Synthesis of safety research related to traffic control and roadway
elements, Volumes 1 and 2, (FHWA-TS-82-233), Washington, DC: Federal Highway
Administration.
Finnish Motor Insurers’ Centre (2006) Final report of fatal accidents investigated by
road accident investigation teams. www.liikennevakuutuskeskus.fi.
Haddon, W., Jr. (1970) A logical framework for categorizing highway safety
phenomena and activity. Paper presented at the 10th International Study Week in
Traffic and Safety Engineering, Rotterdam, 7-11 September.
Häkkinen, S., Luoma, J. 1990. Liikennepsykologia [Traffic psychology]. A Textbook
for traffic psychology students. Otatieto, Hämeenlinna. Finland.
Hale, A. E. (1990) ‘Safety and speed. A systems view of determinants and control
measures’, IATSS Research, 14(1), pp. 59 – 65.
Honkanen, R., Ertama, L., Linnoila, M., Alha, A., Lukkari, I., Karlsson, M.,
Kiviluoto, O., and Puro, M. (1980) ‘Role of drugs in traffic accidents’, British
Medical Journal, 281, pp. 1309 – 1312.
Hurst, P. M., Harte, D. and Frith, W. (1994) ‘The grand rapids dip revisited’, Accident
Analysis and Prevention, 26, pp. 647 – 654.
ICADTS working group on alcohol interlocks (2001) Alcohol ignition inter-lock
devices I: Position paper, ICADTS (ISBN 90-802908-4-x).
ICADTS working group on alcohol interlocks (2005) Alcohol ignition inter-lock
devices II: Research policy and program status 2005, ICADTS (ISBN 9080290858).
Jamson, S. (2006) ‘Would those who need ISA, use it? Investigating the relationship
between drivers’ speed choice and their use of a voluntary ISA system’,
Transportation Research, 9F, pp. 195 – 206.
Jonah, B. A. (1997) ‘Sensation seeking and risking driving: a review and synthesis of
literature’, Accident Analysis and Prevention, 29(5), pp. 651 – 665.
Kantowitz, B. H., Hanowski, R. J., and Garness, S. A. (1999) Development of Human
Factors Guidelines for advanced traveler information systems (ATIS) and commercial
vehicle operations (CVO): Driver memory for in-vehicle visual and auditory
messages (FHWA-RD-96-148). Washington, DC: Federal Highway Administration.
http://www.fhwa.dot.gov/tfhrc/safety/pubs/96148/intro.htm
Katteler, H. (2003) Acceptance of Lane Departure Warning Assistance (LDWA)
Systems: Final Report [Draagvlak voor Lane Departure Warning Assistance (LDWA)
systemen: eindrapport], Report.TM - 03 - C022. Soesterberg, The Netherlands: TNO
Human Factors.
Keskinen, E., Ota, H. and Katila, A. (1998) ‘Older drivers fail in intersections: speed
discrepancies between older and younger male drivers’, Accident Analysis and
Prevention, 30(3), pp. 323 – 330.
Kloeden, C. N., Ponte, G. and McLean, A. J. (2001) Travelling speed and the risk of
crash involvement on rural roads, Australian Transport Safety Report CR 204.
Kuciemba, S. R., Cirillo, J. A. (1992) Safety effectiveness of highway design features:
Volume V, Intersections, (FHWA-RD-91-048), Washington, DC: Federal Highway
Administration.
Lamm, R., Psarianos, B., Mailaender, T. (1999) Highway Design and Traffic Safety
Engineering, McGraw Hill, New York.
Marchau, V. and van der Heijden, R. (1998) ‘Policy aspects of driver support systems
implementation: Results of an international Delphi study’, Transport Policy, 5(4) pp.
249-258.
Matthews, L. R., and Barnes, J. W. (1988). ‘Relation between road environment and
curve accidents’, Proceedings of the 14th ARRB Conference, Canberra, Australia.
Mathijssen, M. P. M. (2005) ‘Drink driving policy and road safety in the Netherlands:
a retrospective analysis’, Transportation Research, 41E, pp. 395 – 408.
Michon, J.A. (1985). ‘A critical view of driver behaviour models. What do we know,
what should we do?’ in: Evans, L. and Schwing R. (eds.) Human behaviour and
traffic safety, Plenum Press, New York.
Milech, D., Glencross, D. and Hartley, L. (1989) Skills acquisition by young drivers:
perceiving, interpreting and responding to the driving environment, Federal Office of
Road Safety Report MR4.
Milton, J. and Mannering, F. (1998) ‘The relationship among highway geometrics,
traffic-related elements and motor-vehicle accident frequencies’, Transportation, 25,
pp. 395 – 413.
Monsere, C., Nolan, C., Bertini, R., Anderson, A. and El-Seoud, T. (2005)
‘Measuring the Impacts of Speed Reduction Technologies: A Dynamic Advanced
Curve Warning System Evaluation’, Transportation Research Record, 1918, pp. 98 –
107.
Movig, K. L. L., Mathijssen, M. P. M., Nagel, P. H. A., van Egmond, T., de Gier, J.
J., Leufkens, H. G. M. and Agberts, A. C. G. (2004) ‘Psychoactive substance use and
the risk of motor vehicle accidents’, Accident Analysis and Prevention, 36, pp. 631 –
636.
Nilsson, L. (1995). ‘Safety effects of adaptive cruise controls in critical traffic
situations’, Proceedings of the 2nd World Congress on Intelligent Transport Systems,
Yokahoma, Japan.
Nilsson, G. (1982) ‘The effects of speed limits on traffic crashes in Sweden’,.
Proceedings of the international symposium on the effects of speed limits on traffic
crashes and fuel consumption, Dublin. Organisation for Economy, Co-operation, and
Development (OECD), Paris.
NHTSA (2001) Driver inattention is a major factor in serious traffic crashes. Traffic
Tech, No. 243, http://www.nhtsa.dot.gov/people/outreach/traftech/TT243.htm
OECD (2006) Young drivers – the road to safety, OECD, ECMT Report.
Owens, D. A., and Sivak, M. (1993) The role of reduced visibility in nighttime road
fatalities, The University of Michigan Transportation Research Institute Report
(UMTRI-93-33).
Pelz, D. and Schuman, S., (1971) ‘Are Young Drivers Really More Dangerous After
Controlling for Exposure and Experience?’, Journal of Safety Research, 3, pp. 68 –
79.
Peirce, S. and Lappin, J. (2003) Acquisition of traveler information and its effects on
travel choices: evidence from a Seattle-area travel diary survey, Technical Report,
13813, USDoT.
Perchonock, K., Ranney, T., Baum, S., Morris, D. and Epich, J. (1978) Hazardous
effects of Highway features and roadside objects Volume 2: Findings, (FHWA - RD 78-202), Washington, DC: Federal Highway Administration.
Penttinen, M., Spyropoulou, I., Bauer, A. and Vaa, T. (2006) ‘Opportunities and
threats of ITS - expert opinions’, Proceedings of the 13th World Congress on
Intelligent Transport Systems, London, 9-12 October 2006.
Persson, J., Towliat, M., Almqvist, S., Risser, R. and Magdeburg, M. (1993) Speed
limiters for cars. A field study of driving speeds, driver behaviour, traffic conflicts
and comments by drivers in town and city traffic. Report, Department of Traffic
Planning and Engineering, University of Lund, Sweden.
PRISM, 2005, Report Reference R1: State of the Art Report on Technology & Patent
Search Data. TUG Graz University of Technology, European Commission.
Rämä P. (2001) Effects of weather-controlled variable message signing on driver
behaviour, (PhD Dissertation), VTT Publications 447, Technical Research Centre of
Finland, Espoo.
Regan, M., Young, K., Triggs, T. J., Mitsopoulos, E. and Tomasevic, N. (2004)
Effects on driving performance of prolonged exposure to multiple in-vehicle
intelligent transport systems: preliminary findings from the Australian TAC
SAFECAR Project. Monash Univerisity Accident Research Centre Report.
Rekveldt, M. G. C. and Labibes, K. (2003) Literature survey on in-vehicle safety
devices. TNO Report 03.OR.BV.037.1/MRE.
Rimmo, P-A. and Aberg, L. (1999) ‘On the distinction between violations and errors:
sensation seeking associations’, Transportation Research, 2F, pp. 151 – 166.
SAFESTAR, Safety Standards for Road Design and Redesign. Project for European
Commission, www.cordis.lu/transport/src/safestar.htm
Sakabe M., Watanabe K. and Ohno H. (2002) ‘Development of a collision warning
system based on the prediction of driver’s braking action’, Proceedings of the 9th
World Congress on Intelligent Transport Systems, Chicago October 14-17 2002.
Sala G., Mussone, L. (2000) ‘The potential impact on traffic safety of lateral support
systems’, Proceedings of the 7th World Congress on Intelligent Transport Systems,
Turin 6-9 November 2000.
SARTRE 2 (1998) The attitude and behaviour of European car drivers to road safety.
SWOV Institute for Road Safety Research, European Commission.
SARTRE 3 (2004) Making our drivers and road safer. Selected results of a European
survey, European Commission.
Smiley, A. (2000) Auto Safety and Human Adaptation. in Issues in Science and
Technology Online. http://www.issues.org/17.2/smiley.htm.
Smink, B. E., Ruiter, B., Lusthof, K. J., de Gier, J. J., Uges, D. R. A. and Egberts, A.
C. G. (2005) ‘Drug use and the severity of a traffic accident’, Accident Analysis and
Prevention, 37, pp. 427 – 433.
Spyropoulou, I., Karlaftis, M., Penttinen, M., Golias, J., and Yannis, G. (2005)
‘Intelligent Transport Systems Today: A European Perspective’, Proceedings of the
European Transport Conference 2005, Strasbourg, 3-5 October, 2005.
Staahl, A., Oxley, P., Berntman, M. and Lind, L. (1995) ‘The use of vision
enhancements to assist elderly drivers’, Proceedings of the First World Congress on
Applications of Transport Telematics and Intelligent Vehicle-Highway Systems.
Stanton, N. A., and Pinto, M. (2000) ‘Behavioural compensation by drivers of a
simulator when using a vision enhancement system’, Ergonomics, 43(9), pp. 13591370.
Sullivan, J. M., and Flannagan, M. J. (2002) ‘The role of ambient light level in fatal
crashes: inferences from daylight saving time transitions’, Accident Analysis and
Prevention, 34(4), pp. 83-94.
Tighe, S., Li, N., Falls, L. C. and Haas, R. (2000) ‘Incorporation road safety into
pavement management’, Transportation Research Record, 1699, pp. 1 – 10.
Treat, J. R., Tumbas, N. S., McDonald, S.T., Shinar, D., Hume, R. D., Mayer, R. E.,
Stansifer, R. L. and Castellan, N. J. (1979) Tri-level study of the causes of traffic
accidents: Final report. US DoT NHTSA Report DOT HS-805-099.
Tijerina, L., Jackson, J.L., Pomerleau, D.A., Romano, R.A. and Perterson, A. (1995)
Run-Off-Road Collision Avoidance Countermeasures Using IVHS Countermeasures,
Robotics Institute, Carnegie Mellon University, USA, Task 3 Report - Volume 2.
Tsugawa, S., Kato, S., Matsui, T., Naganawa, H., and Fuji H. (2000) ‘An Architecture
for Cooperative Driving of Automated Vehicles’, Proceedings of the 2000 IEEE
Intelligent Transportation Systems Conference, Dearborn, MI.
Varhelyi, A. and Mäkinen, T. (2001) ‘The effects of in-car speed limiters: Field
studies’, Transportation Research, 9C, pp. 191–211.
Várhelyi, A. (2002) ‘Speed management via in-car devices: effects, implications,
perspectives’, Transportation, 29, pp. 237–252.
Vanlaar, W. (2005) ‘Drink driving in Belgium: results from the third and improved
roadside survey’, Accident Analysis and Prevention, 37, pp. 391 – 397.
Vincent, A., Noy, I., and Laing, A. (1998). ‘Behavioural adaptation to fatigue warning
systems’. Paper presented at the 16th International Technical Conference on the
Enhanced Safety of Vehicles (ESV), Windsor, Ontario, Canada, May 31- June 4.
Virtanen, N. (2005): Impacts of an automatic emergency call system on accident
consequences. Espoo, Technical Research Centre of Finland (VTT).
Vlassenroot, S., Broekx, S., De Mol, J., Panis, L., Brijs, T. and Wets, G. (2007)
Driving with intelligent speed adaptation: Final results of the Belgian ISA-trial.
Transportation Research, 41(3)A, pp. 267 – 279.
Wakasugi T. and Yamada K. (2000) ‘Driver reaction time to forward vehicle collision
warning - effectiveness of warning system under low awareness level’, Proceedings
of the 7th World Congress on Intelligent Transport Systems, Turin 6-9 November
2000.
Yamada K. (2002) ‘Evaluation of forward obstacles collision avoidance support
system using driving simulator’, Proceedings of the 9th World congress on intelligent
transport systems, Chicago October 14-17 2002.
Zegeer, C. V. and Council, F. M. (1995) ‘Safety relationships associated with crosssectional roadway elements’, Transportation Research Record Issue, 1512, pp. 65 –
74.
Traffic events
Traffic
observations
Not observed
Other (nondriving
related)
observations
No decisions
Wrong decision
Wrong action
Correct
decision
Other decisions
Correct action
Accident
Near accident
"Good luck"
Other actions
Safe driving