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The effect of open-back vehicles on casualty rates: The case of Papua New Guinea
Accid. Anal. & F’rev. Vol. 23. Nos. 2/3. pp. 109-117. Printed in Great Britain.
lwol-4575191 $3.00 + .oo 8 1991 Pergamon Press plc
1991
THE EFFECT OF OPEN-BACK VEHICLES ON CASUALTY RATES: THE CASE OF PAPUA NEW GUINEA School of Civil Engineering,
DONNA C. NELSON Purdue University, West Lafayette, IN 47907, U.S.A.* and
JAMES V. STRUEBERt James V. Strueber Architects and Planners, P.O. Box 2124, West Lafayette, IN 47906, U.S.A. (Received 18 December
1989)
Abstract-Vehicle characteristics and vehicle use are frequently cited in the literature as potentially important factors contributing to the high motor-vehicle-related fatality rates reported by developing countries. Vehicles in developing countries are frequently overloaded, and improper vehicles are used to transport passengers. This paper seeks to estimate the importance of occupancy and vehicle type on the high motor-vehicle-related fatality rates in developing nations, using data for Papua New Guinea, a small South Pacific developing nation. This is achieved by establishing patterns of crash involvement and morbidity rates for vehicle types and ownership. Relative risk of crash involvement and relative risk of casualty for drivers and passengers are examined for open and closed vehicles. Finally, an estimate of the importance of vehicle type on the fatality rate is developed. The results demonstrate the need to disaggregate factors contributing to severity and occurrence in the study of road safety in developing countries.
INTRODUCTION
factors that underlie trends in motor-vehicle-related fatality rates are not well understood. While there is a general feeling that over time a “car culture” evolves that leads to greater road safety (Dondanville 1970; Jacobs and Sayer 1983; Mekky 1984, 1985), differences in motor-vehicle-related fatality rates are broadly attributed to differences in road-user populations, vehicle populations (class), and the road environment. High motor-vehicle-related mortality rates (fatalities/10,OOO registered vehicles) are commonly reported by developing countries. For example, Nigeria (Siddique and Abengowe 1979), Qatar (Eid 1980), Kuwait (Bayoumi 1980; Jadaan and Salter 1982), Libya (Mekky 1985), Papua New Guinea (Wyatt 1980), and other developing countries (Jacobs and Sayer 1983) regularly report motor vehicle mortality rates that are as much as 10 to 20 times higher than those reported in the United States. Various reasons for the mortality rates in developing nations have been offered in the literature. There appears to be a strong link between development and motor-vehiclerelated mortality (Wintemute 1985). Countries with low levels of motorization commonly report high fatality rates (Jacobs and Sayer 1983), a condition that appears to be exacerbated by sudden high-percentage increases in the motor vehicle population (Mekky 1985). Crash involvement patterns exhibited by road-user and vehicle groups vary significantly from country to country. It is thought that these involvement patterns reflect the makeup of the human and vehicle populations in individual countries (Jacobs and Sayer 1983). Researchers have hypothesized that crash involvement and casualty patterns are also affected by vehicle occupancy rates (Mekky 1985), level of motorization (Smeed 1968), economic development, per capita income, and other social, economic, and cultural factors including the quality and availability of emergency medical services.
The
*Present address: Department of Civil Engineering, California State Polytechnic University, Pomona, CA 91768, U.S.A. tPresent address: James V. Strueber, Architects and Planners, P.O. Box 986, Claremont, CA 91711. U.S.A. MP 23:2,3-A
109
110
D. C. NELSON and J.V. STRUEBER
The objective of the work described in this paper was to examine the impact that vehicle use and occupancy have on motor-vehicle-related fatalities experienced by Papua New Guinea (PNG), a Pacific Rim developing country. This has been accomplished in several steps. First, the crash involvement patterns for vehicles (by type and ownership) and the injury patterns associated with driver and passenger were established for PNG. These demonstrate that, while utility vehicles are not overrepresented in crash involvement, they are associated with an unusually high proportion of passenger casualties and fatalities. Second, the relative risk of crash involvement and casualty were calculated for the same groups. These calculations show that open-backed vehicles (mainly small pickups and trucks) are greatly overrepresented in passenger morbidity and mortality rates, apparently because of their extensive use to transport passengers. Third, the extent of their contribution is demonstrated by estimating the reduction in casualties that might occur if the occupancy of open-backed vehicles was limited to the number of seats available for passengers in the cab.
THE
STUDY
SITE
Papua New Guinea (PNG) is a small South Pacific nation situated east of Indonesia and north of Australia. The population of approximately 3.1 million is basically young; 47% are less than 15 years old (King 1984). PNG has a land area of 183,540 square miles and 49,611 registered vehicles. The nearly 19,500 km of road are spread over several isolated road systems. Travel between these road systems can only be accomplished by sea, air, or on foot (Townsend 1984). In 1984, Papua New Guinea reported 188 motor vehicle related-fatalities and 2,574 injuries in a total of 5,771 crashes. The motor vehicle fatality rates were 47.31 fatalities per 10.000 motor vehicles, and 7.9 fatalities per 100,000 population (in contrast to 2.52 fatalities per 10,000 vehicles and 18.4 fatalities per 100,000 population reported by the United States in 1983).
THE
DATA
Data for all property damage and injury crashes were obtained from the Papua New Guinea Computerized Accident Records File in Port Moresby. Vehicle registration statistics (Registered Motor Vehicles 1980-1984) and information on licensed drivers (Licensed Drivers and Riders 1980-1984) were obtained from the National Statistical Office in Port Moresby. Information on vehicle occupancy rates was obtained from traffic count studies conducted in several locations across the country (Puvanachandran 1982) and through field observations during the year the researchers were in the country. As with data sets in most countries, damage-only crashes and slight-injury crashes are likely to be underreported in the PNG data. Generally, analyses of motor vehicle crashes in developing nations have been limited to the use of fatality data, primarily because more detailed data are rarely available. The use of fatality data, while possibly more reliable and usually available, aggregates the effects and interactions of factors leading to crash occurrence and casualty (crash severity). As this analysis demonstrates, potentially significant information on the nature of motor-vehicle-related fatalities in developing countries is masked when only fatalities are used for analysis. Underreporting, in the case of the PNG data, tends to understate the number of damage and slight injury crashes and overstate the average severity of crashes (total casualties/total crashes). While possibly serious, the limitations of the PNG data do not preclude their use here to explore the relationship between vehicle type, crash severity, and crash rates. Unless otherwise noted, data for single-vehicle crashes were used to determine patterns of road user crash involvement. Morbidity data for multi-vehicle crashes are reported as total injuries per event, which makes it impossible to identify which vehicle carried the casualties. Severity ratios reported for vehicle categories are based only on crashes in which an injury was reported. In 1984, single vehicle crashes represented 34% (2,504 crashes) of the total number of crashes.
Effect of open-back vehicles on casualty rates in Papua New Guinea CRASH
INVOLVEMENT
AND
SEVERITY
The relationship between involvement, occupancy, be described by the simple models presented below Fatalities = (Events)
x
111
MODELS
severity, and fatality rates can
(Severity)
An evenf is defined as the occurrence of a crash, Severity is defined as the number of fatalities (or casualties) produced per event. Average severity is the totaf number of casualties divided by the total number of events (crashes). Severity = ~(occupancy,
vehicle design, kinematics, availability of medical services)
Occupancy-the number of persons in the vehicle-affects severity through the number of persons exposed to crash per vehicle-mile of travel and the number of persons exposed to injury should a crash happen. Vehicle type affects severity through design, design occupancy, and protection afforded occupants. Kinematics represent the factors or characteristics of the individual crash that affect the severity of the crash. These include speed, angle of impact, and final resting place of vehicle, and other factors. The availability of medical services determines the survivability of victims for given levels of injury. Crash invofvement and cus~uftypatterns Crash-involvement patterns represent the proportion of events (crashes) that involve specific vehicle groups. Casualty patterns describe the distribution of injuries and fataiities among road-user groups involved in the crashes. The relative involvement of these groups will be used later in this paper to assess the impact that vehicle type and use has on motor-vehicle-related morbidity rates in PNG. Crash involvement patterns and casualty distributions are derived using the relationship below: I = ;* I = Proportion of events in which the group of interest is involved. N = Frequency of involvement of group of interest. T = Total number of events. Relative involvement rates Relative involvement rates for vehicle groups are calculated on the basis of their representation in the crash-involved populations of interest (as the data allow). R.I. = ;. R.I. = Proportion of events in which the group of interest is involved. N = Frequency of involvement of group of interest. P = Proportion of group of interest in poputation. Relative risk met~odofogy The relative risk methodology used here is modeled after the methods used by Evans (1982) and Angel1 (Angel! and Von Buseck 1985). The concept of relative risk (RR) is based on the relative representation of various factors in the crash experience. The relative risk for a set of criteria (drivers, vehicles, etc.) is calculated in reference to other crash-involved groups (Fig. 1). Relative risk is calculated as shown below: RR _ a/(a + b) cf(c + d)’ where a = count in subgroup (k) which is also in subgroup (i). b = count in subgroup (k) which is part of subgroup (i), a + b = total count of subgroup k.
D. C. NELSONand J. V. STRUEBER
112
F-F-
Fig. 1. Relative risk table.
c= d=
c+d=
count in subgroup (I) which is also in subgroup (i). count in subgroup (I) which is part of subgroup (i). total count of subgroup k.
If the relative risk (RR) is greater than 1, then the subgroup is overrepresented in crashes relative to its exposure; if it is less than 1, the subgroup is underrepresented. The strength of this method is the ability to conduct an analysis in which the contributions of various factors can be compared in ways that identify systematic difference in populations. Relative risk of crash involvement The Relative risk of involvement
in a crash can be alternatively
written as:
RR1 = Relative risk of involvement in crash category j. Pa, = The proportion of crashes involving group a which is in subgroup (i). Pri = Proportion of all exposure or population accounted for by subgroup (i) Relative risk of casualty Relative Risk of Casualty can be similarly defined: RRC, = 2 ,
RRC = Relative risk of casualty for roaduser (i) in vehicle type ( j). Pai = Proportion of roaduser casualties (i) in all injuries in vehicle type ( j). Pr, = Proportion of all other roaduser casualties in all other vehicle types. CRASH
INVOLVEMENT
PATTERNS
Road user casualty patterns and vehicle crash involvement patterns are developed in this section. These calculations help to identify patterns and expose the interaction among vehicle type, use, and passenger casualties. Crash distribution by vehicle type Utility vehicles (small pickup trucks) are the primary vehicle in approximately 38% of all reported crashes and comprise 35% of the vehicle fleet, indicating they are not greatly overrepresented in the crash statistics (shown in Table 1). This represents a relative involvement rate of 1.08. When casualty data are examined, they show that the majority of passenger injuries (nearly 65%) are associated with utility vehicles; furthermore, 61% of the casualties associated with utility vehicles in single vehicle crashes are passengers. Passenger account for over 80% of the casualties associated with single unit trucks (known as heavy goods vehicles or HGVs). The number of passenger casualties is probably understated, as passengers falling from vehicles are frequently classified as pedestrians. This practice is also reflected in the high proportion, 39%, of pedestrian casualties associated with motorcycles.
Effect of open-back vehicles on casualty rates in Papua New Guinea
113
Table 1. Crash and casualty patterns by vehicle type Crashes
Casualties in single vehicle crashes
Category
Total
%
Registered vehicles
Utility Auto Bus HGV* Other Motorcycle
2189 759 153 750 153 102
37.9% 30.6% 13.1% 13.0% 2.7% 1.8%
35.5% 35.0% 6.1% 13.4% 3.5% 3.2%
23 31
0.4% 0.5%
Tractor** Bicycle Totals Unassigned
Passengers 666 31 83 226 3 8
3.40/o
64.7% 3.0% 8.0% 21.9% 0.3% 0.8%
r3
5772 653
Drivers
Pedestrians 225 110 42 42 2;:
1.3% -
49.9% 24.4% 9.3% 8.0% 1.6% 6.2%
168 84 21 10 6 36
51.1% 25.5% 6.4% 3.0% 1.8% 10.9%
1059 330 146 278 16 72
0.7% -
Y
1.2% -
20
3
1030 12
Total
451 100
329 855
55.1% 17.1% 7.6% 14.5% 0.8% 3.7% 1.0% -
1921
-Data Not Availabie *Heavy Goods Vehicle (Single unit truck) **Truck tractor
Cars (including passenger cars and very small vans), in contrast show a more even distribution of roaduser casualties across groups. Utility vehicles, then, while not overrepresented in crashes, are greatly overrepresented in passenger casualty statistics. Vehicle ownership
Vehicle ownership is an important factor in crash patterns because of the role ownership plays in vehicle use patterns and potentially in the driving skill and experience of the operator. For example, a commercial vehicle in PNG may have a more experienced driver, be less likely to be overloaded with passengers, and be driven at less risky times of the day and week, namely daylight hours and on week days. The frequency of crashes and casualties by ownership category are reported in Table 2. Private vehicles and company vehicles were the primary vehicle in 47% and 21% of ail crashes, respectively. The highest proportion of casualties of ail types accrue to private vehicles. Crash d~st~ibation by vehicle ownership and vehicle type
When the data are categorized by vehicle type and ownership, more detailed patterns begin to emerge. Ownership statistics were not available by vehicle use groups, only by vehicle type, making it impossible to develop relative involvement rates for specific vehicle uses. Instead, a cross classification of casualty frequencies by vehicle type and ownership is presented in Table 3. It is interesting to note that 17% of ail crash-involved public motor vehicles-PMVs-(essentially jitneys) are in fact open-backed vehicles
Table 2. Crash involvement and casualty patterns by vehicle ownership (PNG 1984) Crashes
Casualties in single vehicle crashes
Category
Total
c%
Govcrnmcnt Commercial Private Taxi BUS PMV” Rental Police Other
6X5 125x 273x 14 6 441 49 359 lb2
iI.Y% 21.8% 47.4% 1.3% 0.1% 7.6% o.xr% 6.2% 2.X%
Totals Unassigned
5772
ioO.0
‘.PMV = Public Motor Vehicle (Jitney)
Passengers 121 1x4 725 0 0 0 72 I6 17 1135 458
10.7 16.2 63.9 0.0 0.0 6.3 1.4 1.5
Pedestrians 5x
120 297 6 0 0 0 0 4 485 68
12.0 24.7 61.2 I.2 0.0 0.0 0.0 0.8
Drivers 49 107 191
1
0 2 6 I1 II
378 50
13.0 28.3 SO.5 0.3 .5 1.6 2.9 2.9
Total 228 411 1213 7 2 78 27 32 1993 576
Il.4 20.6 60.7 0.4 0.1 3.9 1.4 I.6
114
D. C. NELSONand J. V. STRUEBER Table 3. Involved vehicles by type, ownership
Vehicle type
Govt
Commercial
Private
Taxi
Bus
PMV
Hire
Police
Other
Total
Car HGV Tractor* Bus M’cycle Utility Other
137 75 5 56 15 393 3
230 355 15 144 11 477 26
1133 225 3 151 69 1156 1
56 0 0 1 0 17 0
1 0 0 4 0 1 0
1 52 0 363 1 23 0
30 7 0 0 0 12 0
172 34 0 39 6 106 0
4
0
1764 750 23 759 102 2187 30
Totals
684
1258
2738
74
6
440
49
357
9
5615
0
1 4
*Truck Tractor
(utility vehicles or trucks). Heavy duty four-wheel drive trucks are often used in rural areas where 20-passenger vans, the typical PMV, have difficulty negotiating the roads during the wet season. PASSENGER
CASUALTIES
AND
VEHICLE
TYPE
In the following sections, a series of calculations are performed to (i) quantify the contribution of passenger casualties in open backed vehicles to the road safety problem in Papua New Guinea and (ii) estimate the magnitude of reduction of casualties that might occur if the use of utility vehicles and other open trucks for passenger transportation were restricted. Relative risk
ofcasualty
by vehicle type
As shown in the previous sections, open backed vehicles such as utility vehicles and heavy goods vehicles are associated with a high proportion of passenger casualties in PNG. The relative risk of casualty and relative involvement rates for utility vehicles, HGVs, buses, and passenger cars is summarized in Table 4. The relative risk of casualty for drivers in utility vehicles is very nearly 1; e.g. their risk is comparable to that for others in the driving population. The relative risk of casualty to passengers in utility vehicles is 3.1, significantly higher than the risk to drivers in the same vehicle type* Buses (a small proportion of crash-involved vehicles) have a lower risk of passenger casualty, but a higher relative crash involvement rate than utifty vehicles, possibly reflecting greater passenger protection. The relative risk of casualty for drivers in cars is higher than the risk for drivers of buses and utilities, possibly reflecting lower ski11 level of the driver. Casualties per crash: Severity
Utility vehicles have a high relative risk of passenger casualty compared to other vehicles. The importance of occupancy in crash severity is demonstrated in Tables 5 and 6. Thirty-one percent of the casualty crashes involving utilities produce three or more casualties, which is more than their passenger design capacity allows with proper Loading. Table 4. Relative risk of casualty Relative risk of casualty* Vehicle
Passengers
Drivers
Relative crash involvement
Utility Buses Cars
3.1 2.4 1.3
0.99 0.88 1.57
1.07 2.14 0.87
‘ln comparison to all other road users in all other vehicles
Effect of open-back vehicles on casualty rates in Papua New Guinea
115
Table 5. Passenger casualties by vehicle type Single-vehicle crashes: Number of casualties per crash
0
Type
123456789
Utilities HGVs
810 282
123 33
83 25
44 7
22 5
10 2
Buses Cars Tractors M’cycles Other
179 542 10 55 18
21 54
18 7
11 6
24
1
1896
241
Crashes
9 1 624_-_-1Lz1 _1
_-
_1
10
11
2
1
1 _-
:
1
12
13
14
15
7
1
1
1
12__--___1__-----_g_--_--__-_-1 l__-______---_136
68
33
____
15
16
2
7
1
3
1
1
1
-
1
Thirty-three percent of all casualty crashes involving private vehicles produce three or more passenger casualties. Overall, 15% of the casualty crashes involve injuries to more than four persons. Vehicle occupancy and casualties
In this section the use of open vehicles to transport passengers is evaluated for its effect on the total casualties (injuries and fatalities), based on the assumption that occupancy in these vehicles could be restricted to carrying only the number of seated passengers that the manufacturer provided for inside the cab (the designed passenger capacity). The methodology and assumptions are outlined below. The potential reduction of passenger casualties is calculated in the following manner: Passenger casualties for single-vehicle (SV) crashes are summed by vehicle type. Note: these data do not include drivers. The maximum number of passenger casualties expected if occupancy is limited to the designed passenger occupancy of the vehicle. Utility vehicles (in PNG these are usually small Japanese imports) are assumed to have a passenger capacity of one passenger in addition to the driver. Because of their larger cabs, HGVs are assumed to have a passenger capacity of two persons in addition to the driver. The potential reduction in passenger casualties in single vehicle crashes are calculated by subtracting actual passenger casualties from the maximum expected casualties based on designed occupancy. The potential reduction in passenger casualties in all crashes (multiple- and singlevehicle crashes) is estimated based on the proportion of crashes that involve a single vehicle.
Table 6. Passenger casualties by vehicle ownership Single Vehicle Crashes: Number of casualties per crash Ownership
0
1
2
3
4
28 52 139 _
12 34 75 -
9 3 48 -
3 6 18 -
12 -
7
5
1
Government Companies Private Taxi PMV Rental Police Other
270 464 853 15 101 97 92
10 3 9
8 3 4
Crashes
1896
241
136
5
6
7
8
9
10
11
12
13
14
15
1 -
-
-
-
-
1
I-
I
3 12 -
2 -
1 1 5 -
-
3 -
-
-
-
-
-
_
_
_
_
I
I
1
-
1
-
-
_
-
-
-
-
-
-
_
_
_
_
_
j__---_-------_1
I-----------
_
_
_
_
68
33
15
16
2
7
I
3
I
1
1
-
1
D. C. NELSON and J. V STRLJEBER
116
Table 7. Potential reduction in passenger casualties Vehicle tvne
Utilitv
HGV
2 (A, x 0,) (C,-Em)
299 666 1 299 367
2 172 54
(5) (WSV) (E, x .45)
2189 51% 1313 590
750 49% 461 110
Single vehicle crashes Crashes with passenger casualties Passenger casualties (actual) Designed occupancy Expected maximum passenger casualties (E,) Expected reduction in casualties (E,)
A,
Estimated reduction in passenger casualties for all crashes Accidents (actual) Percent single vehicle accidents Estimated passenger casualties (E,) Expected reduction in passenger casualties (E,) Reductions for HGVs and utility vehicles Total motor-vehicle-related casualties Potential total reduction in casualties
The calculation assumptions:
of the potential
reduction
(E,
E rr0Tola, (C,) x C,) x 100
700 2574 21%
in casualties is based on the following
1. that the maximum number of casualties would occur given a crash of sufficient severity to result in a single casualty; and 2. the severity rate (number of persons injured per crash) is the same for singleand multile-vehicle crashes regardless of the number of vehicles actually involved. Both assumptions (1) and (2) underestimate the potential reduction in passenger casualties. Table 7 shows the potential reduction in casualties for PNG during 1984 for utilities and trucks if it were possible to limit utility vehicles and HGVs to their passenger design capacity. In single vehicle crashes alone, the number of passenger casualties could be reduced by 45% for utility vehicles. When the potential reduction in casualties is estimated for the entire crash-involved vehicle population, the reduction in casualties is significantly higher. This is, of course, a very gross, but conservative, estimate. DISCUSSION
Vehicle overloading and the use of open-back vehicles to transport passengers are commonly reported as safety problems in developing countries. The results presented in this paper clearly demonstrate the contribution vehicle type, use, and loading factors have on motor-vehicle-related fatality rates in Papua New Guinea (PNG). Open-back vehicles (extensively used in PNG to transport passengers) exhibit a relatively high number of injuries per crash and appear to be a major contributor to motor vehiclerelated morbidity and mortality. Calculations for PNG show that motor vehicle-related injuries could be reduced by 27% if HGVs and utility vehicles were limited to their designed passenger occupancy. The large potential reductions in passenger casualties demonstrated for PNG suggest that countermeasures directed at discouraging the use of open-backed vehicles to transport passengers would be successful in reducing casualties in those developing countries where open-back vehicles are frequently used to transport passengers. In countries where access to vehicles is limited, simply banning passengers in the cargo beds of utility vehicles has little or no chance of success. The resulting limitations on personal travel would be socially and economically undesirable and the legislation virtually impossible to enforce. In PNG, countermeasures could be developed along two general avenues; (i) those measures that influence the characteristics of the vehicle fleet, and (ii) those that provide more passenger protection in the open-back vehicles that are currently in use. In PNG,
Effect of open-backvehicleson casualtyrates in Papua New Guinea 117 the characteristics of the vehicle fleet can be directly influenced through the import duty structure. Currently a high import duty is placed on nonwork vehicles such as passenger cars and small enclosed vans, raising the sale price of these vehicles considerably over the price of utility vehicles (which have a very low import duty). A duty structure that encourages the purchase of enclosed vehicles over open vehicles would show its effectiveness in a relatively short period of time, as the average vehicle lifespan is only three to five years. It is important to note that minivans are well suited to most of the uses that small utility vehicles now serve in addition to passenger transportation. In addition to overloaded vehicles, there are a number of other vehicle-related factors, such as high vehicle occupancies and lack of emergency medical care, that may further contribute to the high motor vehicle-related casualty rates reported by developing countries. High vehicle occupancy rates are common in developing nations and influence crash severity by increasing the number of occupants exposed to injury or fatality in the event of a crash. For example, average vehicle occupancies in the United States range from 1.3 to 2.0, depending upon trip purpose. In PNG, occupancies range from an average of 2.4 in Port Moresby to over 10 in some rural areas. In rural areas restricted access to medical services also plays an increased role in the severity of accidents. The motor vehicle-related fatality rate (per 10,000 registered vehicles) for PNG is quite high, like that of other developing nations at the same stage of development. The vehicle crash rate (crashes per 10,000 registered vehicles) however, is actually lower than the crash rate reported for the United States (Nelson 1988). While the risk of crash is low, if a crash occurs, the risk of being seriously injured or killed is quite high. This indicates that crash severity is an important factor in producing the high motor vehicle related fatality rates reported by developing countries. The use of open vehicles to transport passengers in developing countries is common. The Papua New Guinea data suggest that a relatively large proportion of the roaduser casualties may result from this practice. In countries where this paradigm is common, this research indicates that a great incremental improvement in road safety would follow the development of effective countermeasures that discourage the use of open-back vehicles to transport passengers. REFERENCES Angell. L.; Von Buseck, C. R. Alcohol related crashes. In: Evans, L.; Schwing, R., eds. Human Behavior and Traffic Safety. New York: Plenum Press; 1985. Bayoumi, A. The epidemiology of fatal motor vehicle accidents in Kuwait. Accid. Anal. Prev. 13:399-348; 1980 Dondanville, L. A. Road safety is no accident. International Road Federation VI World Highway Conference. Montreal, October 1970. Evans, L. Driver fatalities versus car mass using a new exposure approach. Accid. Anal. Prev. 16; 1982. Eid. A. Road traffic accident in Qatar: The size of the problem. Accid. Anal. Prev. 12:287-298; 1980. Jacobs. J. D.; Sayer, I. Road accidents in developing countries. Accid. Anal. Prev. 15:337-353; 1983 Jadaan, K. S.; Salter, R. J. Traffic accidents in Kuwait. Traffic Eng. Control 23:221-223: 1982. King, D. Population of Papua New Guinea. In: King. D.; Ranck, S., eds. Papua New Guinea Atlas: A Nation in Transition. Lae. PNG: Geography Department, University of Papua New Guinea: 1984; 20-21. Licensed Drivers and Riders. Port Moresby, PNG: National Statistical Office; 1980-1984. Mekky, A. Road traffic accidents in rich developing countries: The case of Libya. Accid. Anal. Prev. 16:263277; 1984. Mekky. A. Effects of rapid increase in motorization levels on road fatality rates in some rich developing countries. Accid. Anal. Prev. 17:101-109; 1985. Nelson. D. C. Growth in Vehicle Populations and Motor Vehicle Fatality Rates in Developing Countries. Ph.D. dissertation. Berkeley, CA: Department of Civil Engineering. University of California; May 1988. Puvanachandran. P. V. Popendetta-Orobay traffic study (unpublished). Lae, PNG: Papua New Guinea University of Technology; 1982. Registered Motor Vehicles. Port Moresby. PNG: National Statistical Office; 1980-1984. Siddique. A. K.; Abengowe. C. U. Epidemiology of road traffic accidents in developing countries: Nigeria, an example. Trop. Doct. 9:67-72; 1979. Smeed, R. J. Variations in the pattern of accident rates in different countries and their causes. Traffic Eng. Control 2(7):364-371; 1968. Townsend. D. Road transport. In: King, D.; Ranck. S.. eds. Papua New Guinea Atlas: A Nation inTransition. Lae. PNG: Geography Department. University of Papua New Guinea; 1984; X0-81. Wintemute. G. Is motor vehicle related mortality a disease of development? Accid. Anal. Prev. 17:223-237; 1985. Wyatt. G. B. The epidemiology of road accidents in Papua New Guinea. Papua New Guinea Med. J. 23:6065: 1980.
lwol-4575191 $3.00 + .oo 8 1991 Pergamon Press plc
1991
THE EFFECT OF OPEN-BACK VEHICLES ON CASUALTY RATES: THE CASE OF PAPUA NEW GUINEA School of Civil Engineering,
DONNA C. NELSON Purdue University, West Lafayette, IN 47907, U.S.A.* and
JAMES V. STRUEBERt James V. Strueber Architects and Planners, P.O. Box 2124, West Lafayette, IN 47906, U.S.A. (Received 18 December
1989)
Abstract-Vehicle characteristics and vehicle use are frequently cited in the literature as potentially important factors contributing to the high motor-vehicle-related fatality rates reported by developing countries. Vehicles in developing countries are frequently overloaded, and improper vehicles are used to transport passengers. This paper seeks to estimate the importance of occupancy and vehicle type on the high motor-vehicle-related fatality rates in developing nations, using data for Papua New Guinea, a small South Pacific developing nation. This is achieved by establishing patterns of crash involvement and morbidity rates for vehicle types and ownership. Relative risk of crash involvement and relative risk of casualty for drivers and passengers are examined for open and closed vehicles. Finally, an estimate of the importance of vehicle type on the fatality rate is developed. The results demonstrate the need to disaggregate factors contributing to severity and occurrence in the study of road safety in developing countries.
INTRODUCTION
factors that underlie trends in motor-vehicle-related fatality rates are not well understood. While there is a general feeling that over time a “car culture” evolves that leads to greater road safety (Dondanville 1970; Jacobs and Sayer 1983; Mekky 1984, 1985), differences in motor-vehicle-related fatality rates are broadly attributed to differences in road-user populations, vehicle populations (class), and the road environment. High motor-vehicle-related mortality rates (fatalities/10,OOO registered vehicles) are commonly reported by developing countries. For example, Nigeria (Siddique and Abengowe 1979), Qatar (Eid 1980), Kuwait (Bayoumi 1980; Jadaan and Salter 1982), Libya (Mekky 1985), Papua New Guinea (Wyatt 1980), and other developing countries (Jacobs and Sayer 1983) regularly report motor vehicle mortality rates that are as much as 10 to 20 times higher than those reported in the United States. Various reasons for the mortality rates in developing nations have been offered in the literature. There appears to be a strong link between development and motor-vehiclerelated mortality (Wintemute 1985). Countries with low levels of motorization commonly report high fatality rates (Jacobs and Sayer 1983), a condition that appears to be exacerbated by sudden high-percentage increases in the motor vehicle population (Mekky 1985). Crash involvement patterns exhibited by road-user and vehicle groups vary significantly from country to country. It is thought that these involvement patterns reflect the makeup of the human and vehicle populations in individual countries (Jacobs and Sayer 1983). Researchers have hypothesized that crash involvement and casualty patterns are also affected by vehicle occupancy rates (Mekky 1985), level of motorization (Smeed 1968), economic development, per capita income, and other social, economic, and cultural factors including the quality and availability of emergency medical services.
The
*Present address: Department of Civil Engineering, California State Polytechnic University, Pomona, CA 91768, U.S.A. tPresent address: James V. Strueber, Architects and Planners, P.O. Box 986, Claremont, CA 91711. U.S.A. MP 23:2,3-A
109
110
D. C. NELSON and J.V. STRUEBER
The objective of the work described in this paper was to examine the impact that vehicle use and occupancy have on motor-vehicle-related fatalities experienced by Papua New Guinea (PNG), a Pacific Rim developing country. This has been accomplished in several steps. First, the crash involvement patterns for vehicles (by type and ownership) and the injury patterns associated with driver and passenger were established for PNG. These demonstrate that, while utility vehicles are not overrepresented in crash involvement, they are associated with an unusually high proportion of passenger casualties and fatalities. Second, the relative risk of crash involvement and casualty were calculated for the same groups. These calculations show that open-backed vehicles (mainly small pickups and trucks) are greatly overrepresented in passenger morbidity and mortality rates, apparently because of their extensive use to transport passengers. Third, the extent of their contribution is demonstrated by estimating the reduction in casualties that might occur if the occupancy of open-backed vehicles was limited to the number of seats available for passengers in the cab.
THE
STUDY
SITE
Papua New Guinea (PNG) is a small South Pacific nation situated east of Indonesia and north of Australia. The population of approximately 3.1 million is basically young; 47% are less than 15 years old (King 1984). PNG has a land area of 183,540 square miles and 49,611 registered vehicles. The nearly 19,500 km of road are spread over several isolated road systems. Travel between these road systems can only be accomplished by sea, air, or on foot (Townsend 1984). In 1984, Papua New Guinea reported 188 motor vehicle related-fatalities and 2,574 injuries in a total of 5,771 crashes. The motor vehicle fatality rates were 47.31 fatalities per 10.000 motor vehicles, and 7.9 fatalities per 100,000 population (in contrast to 2.52 fatalities per 10,000 vehicles and 18.4 fatalities per 100,000 population reported by the United States in 1983).
THE
DATA
Data for all property damage and injury crashes were obtained from the Papua New Guinea Computerized Accident Records File in Port Moresby. Vehicle registration statistics (Registered Motor Vehicles 1980-1984) and information on licensed drivers (Licensed Drivers and Riders 1980-1984) were obtained from the National Statistical Office in Port Moresby. Information on vehicle occupancy rates was obtained from traffic count studies conducted in several locations across the country (Puvanachandran 1982) and through field observations during the year the researchers were in the country. As with data sets in most countries, damage-only crashes and slight-injury crashes are likely to be underreported in the PNG data. Generally, analyses of motor vehicle crashes in developing nations have been limited to the use of fatality data, primarily because more detailed data are rarely available. The use of fatality data, while possibly more reliable and usually available, aggregates the effects and interactions of factors leading to crash occurrence and casualty (crash severity). As this analysis demonstrates, potentially significant information on the nature of motor-vehicle-related fatalities in developing countries is masked when only fatalities are used for analysis. Underreporting, in the case of the PNG data, tends to understate the number of damage and slight injury crashes and overstate the average severity of crashes (total casualties/total crashes). While possibly serious, the limitations of the PNG data do not preclude their use here to explore the relationship between vehicle type, crash severity, and crash rates. Unless otherwise noted, data for single-vehicle crashes were used to determine patterns of road user crash involvement. Morbidity data for multi-vehicle crashes are reported as total injuries per event, which makes it impossible to identify which vehicle carried the casualties. Severity ratios reported for vehicle categories are based only on crashes in which an injury was reported. In 1984, single vehicle crashes represented 34% (2,504 crashes) of the total number of crashes.
Effect of open-back vehicles on casualty rates in Papua New Guinea CRASH
INVOLVEMENT
AND
SEVERITY
The relationship between involvement, occupancy, be described by the simple models presented below Fatalities = (Events)
x
111
MODELS
severity, and fatality rates can
(Severity)
An evenf is defined as the occurrence of a crash, Severity is defined as the number of fatalities (or casualties) produced per event. Average severity is the totaf number of casualties divided by the total number of events (crashes). Severity = ~(occupancy,
vehicle design, kinematics, availability of medical services)
Occupancy-the number of persons in the vehicle-affects severity through the number of persons exposed to crash per vehicle-mile of travel and the number of persons exposed to injury should a crash happen. Vehicle type affects severity through design, design occupancy, and protection afforded occupants. Kinematics represent the factors or characteristics of the individual crash that affect the severity of the crash. These include speed, angle of impact, and final resting place of vehicle, and other factors. The availability of medical services determines the survivability of victims for given levels of injury. Crash invofvement and cus~uftypatterns Crash-involvement patterns represent the proportion of events (crashes) that involve specific vehicle groups. Casualty patterns describe the distribution of injuries and fataiities among road-user groups involved in the crashes. The relative involvement of these groups will be used later in this paper to assess the impact that vehicle type and use has on motor-vehicle-related morbidity rates in PNG. Crash involvement patterns and casualty distributions are derived using the relationship below: I = ;* I = Proportion of events in which the group of interest is involved. N = Frequency of involvement of group of interest. T = Total number of events. Relative involvement rates Relative involvement rates for vehicle groups are calculated on the basis of their representation in the crash-involved populations of interest (as the data allow). R.I. = ;. R.I. = Proportion of events in which the group of interest is involved. N = Frequency of involvement of group of interest. P = Proportion of group of interest in poputation. Relative risk met~odofogy The relative risk methodology used here is modeled after the methods used by Evans (1982) and Angel1 (Angel! and Von Buseck 1985). The concept of relative risk (RR) is based on the relative representation of various factors in the crash experience. The relative risk for a set of criteria (drivers, vehicles, etc.) is calculated in reference to other crash-involved groups (Fig. 1). Relative risk is calculated as shown below: RR _ a/(a + b) cf(c + d)’ where a = count in subgroup (k) which is also in subgroup (i). b = count in subgroup (k) which is part of subgroup (i), a + b = total count of subgroup k.
D. C. NELSONand J. V. STRUEBER
112
F-F-
Fig. 1. Relative risk table.
c= d=
c+d=
count in subgroup (I) which is also in subgroup (i). count in subgroup (I) which is part of subgroup (i). total count of subgroup k.
If the relative risk (RR) is greater than 1, then the subgroup is overrepresented in crashes relative to its exposure; if it is less than 1, the subgroup is underrepresented. The strength of this method is the ability to conduct an analysis in which the contributions of various factors can be compared in ways that identify systematic difference in populations. Relative risk of crash involvement The Relative risk of involvement
in a crash can be alternatively
written as:
RR1 = Relative risk of involvement in crash category j. Pa, = The proportion of crashes involving group a which is in subgroup (i). Pri = Proportion of all exposure or population accounted for by subgroup (i) Relative risk of casualty Relative Risk of Casualty can be similarly defined: RRC, = 2 ,
RRC = Relative risk of casualty for roaduser (i) in vehicle type ( j). Pai = Proportion of roaduser casualties (i) in all injuries in vehicle type ( j). Pr, = Proportion of all other roaduser casualties in all other vehicle types. CRASH
INVOLVEMENT
PATTERNS
Road user casualty patterns and vehicle crash involvement patterns are developed in this section. These calculations help to identify patterns and expose the interaction among vehicle type, use, and passenger casualties. Crash distribution by vehicle type Utility vehicles (small pickup trucks) are the primary vehicle in approximately 38% of all reported crashes and comprise 35% of the vehicle fleet, indicating they are not greatly overrepresented in the crash statistics (shown in Table 1). This represents a relative involvement rate of 1.08. When casualty data are examined, they show that the majority of passenger injuries (nearly 65%) are associated with utility vehicles; furthermore, 61% of the casualties associated with utility vehicles in single vehicle crashes are passengers. Passenger account for over 80% of the casualties associated with single unit trucks (known as heavy goods vehicles or HGVs). The number of passenger casualties is probably understated, as passengers falling from vehicles are frequently classified as pedestrians. This practice is also reflected in the high proportion, 39%, of pedestrian casualties associated with motorcycles.
Effect of open-back vehicles on casualty rates in Papua New Guinea
113
Table 1. Crash and casualty patterns by vehicle type Crashes
Casualties in single vehicle crashes
Category
Total
%
Registered vehicles
Utility Auto Bus HGV* Other Motorcycle
2189 759 153 750 153 102
37.9% 30.6% 13.1% 13.0% 2.7% 1.8%
35.5% 35.0% 6.1% 13.4% 3.5% 3.2%
23 31
0.4% 0.5%
Tractor** Bicycle Totals Unassigned
Passengers 666 31 83 226 3 8
3.40/o
64.7% 3.0% 8.0% 21.9% 0.3% 0.8%
r3
5772 653
Drivers
Pedestrians 225 110 42 42 2;:
1.3% -
49.9% 24.4% 9.3% 8.0% 1.6% 6.2%
168 84 21 10 6 36
51.1% 25.5% 6.4% 3.0% 1.8% 10.9%
1059 330 146 278 16 72
0.7% -
Y
1.2% -
20
3
1030 12
Total
451 100
329 855
55.1% 17.1% 7.6% 14.5% 0.8% 3.7% 1.0% -
1921
-Data Not Availabie *Heavy Goods Vehicle (Single unit truck) **Truck tractor
Cars (including passenger cars and very small vans), in contrast show a more even distribution of roaduser casualties across groups. Utility vehicles, then, while not overrepresented in crashes, are greatly overrepresented in passenger casualty statistics. Vehicle ownership
Vehicle ownership is an important factor in crash patterns because of the role ownership plays in vehicle use patterns and potentially in the driving skill and experience of the operator. For example, a commercial vehicle in PNG may have a more experienced driver, be less likely to be overloaded with passengers, and be driven at less risky times of the day and week, namely daylight hours and on week days. The frequency of crashes and casualties by ownership category are reported in Table 2. Private vehicles and company vehicles were the primary vehicle in 47% and 21% of ail crashes, respectively. The highest proportion of casualties of ail types accrue to private vehicles. Crash d~st~ibation by vehicle ownership and vehicle type
When the data are categorized by vehicle type and ownership, more detailed patterns begin to emerge. Ownership statistics were not available by vehicle use groups, only by vehicle type, making it impossible to develop relative involvement rates for specific vehicle uses. Instead, a cross classification of casualty frequencies by vehicle type and ownership is presented in Table 3. It is interesting to note that 17% of ail crash-involved public motor vehicles-PMVs-(essentially jitneys) are in fact open-backed vehicles
Table 2. Crash involvement and casualty patterns by vehicle ownership (PNG 1984) Crashes
Casualties in single vehicle crashes
Category
Total
c%
Govcrnmcnt Commercial Private Taxi BUS PMV” Rental Police Other
6X5 125x 273x 14 6 441 49 359 lb2
iI.Y% 21.8% 47.4% 1.3% 0.1% 7.6% o.xr% 6.2% 2.X%
Totals Unassigned
5772
ioO.0
‘.PMV = Public Motor Vehicle (Jitney)
Passengers 121 1x4 725 0 0 0 72 I6 17 1135 458
10.7 16.2 63.9 0.0 0.0 6.3 1.4 1.5
Pedestrians 5x
120 297 6 0 0 0 0 4 485 68
12.0 24.7 61.2 I.2 0.0 0.0 0.0 0.8
Drivers 49 107 191
1
0 2 6 I1 II
378 50
13.0 28.3 SO.5 0.3 .5 1.6 2.9 2.9
Total 228 411 1213 7 2 78 27 32 1993 576
Il.4 20.6 60.7 0.4 0.1 3.9 1.4 I.6
114
D. C. NELSONand J. V. STRUEBER Table 3. Involved vehicles by type, ownership
Vehicle type
Govt
Commercial
Private
Taxi
Bus
PMV
Hire
Police
Other
Total
Car HGV Tractor* Bus M’cycle Utility Other
137 75 5 56 15 393 3
230 355 15 144 11 477 26
1133 225 3 151 69 1156 1
56 0 0 1 0 17 0
1 0 0 4 0 1 0
1 52 0 363 1 23 0
30 7 0 0 0 12 0
172 34 0 39 6 106 0
4
0
1764 750 23 759 102 2187 30
Totals
684
1258
2738
74
6
440
49
357
9
5615
0
1 4
*Truck Tractor
(utility vehicles or trucks). Heavy duty four-wheel drive trucks are often used in rural areas where 20-passenger vans, the typical PMV, have difficulty negotiating the roads during the wet season. PASSENGER
CASUALTIES
AND
VEHICLE
TYPE
In the following sections, a series of calculations are performed to (i) quantify the contribution of passenger casualties in open backed vehicles to the road safety problem in Papua New Guinea and (ii) estimate the magnitude of reduction of casualties that might occur if the use of utility vehicles and other open trucks for passenger transportation were restricted. Relative risk
ofcasualty
by vehicle type
As shown in the previous sections, open backed vehicles such as utility vehicles and heavy goods vehicles are associated with a high proportion of passenger casualties in PNG. The relative risk of casualty and relative involvement rates for utility vehicles, HGVs, buses, and passenger cars is summarized in Table 4. The relative risk of casualty for drivers in utility vehicles is very nearly 1; e.g. their risk is comparable to that for others in the driving population. The relative risk of casualty to passengers in utility vehicles is 3.1, significantly higher than the risk to drivers in the same vehicle type* Buses (a small proportion of crash-involved vehicles) have a lower risk of passenger casualty, but a higher relative crash involvement rate than utifty vehicles, possibly reflecting greater passenger protection. The relative risk of casualty for drivers in cars is higher than the risk for drivers of buses and utilities, possibly reflecting lower ski11 level of the driver. Casualties per crash: Severity
Utility vehicles have a high relative risk of passenger casualty compared to other vehicles. The importance of occupancy in crash severity is demonstrated in Tables 5 and 6. Thirty-one percent of the casualty crashes involving utilities produce three or more casualties, which is more than their passenger design capacity allows with proper Loading. Table 4. Relative risk of casualty Relative risk of casualty* Vehicle
Passengers
Drivers
Relative crash involvement
Utility Buses Cars
3.1 2.4 1.3
0.99 0.88 1.57
1.07 2.14 0.87
‘ln comparison to all other road users in all other vehicles
Effect of open-back vehicles on casualty rates in Papua New Guinea
115
Table 5. Passenger casualties by vehicle type Single-vehicle crashes: Number of casualties per crash
0
Type
123456789
Utilities HGVs
810 282
123 33
83 25
44 7
22 5
10 2
Buses Cars Tractors M’cycles Other
179 542 10 55 18
21 54
18 7
11 6
24
1
1896
241
Crashes
9 1 624_-_-1Lz1 _1
_-
_1
10
11
2
1
1 _-
:
1
12
13
14
15
7
1
1
1
12__--___1__-----_g_--_--__-_-1 l__-______---_136
68
33
____
15
16
2
7
1
3
1
1
1
-
1
Thirty-three percent of all casualty crashes involving private vehicles produce three or more passenger casualties. Overall, 15% of the casualty crashes involve injuries to more than four persons. Vehicle occupancy and casualties
In this section the use of open vehicles to transport passengers is evaluated for its effect on the total casualties (injuries and fatalities), based on the assumption that occupancy in these vehicles could be restricted to carrying only the number of seated passengers that the manufacturer provided for inside the cab (the designed passenger capacity). The methodology and assumptions are outlined below. The potential reduction of passenger casualties is calculated in the following manner: Passenger casualties for single-vehicle (SV) crashes are summed by vehicle type. Note: these data do not include drivers. The maximum number of passenger casualties expected if occupancy is limited to the designed passenger occupancy of the vehicle. Utility vehicles (in PNG these are usually small Japanese imports) are assumed to have a passenger capacity of one passenger in addition to the driver. Because of their larger cabs, HGVs are assumed to have a passenger capacity of two persons in addition to the driver. The potential reduction in passenger casualties in single vehicle crashes are calculated by subtracting actual passenger casualties from the maximum expected casualties based on designed occupancy. The potential reduction in passenger casualties in all crashes (multiple- and singlevehicle crashes) is estimated based on the proportion of crashes that involve a single vehicle.
Table 6. Passenger casualties by vehicle ownership Single Vehicle Crashes: Number of casualties per crash Ownership
0
1
2
3
4
28 52 139 _
12 34 75 -
9 3 48 -
3 6 18 -
12 -
7
5
1
Government Companies Private Taxi PMV Rental Police Other
270 464 853 15 101 97 92
10 3 9
8 3 4
Crashes
1896
241
136
5
6
7
8
9
10
11
12
13
14
15
1 -
-
-
-
-
1
I-
I
3 12 -
2 -
1 1 5 -
-
3 -
-
-
-
-
-
_
_
_
_
I
I
1
-
1
-
-
_
-
-
-
-
-
-
_
_
_
_
_
j__---_-------_1
I-----------
_
_
_
_
68
33
15
16
2
7
I
3
I
1
1
-
1
D. C. NELSON and J. V STRLJEBER
116
Table 7. Potential reduction in passenger casualties Vehicle tvne
Utilitv
HGV
2 (A, x 0,) (C,-Em)
299 666 1 299 367
2 172 54
(5) (WSV) (E, x .45)
2189 51% 1313 590
750 49% 461 110
Single vehicle crashes Crashes with passenger casualties Passenger casualties (actual) Designed occupancy Expected maximum passenger casualties (E,) Expected reduction in casualties (E,)
A,
Estimated reduction in passenger casualties for all crashes Accidents (actual) Percent single vehicle accidents Estimated passenger casualties (E,) Expected reduction in passenger casualties (E,) Reductions for HGVs and utility vehicles Total motor-vehicle-related casualties Potential total reduction in casualties
The calculation assumptions:
of the potential
reduction
(E,
E rr0Tola, (C,) x C,) x 100
700 2574 21%
in casualties is based on the following
1. that the maximum number of casualties would occur given a crash of sufficient severity to result in a single casualty; and 2. the severity rate (number of persons injured per crash) is the same for singleand multile-vehicle crashes regardless of the number of vehicles actually involved. Both assumptions (1) and (2) underestimate the potential reduction in passenger casualties. Table 7 shows the potential reduction in casualties for PNG during 1984 for utilities and trucks if it were possible to limit utility vehicles and HGVs to their passenger design capacity. In single vehicle crashes alone, the number of passenger casualties could be reduced by 45% for utility vehicles. When the potential reduction in casualties is estimated for the entire crash-involved vehicle population, the reduction in casualties is significantly higher. This is, of course, a very gross, but conservative, estimate. DISCUSSION
Vehicle overloading and the use of open-back vehicles to transport passengers are commonly reported as safety problems in developing countries. The results presented in this paper clearly demonstrate the contribution vehicle type, use, and loading factors have on motor-vehicle-related fatality rates in Papua New Guinea (PNG). Open-back vehicles (extensively used in PNG to transport passengers) exhibit a relatively high number of injuries per crash and appear to be a major contributor to motor vehiclerelated morbidity and mortality. Calculations for PNG show that motor vehicle-related injuries could be reduced by 27% if HGVs and utility vehicles were limited to their designed passenger occupancy. The large potential reductions in passenger casualties demonstrated for PNG suggest that countermeasures directed at discouraging the use of open-backed vehicles to transport passengers would be successful in reducing casualties in those developing countries where open-back vehicles are frequently used to transport passengers. In countries where access to vehicles is limited, simply banning passengers in the cargo beds of utility vehicles has little or no chance of success. The resulting limitations on personal travel would be socially and economically undesirable and the legislation virtually impossible to enforce. In PNG, countermeasures could be developed along two general avenues; (i) those measures that influence the characteristics of the vehicle fleet, and (ii) those that provide more passenger protection in the open-back vehicles that are currently in use. In PNG,
Effect of open-backvehicleson casualtyrates in Papua New Guinea 117 the characteristics of the vehicle fleet can be directly influenced through the import duty structure. Currently a high import duty is placed on nonwork vehicles such as passenger cars and small enclosed vans, raising the sale price of these vehicles considerably over the price of utility vehicles (which have a very low import duty). A duty structure that encourages the purchase of enclosed vehicles over open vehicles would show its effectiveness in a relatively short period of time, as the average vehicle lifespan is only three to five years. It is important to note that minivans are well suited to most of the uses that small utility vehicles now serve in addition to passenger transportation. In addition to overloaded vehicles, there are a number of other vehicle-related factors, such as high vehicle occupancies and lack of emergency medical care, that may further contribute to the high motor vehicle-related casualty rates reported by developing countries. High vehicle occupancy rates are common in developing nations and influence crash severity by increasing the number of occupants exposed to injury or fatality in the event of a crash. For example, average vehicle occupancies in the United States range from 1.3 to 2.0, depending upon trip purpose. In PNG, occupancies range from an average of 2.4 in Port Moresby to over 10 in some rural areas. In rural areas restricted access to medical services also plays an increased role in the severity of accidents. The motor vehicle-related fatality rate (per 10,000 registered vehicles) for PNG is quite high, like that of other developing nations at the same stage of development. The vehicle crash rate (crashes per 10,000 registered vehicles) however, is actually lower than the crash rate reported for the United States (Nelson 1988). While the risk of crash is low, if a crash occurs, the risk of being seriously injured or killed is quite high. This indicates that crash severity is an important factor in producing the high motor vehicle related fatality rates reported by developing countries. The use of open vehicles to transport passengers in developing countries is common. The Papua New Guinea data suggest that a relatively large proportion of the roaduser casualties may result from this practice. In countries where this paradigm is common, this research indicates that a great incremental improvement in road safety would follow the development of effective countermeasures that discourage the use of open-back vehicles to transport passengers. REFERENCES Angell. L.; Von Buseck, C. R. Alcohol related crashes. In: Evans, L.; Schwing, R., eds. Human Behavior and Traffic Safety. New York: Plenum Press; 1985. Bayoumi, A. The epidemiology of fatal motor vehicle accidents in Kuwait. Accid. Anal. Prev. 13:399-348; 1980 Dondanville, L. A. Road safety is no accident. International Road Federation VI World Highway Conference. Montreal, October 1970. Evans, L. Driver fatalities versus car mass using a new exposure approach. Accid. Anal. Prev. 16; 1982. Eid. A. Road traffic accident in Qatar: The size of the problem. Accid. Anal. Prev. 12:287-298; 1980. Jacobs. J. D.; Sayer, I. Road accidents in developing countries. Accid. Anal. Prev. 15:337-353; 1983 Jadaan, K. S.; Salter, R. J. Traffic accidents in Kuwait. Traffic Eng. Control 23:221-223: 1982. King, D. Population of Papua New Guinea. In: King. D.; Ranck, S., eds. Papua New Guinea Atlas: A Nation in Transition. Lae. PNG: Geography Department, University of Papua New Guinea: 1984; 20-21. Licensed Drivers and Riders. Port Moresby, PNG: National Statistical Office; 1980-1984. Mekky, A. Road traffic accidents in rich developing countries: The case of Libya. Accid. Anal. Prev. 16:263277; 1984. Mekky. A. Effects of rapid increase in motorization levels on road fatality rates in some rich developing countries. Accid. Anal. Prev. 17:101-109; 1985. Nelson. D. C. Growth in Vehicle Populations and Motor Vehicle Fatality Rates in Developing Countries. Ph.D. dissertation. Berkeley, CA: Department of Civil Engineering. University of California; May 1988. Puvanachandran. P. V. Popendetta-Orobay traffic study (unpublished). Lae, PNG: Papua New Guinea University of Technology; 1982. Registered Motor Vehicles. Port Moresby. PNG: National Statistical Office; 1980-1984. Siddique. A. K.; Abengowe. C. U. Epidemiology of road traffic accidents in developing countries: Nigeria, an example. Trop. Doct. 9:67-72; 1979. Smeed, R. J. Variations in the pattern of accident rates in different countries and their causes. Traffic Eng. Control 2(7):364-371; 1968. Townsend. D. Road transport. In: King, D.; Ranck. S.. eds. Papua New Guinea Atlas: A Nation inTransition. Lae. PNG: Geography Department. University of Papua New Guinea; 1984; X0-81. Wintemute. G. Is motor vehicle related mortality a disease of development? Accid. Anal. Prev. 17:223-237; 1985. Wyatt. G. B. The epidemiology of road accidents in Papua New Guinea. Papua New Guinea Med. J. 23:6065: 1980.