Fact sheet

Trucks and delivery vehicles

Summary

Crashes in which trucks or delivery vehicles are involved are often serious, especially for the crash opponent. Trucks are not only involved in crashes because of their drivers' unsafe behaviour (freights falling off, rollovers, jack-knifing), but also because other road users take too little account of the trucks' specific characteristics (blind spot, swerving out). Although delivery vehicles are smaller than trucks, they still are bigger and heavier than cars and their rear view is not as good. Infrastructural measures, such as target-group lanes and not allowing heavy freight vehicles in urban areas, can result in safer freight and delivery traffic. In addition, (intelligent) instruments can be used inside the vehicle, such as speed limitation devices and rear view systems for delivery vehicles and monitoring and detection systems, for example for the blind spot, for trucks. Furthermore, it is important to encourage a safety culture in transport companies. Finally, it is important to inform other road users about the specific hazards of trucks in traffic.

Background and content

Trucks and delivery vehicles share the road with other vehicles. The difference in mass explains why crashes between trucks and cars are often serious. To a lesser extent this is also the case for crashes involving vans, and their number in traffic is growing. In 1986, the proportion of delivery vehicles in the motorized vehicle fleet (not including mopeds and light mopeds) was slightly more than 5% and in 2005 this had risen to more than 10%. In 2006, a slight decline could be observed so that the proportion of delivery vehicles was 8% in 2015 (CBS Statline, 2016). Over the years, the proportion of trucks (not including special vehicles) has dropped very slightly, from 1.8% in 1986, to 1.4% in 2015. This fact sheet briefly discusses the road safety problems of freight and delivery traffic, the size of the problem, the crash types in which trucks and vans are involved, and possible measures to improve road safety.

Facts

What is the size of the problem?

Crashes with trucks or delivery vehicles are much more frequently fatal for the crash opponents than for the occupants of the trucks and delivery vehicles (see Table 1).

Table 1. Numbers of road deaths in the Netherlands in crashes involving trucks and delivery vehicles, divided by fatalities among their ‘own' occupants and among crash opponents. Crashes involving both a truck and a delivery vehicle are included in the total only once. Source: Ministry of Infrastructure and the Environment (IenM) – Police Road Crash Registration in the Netherlands (BRON).

 

The data in Table 1 show that, despite the fact that there are approximately six times more delivery vehicles than trucks in Dutch traffic, there are more fatalities among the trucks' crash opponents than among those of the delivery vehicles. This is, among others, due to the fact that trucks travel more kilometres per vehicle. There are also many trucks from other countries in Dutch traffic. This results in the total of kilometres travelled by delivery vehicles being only a factor 2.5 higher than that of trucks and trailers. Therefore, we take the fatality rate as the comparison unit: the number of fatalities among the crash opponent per billion kilometres travelled by truck or delivery vehicle. Table 2 shows the Dutch fatality rates for crash opponents of trucks and delivery vehicles with the fatality rate for crash opponents of passenger cars as a comparison.
The data in Table 2 indicate that the fatality rate for crash opponents of trucks – compared to that of passenger cars - is more than six times higher. The fatality rate for the crash opponents of delivery vehicles is twice as high as that for the occupants of passenger cars.

Table 2. The Dutch fatality rate (the average of 2010-2015) defined as the number of fatalities among occupant and crash opponent per billion kilometres travelled by delivery vehicles, trucks and passenger cars respectively (Sources: BRON - IenM; Statistics Netherlands).

 

Which crash types are trucks and delivery vehicles involved in?

Driving a truck is different from driving a passenger car in more than one aspect. Among other things, this is caused by characteristics of the vehicle: trucks are larger, less manoeuvrable, they accelerate more slowly than passenger cars and have a longer braking distance. Other differences can be found in characteristics of the task (dealing with freight, long and therefore tiring journeys), and in characteristics of the driver (lifestyle, susceptibility to fatigue). In addition, crashes may be caused by the interaction with other road users. Many road users, for example, are not aware that they could be in the blind spot of the truck, or that a truck could swerve. What applies to trucks, to a lesser extent also applies to delivery vehicles: they are usually larger and heavier than passenger cars, and have a more limited rear view. Because of all of these differences, a disproportionately large share of the crashes involving trucks and delivery vehicles are specific types of crashes.

Manoeuvres

In comparison with collisions between passenger cars, there are relatively many rear-end, side, and head-on collisions and fewer lateral collisions between trucks and cars (Van Kampen & Schoon, 1999; AVV, 2006). Annually, an average of 50 crashes in which a truck turns over on a motorway are registered in the Netherlands. Davidse & Van Duijvenvoorde (2012) carried out an in-depth study of crashes involving delivery vehicles. They identified five frequent crash types with similar characteristics:

  • reversing driver collides with unnoticed vulnerable crash opponent;
  • driver turning right fails to notice cyclist or light moped rider going straight on;
  • driver not alert to intersecting traffic;
  • driver and intersecting traffic cannot see each other due to a short approach sight distance;
  • driver in unclear traffic situation that requires extra attentiveness.

Age

Young, novice drivers have a higher crash rate than older, more experienced drivers (see SWOV Fact sheet 18- to 24-year-olds: young drivers). This is also to the case for young truck and van drivers. Although there is no concrete data, there are indications that transport companies often employ young van drivers because they are cheaper (Bax et al. 2014). Bos & Twisk (1999) found that young, novice van drivers more often have crashes with oncoming traffic, and are more often involved in rear-end collisions in which the van is crashed into; possibly due to unpredictable behaviour.

Fatigue

There are indications that truck drivers are relatively more frequently involved in fatigue-related than car drivers (ETSC, 2001; McKernon, 2008; see also SWOV Fact sheet Fatigue in traffic: causes and effects).

Blind spot

Trucks and delivery vehicles have a blind spot: from their position, truck drivers cannot (properly) see other road users in certain locations. In the Netherlands, trucks turning right cause around 10 fatalities among cyclists per year, despite the fact that the blind spot mirror became mandatory in 2003 (see the archived SWOV Fact sheet Blind spot crashes).

Which measures can make freight and delivery traffic safer?

Several measures can be employed to make freight and delivery traffic safer: measures in relation with the road, measures inside the vehicle, measures focusing on the skills of drivers, and measures in the transport sector. Finally, it is important to inform other road users about the specific dangers of trucks and delivery vehicles.

Road

Measures at the road level can involve either infrastructural measures or traffic organization. Target-group lanes on which freight traffic and other vehicles are separated as much as possible The vision Advancing Sustainable Safety (Wegman & Aarts, 2006) proposes a logistics system in which heavy freight traffic only uses the main road network with grade separated intersections. Light freight vehicles, which have better safety provisions, only use the secondary road network. The development of a so-called Quality Network Freight Transport (KNG) is also in line with this vision (CROW, 2006).

Furthermore, measures can be used to regulate freight traffic in urban areas. This can for instance be done by adapting the times for loading and unloading or by not allowing trucks during the morning rush hour. Dijkstra (2009) shows to what extent heavy vehicles are involved in crashes with slow traffic, especially on 50 and 80 km/h roads. The report gives an overview of measures and facilities that can improve road safety on these road types.

Vehicle

Lower speeds reduce the risk of crashes as well as their severity. Systems inside the vehicle, such as ISA (Intelligent Speed Assistance) or speed limiters, can reduce the driving speed (see also SWOV Fact Sheet Speed and speed management). In addition to speed limiters, other intelligent in-vehicle systems can also be used, such as rear view systems for delivery vehicles and monitoring systems in trucks, for example OBD (On Board Diagnostics)-systems that can be used to read information from the vehicle.

Blind spot Detection and Signalling Systems (DDSS) can warn the truck driver about the presence of other road users in his blind spot (Connekt, 2010). Crash prevention systems (such as Advanced Cruise Control or Lane Departure Warning Assistant) can help prevent a crash, but, on the other hand, they can also distract the attention of the driver (Eenink, 2009; see also SWOV Fact sheet Intelligent Transport Systems (ITS) and road safety).

In the Netherlands, open side-underrun protection and closed side-underrun protection is mandatory for trucks to prevent vulnerable road users sliding under the back wheels (Van Kampen & Schoon, 1999).

Retroreflective contour marking can improve the trucks’ visibility in the dark (Wijnen et al., 2015).

Blow-out tyres are frequent among trucks, but it is unclear how often blow-out tyres lead to crashes (Onderzoeksraad voor Veiligheid, 2012).

Finally, there are measures in relation with load and loading, such as checks on overloading and incorrect loading, and rollover warning systems.

Driver

Regarding the human factor, two types of measures can be distinguished:

  1. measures aimed at improving competence (knowledge and skills);
  2. measures aimed at increasing task capability (the degree of fitness to drive) and task readiness (the willingness to drive safely).

Improving competence involves matters such as driver training, driving examination, professional driver diploma, driving experience, and extra training. For example, it is mandatory for every professional truck driver to take a supplementary training after which the licence is marked with a ‘code 95’ indicating professional skills. Since January 2013, the C1 licence has been available which allows driving a light truck with a weight between 3500 and 7500 kg (Ministry of Infrastructure and the Environment, 2012).

Drivers of delivery vehicles only need an extra driving licence when they drive with a heavy trailer.

Increasing the task capability and task readiness involves fitness and motivation. A driver is less hindered by fatigue if the legal working hours and driving time regulations are correctly adhered to. To check the compliance, the tachograph was introduced. Also available are the journey data recorder (black box), the crash data recorder and the dash cams, which can but need not be connected to the vehicle’s OBD. If these devices are used in combination with improving the company safety culture, it can have a positive road safety effect (Bax, Goldenbeld & Korving, 2014).

Transport sector

Safety improvement in freight and delivery transport also requires enhancement of the safety culture within transport companies. A study of five Dutch transport companies (Gort et al., 2002) showed that promoting the safety culture had no priority in these companies. The safety culture of transport companies in the Netherlands can be improved by initiating group discussions with staff about safety, rewarding safe behaviour and training ' higher order skills' such as hazard perception, risk perception and risk acceptance (Vlakveld et al., 2014). In addition, the safety culture can be improved 'indirectly', by, for example, fatigue management or plans for damage control (Lindeijer, Rienstra & Rietveld, 1997).

Other road users

The 'guilty party' in crashes between trucks and other road users is the truck driver as often as it is the crash opponent (Van Raamsdonk, 2002). Often the other road users take (too) little account of the specific characteristics of a truck (blind spot, swerving out). Education in primary and secondary schools should, more than is now the case, deal with how crashes with trucks and delivery vehicles can be prevented (for example by not being in the blind spot; TLN & VVN, 2016). Also, there could be more attention for the vehicle characteristics of heavy goods vehicles in the regular driver training and public information campaigns.

Conclusions

Road crashes with trucks and delivery vehicles have more serious consequences for the crash opponent than crashes between passenger cars. The crash types also differ: crashes involving a truck are often many rear-end, side, and head-on collisions. Crashes involving a delivery vehicle often occur during reversing and with intersecting traffic. For safer freight and delivery traffic infrastructural measures can be used, such as separate target group lanes and banning heavy goods vehicles from urban areas. Much safety benefit can also be achieved by intelligent vehicle devices such as speed limiters and rear view systems for delivery vehicles and monitoring systems for trucks. Furthermore, promoting safety culture within companies is of major importance. Finally, it is important to make other road users aware of the specific dangers of trucks and delivery vehicles.

Publications and sources

AVV (2006). Ongevallen met vrachtauto's op rijkswegen. Frequentie, oorzaken, consequenties en oplossingen. Rijkswaterstaat, Adviesdienst Verkeer en Vervoer, Rotterdam.

Bax, C.A., Goldenbeld, Ch., Knapper, A., Vaartjes, I. & Groot-Mesken, J. de (2014). Vracht- en bestelverkeer: veld van actoren en veiligheidsissues. R-2014-27A. SWOV, Leidschendam.

Bax, C.A., Goldenbeld, C. & Korving, H. (2014). Veiligheidscultuur in de praktijk : motieven, uitvoering en effecten. R-2014-33. SWOV, Den Haag.

Bos, J.M.J. & Twisk, D.A.M. (1999). Rijbewijs B: te veel bevoegdheden; Veiligheidsconsequenties van de discrepantie tussen de rijexamen-B-eisen en de benodigde vaardigheden voor het besturen van de voertuigtypen waarvoor rijbewijs B rijbevoegdheid verleent. R-98-67. SWOV, Leidschendam.

CBS Statline (2016). www.cbs-statline.nl. Centraal Bureau voor de Statistiek, Voorburg.

Connekt (2010). Dode hoek Detectie- en Signalerings-Systemen (DDSS): Onderzoek naar de werking en de mogelijkheden. Connekt, Delft.

CROW (2006). Handleiding kwaliteitsnet goederenvervoer. CROW, Ede.

Davidse, R.J. & Duijvenvoorde, K. van (2012). Bestelauto-ongevallen: karakteristieken, ongevalsscenario's en mogelijke interventies. R-2012-18. SWOV, Leidschendam.

Dijkstra, A. (2009). Ongevallen met langzaam verkeer en zwaar verkeer op wegen met een snelheidslimiet van 50 of 80 km/uur; Aanzet tot aanvullende veiligheidscriteria voor een Kwaliteitsnet Goederenvervoer. D-2009-3. SWOV, Leidschendam.

Eenink, R.G. (2009). Verkeersveiligheidseffecten van Anti-Ongevalsystemen; Schatting van de effecten op ongevallen met vrachtauto's op autosnelwegen. R-2009-11. SWOV, Leidschendam.

ETSC (2001). The role of driver fatigue in commercial road transport crashes. European Transport Safety Council, Brussel.

Goldenbeld, Ch., Davidse, R.J., Mesken, J. & Hoekstra, A.T.G. (2011). Vermoeidheid in het verkeer: prevalentie en statusonderkenning bij automobilisten en vrachtautochauffeurs: een vragenlijststudie onder Nederlandse rijbewijsbezitters. R-2011-4. SWOV, Leidschendam.

Gort, J., Swuste, P., Henstra, D., Schoon, C.C. & Waterbeemd, H. (2002). Safety Culture in het goederentransport over de weg. Rijkswaterstaat, Adviesdienst Verkeer en Vervoer, Rotterdam.

Kampen, L.T.B. van & Schoon, C.C. (1999). De veiligheid van vrachtauto's; Een ongevals- en maatregelenanalyse. R-99-31. SWOV, Leidschendam.

Lindeijer, J.E., Rienstra, S.A. & Rietveld, P. (1997). Voorbeeld van bedrijfseconomische kosten/baten van schadepreventiemaatregelen; Kosten/effectenindicaties van veiligheidsmaatregelen, als onderdeel van een schadepreventiebeleid van bedrijven met een transportfunctie van goederen langs de weg. R-97-42. SWOV, Leidschendam.

McKernon, S. (2008). Driver fatigue literature review. Research Report 342. Land Transport New Zealand, Wellington.

Mesken, J. & Schoon, C.C. (2011). Stedelijke distributie: conceptuele aanpak verbetering verkeersveiligheid. H-2011-2. SWOV, Leidschendam.

Ministerie van Infrastructuur en Milieu (2012). Nieuwe regels voor het rijbewijs. Het rijbewijs na implementatie van de derde Europese rijbewijsrichtlijn. Ministerie van Infrastructuur en Milieu, Den Haag.

Onderzoeksraad voor Veiligheid. (2012). Vrachtwagenongevallen op snelwegen. Onderzoeksraad voor Veiligheid, Den Haag.

Raamsdonk, M. van (2002). Interactie tussen vrachtautochauffeur en automobilist, Fase 2b: ongevallenanalyse 1998-2000. Rijkswaterstaat, Adviesdienst Verkeer en Vervoer, Rotterdam.

TLN & VVN (2010). Veilig op weg. Lespakket. Transport en Logistiek Nederland TLN/Veilig Verkeer Nederland.

Vlakveld, W.P., Goldenbeld, C., Knapper, A. & Bax, C.A. (2014). Veiligheidscultuur in het wegtransport. SWOV, Den Haag.

Wegman, F. & Aarts, L. (red.) (2006). Advancing Sustainable Safety; National Road Safety Outlook for 2005-2020. SWOV, Leidschendam.

Wijnen, W., Bax, C.A., Stipdonk, H.L., Wegman, R.W.N. & Bos, N.M. (2015). Invoering van contourmarkering voor het bestaande vrachtwagenpark; effecten en kosteneffectiviteit van retrofit in Nederland en in Europa. R-2015-2. SWOV, Den Haag.

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Updated

13 Dec 2016