A methodology for aiding hazardous materials transportation decisions

European Journal North-Holland

of Operational

Research

32 (1987)

231-244

231

A methodology for aiding hazardous materials transportation decisions Jacques F.J. van STEEN Department of Industrial Safety, Netherlands Organization for Applied Scientific Research TNO, P.O. Box 342, 7300 AH Apeldoorn, Netherlands

Abstract: Transportation

through tunnels is currently prohibited in the Netherlands for certain classes of hazardous materials. Abolishing this regulation is under consideration. However, risks to the public are associated with both tunnel routes and diversions. The question then is: which of these routes is, for a specific location, more acceptable in terms of risk? This paper presents a methodology that was developed for aiding such transportation decisions. It is based on the decision analysis approach, which is argued to offer the appropriate perspective in dealing with acceptable risk problems, and includes an approach for providing insight into potential value judgments required for arriving at a decision. Outcome valuation is treated from a monetary perspective. The necessary computing can be performed with a desk calculator (which was a requirement put forward by the client). The paper also presents an application to a specific decision problem. Keywords: Decisions, transportation,

risk, safety

1. Introduction

Transportation through tunnels is currently prohibited in the Netherlands for certain classes of hazardous materials. The purpose of this regulation was to prevent irreparable tunnel damage, which might be caused by an accident involving the release of such materials. This regulation, however, implies diverting these shipments and consequentially increases the risk to the public, as diversions usually are closer to residential areas than are highways with tunnels. Thus abolishing (or at least relaxing) the regulation in question is under consideration. Allowing hazardous materials to be transported through tunnels, on the other hand, will open up the possibility of tunnel destruction, which might involve many fatalities and would certainly imply great material and economic damage. It follows that the choice between maintaining the regulation and allowing the shipments in quesReceived

August

fl377-2217/87/$3.X10

1986 1987, Elsevier

Scienke

Publishers

tion through tunnels is not an obvious one. Consequently the Dutch Rijkswaterstaat, which controls most of the Dutch tunnel locations, expressed the need for a methodology to aid decisions on the tunnel transportation of hazardous materials. Assuming the shipments in question to take place, the scope of the problem was limited to somparing different routes. Thus the methodology to be developed was required to allow a comparison between tunnel routes and diversions, in particular from the view-point of the risks associated with the transportation of hazardous materials. More specifically, the following requirements had to be met in view of the ultimate application of the methodology to each of the Dutch tunnel locations: - the possible consequences of so-called undesirable events, which may occur during road and tunnel transportation of hazardous materials, must be estimated quantitatively, - insight has to be provided into the value judgments that might be necessary to make a choice between the different routes,

B.V. (North-Holland)

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- the necessary computing has to be performed by means of a desk calculator. This paper discusses the methodology developed. Section 2 presents some methodological considerations. Sections 3 and 4 describe the methodology, in terms of four steps that are distinguished, and an application to a specific decision problem. The paper concludes with a discussion in section 5.

2. Methodological

considerations

The purpose of the methodology is to find an answer to the following question: which route is, for a specific location, more acceptable in terms of risk: tunnel route or diversion? Risk analysis provides insight into the risks associated with the transportation of hazardous materials via both routes, where risk refers to the possibility of undesirable consequences such as fatalities. How can we use risk analysis results in answering the acceptability question? One approach would be to try to define risk acceptability criteria. The idea behind this approach is that acceptable risk levels do exist and can be derived from past behavior. Fischhoff et al., in discussing three major approaches, call this particular approach bootstrapping and distinguish four bootstrapping methods: risk compendiums, revealed preferences, implied preferences and natural standards [4]. Proponents of risk compendiums assume that a risk which is small in comparison with existing risk levels, is negligible and consequently should be acceptable. Risk compendiums are intended to make such comparisons possible. For example, tables exist of activities that increase the chance of death in any year by 1 X lo-$ typical activities include smoking 1.4 cigarettes, drinking 0.5 litre of wine, spending 1 hour in a coal mine, travelling 150 miles by car, living 2 days in New York or Boston, eating 100 charcoal-broiled steaks, living 150 years within 20 miles of a nuclear power plant, eating 40 tablespoons of peanut butter (taken from [4]). The revealed and implied preferences methods are involved with trying to find relationships between risks that were accepted in the past and other characteristics of the respective activities. The former is discussed by Starr [20]. He derived several hypotheses from statistical analyses of his-

materials

transportation

torical data, for example: The acceptable level of risk is roughly proportional to the third power of the benefits. The public is willing to accept risks from voluntary activities that are roughly a thousand times greater than those it will tolerate from involuntary activities having the same level of benefit. Finally, the natural standards method looks to the geologic past and is focussed on characteristic levels in which a species evolved. All these bootstrapping methods for.evaluating absolute risk levels have received strong criticism [2,4,14,17]. For example, one of the conclusions of a study for the Commission of the European Communities is that it is unrealistic to attempt to correlate the acceptability or acceptance of a risk with the absolute level of that risk [14]. A similar conclusion has been drawn from research within the social sciences on judging risks: risk estimates of non-experts are only moderately to poorly correlated with absolute risk levels. Apparently other factors than the absolute risk level alone influence public perceptions of risk. Consequently a number of studies have been directed at identifying factors which determine risk perception. Several reviews of this research are available [1,15,19]. Prominent contributions to research on aspects of risk perception have been provided by Fischhoff et al. [3] and Slavic et al. [18] in the USA and by Vlek and Stallen [21,22] in the Netherlands. The research approach in each case was as follows: (1) Make assumptions about factors which might determine risk perception. (2) Conduct interviews, in which subjects are asked to judge the ‘riskiness’ of a number of activities and the degree to which the different factors apply to these activities. Vlek and Stallen also investigated ‘beneficiahty’ and ‘acceptability’. (3) Analyze the results, in order to try to identify relationships between ‘riskiness’ and the different factors. The size of a potential accident, controllability and benefit-related aspects appeared to be .essential factors in risk perception. The apparent importance of the first of these factors is consistent with the existence of a phenomenon called risk aversion: usually a larger weight is assigned to large accidents or losses in comparison with small ones. l

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From this psychological research it appears that people tend to consider risky activities as a whole, and to weigh benefits against disadvantages implicitly or explicitly. This makes decision anafysis an appropriate approach for dealing with acceptable risk problems. Decision analysis is a discipline that deals explicitly with uncertainty and with value judgments like trade-offs and risk aversion [6,9,10]. Adopting this approach for acceptable risk problems is equivalent to recognizing that these problems essentially involve a decision among alternatives which are characterized by a number of aspects, of which risk is just one. It is possible, therefore, to incorporate in a natural way an aspect that happened to be one of the major motives for the existing regulation: material and economic damage. The decision analysis methodology consists of a number of steps: (1) Define the alternatives. (2) Specify the relevant aspects. (3) Define outcome measures. (4) Quantify the possible consequences of each alternative. (5) Evaluate and compare the alternatives. In practice, this approach often takes the form of an iterative procedure, in which sensitivity analyses are used to ensure that the essential characteristics of the decision problem under consideration get most of the attention. Within decision analysis several approaches can be distinguished. Merkhofer [13] presents a clear discussion of this subject. The ‘social decision analysis’ approach [7] is followed in this paper. Within this approach, valuation of outcomes is treated from a monetary perspective. The problem under consideration represents a special case of the general acceptable risk problem: the advantages of the alternatives are considered equal. This implies that the alternatives differ only by their disadvantages [2]. Anticipating the next section, it appears that the disadvantages are limited to fatalities, material damage and economic costs. Treating outcome valuation from a monetary perspective, the main trade-off then relates to valuing human life. Different procedures exist for placing a value on the loss of human life. Several reviews of this subject are available [12,23]. Within the decision analysis approach, Howard has suggested a model that can be used to calculate’s ‘small-risk value of

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life’ [8]; however, this model is somewhat difficult to apply in practice. As a result, the willingnessto-pay perspective is often adopted for dealing with the problem of valuing human life [13]. It should be noted, that the various methods for determining the value of a life have yielded a considerable range of values. This underlines the necessity of performing sensitivity analyses on this aspect [4,5]. The discussion presented above can be summarized as follows: Decision analysis is an appropriate approach for dealing with acceptable risk problems. More specifically, the ‘social decision analysis’ approach is followed in this paper. Outcome valuation is treated from a monetary perspective. The willingness-to-pay perspective is adopted for valuing human life. Sensitivity analysis on the value of life must be possible. How these considerations, together with the requirements listed in section 1, were translated into a model that is applicable in practice, is shown in the next section which describes the methodology that was developed for aiding hazardous materials transportation decisions. It will appear that simplifications were unavoidable, partly due to the requirement of a desk calculator being sufficient for the necessary computing. l

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3. Methodology

description

The methodology developed consists of a number of consecutive activities. Figure 1 presents a schematic representation of methodology structure. Four principal steps are distinguished: (1) Characterize the alternatives; one of the activities here is investigating maps of the routes and their environment. (2) Employ a calculation scheme that leads to the so-called primary result. (3) Analyse the decision problem on the basis of the primary result. Relevant activities here are determining the category to which the decision problem belongs, employing value relationships and calculating the respective components of the primary result. (4) Perform a sensitivity analysis that might lead to one of several supplemental steps.

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EMPLOY VALUE RELATIONSHIPS

/ Decision

I

aid /or hazardous

1 CALCULATE

materials

COMPONENTS

transportation

)

I

Figure 1. Schematic representation of methodology structure

The following sections present discussion of these steps.

a more detailed

3.1. Step I: Characterize the alternatives Characterizing the alternatives involves the following activities: determine road types, divide routes into road sections, determine population and value categories, . characterize constructions (e.g. the tunnel), determine hazardous materials distribution across shipments. l

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Determining road types is relevant for calculation of fatalities and damage on the road itself and for accident probabilities. Fatalities and damage on the road itself depend on the road being a one-lane road or a dual carriage-way. Four road types are distinguished with different accident probabilities. If available, specific data on accident probabilities may be used. The routes have to be divided into homogeneous road sections of at least 500 m length. The condition ‘homogeneous’ refers to densities of population and value up to a distance of 100 m from the road and in the area between 100 m and

J. F.J. oan Steen / Decision Table 1 Formal for the data characterizing

aid for hazardous

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transportation

235

an alternative

250 m from the road. These length and distance requirements relate to possible effect areas. Once densities of population and value have been determined, the road sections are allocated to density categories. Both for population and for value four categories are distinguished. Tunnels are characterized by length, value, number of tubes and lanes, and accident probability. The distribution of different types of hazardous materials across shipments is determined by calculating the fractions of vehicles carrying the various categories of hazardous materials. Five categories are distinguished: explosives, flarnmable gases, flammable liquids, toxic liquids and toxic gases. A standardized distribution is used within the calculation model. As with accident probabilities, specific data on the actual distribution may be useful. The format for the data characterizing an alternative (excluding those on the tunnel and on the hazardous materials distribution) is given in Table 1. 3.2. Step 2: Employ calculation scheme

The results of the previous stage serve as ilLput to the calculation scheme, together with the collection of damage factors, which were calculated in advance for each combination of road type and environment category. Starting from the respective road sections, the corresponding damage factors are collected. Employing the calculation scheme

then leads to the so-called primary result. A model was used to calculate the damage factors. This cakulation model is explained first. Calculation model

The calculation model used for calculating probability distributions of fatalities and material/economic damage, consists of a damage model and a probability model (see Figure 2). The damage model serves to quantify the consequences of the alternatives. Consequences considered are limited to the following major attributes: fatalities, due to traffic accidents which are followed by hazardous materials release, * fatalities, due to traffic accidents without release occurring, material and economic damage, due to traffic accidents which are followed by hazardous materials release, * material damage, due to traffic accidents without release occurring, . additional transportation costs, associated with the longest of the two routes. Environmental aspects (air and noise pollution) have also been assessed, but appeared to be of little consequence. The attributes mentioned above can be divided into two categories: fatalities and material/economic damage. The damage model calculates number of fatalities and size of material/economic damage depending upon four variables: accident location, substance, accident scenario and l

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aidfor

hazardous

materials

transportation

. environment

TECHNICAL

DATA

t

I j ~czzi~~i~elocation

::

j

1 \

PROBABILITY MODEL

accident

scenario \

type

of

Figure 2. Schematic representation of calculation model

damage

r

i

fatalities

-

material and economic damage

-

probability

DAMAGE MODEL

\ \ \ \ \ \

.

\ '\I, ' '1 \ \ \ t' '\ 1 \\ \ '\\ \ '\\ \;,I\ \a4

.

structure

type of damage. The last variable relates to the possibility of different types of damage within one accident scenario. The probability model calculates the probabilities associated with the possible damages. From the probability distributions calculated, damage factors were derived on an expected value basis for each combination of road type and environment category, as well as for the tunnel. These damage factors are divided into factors for fatalities and factors for material/economic damage. In addition, damage factors were calculated which separate out the contribution of large accidents to the respective expected values. Primary result calculation

The primary result consists of a number of expected values for each route. These expected values are calculated by multiplying damage factor, length and accident probability for each road

section type and then adding up: EV = c(EyL& i

ELA= C (ELAjLjq) j

+ CCL,, i (2)

where EV = expected value for the route, ELA = expected value of large accidents for the route (= contribution of large accidents to EV), EVj = EV-factor for combination j of road type and environment category, ELA,= ELA-factor for combination j of road type and environment category, = total length of type j road section, Li = accident probability of type j road sec5. tion, c = transportation costs per unit of road length.

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/ Decision

fatalities

alternative

1

aid for hazardous material/economic

alternative

alternative

2

EV

EV

ELA

ELA

1

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231

damage

alternative

2

L

Figure

3. Primary

result

presentation

The term in C only applies for calculating material/economic damage expected value. Both formulae are applied twice for each alternative. Thus, each alternative is characterized by four numbers (subscripts f and m refer to fatalities and material/economic damage respectively): (1) Total expected value of fatalities (EV,). (2) Expected value of fatalities due to large accidents (ELA r). (3) Total expected value of material/economic damage (EV,,, ). (4) Expected value of material/economic damage due to large accidents (ELA,). Together these numbers constitute the primary result, which is presented in the form shown in Figure 3. 3.3. Step 3: Analyse the decision problem On the basis of the primary result, a preliminary decision may be made. However, this decision may not prove to be a robust one over all situations. Moreover, situations may occur, in which even a preliminary decision is not obvious based purely on the primary result. This could occur when: the differences between the alternatives are small, large accidents require that risk aversion is taken into account, a trade-off beteen fatalities and material/ economic damage is required. In these situations further analysis of the decision problem is necessary, taking the primary result as starting point. First, the decision problem is characterized by determining the category to which it belongs. Next, category-specific value relationship are employed, directed at providing insights into the trade-off and risk aversion aspects of the decision problem. These value relationships are based on a simple value model. l

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Characterize the decision problem Characterizing the decision problem is done by determining the category to which it belongs. The classification of categories is based on whether value judgments are necessary or not, and, if so, on whether the required value judgments are related to trade-off, to risk aversion or to both. Considering all possible patterns, four principal categories exist: * category 1: no value judgments necessary, * category 2: trade-off relevant, * category 3: risk aversion relevant, . category 4: both trade-off and risk aversion relevant. Category 4 is the most complicated one and also the largest. However, in most of the situations some simplification is possible. Thus category 4 is divided into two sub-categories: * category 4A: separable problems, category 4B: non-separable problems. All categories are presented in Figure 4. Here, the primary result is used only qualitatively: the preferred alternative is indicated by an asterisk for each of the four characteristic numbers. (Within each category two possible situations exist, of which just one is shown; the other one is its mirror image.) Different procedures apply for each case. If category 1 occurs, then alternative 1 is clearly preferred. Further analysis is limited to checking robustness of the result. Category 2 shows clear preferences in terms of each of fatalities and material/economic damage expected values, but they are different. In this case the choice depends upon the trade-off between fatalities and material/economic damage. Category 3 shows different preferences for total expected values and large accident expected values. In this case the choice depends upon the degree of risk aversion. Within category 4 risk aversion and the trade-off l

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materials

trakportation

possible by separation of these two* value judgments. Category 4A shows a different preference for

between fatalities and material/economic damage must in principle be considered together. However, in a number of situations simplification is FATALITIES

MATERIAL/ECONOMIC

DAMAGE

Category

'

::,

Far

I

( alternative

2 ,

,alternr

I rnative

2 ,

Category

*

::,

~ltet-nafve

1

1 alternative

2 ,

, alternative

1 ~altern;ive

2 ,

Category

3

::,

ra;ve

I

r;

2,

r;ive

I r;

2

,

Category

4Al

Category

4A2

II;

rar

1

1.11).1.e

2 ,

r;ive

I r;

2

,

Category

4A3

::,

[lt.erna;ve

1

ialtern;

2 ,

,altern;ive

1 I;lternative

2 ,

Category

4A4

::,

r;ve

1

rr

2 ,

r';

1 rative

2 ,

Category

4B

::,

rzve

1

r;

2 ,

r"

1 Five

2

Figure 4. Decision problem characterization

ive

,

J. F.J. van Steen

/ Decision

aid for

hazardous

one of the four characteristic numbers. In these cases the choice may depend upon either the degree of risk aversion or the trade-off between fatalities and material/economic damage. With category 4Al for example, alternative 1 is preferred for strong risk aversion or for a strong weighing of fatalities. The problem belongs to category 4B, if neither is the case. Within category 4B the joint effect of trade-off and risk aversion determines which alternative is preferred.

D, = ev,.

It appeared that category dependent simplifications are possible, leading to convenient value relationships in terms of break-even points and preference conditions. To employ these value relationships first requires a calculation of differences between absolute values. The notation is as follows:

D,., = (evr - ela,) + u ela, = ev, + (u - l)ela,.

(5) Again, a similar formula applies to material/economic damage. In these formulae the attributes ‘ fatah ties’ and ‘ material/economic damage’ are considered separately. Joint treatment of these two attributes is effected by introducing a trade-off factor b, which is equivalent to the amount of material/economic damage one is willing to accept in order to prevent one fatality. In (6) both risk aversion and trade-off are considered:

(3)

where = total expected value of fatalities, w subscript 1 = alternative 1, subscript 2 = alternative 2.

D=b[ev,+(u-l)ela,]

Similar formulae apply to the other characteristic numbers. Thus four difference values are calculated (see Figure 5).

alternative EV

ELA

1

EVfl

E"f2

ELAfl

ELAf2

1

Figure 5. Calculation of character&k

2

It is also possible to concentrate

alternative EV

ELA

I

+ev,,,+(u-l)ela,. (6)

material/economic alternative

(4)

A similar formula applies to material/economic damage. It must be possible, however, to take into account the impact of risk aversion. This is done by introducing a risk aversion factor u, which weights the contribution of large accidents (u > 1):

Employ value relationships

fatalities

239

transportation

The respective value relationships were derived from several formulae, which represent differences between alternatives from various perspectives. If all consequences of accidents are valued equally, regardless of size, then the difference D between the alternatives is

just

ev, = EV, - EV,

materials

1

damage alternative

EVml

EVm2

ELAml

EL%2

I

numbers which represent the decision problem

2

on trade-off

J. F.J. oan SIeen / Decision

240 Table 2 Summary points Category

of

category

2/3

Break-even first

2

bo-$

3

u,=l--

value

relationships:

aid /or hazardous

break-even

points

approximation

corrected

value

b = _ ev,+(u-l)ek 1 ev, + (a - 1)elar ev, +ev,/b u,=lelar + ela,/b

I evr ela,

between fatalities and material/economic without considering risk aversion, as in:

All value relationships were derived from the above formulae. These relationships are category dependent, and are summarized in Tables 2 and 3. Choice in a category 2 decision problem depends upon the trade-off between fatalities and material/economic damage. To this end the break-even point for b is determined, which is the value of b for which the difference between the alternatives is zero. A first approximation for this break-even point (6,) is obtained from (7). The choice then is made by comparing 6, with the decision maker’s value (bdm); alternative 1 is preferred if b,, > b,. This first approximation for b does not consider risk aversion. A corrected value for the break-even point (b,) is obtained from (6). Similarly, risk aversion break-even points can be derived for category 3 decision problems by applying (5) and (6). Again the choice is made by comparing z+ or U, with the decision maker’s value (u,,). Alternative 1 is preferred if udm is smaller than the risk aversion break-even point.

Category

Condition

4Al

U.jm 2 1 - 2

4A2

4A3 4A4

“drn

1

conditions Condition

m

b&-2

6 1 - 5% ela,

Udm 2 1 - JYk ela,

indicat2

I ela,

hrnz-~ b,,,/$’ f%mB-~

I r ela, I

Category 4Al will serve as an example of how to derive value relationships for category 4A. It appears that three difference values have the same sign. Alternative 1 is preferred if the difference D between alternative 1 and alternative 2 is negative: +ev,+(u-l)ela,
(8)

In this case ev,, ela r and ela m are negative, whereas ev, is positive. The condition represented by equation (8) is certainly met, if

(7)

Table 3 Summary of category 4 value relationships: ing preference for alternative 1

rransporrarion

b[ev, + (U - l)ela,]

damage,

D b.ev= bev, + ev,.

maferiob

ev, + (II - l)ela, This equation 4Al:

5 0.

leads to condition I for category

udm 2 1 - ev,,/ela m.

(9)

If condition 1 is met, then alternative 1 is preferred regardless of the value of 6: for strong risk aversion, the decision problem is not a trade-off problem The condition that is represented by equation (8) is also certainly met, if bev, + ev,,, I 0. This equation 4Al:

leads to condition 2 for category

bdm r - ev,,,/ev,,

(10)

If condition 2 is met, then alternative 1 is preferred regardless of the value of u: for a strong trade-off on fatalities, the decision problem is not a risk aversion problem. If neither condition is met, alternative 1 could still be the preferred alternative. However, the decision problem then becomes a category’ 4B problem. The choice at a category 4B problem depends upon the joint effect of trade-off and risk aversion. These problems cannot be simplified by indicating separate conditions for trade-off or risk aversion. Calculate primary result components The damage factors EVj and ELA,, introduced in formulae (1) and (2), are aggregated factors. Since the components of these damage factors are known, it is possible to divide the primary result into its components.

J.F.J.

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Three categories of fatalities can be distinguished: . fatalities on the road/in the tunnel, due to the release of hazardous materials, . fatalities in the environment, due to the release of hazardous materials, fatalities due to traffic accident with no release. Five categories of material/economic damage can be distinguished, of which the first three are similar to those mentioned above. The other two are: . damage due to the difference in transportation costs between the two alternatives, economic losses, caused by irreparable tunnel damage. Finally, the contributions of the different categories of hazardous materials to the primary result can be distinguished. l

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3.4. Step 4: Perform sensitivity analysis After the preceding three steps a choice between the alternatives can be made. It is important however to get insight into the robustness of this choice. By performing sensitivity analyses it becomes possible to investigate the impact of input data variations on the primary result and on the parameters that are derived from the primary result: differences between the alternatives, break-even points and preference conditions. Thus it can be determined: whether the final results change significantly due to possible input data variations, * whether factors exist, which require further investigation because they can have significant impact on the final results, what the impact on the final results is, if factors are corrected, e.g. in case new data becomes available. In the study sensitivity analysis relationships were developed which describe the relation between changes in expected value differences (ev and ela values) and changes in the following factors: hazardous materials distribution across shipments, accident probabilities, * population densities, * value densities, large accident threshold values, . scenario probabilities. l

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Furthermore, relationships were developed which indicate how changes in expected value differences influence break-even points and preference conditions. The analyses performed may m some circumstances show that it is impossible or inadvisable to make a choice on the basis of the results calculated. Two situations may occur: one or more potentially variable factors may appear to be very influential, or the decision problem may be a borderline case. In those situations supplemental steps are to be considered. In the first case specific research into the factor in question might be possible. In the second case other aspects than fatalities and material/economic damage may be determining. One could also consider a more detailed analysis with computer calculations.

4. Application The methodology described above has been applied to a specific tunnel location. The lengths of the tunnel route and the diversion were 7.65 km and 15.9 km respectively. The results of the calculations are presented in Figure 6. Figure 6a shows the primary result. The tunnel route is preferred from the point of view of fatalities. Material/economic damage results do not show an unambiguous preference. Indicating the preferences by an asterisk leads to Figure 7. It appears that this decision problem is a category 4A2 problem. Conditions indicating preference for alternative 1, the tunnel route, can be obtained from Table 3. Substitution of the numbers calculated learns that the tunnel route is preferred if one of the following conditions is met: (1) (2)

I(&,, < 740, bdm 2 Dfl. 500000 per fatality.

These conditions imply, that the tunnel route is preferred if large accidents are weighed less than 740 times as much as small accidents or if material/economic damage that one is willing to accept to prevent a fatality is at least Dfl. 500000. These value judgments have to be made by the decision maker. Figure 6b shows the contributions of the respective components to the primary result. The following components appear to dominate the

242

J.

a) Primary

F.J. van

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transportorion

result fatalities [IO-'

material/economic

fatalities/shipment1

damage

[10m3 Ofl./shipmentl

b) Components

Hazardous

materials

Hazardous materials (road + tunnel)

Hazardous materials (environment)

Traffic accidents (no release)

Transportation

costs

Economic losses, caused by irreparable tunnel damage

Figure 6. Application:

Results

characteristic numbers: - (EV,): traffic accidents without release occurring, - (ELA,): road/tunnel, due to hazardous materials release, fatalities

tunnel EV

ELA

route

mterial/economic diversion

tunnel

I

EV

*

ELA

Figure 7. Application:

- (EV,,,): transportation costs, - (ELA,,): tunnel, due to hazardous materials release (both direct damage and economic losses). Concentrating on the contributions of the different categories of hazardous materials, it ap-

Problem characterization

route

damage

diversion

*

*

J. F. J. van Steen / Decision fatalities [lo-'

aid for hazardous material/economic

fatalities/shipment]

tunnel

route

[lo-3

diversion

tunnel

EV

1.41

4.73

EV

ELA

0.11

0.32

ELA

Figure 8. Application:

materials

transportation

243

damage

Ofl./shipment! diversion

route

11496

23903

5.86

0

Sensitivity analysis results for accident probabilities

peared that the expected values calculated were dominated by the flammable gases category. Figure 8 shows sensitivity analysis results when specific accident probabilities are applied instead of standard values. The decision problem remains a category 4A2 problem. However, the conditions for tunnel route preference change: (1)

r/,, Q 2100,

(2)

bdm2 Dfl. 300000 per fatality.

5. Discussion The methodology presented is subject to a number of limitations. These are caused by the attributes considered, by the nature of the information available, by necessary simplifications and by the incorporation of value judgments. Estimates are limited to some main attributes: fatalities and material/economic damage. Environmental aspects proved to have little influence. Other attributes have not been considered. Estimates used are based on available information, e.g. accident statistics. These estimates are characterized by large uncertainties. However, the methodology is directed toward comparing alternatives, and consequently accuracy of absolute values is less important. Furthermore, simplifications were required. For example: fatalities and material damage which are joint consequences of an accident scenario, are considered separately. These simplifications were partly caused by the managerial requirement that the necessary computing should be performed by means of a desk calculator. Besides, the damage

factors calculated are based on a limited number of scenarios that are considered representative for the different hazardous materials categories. Finally, a simple value model was used. For example: fatality weighting is independent of fatality level and damage level, the weighting of large accidents is described by a constant factor above certain threshold values, with the same factor employed for fatalities and damage. One could also argue that the level of aggregation, involved in transforming the primary result into only two basic value parameters, is too great [16]. However, this aspect of the methodology does not prevent the decision maker from considering the primary result and its components. Furthermore, one could partly meet this objection by transferring the model (or preferably a more sophisticated version of it) into an interactive microcomputer program, which is currently considered as being an interesting direction for further development. This would also facilitate evaluation of actual applications by other parties or individuals involved in the decision-making process. This would enable the decision-maker to take into account more easily the preferences of the affected individuals, which is recognized to be an important aspect of societal decision-making [ll] In conclusion, the methodology presented is believed to be, despite its limitations, a useful aid in decision-making about the transportation of hazardous materials: it provides a consistent picture of the decision problem along some key attributes. This makes a preliminary choice possible which can be analyzed further. This approach leads to arguments in favour of one alternative and offers a good starting-point for investigating the effect of other attributes. Finally, it is noted that the Dutch Rijkswa-

J.F.J.

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aid for hazardous

terstaat, which controls most of the Dutch tunnel locations, is now in the process of applying the methodology described in this paper to each of those locations. Acknowledgment Cees Jansen, Loet Janssen and especially Coen Cieraad provided important contributions to the research project on which large parts of this paper are based. The research project in question was performed for the Rijkswaterstaat. The author is grateful to the Rijkswaterstaat for permission to publish this paper. This agency does not necessarily agree with any views presented here. Jonathan Rosenhead and Mirko Vujosevic provided valuable comments on earlier drafts of this paper. References [l]

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