dangerous good transportation by road from risk analysis

7/27/2019 Dangerous Good Transportation by Road From Risk Analysis

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Dangerous good transportation by road: from risk analysis

to emergency planning

B. Fabiano*, F. Curro, A.P. Reverberi, R. Pastorino

DIChePChemical and Process Engineering Department, G.B. Bonino, University of Genoa, Via Opera Pia, 15-16145 Genoa, Italy

Abstract

Despite the relative recent move towards inherent safe materials, the relentless drive of consumerism requires increased quantities of

dangerous goods to be manufactured, transported, stored and used year on year. The safety and effectiveness of road transport systems is to beconsidered a strategic goal in particular in those countries, like Italy, in which 80% of goods are transported by this means. In this paper, we

face the risk from dangerous good transport by presenting a site-oriented framework for risk assessment and developing a theoretical

approach for emergency planning and optimisation. In the first step, we collected field data on a pilot highway and developed a database

useful to allow a realistic evaluation of the accident frequency on a given route, by means of multivariate statistical analysis. To this end, we

considered both inherent factors (such as tunnels, bend radii, height gradient, slope etc), meteorological factors, and traffic factors (traffic

frequency of tank truck, dangerous good truck etc.) suitable to modify the standard national accident frequency. By applying the results to a

pilot area, referring to flammable and explosive scenarios, we performed a risk assessment sensitive to route features and population exposed.

The results show that the risk associated to the transport of hazardous materials, in some highway stretches, can be at the boundary of the

acceptability level of risk set down by the well known F/N curves established in the Netherlands. On this basis, in the subsequent step, we

developed a theoretical approach, based on the graph theory, to plan optimal emergency actions. The effectiveness of an emergency planning

can normally be evaluated in term of system quickness and reliability. As a case study, we applied the developed approach to identify optimal

consistency and localisation in the pilot area of prompt action vehicles, properly equipped, quick to move and ready for every eventuality.

Applying this method results in an unambiguous and consistent selection criterion that allows reduction of intervention time, in connectionwith technical and economic optimisation of emergency equipment.

q 2005 Elsevier Ltd. All rights reserved.

Keywords: Accident frequency; Hazardous materials; Emergency; Transportation

1. Introduction

Despite the relative recent move towards inherent safe

materials, the relentless drive of consumerism has required

increased quantities of dangerous goods to be manufactured,

transported, stored and used year on year (Thomson, 1998).

Of the different way of transportation, rail has higher

damage potential, as larger quantities are transported by this

means. However, considering the damage it may cause to

life and properties, transport by road is more hazardous, as

roads often pass through populated areas, especially in

developing countries. The recent EEC Directive 96/82/EC

implies the evaluation of risk in highly industrialised areas

by means of Quantitative Area Risk Analysis techniques. It

must be evidenced that certain dangerous substances are

transported along particular Italian road routes in quantities

that would exceed the threshold for safety notification or

declaration, set down in Italy by Seveso II Directive, if

stored in a fixed installation. On the other side, it must be

remembered that EEC Directive 94/55/EC implies the

harmonisation of the different national legislation on

transport of hazardous materials by road. The safety and

efficiency of road transport is to be considered a strategic

goal in particular in those countries, like Italy, in which

about 80% of goods is transported by this means, with a

30% increase with reference to the 2010 forecast. In

particular, Italian highways are very crowded with trucks,

considering that 17% of the whole good traffic by road of

EU (15 Countries) is transported on these highways.

Moreover, the number of cars is still steadily increasing,

Journal of Loss Prevention in the Process Industries 18 (2005) 403413

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0950-4230/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.jlp.2005.06.031

* Corresponding author. Tel.:C39 010 3532585; fax:C39 010 3532586.

E-mail address: [email protected] (B. Fabiano).
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making a place on the road increasingly a scarce

commodity. Empirical evidence shows that though

improvement in transport safety, in Italy a consistent

number of serious accidents on motorways and highways

keeps occurring, evidencing that the risk connected to

dangerous goods transport is comparable with the fixed

plants one.Analysis of the risks presented by the transportation of

hazardous materials presents a very different risk than a

fixed facility: detailed information on shipments is not

available on a national, regional, or local level in contrast

with fixed facility inventories (Pine & Marx, 1997). As

reported by different researchers, a specifically tailored

QRA methodology can represent an effective tool to assess

the risk to people associated with the transport of dangerous

substance. The selection of the best route for transport has

been widely investigated (List, Mirchandani, Turnquist, &

Zografos, 1991) and was recently formulated as a

minimum cost flow problem, which consists of determin-

ing, for a specific hazardous substance, the cheapest flow

distribution, honouring the arc capacities, from the origin to

the destination vertices (Leonelli, Bonvicini, & Spadoni,

2000). Poor appreciation of factors related to road

conditions such as road class, designated speed limits,

traffic density, as well as of the population characteristics, is

likely to result in a risk assessment insensitive to route

specifics and over- or under-estimating the overall level of

risk (Davies, 1999).

In this paper, a site-oriented risk analysis procedure is

tested in a pilot area, starting from an in-depth inventory of

hazardous materials transported and from a statistical

analysis of traffic and accidents observed in the area. Infact, it must be observed that to ensure that a local

emergency plan is complete, it must take into account the

nature and extent hazardous materials are transported by

road in the area. The results are then discussed and a

mathematical model for optimising emergency planning is

presented. A main focus in the management of emergencies

has been on resources and logistics; in other words, having

what and who you need it to meet the crisis within an urgent

time frame (Kowalski, 1995). The importance of the ability

of the emergency response services to minimize the damage

was recently highlighted by a pilot project carried out in the

Netherlands, where the evaluation method of external safetyrisk included three new criteria, additional to individual risk

and societal risk (Wiersma & Molag, 2004):

self-rescue i.e. the ability of the people in the vicinity

of the accident to safe themselves;

controllability i.e. emergency response services;

consequences i.e. analysis of representative scenarios

in terms of number of fatalities, injuries and material

damage.

The criterion of controllability is focused on the ability of

the emergency response services to minimize the magnitude

and to prevent escalation of the accident. In case of accident

in hazmat transportation and subsequent release into the

environment, it is very important to have at ones disposal

information on each chemical hazardous product involved,

trained and skilful personnel and suitable prompt action

vehicles, properly equipped to be employed if the above

mentioned hazardous release would happen. To this end, inthe last phase of this paper, the optimisation algorithm is

developed for solving the problem of optimal location of

emergency vehicles in the pilot area.

2. Theoretical structure

2.1. Transportation risk analysis

Generally speaking, the concept of risk is the relation

between frequency and the number of people suffering from

a specified level of harm in a given population from the

realization of specified hazards (Vrijling, Van Hengel, &

Houben, 1995).

The model required for our purposes is focused on a

proper evaluation of the expected frequency of accidents. If

the route is divided into road stretches, each characterized

by different characteristics, the expected number of fatalities

as consequence of an accident occurred on the road stretch r

and evolving according to a scenario S, can be expressed as:

DrZX

S

frNr;SPS (1)

where:fr frequency of accident in the rth road stretch

[accident yearK1]

Nr,S number of fatalities caused by the accident evolving

according to a scenario S in the rth road stretch

[fatalities accidentK1]

PS probability of evolving scenarios of type S, following

the accident initialiser (i.e. collision; roll-over; failure

etc.) []

Transportation network can be considered as a number of

vertices linked one another by a number of arcs. As shown

in the following paragraph, the vertices represent origin-destination points, tool-gates, storage areas on the transpor-

tation network and the arcs are the roads connecting

vertices. An arc between two vertices is characterized by

a different number of road stretches and the expected

number of fatalities for the arc is:

DZX

r

X

S

frNr;SPS (2)

where Nr,S is the total number of fatalities according to

Eq. (3):

Nr;SZ AinS kvCA

offS dPPF;S (3)

B. Fabiano et al. / Journal of Loss Prevention in the Process Industries 18 (2005) 403413404


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being the in-road and the off-road number of fatalities

calculated respectively as:

Ninr;SZA

inS kvPF;S (4)

Noffr;SZA

offS dPPF;S (5)

where:AinS consequence in-road area associated with scenario S

[m2]

AoffS consequence off-road area associated with scenario S

[km2]

PF,S probability of fatality for accident scenario S []

k average vehicle occupation factor []

v vehicle density on the road area [vehicle mK2]

dP population density [inhabitants kmK2]

The frequency of an accident involving a scenario S, on

the r-th road stretch, can be expressed as:

fr;SZfrPS (6)

frZgrLrnr (7)

grZg0;r

Y6

jZ1

hj (8)

where:

gr expected frequency on rth road stretch

[accident kmK1 vehicleK1 yearK1]

Lr road length [km]

nr number of vehicles [vehicle]

g0,r national accident frequency [accident kmK1

vehicleK1 yearK1]

hj local enhancing/mitigating parameters []

As is well known, various factors influence the accidents:

mechanical, environmental, behavioural, physical, road

intrinsic descriptors. A statistical multivariate analysis was

performed, by comparing historical accident data related to

the whole regional highways and data directly collected on

the field on each stretch, in order to highlight relevant

intrinsic road factors and meteorological, traffic conditions

etc. (Fabiano, Curro, Palazzi & Pastorino, 2002).

Table 1 shows the parameters suitable to influence

accident rates and grouped into three categories: intrinsic

characteristics, meteorological conditions and traffic con-

ditions. Thevalues of the parameters are in the range 0.82.5.

2.2. Emergency planning

The effectiveness of an action aimed at facing an

emergency situation can normally be evaluated in terms of

systems quickness and reliability. To approach the

optimisation problem we adopted the graph-theory,

recently introduced by Beroggi and Wallace (1994) in

computing optimal course of action for emergency

response. Generally speaking, a linear graph may be

defined as a set N of objects named vertices Vi (iZ1,.,n)

and a set A of arcs linking couples of vertices (Vi,Vj). Indetails, a graph is a couple G(N,A) where NZ[V1,.,n] is a

set of vertices and AZai;jZVi; VjjVi; Vj2N is a class

of elements called arcs. Between two vertices, several

oriented arcs may exist: the maximum number of the same

oriented arcs between two vertices is called P and the

graph is a P-graph. The set of vertices N can be run

according to different ways: as tail, leading to the research

in breadth (breadth-first), or as a pile, leading to research in

depth (depth-first) or backtracking (Tarjan, 1972). In order

to solve the minimum intervention time problem, a label

d(i) is assigned to every vertex Vi, defining the path

between vertices and a pointer pred(i), which shows the

predecessor of Vi in the considered path. The sequence

starts from a temporary value for d(i) which has to be

modified, by iteration, as to reach the right value. After a

comparative survey on various shortest path algorithms

(Dreyfus, 1969) we considered the Dijkstra algorithm

(Dijkstra, 1959) of label setting, as follows:

1. d(s)Z0; d(i)Zx; pred(i)Zs;

2. d(h)Zmin[d(i)/d(i) not exact]; d(h) becomes exact;

3. if Vi2A(h) and di not exact, d(i)Zmin[d(i),d(h)Cc];

eventually pred(i)Zh;

4. if every value d(i) is exact, then stop, if not go to 2.

Table 1

Local enhancing and mitigating parameters

Parameter

Intrinsic characteristics h1 Straight road

Road bend (radiusO200 m)

Road bend (radius!200 m)

h2 Plane road

Slope road (gradient!5%)

Steep slope road (gradientO5%)

Downhill road (gradient!5%)

Steep downhill road (gradientO5%)

h3 Two lanes for each carriageway

Two lanes and emergency lane for

each carriageway

Three lanes and emergency lane for

each carriageway

h4 Well lighted straight tunnel

Other tunnels

Bridge

Meteor. cond. h5 Fine weather

Rain

Heavy rainFog

Snow/ice

Traffic charact. h6 Low intensity!500 vehicle/h

Medium intensity!1250 vehicle/h

with heavy traffic!125 truck/day

High intensityO1250 vehicle/h

High intensityO1250 vehicle/h with

heavy trafficO250 truck/day

B. Fabiano et al. / Journal of Loss Prevention in the Process Industries 18 (2005) 403413 405


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We developed the optimisation algorithm as schematised

in Fig. 1. Every vertex corresponds to a toll gate, a fire

brigade station or to a storage area and the algorithm allots

the exact value for d(i) at the last iteration for every vertex.

3. Case-study

The methodology previously presented was applied to a

pilot area, referring to the routes starting from the Genoa

port area (the most important in the Mediterranean basin)

towards four direction: the industrialized North Italian and

Central Europe districts, France and South of Italy (Fig. 2).

All of these highways are characterized by high truck

traffic (mainly ADR) and inherent factors (ascribed to road

out-of-date: the year of construction of A7 is 1935)

determining to a major accident risk, with reference to both

individual and social risk, defined according to the Dutch

limits.

3.1. Data collection and analysis

The value of traffic and accidents for the four highways

in the area are shown in Table 2. In particular, it can be

noticed that A7 highway is characterized by values higher at

least an order of magnitude than the accident frequency

(6.0!10K8

) calculated by other researchers for certain typeof load threatening accidents (James, 1986), thus approach-

ing the calculated values for urban road.

By considering the daily ADR traffic on the different

highway sections, it results that the higher values of

dangerous goods fluxes correspond to the intersection

between the highways A10 (West riviera) and A12 (East

riviera), in the stretch between the towns of Bolzaneto and

Busalla and in the starting stretch, from the central port of

Genoa (Genova Ovest toll-gate) to the connection between

the highways A10 and A7.

The substances transported are shown in Fig. 3: it is

important to notice the high striking transport percentages of

chlorine and ethylene oxide.

The immediate causes of accident are summed up in

Fig. 4.

The proportion of severe accidents on these highways

during the years 19951999 is in the range 6070% of the

total accidents, defining a severe incident as one involving

death, serious injuries, a fire or explosion, or more than EUR

25000 worth of damage.

3.2. Modelling

In order to obtain a correct evaluation of the density ofthe population which might be exposed to Hazmat hazards

from transport, it is necessary to include data on the

population density along the route and on the so-called

motorist density, taking into account, as well, the proportion

which may be considered particularly vulnerable or

protected. Otherwise, all individuals within a threshold

distance from road stretches run the same risks regardless of

their location.

The population density along a route segment can vary

with time, such as from day to night, and from month to

month. The average density on the route can be calculated

starting from the collected statistical data relevant to

average daily traffic, average speed and geometrical data

of carriageway and lanes, in each highway stretch

considered.

Also on-road population can vary during the day: in order

to evaluate correctly the number of on-road population

involved in the accident, the response and the variations in

the motorist density as a consequence of an accident, were

considered.

Two classes of motorist density are to be considered: the

former refers to the carriageway, where the accident occurs,

the latter considers the opposite carriageway, were the

ghoul effect causes the slowing down of the traffic.

Fig. 1. Modified Dijkstra algorithm.



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In order to evaluate the probability of death in the area

involved, the consequence model was applied making

reference to event trees for every type of accident

consequence: pool fire, flash fire, jet fire, BLEVE, fireball, UVCE, release of toxic substances.

Average individual risk, defined as the frequency at

which an individual may be expected to sustain a given level

of harm from the realization of a specific hazard (Dantzig

& Kriens, 1960), has been determined averaging the

estimating individual risk levels for all the individuals in

the selected area, as above-described. For individual risk,

we considered the upper acceptability criterion set down in

the Netherlands in new situations or new developments,

corresponding to 10K6 yearK1.

The same technique was secondly adopted for the

evaluation of societal risk. Societal risk analysis can lead,via the generation of expectation values (average number of

lives lost) to the consideration of the need for, and cost

benefit, of risk reduction measures, even if it involves many

generalising assumptions and averaging (Purdy, 1993). In

all concepts, the most stringent of the personally and the

socially acceptable level of risk determines the acceptable

level of risk. So both criteria have to be satisfied ( Vrijling

et al., 1995). The same acceptability criterion for individual

Fig. 2. Pilot area.

Table 2

Daily traffic and accident frequency

Highway Daily traffic [n] Accident per km per 10 millions of vehicles

Length [km] Total Heavy vehicle Light vehicle Total Heavy vehicle Light vehicle

A26 83.7 34946 7172 27774 6.29 6.63 4.97

A12 48.7 52105 8139 43966 5.16 5.22 4.84

A10 45.5 55025 9190 45835 6.06 7.20 6.95

A7 50.0 33721 6353 27368 9.83 9.92 9.81

A7-Stretch 1 1.9 30075 6123 23952 8.63 11.77 7.83

A7-Stretch 2 3 31620 6235 25385 4.04 7.32 3.24

A7-Stretch 3 2.9 27758 5134 22624 6.47 11.04 5.43

A7-Stretch 4 14.3 15476 3250 12226 6.56 7.66 6.27

A7-Stretch 5 5 12289 2768 9521 13.4 17.82 12.09

A7-Stretch 6 5.8 12042 2746 9296 7.45 6.88 7.62

A7-Stretch 7 6.6 11823 2512 9311 4.56 6.61 4.01



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and societal risk was considered by Alp and Eelensky

(1996), in developing a rigorous mathematical platform on

which risk assessment can be built. It must be evidenced that

the societal acceptable risk criterion is not standardized in

the different EU Countries. So, in the absence of a national

statistical reference, we adopted again the F/N limit curves

established in the Netherlands, dividing as well the so-called

Alarp region into two bands: the acceptability criterion of

the risk so modified is explained in Table 3, where P is the

cumulative frequency per year and Nthe number of fatalities

(Hj & Kroger, 2002).The results of the risk evaluation for the pilot area are

summarized in Fig. 5.

To reduce intervention time, the localization of prompt

action vehicles must found on scientific statement, as

previously explained, taking into consideration the concept

of minimum pathway. The main constraint the theoretical

approach is based upon is that the emergency vehicle can

not be placed in any site of the concerned provincial

territory, but only in the Fire Brigades Central Department

or in one of the six detachment. Central Department and the

six detachments are to be considered as vertices in the final

graph, which will solve the problem.

In the same way, it is possible to indicate the hazardousareas, where production, transformation and hazardous

substances storage take place, as vertices of the final

Fig. 3. Inventory of hazardous material traffic.

Fig. 4. Immediate causes of accident.



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graph. To complete the construction of the final graph, it

was necessary to take into consideration highly hazardous

areas along the highway. We considered as vertices of the

graph inlet and outlet toll-gates, fire brigade districts and

central department, as well as production, transformation

and storage sites of dangerous substances. The obtained

final graph is depicted in Fig. 6.

The arcs, which link vertices together, represents normal

way and highway units, between all areas taken into

consideration.In order to evaluate the considered graph, we allotted to

each arc a corresponding scalar, defined cost of the arc.

This scalar value corresponds to a time: the Average Run

Time (in minutes) needed to reach from a vertex the

subsequent one. Time was calculated considering distances

between vertices and assuming two average speeds:

80 km hK1 as highway speed and 30 km hK1 as urban

speed.

4. Results and discussion

The results of transport risk analysis in the area show that

the risk associated with the transport of hazardous materials

on the highways considered, in a number of stretches

(Fig. 5), is at the limit of the acceptable level of risk set

down according to the criterion schematised in Table 3 and

previously discussed. The results are similar to those

reported by Milazzo et al. (2002) who presented risk

analysis in an urban area, where flammable substances wereprevalent in road transportation. They concluded that

overall societal risk was not acceptable on the basis of the

Dutch risk criteria and that the risk associated with the road

transportation is higher for N!20, while the risk associated

with railway transportation become dominant for NO20.

On these bases, strategies for the reduction of risks and

emergency management in the area must be developed. As a

first approach, the opportunity of limiting hazardous

Table 3

Acceptability criterion of the risk

Evaluation of the risk Criterion Explanation

Acceptable risk P!(10K5/N2) No need for detailed studies. Check that risk maintains at this level

Tolerability region A (10K5/N2)!P!(10K4/N2) Tolerable risk if cost of reduction would exceed the improvements gained

Tolerability region B (10K4/N2)!P!(10K3/N2) Tolerable only if risk reduction is impracticable or if its cost is grossly in disproportion to

the improvement gained

Unacceptable risk PO(10K3/N2) Risk intolerable: risk cannot be justified even in extraordinary circumstances

Fig. 5. Risk characterization in the different highways stretches.



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materials travelling during particular time bands must be

considered. As an example, about 53% of ADR traffic is

focused in the time interval 8 a.m.1 p.m.

A second strategic opportunity consists in imposing a

different highway route for hazardous materials transport.

For example, an alternative route from Genoa to Milan is

represented by A26 highway, from Genoa Voltri towards

Alessandria: this highway, being more recent and charac-

terized by lower intrinsic risk factors, could gather also the

traffic from East and Genoa central port. However, the

practical utilization of this option is made difficult by

the need of crossing a long urban stretch, characterized by

high risk level (as depicted in Fig. 5). A solution for risk

reduction is therefore the construction of a slip road

connecting Genoa central port and highway A26, even if

the feasibility of this option is obviously constrained by

economical and environmental impact issues. Therefore arisk reduction could pass through a redefinition of the

transportation network and in defining proper emergency

plans.

Table 4 depicts a selection of the complete flow sheet

reporting the vertices and the costs of the arcs. In the first

row are indicated all 36 vertices of the graph. In the

following rows, are reported the distances of each vertex

from the other ones and respective forerunner, correspond-

ing to the cost of the arc. From Table 4, we can notice that

the route stretching from vertex 1 to vertex 4 and including

vertices 2 and 3 has a total cost of 31 min. In the same way,

vertex 36 is distant from vertex 1 130 min, according to the

considered route. By applying the method to the whole

transportation network, it is possible to know all distances

(in minutes) of each vertex 1, 2, 3, ., 36 from all the other

36 vertices.

In column B it is indicated maximum time needed to

reach, from a vertex, all the other ones. For example, vertex1 is distant from all the other vertices 130 min as a

Fig. 6. Final graph.

Table 4

Summary of the results (one prompt action vehicle)

Vertex 1 2 3 4 5 6 7 [.] 33 34 35 36 A B

Distance from 1 0 13 22 31 40 44 44 105 114 115 130 130 130

Forerunner 0 1 2 3 4 5 5 32 33 34 34 34

[.]

Distance from 28 66 53 44 35 44 48 48 39 48 49 64 67 67

Forerunner 2 3 4 8 4 5 5 32 33 34 34 34

[.]



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maximum. It means that, starting from vertex 1, it is

possible to reach vertex 36 (the further one from vertex 1) in

a maximum time of 130 min. All other vertices can be

reached in a shorter time.

Moreover, starting from vertex 28, it is possible to reach

all the other vertices in a maximum time of 67 min. From

the final graph, we can observe that vertex 28 corresponds toNervi Highway toll-gate. It is obvious that if the prompt

action vehicle is located near vertex 28, it will be able to

reach all the other vertices corresponding to hazardous

areas, in a maximum time of 67 min, representing, under

this constraint, the minimum intervention time. Of course,

the prompt action vehicle has to be placed inside the Fire

Brigades Central Station or in one of the six Provincial

District.

Let us now take into consideration the option of two

prompt action vehicles: also in this case they are to be

placed inside the Fire Brigades Central Station or in one of

the six Provincial District. From the elaboration (Table 5

being a selection of this example), it results that the prompt

action vehicles can be placed in vertices:

1013 1015 1021 1026 1031 1032

1315 1321 1326 1331 1332 1521

1526 1531 1532 2126 2131 2132

2632 631 3132

If we place the two vehicles in vertices 15 and 31, vertex

1 can be reached, starting from vertex 15, in 61 min and

starting from vertex 31, in 88 min. In order to reduce action

time to reach vertex 1, it is convenient to start from vertex15. The same logic must be followed for every vertex of the

final graph. In the end, placing the two prompt action

vehicles in vertices 15 and 31 it will be possible to reach all

the vertices in a maximum time of 61 min. From data of

Table 5, if we place the two vehicles in vertices 26 and 31 or

26 and 32, it derives that all vertices can be reached in a

maximum time of 58 min (column A).

In conclusion, if we place the two prompt action

vehicles in vertices 26 and 31 (Genova-Est and Rapallo

highway toll-gates) or in vertices 26 and 32 (Genova-Est

and Chiavari highway toll-gates), it will be possible to

intervene in a maximum time of 58 min, which represents

the minimum intervention time, under these conditions.

This same logic can be followed whether three or more

prompt action vehicles are available. It is noteworthy

noting that, if we have at our disposal three prompt action

vehicles placed in vertices 102132, it is possible to reach

all vertices in a maximum time of 41 min (from Table 5).

The same result is obtained in case we have at our disposal

seven prompt action vehicles placed in the seven Fire

Brigades provincial stations. It must be remarked that the

resources necessary to control an accident and mitigate its

consequences include an emergency management plan,

trained manpower, appropriate equipment, available

communication, plus knowledgeable and decisive leaders

(Kowalski, 1995).

5. Conclusions

The risk from transporting dangerous goods by road andthe strategies proposed to select road load/routes are faced

in this paper, by developing a site-oriented framework

sensitive to route specifics and population exposed. The

results of the risk evaluation evidenced some critical

situations of the road transportation network in those

highway stretches crossing urban areas and starting from

Genoa port. Strategies for the reduction of risks may include

distribution and limitation of ADR road traffic, improve-

ment of highway section, alternative routes and appropriate

emergency management. Considering this last issue, an

optimisation algorithm, based on the graph theory was

developed to select optimal consistency and localisation in

the area of prompt action vehicles, properly equipped, quick

to move and ready for every eventuality. Applying this

method results in an unambiguous and consistent selection

criterion that allows reduction of intervention time (41 min

maximum), in connection with technical and economic

optimisation of emergency equipment (three vehicles).

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