Risky driver behaviour in the commercial transport and freight industry has a direct impact on an organisation’s fuel costs, tyre costs and other fleet operating costs, as well as its corporate image and performance. Without managing risky driver behaviour, businesses expose themselves to major risks and unnecessary costs. Organisations that focus their management activities on influencing or changing risky driver behaviour through proactive initiatives not only reduce the risk and cost of unsafe driving but can also improve organisational profitability. To improve road safety, it is imperative to understand and influence drivers’ behaviour. The ability of drivers to identify risky or hazardous situations is an important skill that allows drivers to overcome the complex cognitive requirements caused by the traffic environment (Borowsky, Shinar & Oron-Gilad 2010).

The research approach that is followed is therefore primarily qualitative, with a case study application. A literature review is firstly used to determine the driver behaviours that are generally considered to be the riskiest and most likely to result in negative business impacts. These are then compared to the South African context, firstly, in terms of the available literature and, secondly, based on an analysis of six fleets comprising in excess of 2450 vehicles, to determine the riskiest driver behaviours in the South African road environment. The literature review is then expanded to determine the typical business impacts of risky driver behaviour and the potential severity thereof.

There are many products available in the market that claim to be capable of providing full or partial solutions to the risks associated with risky driver behaviour. A brief analysis is performed on some of the most commonly used on-board safety monitoring systems to provide the basis for the establishment of a driver risk management framework.

Following the determination of a driver risk management framework, a case study is used to test the framework. As an ‘empirical inquiry that investigates a contemporary phenomenon within its real life context’ (Yin 2009:18), a case study is intended to focus on a particular feature within an organisation. In this article, a leading on-board safety monitoring system is analysed using data collected from three of the fleets where the system has been implemented. The selection of these fleets was based on the willingness of the organisations to participate in the review and provide information, as well as the size of their respective fleets and therefore the extent of data. Using a ratio data methodology, critical incident triangles were determined for the fleets under review. These data were then used to test the framework.

alcohol and drug use; speeding; frequent or rapid lane changes; failing to signal; tailgating; failing to wear a seatbelt; driving while drowsy; and engaging in distracting behaviour (e.g. cell phone use). (pp. 1–2)

Klauer et al. (2006) describe only four driver behaviours as the riskiest: excessive speed, safety belt use, driver distraction or inattention and driver drowsiness.

When considering driver behaviour in a fleet context, the picture may be somewhat different. In a study conducted in the United Kingdom, the riskiest fleet driver behaviours in the were identified as harsh braking, followed by, in order, sharp cornering, lane handling, harsh acceleration and speed (Milnes 2012). This is contrary to the situation in North America, where ‘speeding is the most dominant unsafe driving behaviour’ (Milnes 2012:1). This is followed by sharp cornering, harsh braking, lane handling and harsh acceleration.

The World Health Organization lists the leading causes of road injuries as speed, pedestrians and cyclists, young drivers and riders, alcohol, medicinal and recreational drugs, driver fatigue, hand-held mobile telephones, inadequate visibility, road-related factors and vehicle-related risk factors (Peden et al. 2004). Those relating to driver behaviour are speed, alcohol, medicinal and recreational drugs, driver fatigue and hand-held mobile telephones. In a report on road traffic fatalities in Africa (Eshbaugh et al. 2012), the authors use the Haddon matrix to group the key reasons for fatalities in Africa as human factors (licensing, training, driver attitude, impairment, distractions), vehicle factors (braking, vehicle design, maintenance, passengers or loads, safety equipment), environmental factors (regulation, enforcement, hazards, natural hazards, driver distractions, built environment, road design or maintenance) and social factors (enforcement, awareness, insurance incentives, rider peer pressure). Those that are influenced by driver behaviour are driver attitude, impairment, distractions, braking, passengers or loads and driver distractions. Similarly:

The American Transportation Research Institute indicates that a significant portion of collisions are triggered by ‘at risk’ driver behaviour, which includes such actions as: following too closely, driving too fast for conditions, failing to obey traffic signals or other ‘traffic warning’ devices. (Zurich 2010:9)

The American Transportation Research Institute, in their 2006 and 2011 studies, identified a number of risky behaviours which would increase the likelihood of an accident. These are: failure to use or improper signal conviction, past crashes, improper passing violation, improper turn conviction, improper or erratic lane change conviction; improper lane or location conviction, failure to obey traffic sign conviction, speeding more than 15 mi/h (24 km/h) over speed limit conviction, any conviction and a reckless, careless, inattentive or negligent driving conviction (American Transportation Research Institute 2011). Key risky driver behaviours are therefore speeding, risky lane changes, failure to obey traffic signals and inattentive or negligent driving.

Stuster, in a Highway Safety Information System report (US Department of Transportation 2004), ranked the criticality of unsafe driving acts as follows (in order): driving inattentively, merging improperly into traffic, failure to stop for a stop sign or light, failure to slow down in a construction zone, unsafe speed, following too closely, failure to slow down in response to environmental conditions, changing lanes abruptly in front of a truck, driving in the ‘no zones’ (left rear quarter, right front quarter and directly behind), unsafe turning, unsafe passing, pulling into traffic from roadside in front of truck without accelerating sufficiently, driving whilst impaired by alcohol or other drug, changing lanes in front of a truck then braking, unsafe crossing, driving left of centre into opposing traffic, failure to permit a truck to merge, failure to discern that the trailer of a manoeuvring truck is blocking the roadway, nearly striking the front or rear of a truck or trailer whilst changing lanes, manoeuvring to the right of a truck that is making a right turn, operating at dawn or dusk without headlights, crossing a lane line near the side of a truck or trailer whilst passing, driving between large trucks, nearly striking the rear of a truck or trailer that is stopped or moving slowing in traffic, nearly striking an unattended or parked truck at roadside and abandoning vehicle in travel lane or impeding traffic. These primarily relate to inattentive driving, inappropriate lane changes, failure to observe traffic signals, following too closely, unsafe braking and driving under the influence of alcohol or drugs.

The results of the literature review therefore indicate that, globally, the following are the key unsafe driving practices:

• alcohol and drug use
• speeding
• frequent or rapid lane changes
• failing to signal
• following too closely
• safety belt use
• driver fatigue
• sharp cornering
• unsafe braking and acceleration
• driver attitude
• failure to obey traffic signals
• driver distraction or inattention (e.g. mobile phone use or passengers in vehicle).

impaired vision, reduced reaction times, reduced concentration and vigilance, feeling more relaxed and drowsy, which may cause a driver to fall asleep at the wheel, difficulty in understanding sensory information, difficulty doing several tasks at once (e.g. keep in the lane and in the right direction, while concentrating on other traffic), failure to obey road rules and over confidence, which may lead to risk taking. (Drug Info 2012:1)

In South Africa it is estimated that one of the leading causes of road fatalities is driving under the influence of alcohol (Mbanjwa 2013).

the risk of a casualty crash approximately doubles with each 5 km/h increase in speed on a 60 km/h speed limited road, or with each 10 km/h increase in speed on 110 km/h roads. (Government of South Australia Transport 2012:1)

Speeding also impacts aspects such as fuel consumption and braking ability: ‘the faster a vehicle goes, the further it takes to stop’ (Land Transport NZ n.d.:4).

drivers [need] to divide their attention between monitoring the forward roadway, their surroundings, steering the vehicle, regulating the vehicle’s speed, and using the turn signal. Drivers who are pressed to change lanes may exhibit degraded performance in one or more of these subtasks. (US Department of Transportation 2009:65)

As drivers have an average of two seconds to respond to lane change events, typical responses to these are a combination of steering and braking manoeuvres.

Added to this:

‘Rear enders’ are the most common type of injury car accidents. Most rear end accidents involve one or both of three key factors: driver inattention by the driver behind, following too closely, or a rapid deceleration by the driver whose car is hit. (Calloway & Wolf n.d.:1)

Typically, the most common reason for rear-end crashes is following too closely.

when the footbrake is used the road speed that has been lost has to be made up by using the accelerator, thereby burning fuel. If it becomes necessary to change down a gear or half gear then even more fuel is used. The load is also more likely to shift under heavy braking. Harsh braking uses more fuel and require an increase in the number of gear changes that you will subsequently have to make. (Department of Transport 2009:12)

The consequences for not obeying traffic laws include personal injury, death, and damage to your vehicle or other property. You may also be ticketed, which may result in a fine or license suspension. (New York Defensive Driving 2013:1)

Driver inattention occurs whenever the operator of a vehicle diverts his or her attention away from the driving task. Driver distraction, on the other hand, has been defined to occur when this inattention leads to a delay in the recognition of information that is necessary to accomplish the driving task safely Thus, distraction occurs when inattention leads to a critical incident. (Hanowski, Perez & Dingus 2005:442)

Evidence suggests that driver distraction is a primary factor in 12.9% of all crashes (Hanowski et al. 2005). Driver distraction includes such elements as talking on cell phones, talking to passengers, reaching to floor or into a pocket et cetera, smoking, eating or drinking, looking outside and so on.

The 2010 road traffic offence survey – one of the primary sources for the 2011 Road traffic report (RTMC 2011) – is an independent survey which determines the level of lawlessness with regard to selected critical offences that mostly contribute to the occurrence of road crashes in South Africa. These are identified as speeding, traffic signal offences, overtaking across the barrier line, alcohol levels, driving licences, wearing of seatbelts, vehicle fitness, correlation between vehicle registration plate and licence disk, pedestrian compliance and presence of traffic officers, pedestrians, cyclists and animals on the roads. It is evident that the five of the first six offences are driver behaviour related.

An analysis of selected South African transport organisations¹ revealed the top 10 riskiest driver behaviours as (in order from most to least):

• harsh braking and aggressive accelerating
• driver unbelted and driver distractions
• not looking far enough ahead
• sharp cornering
• following too close
• disregarding traffic signals
• speed policy violation
• judgement errors
• mirrors not checked
• driver fatigue.

It is evident from the above that the riskiest driver behaviours in South Africa demonstrate very similar patterns to driver behaviour elsewhere in the world.

Many different interventions can be used to change risky driver behaviour to safer and desired driving habits. Risky driving behaviour can be identified through various ways, such as customer or operational complaints, driving observations, drivers receiving fines for any traffic violations, when drivers are involved in accidents or near-misses, or with an on-board safety monitoring (OBSM) system. It has become common practice to utilise sophisticated technologies to improve risky driving behaviour and to reduce the associated risk. An OBSM allows fleet managers to collect detailed information associated with driver behaviour and vehicle performance. In recent years, OBSM devices have become increasingly advanced. Different types of devices exist which vary significantly in terms of complexity, data extraction sources and diverse features. Some devices gather data directly from the vehicle on-board diagnostics, whilst others have embedded GPS functionality or built-in accelerometers. These devices monitor numerous driver or vehicle parameters and obtain performance data such as vehicle speed, location, braking and acceleration and fuel consumption. Some of the commonly used OBSM systems that are available, with their primary attributes, are described below (Insurance Institute for Highway Safety 2013; Volunteer Fireman’s Insurance Services 2012).

To provide comprehensive and coherent driver risk management, a system should be able to combine the best functionalities of the above listed systems, but must also be extended to provide pertinent and intelligent data so that this information can be used as a management tool. In order to create this type of driver risk management system, relevant information needs collected, filtered, analysed and used to predict and influence risky driver behaviour.

A broad range of risky driver behaviours exist that increases the probability of road traffic accidents and directly impacts organisations fleet operating costs. Organisations need a pre-emptive and comprehensive risk management platform to effectively manage and reduce risky driver behaviour. An effective driver risk management system (DRMS) should contain the following segments: system parameters, activation, event analysis and a management tool (Figure 1).

FIGURE 1: Framework for a driver risk management system.

The DRMS must be able to monitor driver and vehicle performance and through intelligent activation, as well as capture and upload only those risky driving events which matter. The risky driving events must be analysed and scored to determine the most appropriate actions. The DRMS should also provide an uncomplicated and easy accessible management tool that profiles risky drivers with relevant supporting evidence that can be used to provide feedback to the organisation and driver. The individual driver risk profile enables management to initiate appropriate coaching interventions to address risky driving habits and monitor the effectiveness of such coaching interventions. The driver should also however have the ability to identify his own risky actions and modify them. A good DRMS should therefore provide feedback to both the driver as well as the organisation, so that, together, risky driver behaviour is minimised.

System parameters require that a system should be set up so that it is able to monitor vehicle telematics and should thus include aspects such as GPS and accelerometers. The DriveCam system provides this functionality and allows for the system to be parametised for a specific type of vehicle so that activation is appropriate for the vehicle type. The DriveCam system provides for intelligent activation to ensure that only relevant events are taken into account. It also filters out events caused by road noise so that events triggered by risky driver behaviour can be managed. In addition to the automated activation, the DriveCam system allows for manual activation, so that, should the driver feel that it is warranted, a recording of an event can be uploaded and stored in the system. The driver should also be aware of system activation. As the purpose of the system is modification of risky driver behaviour, consciousness of system activation will assist the driver in awareness of risky driver behaviour and the modification of their own risky behaviour patterns.

The most critical aspect of the framework is the analysis of the events that have triggered the system. DriveCam-recorded events are analysed off-shore by trained DriveCam analysts, which allows for consistency, fairness and impartiality. Based on this, events are scored against a pre-determined scoring system, so that the organisation, as well as the driver, can focus their risky behaviour prevention efforts on only the behaviour that is likely to result in a serious event or incident. Each system trigger could consist of one or more transgressions, each carrying a numerical score ranging from 0 to 10 points. These are added together to give a total event score. For example, a driver may have triggered the system by harsh braking. The analysis of the event may show that the driver jumped a stop street and was then required to brake quickly. In this instance there are at least two events, each of which carries a score of between 0 and 10. When the total event score is calculated, the sum of these two events is used. In general, the higher the total event score, the more risky the event (and the driver’s actions).

In the DriveCam system, events that triggered a recording are not viewed in isolation, but are rather used to develop driver profiles to determine which drivers are most at risk of accidents and which of their behaviours are most likely to result in such events. DriveCam not only performs driver profiling, but also provides a fleet profile dashboard, so that the organisation is able to, at a glance, recognise potential problem areas and manage these accordingly.

When considering common cause hypotheses, reference is frequently made to popular ratio data studies such as Bird (1966), Corbett et al. (2008), Heinrich (1931), Tye and Pearson, in Rausand (2011) and Salminen et al. (1992). Ratio triangles or icebergs are used freely in industry today. Although these are often used to imply a ratio relationship of causes, Heinrich’s original triangle was not intended for this purpose, but was rather an attempt to illustrate that:

prevention need not wait until an accident occurred, and that prevention should not only be aimed at the most severe consequences but also to events at the lower levels of the triangle. (Wright & Van der Schaaf 2004:105)

Based on evidence provided by Heinrich (1931), ‘reduction of events at the bottom of the triangle should lead to a reduction in the number of events at the top of the triangle’ (Wright & Van der Schaaf 2004:106). Figure 2 outlines the methodology that was used to obtain the critical incident triangles for the fleets under review.

FIGURE 2: Critical incident triangle methodology.

For the purposes of the evaluation, the DriveCam scoring system was used to determine the values for the critical incident triangles. DriveCam uses a numerical scoring system, ranging from 0 to 10 points, which indicates the severity of the event. To determine the number of incidents at the base of the triangle, risky driver behaviour was defined as:

• traffic violations (such as not slowing or stopping for stop streets and red lights, speeding, etc.)
• poor awareness (such as not looking far enough ahead, not scanning the roadway or intersection, not checking mirrors, etc.)
• fundamentals (such as following too closely, driving too fast for conditions, unsafe lane changes, etc.)
• distractions (such as using cell phones, eating and drinking, passenger in vehicle, etc.)
• driver conduct (such as judgement errors, aggressive behaviour, reckless driving, etc.)
• driver condition (such as drowsiness or falling asleep)
• custom event detail (as defined by the specific organisation, which may include aspects such as reversing on an active roadway, fast cornering, etc.).

Table 2 provides a summary of the fleets that were analysed. A total of over 160 000 system events were analysed, of which almost 31 300 were considered to constitute risky driver behaviour. Events that received a total event score of zero were excluded from the analysis.

Figure 3 illustrates some of the critical incident triangles obtained from the fleets under review. Although the critical incident triangle ratios differ considerably from fleet to fleet (there is no ideal ratio), managers need to be aware of these ratios, so that these can be monitored over time. The purpose of ratio data analysis is to understand and manage the risky driver behaviours at the base of the triangle that can result in more serious incidents and collisions.

FIGURE 3: Critical incident triangles for Fleets 1–3 in this study.

FIGURE 4: Critical incident triangles for Fleets 1–3 in this study.

FIGURE 5: Fleet 2 – Average number of incidents per vehicle (tankers).

FIGURE 6: Fleet 3 – Average number of incidents per vehicle (couriers).

Similar characteristics are evident in South African, with the top 10 riskiest driver behaviours being (in order): harsh braking and aggressive accelerating, driver unbelted or driver distractions, not looking far enough ahead, sharp cornering, following too close, disregarding traffic signals, speed policy violation, judgement errors, mirrors not checked and driver fatigue.

The impact of risky driving behaviour on businesses is substantial. Inefficient and unsafe driving habits increase vehicle operating costs (e.g. higher fuel consumption and maintenance costs), insurance costs (i.e. increased risk profile), claims and accident related pay-outs and damage corporate image. Improving driving habits can lead to lower insurance costs, less loss or damage to cargo, increased vehicle fleet reliability and effectiveness and improve driver health, productivity and morale.

A DRMS helps proactively to reduce liability, property damage and workers’ compensation costs and reduces a firm’s financial exposure in the event of a collision by providing unbiased evidence to assess and manage exposure to risk. Video evidence also protects firms against fraudulent claims, can exonerate drivers and provides them with added security whilst driving. The DRMS also ensures that drivers adhere to corporate driving policies which guarantee a safe and comfortable journey. An effectively managed DRMS can also significantly reduce fleet operating costs by fostering improved driving behaviour that improves fuel efficiency, cuts vehicle maintenance costs and improves operations by ensuring vehicles are kept on the road where they generate revenues.

For implementation to be effective, it is recommended that a DRMS should:

• Be able to monitor driver performance and, through intelligent activation, capture and upload only those risky driving events which matter.
• Create driver awareness of system activation so that driver is able to modify their own risky behaviour patterns.
• Analyse and score risky driving events to determine the extent of the risk.
• Provide the opportunity to identify and reduce the frequency of unsafe driving behaviour by implementing corrective measures (i.e. coaching) before further incidents or collisions occur.

In this research, a case study was used to test a framework for the management of risky driver behaviour. As a case study methodology only focuses on a particular feature within an organisation, it is recommended that future research be expanded to incorporate fleets of varying sizes and complexities to establish the replicability of the framework. Future research should also incorporate return on investment analyses to establish whether system implementation and the resultant reduction in risky driver behaviour lead to quantifiable benefits for the organisation.

American Transportation Research Institute, 2011, Predicting truck crash involvement: A 2011 update, viewed 22 February 2013, from http://www.atri-online.org/research/results/ATRI_Crash_Predictor One_Pg_Summary_Apr_2011.pdf

Automotive Fleet, 2008, DriveCam studies role of following distance in rear-end collisions, viewed 26 February 2013, from http://www.automotive-fleet.com/channel/safety-accident-management/news/story/2008/08/drivecam-studies-role-of-following-distance-in-rear-end-collisions.aspx?prestitial=1

Bird, F., 1966, Damage control, Insurance Company of North America, Philadelphia.

Borowsky, A., Shinar, D. & Oron-Gilad, T., 2010, ‘Age, skill and hazard perception in driving’, Accident Analysis and Prevention 42, 1240–1249. http://dx.doi.org/10.1016/j.aap.2010.02.001

Calloway & Wolf, n.d., Rear end accidents, viewed 18 February 2013, from http://www.callawayandwolf.com/rear-end-accident-injuries/

Canadian News, 2013, Improper lane changes to blame for several crashes: Toronto police, viewed 27 February 2013, from http://myoor.com/english/improper-lane-changes-to-blame-for-several-crashes-toronto-police

Centre for Accident Research and Road Safety – Queensland, 2011, State of the road, viewed 26 February 2013, from http://www.carrsq.qut.edu.au/publications/corporate/fatigue_fs.pdf

Corbett, E., Rice, S., Wilde, E., Young, C. & Jackson, K., 2008, Accidents in the transport industry, Health and Safety Executive, Buxton.

Department of Transport, 2009, The fuel efficient truck driver’s handbook, viewed 26 February 2013, from http://postconflict.unep.ch/humanitarianaction/documents/02_08-04_06-04_02-22.pdf

Drug Info, 2012, How does alcohol affect driving?, viewed 21 February 2013, from http://www.druginfo.adf.org.au/topics/how-does-alcohol-affect-driving

Edgar Snyder & Associates, 2011, Seat belt statistics, viewed 18 February 2013, from http://www.edgarsnyder.com/product-injuries/auto/seat-belts/seat-belts-statistics.html

Eshbaugh, M., Maly, G., Moyer, J.D. & Torkelson, E., 2012, Putting the brakes on road traffic fatalities in Africa, viewed 25 February 2013, from http://www.issafrica.org/futures/uploads/African_Futures_Brief_3.pdf

Government of South Australia Transport, 2012, Road safety: Speed facts, viewed 26 February 2013, from http://www.dpti.sa.gov.au/__data/assets/pdf_file/0019/81604/Speeding_penalties_leaflet_for_drivers.pdf

GreenRoad, 2012, GreenRoad 2011 worldwide fleet driver performance benchmark report, viewed 19 February 2013, from http://greenroad.com/press-releases/greenroad-releases-first-fleet-driver-performance-benchmark-report/

Hall, J.E., n.d., Alcohol and other drug use in commercial transportation, viewed 23 February 2013, from http://druglibrary.org/schaffer/misc/driving/s1p1.htm

Hanowski, R.J., Perez, M.A. & Dingus, T.A., 2005, ‘Driver distration in long-haul truck drivers’, Transportation Research Part F 8(6), 441–458. http://dx.doi.org/10.1016/j.trf.2005.08.001

Heinrich, H.W., 1931, Industrial accident prevention, McGraw-Hill, New York.

Hub pages, 2010, Let’s ‘recall’ the dangers of speeding, viewed 24 February 2013, from http://safe-driver.hubpages.com/hub/Lets-Recall-the-Dangers-of-Speeding

Insurance Institute for Highway Safety, 2013, Crash avoidance technologies, viewed 25 July 2013, from http://www.iihs.org/iihs/topics/t/crash-avoidance-technologies/topicoverview

Iversen, H., 2004, ‘Risk-taking attitudes and risky driving behaviour’, Transportation Research Part F May, 135–150. http://dx.doi.org/10.1016/j.trf.2003.11.003

Jonck, J., 2010, ‘What are the effects of fatigue on driving?’, Arrive Alive Road Safety Blog, viewed 19 February 2013, from http://roadsafety.co.za/2010-10/what-are-the-effects-of-fatigue-on-driving/

Klauer, S.G., Sudweeks, J., Hickman, J.S. & Neale, V.L., 2006, How risky is it?, viewed 22 February 2013, from https://www.aaafoundation.org/sites/default/files/RiskyDrivingReport.pdf

Land Transport NZ, n.d., Heavy combination vehicle stability and dynamics, viewed 17 February 2013, from http://www.nzta.govt.nz/resources/heavy-learner/heavy-combination-vehicles/docs/heavy-combination-vehicles.pdf

Mbanjwa, X., 2013, Stats don’t lie: More road carnage to come, viewed 26 November 2013, from http://www.citypress.co.za/news/stats-dont-lie-more-road-carnage-to-come/

Milnes, J., 2012, Harsh braking most common risky driver behaviour, viewed 22 February 2013, from http://www.racplus.com/cool-van-man/harsh-braking-most-common-risky-driver-ehaviour/8628865.article

Monahan, J., 2005, Seat belts: Safety on the road, viewed 27 February 2013, from http://ezinearticles.com/?Seat-Belts:-Safety-On-The-Road&id=114354

New York Defensive Driving, 2013, Chapter 3 - Traffic laws and procedures I, viewed 22 February 2013, from http://www.newyorkdefensivedriving.com/course_sample.html

Özkan, T., Öz, B. & Lajunen, T., 2013, ‘An investigation of professional drivers: Organizational safety climate, driver behaviours and performance’, Transportation Research Part F 16, 81–91. http://dx.doi.org/10.1016/j.trf.2012.08.005

Peden, M., Scurfield, R., Sleet, D., Mohan, D., Hyder, A.A., Jarawan, E. et al., 2004, World report on road traffic injury prevention, viewed 27 February 2013, from http://whqlibdoc.who.int/publications/2004/9241562609.pdf

Rausand, M., 2011, Risk assessment: Theory, methods, and applications, John Wiley & Sons, Hoboken. http://dx.doi.org/10.1002/9781118281116

Road Traffic Management Corporation, 2011, Road traffic report, viewed 26 February 2013, from http://www.arrivealive.co.za/documents/March%202011%20Road%20Traffic%20Report.pdf

Salminen, S., Saari, J., Saarela, K.L. & Rasanen, T., 1992, ‘Fatal and non-fatal occupational accidents: Identical versus differential causation’, Safety Science 15, 109–118. http://dx.doi.org/10.1016/0925-7535(92)90011-N

Salmon, P., Regan, M. & Johnston, I., 2005, Human error and road transport, Monash University Accident Research Centre, Clayton.

Starr Austen, 2012, What if alcohol or drug use was a factor in my truck accident?, viewed 17 February 2013, from http://www.starrausten.com/what-if-alcohol-or-drug-use-was-a-factor-in-my-truck-accident/

Steyn, L., 2013, SA ranked worst out of 36 in global road safety report, viewed 26 November 2013, from http://mg.co.za/article/2013-05-23-south-africa-ranked-worst-in-global-road-safety-report

Suk, C.J., 2012, Consequences of failing to use turn signals, viewed 26 February 2013, from http://rochester.legalexaminer.com/automobile-accidents/consequences-of-failing-to-use-turn-signals.aspx?googleid=300618

Tyre Savings, 2013, Tyre wear, viewed 23 February 2013, from http://www.tyresavings.com/articles/safe-driving/looking-after-your-tyres/tyre-wear

US Department of Transportation, 2004, An examination of fault, unsafe driving acts, and total harm in car-truck collisions, viewed 19 February 2013, from http://www.fhwa.dot.gov/publications/research/safety/humanfac/04085/04085.pdf

US Department of Transportation, 2009, Analysis of lane-change crashes and near-crashes, viewed 21 February 2013, from http://www.nhtsa.gov/DOT/NHTSA/NRD/Multimedia/PDFs/Crash%20Avoidance/2009/811147.pdf

Volunteer Fireman’s Insurance Services, 2012, Risk communique – Driver monitoring systems, viewed 20 February 2013, from http://www.vfis.com/documents/DriverMonitoringSystems.pdf

Wright, L. & Van der Schaaf, T., 2004, ‘Accident versus near miss causation: A critical review of the literature, an empirical test in the UK railway domain, and their implications for other sections’, Journal of Hazardous Materials 111, 105–110. http://dx.doi.org/10.1016/j.jhazmat.2004.02.049

Yin, R.K., 2009, Case study research: Design and method, 4th edn., Sage, London.

Zamorski, M.A. & Kelley, A.M., 2011, Chapter 5 – Risky driving behaviour, viewed 22 February 2013, from http://ftp.rta.nato.int/public//PubFullText/RTO/TR/RTO-TR-HFM-164///TR-HFM-164-05.pdf

Zurich, 2010, Zoom in on your fleet risks: Revealing the fleet risk revolution, viewed 25 February 2013, from http://www.zurich.com/internet/main/sitecollectiondocuments/insight/telematics-fleet-risk-management-insight.pdf