ES2372945B2 - INTELLIGENT METHOD AND SYSTEM FOR EMERGENCY MANAGEMENT IN ROAD TUNNELS - Google Patents

Abstract

Method for managing an emergency through support for safety decision making in a road tunnel, which includes the steps of: identifying a road tunnel scenario through a first evacuation area and a second evacuation area, where the first is the area where vehicles and people directly involved in the emergency situation and people in imminent danger are located; and the second is adjacent to the first, where the trapped vehicles are located; identify the possibility of bidirectionality (a? {sub, BIDIREC}) in the process of evacuation of the first area; calculate: the distance (d {sub, SI}) from the countercurrent mouth of the tunnel to the place of occurrence of the incident; the distance (d? {sub, I1}) from the countercurrent mouth of the tunnel to the first area; the distance (d? {sub, I2}) from the countercurrent mouth of the tunnel to the end of the first area; calculate the number of people to evacuate; calculate the evacuation time of one; Propose an appropriate decision. Intelligent system integrated and automated.

Description

Intelligent method and system for emergency management in road tunnels.

Field of the Invention

The present invention belongs to the field of Industrial Engineering, and more specifically to the development of intelligent methods and systems for emergency management.

Background of the invention

Currently there are Intelligent Systems applied to different areas of society (irrigation or washing systems, lighting systems ...), these being understood as systems that try to emulate the characteristics and behaviors similar to those of human or artificial intelligence.

Within the field of transport, these systems have been applied to improve the operability and safety of urban, rural, rail roads ..., offering technological solutions for automatic toll collection and even automatic infraction surveillance.

It is possible to find in the literature not very extensive statistical information related to accidents in road tunnels worldwide. The World Tunnel Disasters since 1970 of the Institute of Advanced Motorists (IAM), 2008 and The Future of Tunnel Testing: Serious Tunnel Accidents since 1970, of the EuroTAP, collect information about the most important incidents in road tunnels since the 1970s .

Regarding evacuation systems, after a review of the existing computer evacuation models of their capabilities, applications and functionalities based on the review carried out by the NIST (National Institute of Standards and Technology) (Kuligowski, ED and Peacock, RD , 2005. Review of Building Evacuation Models, Technical Note 1471, Natl. Inst. Stand. Technol., Gaithersburg, MD), it was concluded that there is currently no evacuation model that represents the specific characteristics of tunnel evacuation. of road.

As for decision-making, there is no known intelligent system that is applied to road tunnels optimizing the decisions to be taken by the person in charge of managing a certain emergency.

On the other hand, in 2006, Royal Decree 635/2006 (BOE No. 126 of May 27, 2006) was approved in Spain, on minimum safety requirements in the state's road tunnels. Regarding the management of emergency situations, the regulations prescribe the development of an operating manual specifying the necessary measures to guarantee the safety of users, but without specifying the action protocols integrated in the response plans. to emergency situations.

Despite the advances in prevention and protection facilities installed in the tunnels, decision-making in the necessary actions in case of emergency is a critical task for the safety of users and can lead to the person in charge of managing such actions, a stress situation that does not allow you to make the most optimal decisions or at the time in which you would act in a normal situation.

Summary of the Invention

The present invention tries to solve the aforementioned inconveniences by means of an intelligent method and system, through which the personnel in charge of managing the security in a road tunnel have an intelligent system that, working in real time, provides adequate decision proposals in emergency dependence detected.

In addition to the proposal for decisions, the system performs an analysis of similar preceding events, based on the statistical analysis of the accident in road tunnels, offering, under a given probability, an estimate of the scope and consequences of the recorded incident, in relation to the number and Injury of people involved in the accident, in addition to those caught in the tunnel.

In the case of decreeing the need to evacuate the tunnel due to the severity of the event, the method and system performs a prognosis about the total time required to proceed to the total evacuation of the tunnel.

The system comprises three independent subsystems: an incident subsystem, an evacuation subsystem and a decision making subsystem.

Specifically, in a first aspect of the present invention, an automated method of emergency management is provided by supporting security decision making in an emergency situation in a road tunnel.

The method comprises the steps of: identifying a road tunnel scenario through a first evacuation area (Evacuation Area I) and a second evacuation area (Evacuation Area II), where said first evacuation area is the zone where the vehicles are located and therefore people directly involved in the accident, as well as people who are in imminent danger due to the emergence of fire

or discharge of dangerous substances; and said second evacuation area is an area adjacent to the first evacuation area, where vehicles are trapped in the traffic jam caused by the accident; identify the possibility of bidirectionality in the evacuation process of the first evacuation area; calculate the distance from the countercurrent mouth of the tunnel to the place of occurrence of the incident; calculate the distance from the countercurrent mouth of the tunnel to the beginning of the first evacuation area (Evacuation Area I).

Calculate the distance from the countercurrent mouth of the tunnel to the end of the first evacuation area (Evacuation Area I); calculate the number of people to evacuate in the first evacuation area and in the second evacuation area; calculate the evacuation time of each person; propose an appropriate decision based on the emergency detected.

In another aspect of the invention, an integrated intelligent system for emergency management due to an incident in a road tunnel is provided, comprising: an incident subsystem, configured to perform a predictive analysis of the possible development and consequences of an emergency , where said event is any accident, caused by one or more motor vehicles that pass through the tunnel, which causes real damage

or potential on people who are inside and eventually advise their evacuation; an evacuation subsystem; and a decision making subsystem. The integrated intelligent system is configured to carry out the steps of the method described above.

The advantages of the invention will become apparent in the following description.

Brief description of the fi gures

In order to help a better understanding of the characteristics of the invention, in accordance with a preferred example of practical realization thereof, and to complement this description, a set of drawings is attached as an integral part thereof, whose character is Illustrative and not limiting. In these drawings:

Figure 1 shows a possible characterization of a road tunnel, according to an implementation of the invention.

Figure 2 shows a diagram of the dead zone of the CCTV camera (Closed Circuit Television) inside a tunnel, according to an implementation of the invention.

Figure 3 shows a decision tree for notification to operating personnel in accordance with an embodiment of the present invention.

Figure 4 shows a decision tree for prevention of emergency services, in accordance with an embodiment of the present invention.

Figure 5 shows a decision tree for the activation of emergency services, in accordance with an embodiment of the present invention.

Figure 6 shows a decision tree for the need for evacuation, in accordance with an embodiment of the present invention.

Figure 7 shows a decision tree for the bidirectionality of the first evacuation area, in accordance with an embodiment of the present invention.

Figure 8 shows a decision tree for the number of lanes to be closed, in accordance with an embodiment of the present invention.

Figure 9 shows a decision tree for closing the tunnel, in accordance with an embodiment of the present invention.

Figure 10 shows a decision tree for activation of the maximum level of illumination, in accordance with an embodiment of the present invention.

Figure 11 shows a decision tree for informing users, in accordance with an embodiment of the present invention.

Figure 12 shows an example of the implementation of a tunnel data file that provides the data that introduce the geometry and characteristics of the specific tunnel of application of the integrated host system.

Figure 13 shows an example of implementation of the introduction of a series of input variables that define a detected emergency situation.

Figure 14 shows an example of implementation of the proposed decisions to be taken by the tunnel control center operator based on the severity and conditions of the incident.

Figure 15 shows an example of implementation and introduction of additional event information.

Figure 16 shows an example of implementation and introduction of additional information about the event, specifically the input variables for evacuation models.

Figure 17 shows an example of evacuation analysis results in the first evacuation area.

Figure 18 shows an example of evacuation analysis results in the second evacuation area.

Figures 19 and 20 show two layout examples of two possible scenarios.

Figures 21 to 24 show the results obtained from the execution exemplified in Figures 19 and 20, both in the first and second evacuation areas.

Detailed description of the invention

In this text, the term "comprises" and its variants should not be understood in an exclusive sense, that is, these terms are not intended to exclude other technical characteristics, additives, components or steps.

In addition, the terms "approximately", "substantially", "around", "ones", etc. they should be understood as indicating values close to which these terms accompany, since due to calculation or measurement errors, it is impossible to achieve those values with total accuracy.

The following preferred embodiments are provided by way of illustration, and are not intended to be limiting of the present invention. In addition, the present invention covers all possible combinations of particular and preferred embodiments indicated herein.

For those skilled in the art, other objects, advantages and characteristics of the invention will be derived partly from the description and partly from the practice of the invention.

The intelligent method and system for the management of an emergency in road tunnels is detailed below by supporting decision-making, and specifically, of the stages and different subsystems that form it: an incident subsystem, a subsystem of evacuation and a decision subsystem. The integration of the subsystems in the intelligent system is also detailed.

1 Incident Subsystem

This subsystem allows a predictive analysis of the possible development and consequences of an incident, understanding this as any accident, caused by one or more motor vehicles that pass through the tunnel, which causes real or potential damage to people in their interior and eventually advise their evacuation.

The subsystem offers results working based on the data obtained about the accidents recorded in the literature that occurred in road tunnels at an international level, in relation to the number of people killed due to the accident, number of minor (minor) injuries, number of seriously injured and summarized in Table 1.

TABLE 1

List of victims of Road Tunnel Accidents between 1979 and 2007. (Source: World Tunnel Disasters since 1970. Institute of Advanced Motorists (IAM). 2008 and EuroTAP -The Future of Tunnel Testing: Serious Tunnel Accidents since 1970)

To obtain the incident subsystem, a mathematical algorithm consisting of a set of expressions that relate a series of input and output variables has been developed. These expressions are based on the pre-established Action Protocols for the Tunnel Control Center Operators.

First, a road tunnel scenario is characterized as follows (see Figure 1):

•

Definition or identification of a first evacuation area (Evacuation Area I) or area where the vehicles are located and therefore the people directly involved in the accident, as well as people who are in imminent danger due to the occurrence of fire or discharge of dangerous substances.

•

De fi nition or identification of a second evacuation area (Evacuation Area II) or area adjacent to the first evacuation area, to the tunnel entrance, where vehicles are trapped in the traffic jam caused by the closure of the tunnel because of the closure accident.

Figure 1 illustrates these two evacuation areas. Figure 1 also shows the distance from the tunnel countercurrent mouth to the approximate (center) place of occurrence of the dSI incident, the distance from the tunnel countercurrent mouth to the end of the second evacuation area d 'I1 and the distance from the countercurrent mouth of the tunnel to the end of the first evacuation area d 'I2. The expressions that comprise the mathematical algorithm of the incident subsystem are the following:

one.

The number of tunnel lanes that are closed to the mCAR traffic must be equal to the number of obstructed lanes resulting from the m ’CAR accident:

2.

The need to notify operating personnel to go to the site of the event, represented by the expression 'APE, must be conditioned by the very occurrence of an incident, represented by the expression aSI:

3.

The need to prevent emergency services, represented by the expression 'PSE, is defined by the closure of at least one lane of the tunnel, provided that the tunnel has more than one lane:

where mMAX represents the maximum number of tunnel lanes and mCAR is the number of tunnel lanes that are closed to traffic.

Four.

The need to activate emergency services, represented by the expression 'ASE, is conditioned by the total closure of the tunnel (when the number of tunnel lanes that close to the mCAR circulation is equal to the maximum number of lanes in the mMAX tunnel), the detection of a fire in it (represented by the expression aFIRE), or a spill (escape) of a dangerous substance (represented by the expression aVSP) or the suspicion that there is at least one injured product of the accident (represented by the expression nHER):

5.

The need for immediate evacuation of the tunnel, represented by the expression 'EVAC, is determined by the emergence of a fire in it (represented by the expression aFIRE) or the discharge or escape of a hazardous substance (represented by the expression aVSP) , as a result of the incident:

6.

The bidirectionality of the possible immediate evacuation of the tunnel, represented by the expression ’BIDIREC (Boolean variable that indicates the possibility of bidirectionality in the evacuation process), is defined by its total or partial obstruction. If the obstruction is total (when the number of lanes in the tunnel that are closed to the mCAR circulation is equal to the maximum number of lanes in the mMAX tunnel) the bidirectionality is false:

7. The distance from the countercurrent mouth of the tunnel to the beginning of the first evacuation area can be calculated using a series of expressions that we detail below:

Initially, the distance from the countercurrent mouth of the tunnel to the approximate (center) place of occurrence of the dSI accident is calculated:

where:

dSI - As can be seen in Figure 1, it is the distance from the tunnel's countercurrent mouth to the approximate location of the incident;

dCAM (nCCTV) - distance from the tunnel entrance to the CCTV camera (Televi Closed Circuit)

sion) nCCTV number (the camera number that detects the incident automatically at the Control Center);

δZM - distance from the dead zone of the chamber;

δCCTV - approximate location of the tunnel sector captured by the CCTV camera where the incident occurred. This

sector is commonly less than or equal to 150myes known for each tunnel. If for example δCCTV = 1, the event occurred in the first half of the tunnel.

ΔCAM-distance between cameras.

The distances from the countercurrent mouth to each of the tunnel chambers can be calculated by the expression:

where: dCAM1 - distance between the countercurrent mouth of the tunnel to the first CCTV camera; ΔCAM - distance between the tunnel chambers.

Figure 2 shows a diagram of the dead zone of the CCTV camera (CCTV) inside the tunnel. Figure 2 includes:

where: hCAM - the height of the cameras from the plane of the road inside the tunnel; βEF-angle of the focal axis of the camera with respect to the horizontal plane; aVIS-camera viewing angle.

for what you get:

where:

hCAM - height of the cameras from the plane of the road inside the tunnel;

βEF-angle of the focal axis of the camera with respect to the horizontal plane;

aVIS-camera viewing angle;

γ-angle of the dead zone of the chamber;

δZM - distance from the dead zone of the chamber;

Then the distance d ’I1 from the countercurrent mouth of the tunnel to the end of the second evacuation area (distance from the countercurrent mouth of the tunnel to the beginning of the first evacuation area):

where: dGALi - distance from the countercurrent entrance of the tunnel to the i-th gallery; dGALj -distance from the countercurrent entrance of the tunnel to the j-th gallery; dSI - distance from the countercurrent mouth of the tunnel to the approximate (center) place of occurrence of the incident; AG -variable indicating if there is a gallery before the gallery i.

The previous expression indicates that the distance d ’I1 must be chosen equal to the distance to the immediate previous gallery against the current of the incident, if it exists. Otherwise, this distance is assumed equal to zero (tunnel entrance).

The distances to the different tunnel galleries are calculated as:

where: dGAL1 -distance from the countercurrent mouth of the tunnel to the first gallery; ΔGAL - distance between the tunnel galleries; mGAL - total amount of tunnel galleries.

8. The distance from the countercurrent mouth of the tunnel to the end of the first evacuation area is calculated by the expression d ’I2:

where:

and where:

dGALk - distance from the countercurrent entrance of the tunnel to the k-th gallery;

LT-tunnel length;

dSI - distance from the countercurrent mouth of the tunnel to the approximate (center) place of occurrence of the incident;

ΔCAM - distance between the tunnel chambers;

AG1 - indicates the existence of a gallery after the incident;

a ’BIDIREC -bidirectionality of tunnel evacuation.

The expression re fl ects that the distance d ’I2 is, for cases of possibility of bidirectional evacuation, equal to the distance to the immediate subsequent countercurrent gallery of the place of occurrence of the event, if such a gallery existed,

or equal to the length of the tunnel otherwise. If the evacuation has to be unidirectional, the distance d ’I2 equal to the distance to the accident is assumed plus a spatial margin equal to one third of the distance between CCTV cameras.

9. The total number of people to evacuate from the first evacuation area n ’IT is preferably a pessimistic estimate to be made based on the number of vehicles directly involved in the accident and their types.

The pessimistic nature of this estimate implies assuming reasonable maximum values that imply simulated evacuation times that are equally reasonable. Therefore, the expression of this variable can be:

where: kVL - passenger occupation assumed by light vehicle; kVPP - passenger occupation assumed by heavy vehicle; kVA - passenger occupation assumed by coach; nVLDI - number of light vehicles directly involved in the incident; nVPDI - number of heavy vehicles directly involved in the incident; nVADI - number of buses directly involved in the incident.

10. The numbers of people to evacuate with different types of mobility in the first evacuation area are also preferably based on pessimistic estimates. Two situations differ: if an incident is serious or if it is not. But this can be assumed with Bayesian probabilities. In the case of a serious event:

where: n ’IMN - number of people with normal mobility; n ’IMR - number of people with reduced mobility; n ’IMA - number of people with assisted mobility; P (MN | AGSI) -probability that following the occurrence of a serious incident, a person's mobility is normal; P (MR | AGSI) -probability that after the occurrence of a serious incident, a person's mobility is reduced; P (MA | AGSI) -probability that following the occurrence of a serious incident, the mobility of a person is assisted;

n ’IT - total number of people trapped in Evacuation Area I.

If the seriousness of the incident is considered false, which by definition presumes the absence of serious injuries, the above expression takes the following form:

where:

P (MN | A¯GSI) -probability of a person's mobility being normal in case the incident is not serious;

P (MR | A¯GSI) -probability of a person's mobility being reduced in case the incident is not serious;

On the other hand, the conditional probabilities used are related to the probabilities of occurrence of minor, serious and deceased injuries when a serious accident occurs in a road tunnel. This means that:

where: P (MR | AGSI) -probability that after the occurrence of a Serious Incident, the mobility of a person is reduced; P (HL | AGSI) is the probability that those involved in an accident suffer minor injuries if the hypothesis that the

Event was serious.

Similarly:

where:

P (MA | AGSI) -probability that following the occurrence of a Serious Incident, the mobility of a person is assisted;

P (HG | AGSI) - Probability that those involved in an accident suffer serious injuries if the hypothesis that the event was serious is true;

P (F | AGSI) - Probability that those involved in an accident will die if the hypothesis that the event was serious is true.

The pessimistic approach of the model from the point of view of evacuation is implicit in the previous expression, since we consider that both the seriously injured and the deceased are included in the category of potential evacuees with assisted mobility. In consecuense:

where: P (MN | A¯GSI) -Probability that a person's mobility is normal in case the incident is not serious;

On the other hand:

where:

P (MR | A¯GSI) -Probability of a person's mobility being reduced in case the incident is not serious;

P (HL | A¯GSI) -is the probability that those involved in an accident suffer minor injuries if the hypothesis that the event was serious is false.

And finally:

where: P (MN | A¯GSI) -Probability that a person's mobility is normal in case the incident is not serious;

11. The distance from the countercurrent mouth of the tunnel to the end of the Evacuation Area II is assumed equal to the distance to the CCTV camera that has captured the incident, that is:

where: dCAM (nCCTV) - represents the distance to the CCTV camera that has captured the incident.

12. The number of vehicles in the second evacuation area (evacuation area II) whose drivers and passengers must be evacuated n 'IIV can be calculated in a simple way, subtracting the number of vehicles trapped in the tunnel since the occurrence of the event nVAT, the total number of vehicles directly involved in the nVDI accident:

13. The delay time in incident detection is evidently equal to the homologous input variable:

Table 2 describes the parameters used in the expressions described above. TABLE 2

Summary of Incident Subsystem parameters

2 Evacuation Subsystem

Incidents in road tunnels carry such seriousness and risk that it is often advisable to decree the immediate evacuation of the people inside. As indicated in the previous section, two scenarios are distinguished in a road tunnel: a first evacuation area (Evacuation Area I) and a second evacuation area (Evacuation Area II). Due to the intrinsic differences in each of the areas considered, a mathematical algorithm has been constructed for each of the scenarios, as described below.

2.1 Subsystem for Evacuation Area I

For the definition of the evacuation subsystem (SE) for the first evacuation area, the different variables that define the mathematical algorithm have been identified.

The SE depends directly on the results of the Incident Subsystem (SSI) previously developed, so that some SSI output variables serve as input to the SE, such as:

•

a ’BIDIREC - a Boolean variable that indicates the possibility of bidirectionality in the evacuation process of the first evacuation area.

•

d ’I1 -distance from the countercurrent mouth of the tunnel to the beginning of the first evacuation area. Recall that it is defined as the distance from the mouth to the gallery immediately before the place of occurrence of the incident, in case that gallery existed or, otherwise, would be equal to zero.

•

d 'I2 -distance from the countercurrent mouth of the tunnel to the end of the first evacuation area, assumed equally, for the case in which the bidirectionality of the evacuation was false, at the distance to the center of the incident (dSI) plus the third of the distance between the CCTV cameras (ΔCAM).

•

n 'IT - total number of people to evacuate from the first evacuation area that corresponds to the sum of people with normal mobility (n' IMN), with reduced mobility (n 'IMR) and with assisted mobility (n' IMA ), directly involved in the incident.

As the mathematical algorithm that details the operation of the SE was defined, there was a need to define new input variables.

The scenario defined as the first evacuation area results in the analysis that an extremely complex and highly uncertain scenario will be carried out for the following reasons:

•

Ignorance of the location of people to evacuate within this first evacuation area. The SSI development assumes predictable knowledge of the amount of light, heavy vehicles and buses within the first evacuation area, but it is extraordinarily difficult to know their distribution as well as the exact amount and location of people on stage.

•

Ignorance of lengths and characteristics of possible evacuation routes. The ignorance of the position of the people on the stage makes it practically impossible to know the distances to travel as well as the complexities of the evacuation routes.

Due to these uncertainties in the study of the evacuation process, the algorithm developed is presented as a stochastic and simple character. The stochastic approach allows to cover the multiple and unpredictable situations that can be found in the first evacuation area, while a simple approach allows to avoid the great diversity of possible situations that may arise with respect to human behaviors.

The subsystem is responsible for estimating the evacuation process with respect to the maximum time necessary for all people trapped on the stage to reach a safe place from the moment of occurrence of the event, so that additional information on the consequences of the event The subsystem uses the following expression to calculate the evacuation time of each person:

where:

tEIi - evacuation time of the first evacuation area of the i-th person;

tpmi - pre-movement time of the i-th person, corresponding to the time elapsed between the moment the accident occurs until this person begins the movement;

dmovi - distance traveled by the i-th person from their original position on stage to leave it;

vmovi - average displacement speed of the i-th person during his departure from the first evacuation area.

The evacuation time of the ith person, tEIi, is a random variable due to the randomness of the parameters on which it depends according to the previous expression. It is logical to assume that pre-movement times and average travel speeds have different probability distribution functions for different mobility groups. Although the type of distribution function is the same, the parameters that characterize it (mathematical hope, standard deviation) are different for people with normal, reduced or assisted mobility.

The same does not happen with the distance traveled by the i-th person since regardless of their mobility they may initially be in any position within the stage and travel an escape route from it that is independent of mobility.

Knowing the distribution functions or probability density functions of the tpmi and vmovi variables for each of the mobility groups, and the unique function for the dmovi variable, as well as the number of people in each group on the stage, you can Simulate using Monte Carlo methods, each hypothetical case of evacuation of all those involved in this process. The selection of the longest resulting time in each simulated case corresponds to the total evacuation time of the first evacuation area (TTEI).

The repetition of this niter process sometimes provides a sample of niter volume of TTEI and its statistical processing allows its adjustment to a known distribution function or its estimation by the histogram, as well as the calculation of its main quantitative characteristics. We will consider that the main output variable of the model will be the percentile for a given probability p (usually greater than or equal to 0.9) of the total evacuation time of the first evacuation area.

This has a total evacuation time value such that the real value in any case, with a probability p, will be less than or equal to it. For the reasons explained above, a mathematical algorithm was developed as follows:

where: TTEIj - Total Evacuation Time of Scenario I (first evacuation area) for the jth implementation; t (MN)

-

evacuation time of the ith person with normal mobility in the jth embodiment;

EIij t (MR)

-

evacuation time of the ith person with reduced mobility in the jth embodiment;

EIij t (MA)

-

evacuation time of the i-th person with assisted mobility in the j-th embodiment;

EIij

max {...} - maximum value of the set.

Likewise:

where:

t (MN)

pmij - pre-movement time of the i-th person with normal mobility in the j-th iteration;

t (MR)

pmij - pre-movement time of the i-th person with reduced mobility in the j-th iteration;

t (MA)

pmij - pre-movement time of the i-th person with assisted mobility in the j-th iteration;

dmovij - distance traveled by the i-th person in the j-th iteration;

v (MN)

-

average speed of movement of the i-th person with normal mobility in the j-th iteration;

movij

v (MR) - average displacement speed of the ith person with reduced mobility in the jth iteration;

movij

v (MA)

-

average speed of movement of the i-th person with assisted mobility in the j-th iteration.

movij

From the defined input variables, corresponding to the SSI output variables, as well as considering the distribution laws that define the random variables corresponding to the different pre-movement times, distances traveled and travel speeds, the The subsystem generates four samples of maximum evacuation times each of equal volume number of iterations selected, three of the samples correspond to the maximum evacuation times by type of mobility and the fourth collects the maximum evacuation time in each iteration. Each sample is statistically processed using the following methodology:

1) The main quantitative characteristics of the sample are calculated.

2) Adjustment or estimation of the distributions of the TTEI random variable is made. This initially involves adjusting the sample to the normal, normal logarithmic or uniform distributions. D'Agostino K2 tests were used for the first two (previously calculating the natural logarithm of the elements of the sample in the case of checking the normal logarithmic law) and the modified Anderson-Darling test for the distribution case uniform If none of the three distributions is satisfied, the distribution is estimated by its histogram, using the Freedman and Diaconis rule to calculate the width of the sample interval.

3) The percentile is calculated for a probability of confidence p that can be represented by the following generic formula:

where:

F − 1

TEI (p) - TTEI inverse distribution function evaluated for the probability of trust p.

2.2 Subsystem for Evacuation Area II

As was the case for the first evacuation area, this subsystem is dependent on the SSI, so that certain input variables of the SEII correspond to some output variables of the SSI:

1) d ’II - distance from the countercurrent mouth of the tunnel to the end of the second evacuation area.

2) n ’IIV - total number of vehicles in the second evacuation area whose drivers and passengers must be evacuated.

In the analysis of evacuation in this scenario, it is assumed that the mobility of trapped persons is not affected by the event, so continuing with the methodology followed for the first evacuation area, the mathematical algorithm that defines the evacuation subsystem for This scenario is:

where:

TTEIIj -Total Evacuation Time of Evacuation Area II (second evacuation area) for the jth implementation;

tTEIIij - evacuation time of the i-th person in the j-th embodiment;

qj-amount of people to evacuate in the j-th realization;

niter - amount of iterations (realizations);

max {...} - maximum value of the set.

The number of people to evacuate in each embodiment that is defined by the expression:

where: n ’IIVL - quantity of light vehicles in the second evacuation area (Evacuation Area II);

-

number of coaches on the Stage (second evacuation area);

-

number of heavy vehicles on the Stage (second evacuation area);

P (AVPA) -probability that a vehicle, from the set of heavy vehicles and coaches that usually travel through the tunnel, is a coach;

Evidently:

k˜VLj - coefficient of occupation of a light vehicle in the j th iteration, which is assumed as a random integer uniformly distributed between 1 and 5;

k˜VPj - coefficient of occupation of a heavy vehicle in the jth iteration, which is assumed as a random integer uniformly distributed between 1 and 2;

k˜VAj - coefficient of occupation of a coach in the j th iteration, which is assumed as a random integer uniformly distributed between 20 and 40.

The evacuation time of each person in each iteration is calculated using the formula:

3. Decision Making Subsystem

Considering the decisions to be taken by the operator of the tunnel control center, an algorithm was developed based on decision trees for Expert Systems as expressed below:

3.1 Notice to Exploitation Personnel This decision is conditioned by the operator's finding of the occurrence of an incident:

That is, the aSI variable is true. Figure 3 shows a decision tree for notification to operating personnel. The logical expression expressed by the notice to operating personnel is as follows:

3.2 Emergency Services Prevention (112)

According to the protocols (sheets) of operation of the control center operators, prevention of emergency services is subject to the closure of at least one tunnel lane provided that the tunnel has more than one lane,

Or what is the same:

Figure 4 shows a decision tree for prevention of emergency services.

3.3 Activation of Emergency Services

There are a number of events that lead to the activation of emergency services and their personi fi cation in the tunnel: the total closing of the tunnel, the occurrence of a fire inside, a spillage of dangerous substance or the suspicion of the existence of least one injured because of the accident. Figure 5 shows a decision tree for the activation of emergency services, in accordance with an embodiment of the present invention. The expression that indicates the need to make the decision to activate emergency services is as follows:

3.4 Need for immediate evacuation

The decree of the need to evacuate the tunnel is determined by the emergence of a fire or the spillage or escape of dangerous substances. Figure 6 shows a decision tree for the need for evacuation, in accordance with an embodiment of the present invention.

3.5 Bidirectionality of Scenario I (first evacuation area) for evacuation

The possibility of evacuation in both directions (bidirectionality of Evacuation Area I) is given by the total or partial obstruction of the tunnel. Figure 7 shows a decision tree for the bidirectionality of the first evacuation area, in accordance with an embodiment of the present invention. Figure 7 shows the decision tree to which the mathematical expression of the possibility of bidirectionality responds.

3.6 Number of tunnel lanes that are closed to traffic

It is assumed that the operator is able to determine the number of obstructed lanes by means of CCTV images.

Figure 8 shows a decision tree for the number of lanes to be closed, in accordance with an embodiment of the present invention. The number of lanes to be closed is equal to the number of clogged lanes as indicated by the expression:

3.7 Total tunnel closure

In the event that the total number of obstructed lanes is equal to the total number of lanes in the tunnel, the total tunnel closure must be decreed, as indicated by the expression:

Figure 9 shows a decision tree for closing the tunnel, in accordance with an embodiment of the present invention.

3.8 Maximum lighting in the tunnel

The maximum lighting sequence is activated in case of fire inside the tunnel or spillage of dangerous substance, as indicated by the expression:

Figure 10 shows a decision tree for activation of the maximum level of illumination, in accordance with an embodiment of the present invention.

3.9 Information to Users

The need to inform users in the event of the occurrence of an incident is conditioned by the detection and confirmation by the operator of an incident, which is defined by the expression:

Figure 11 shows a decision tree for informing users, in accordance with an embodiment of the present invention.

4. Integrated System

Based on the method and the three subsystems, an intelligent system for emergency management has been implemented, which is accompanied by a secondary system that generates and / or modifies the data files that introduce the geometry and characteristics of the specific application tunnel of the integrated main system. An example of a tool for generating this data is illustrated in Figure 12. Figure 13 shows an example of the implementation of the introduction of a series of input variables that define a detected emergency situation.

The integrated system allows you to enter a series of input variables that define the detected emergency situation. An example of a tool that allows you to enter these input variables is illustrated in the figure

13. First, for the system to work properly, the data file that defines the geometry and characteristics of the tunnel (generated with the secondary system) must be selected. The user of the system must indicate by means of five Boolean variables, the veracity of the occurrence of an Incident, of a Fire, of Discharge, of Injury (s) and if it is an incident classified as serious. You enter another group of numerical variables.

•

Number of clogged lanes.

•

Camera number showing the incident.

•

Approximate location of the incident in the sector.

•

Number of light vehicles involved in the incident.

•

Number of heavy vehicles involved in the incident.

•

Number of coaches involved in the incident.

•

Total number of vehicles trapped inside the tunnel.

•

Amount of heavy vehicles and buses trapped inside the tunnel.

•

Delay time in incident detection.

Once the input variables have been introduced, firstly, by clicking on the Decisions button of the exemplified tool, the system generates the proposal for decisions to be taken by the operator of the tunnel control center depending on the severity and conditions of the incident , among the following, which are exemplified in Figure 14:

•

Notice to operating personnel.

•

Emergency services prevention.

•

Activation of emergency services.

•

Need for immediate evacuation.

•

Tunnel closure

•

Lighting level

•

Inform users.

•

Bidirectionality of the first evacuation area.

•

Number of lanes to close.

In any case, the system allows, through the button Additional information of the event, to display information that can be useful when analyzing the scope and severity of the incident, as shown in Figure 15 and offering a series of numerical results.

•

Distance to the beginning of Evacuation Area I.

•

Distance to the end of Evacuation Area II.

•

Total number of people to evacuate in Evacuation Area I.

•

Number of people with normal mobility in Evacuation Area I.

•

Number of people with reduced mobility in Evacuation Area I.

•

Number of people with assisted mobility in the Evacuation Area I.

•

Distance to the end of Evacuation Area II.

•

Number of vehicles to evacuate from Evacuation Area II.

On the other hand, in the event that the need for immediate evacuation is activated in the decision proposal, the system allows the option of obtaining a prognosis on the evacuation time for each of the evacuation areas considered. By clicking on the Evacuation Analysis button, the evacuation subsystem starts working, requiring the user to enter a series of Input Variables for the evacuation models, as shown in Figure 16. These variables can be divided into three groups :

•

General variables of the models: Number of iterations generated by the Monte Carlo Method, level of confidence or significance for adjusting the distribution (to choose between 0.1 0.05 and 0.01) and level of confidence for the calculation of the percentile (a choose between 0.9, 0.95 and 0.99).

•

Variables typical of the Evacuation Area I: It allows the user to introduce the behavioral variables corresponding to the people trapped in what has been defined as the first evacuation area according to their mobility. Mean (M) and standard deviation (Sigma) for pre-movement times and travel speeds for normal, reduced and assisted mobility.

•

Variables typical of Evacuation Area II: In the same way as for the first evacuation area, the program allows you to enter the values for the behavioral variables of people trapped in the second evacuation area (Mean M and standard sigma deviation for time of pre movement and speed of movement of the users to evacuate). The value of the response time (tpm0) and travel speed (vmov0) of the person initiating the evacuation (sender) is entered. In this block, the occupancy coefficients by type of vehicle (light VL, heavy VP and VA coaches) are also defined by a range of values, as well as the probability that the coach and heavy vehicle set is a coach.

Once all the own input variables have been entered for the evacuation models, pressing the continue evacuation subsystem button consecutively shows the results obtained on the evacuation analysis, as shown in Figures 17 and 18 (first and second evacuation areas, respectively). Analyzing the samples obtained, the system obtains as a main output data the percentile of the total evacuation time, in addition to the type of probability distribution law to which the corresponding sample is adjusted, mathematical expectation, standard deviation and range of values.

Example of embodiment of the invention

The intelligent system was applied to the Lantueno tunnel of the A-67 Highway (Cantabria -La Meseta), defining the data file with the characteristics of the tunnel. The following two types of scenarios were assumed (Scenario A and Scenario B), whose layout is illustrated, respectively, in Figures 19 and 20.

Scenario A corresponds to an accident that occurred in the area near the exit, with the greatest possible number of vehicles being trapped. They are considered a series of cases.

•

Case 1.1.-Accident with fire (no spillage). Considering the location of the accident in zone 3, two obstructed lanes and the existence of injuries, the following scenario variants were considered:

◦

Case 1.1.1. Accident between two light vehicles.

◦

Case 1.1.2 Accident between a light vehicle and a heavy vehicle.

◦

Case 1.1.3. Accident between two heavy vehicles.

•

Case 1.2.-Accident with fire (with spillage). Considering the other values indicated above in addition to the following variants:

◦

Case 1.2.1. Accident between two light vehicles.

◦

Case 1.2.2. Accident between a light vehicle and a heavy vehicle.

◦ Case 1.2.3. Accident between two heavy vehicles.

•

Case 1.3.-Accident without fire (with spillage). Considering the other values indicated above in addition to the following variants:

◦

Case 1.3.1. Accident between two light vehicles.

◦

Case 1.3.2. Accident between a light vehicle and a heavy vehicle.

◦

Case 1.3.3. Accident between two heavy vehicles.

•

Case 1.4.-Accident with fire (with spillage). Considering the other values indicated above in addition to the following variants:

◦

Case 1.4.1. Accident between three light vehicles and a heavy vehicle.

◦

Case 1.4.2. Accident between a light vehicle and three heavy vehicles.

◦

Case 1.4.3. Accident between three heavy vehicles and three light vehicles.

Scenario B corresponds to the occurrence of an incident in the intermediate zone of the tunnel, considering the most probable cases according to the tunnel incident log. A series of cases were considered.

•

Case 2.1. Accident with fire (with spillage).

◦

Case 2.1.1. Light vehicle

◦

Case 2.1.2. Heavy vehicle.

•

Case 2.2. Accident with fire (no spillage).

◦

Case 2.2.1. Light vehicle

◦

Case 2.2.2. Heavy vehicle.

•

Case 2.3. Accident without fire (with spillage).

◦

Case 2.3.1. Light vehicle

◦

Case 2.3.2. Heavy vehicle.

Input Data

In Table 3, the input data of the system is collected. TABLE 3

Input data for the integrated Smart System

Results on the proposed decisions

The system offers the recommended decisions to be undertaken by the operator of the tunnel control center (Tables 4 and 5), coinciding with those contained in the protocols (action sheets) according to the emergency situation considered:

TABLE 4

Decisions to be taken by the operator: Scenario A

TABLE 5

Decisions to be taken by the operator: Scenario B Results on additional incident information

With respect to the numerical results on the incident, the system allows the option to display additional information that may be useful when communicating with external services when estimating the scope of events.

Tables 6 and 7 show the results on the estimation of the people involved and their effects.

TABLE 6

Numerical results of the incident: Scenario A

TABLE 7

Numerical results of the incident: Scenario B

With respect to the additional information of the incident, they are results that vary in each case because it is a random variable that depends on a probability of occurrence obtained. The characteristics of the event are decisive when estimating the number of people involved and their involvement.

Results on evacuation in the AyB Scenario

In Figures 21-24, the results obtained are shown. For the cases of Scenario B, due to the severity assumed, the estimate reflects that no user will have normal mobility. Observing the results obtained in both Scenarios, it is verified that for the cases of Scenario A, the percentile of the Total Evacuation Time is higher than the cases of Scenario B, due to the assumption of a greater number of vehicles trapped in Evacuation Area II .

In summary with respect to conventional systems: There are currently some intelligent systems applied to different areas including transport but aimed at promoting operability, as in the case of automatic toll systems, but none has been applied to the study of safety in road tunnels. As for the incident subsystem, no similar system is included in the literature that analyzes the recorded accident by offering an estimate of the possible consequences of the accident.

On the other hand, with regard to evacuation subsystems, for the first time an evacuation model has been proposed that represents the specific characteristics of evacuation in a road tunnel. The same applies to the decision subsystem, since no decision system or subsystem is known that applies to environments such as road tunnels.

Although only particular embodiments of the invention have been represented and described herein, the person skilled in the art will know how to introduce modifications and replace some technical characteristics with equivalent ones, depending on the requirements of each case, without departing from the scope of protection defined by the attached claims.

Claims (16)

1. A method for managing an emergency by supporting security decisions in a road tunnel, which includes the stages of:

•

Identify a road tunnel scenario through a first evacuation area (Evacuation Area I) and a second evacuation area (Evacuation Area II), where the first evacuation area is the area where vehicles and people are located directly involved in the emergency situation, as well as people who are in imminent danger due to the occurrence of fire or spillage of dangerous substances; and the second evacuation area is an area adjacent to the first evacuation area, where vehicles are trapped in the traffic jam caused by the emergency situation;

•

Identify the possibility of bidirectionality (a ’BIDIREC) in the evacuation process of the first evacuation area;

•

Calculate:

-

The distance (dSI) from the countercurrent mouth of the tunnel to the place of occurrence of the incident;

-

The distance (d ’I1) from the countercurrent mouth of the tunnel to the beginning of the first evacuation area (Evacuation Area I);

-

The distance (d ’I2) from the countercurrent mouth of the tunnel to the end of the first evacuation area (Evacuation Area I);

•

Calculate the number of people to evacuate;

•

Calculate the evacuation time of each person;

•

Propose an appropriate decision based on the emergency detected.

2. The method of claim 1, wherein said step of calculating the distance from the countercurrent mouth of the tunnel to the place of occurrence of the incident (dSI) is performed by the following expression: where: dSI -is the distance from the countercurrent mouth of the tunnel to the approximate location of the incident; dCAM (nCCTV) - distance from the countercurrent mouth of the tunnel to the CCTV camera number nCCVT (the number of camera that detects the incident automatically in the Control Center); δZM - distance from the dead zone of the chamber; δCCTV - approximate location of the tunnel sector captured by the CCTV camera where the incident occurred. This sector is commonly less than or equal to 150myes known for each tunnel. If for example δCCTV = 1, the event occurred in the first half of the tunnel; ΔCAM-distance between cameras. 3. The method of claim 2, wherein the distances to each of the tunnel chambers from the inlet port can be calculated by the expression: where: dCAM1 -represents the distance between the countercurrent mouth of the tunnel to the first CCTV camera; ΔCAM - distance between the tunnel chambers. 4. The method of any one of the preceding claims, wherein said step of calculating the distance (d'I1) from the countercurrent mouth of the tunnel to the beginning of the first evacuation area is performed by the following expression: where: dGALi - the distance from the countercurrent entrance of the tunnel to the i-th gallery; dGALj -the distance from the countercurrent entrance of the tunnel to the j-th gallery; dSI - the distance from the countercurrent mouth of the tunnel to the approximate (center) place of occurrence of the incident; AG -variable indicating if there is a gallery before the gallery i. 5. The method of claim 4, wherein the distances to the various tunnel galleries are calculated by the expression: where: dGAL1 is the distance from the countercurrent mouth of the tunnel to the first gallery; ΔGAL is the distance between the tunnel galleries; mGAL is the total number of tunnel galleries. 6. The method of any of the preceding claims, wherein said step of calculating the distance (d 'I2) from the countercurrent mouth of the tunnel to the end of the first evacuation area (Evacuation Area I) is calculated by the expression: and where: dGALk -is the distance from the countercurrent entrance of the tunnel to the k-th gallery; LT - is the length of the tunnel; dSI -is the distance from the countercurrent mouth of the tunnel to the approximate (center) place of occurrence of the incident; ΔCAM - represents the distance between the cameras of the tunnel; AGI - indicates the existence of a post-incident gallery; a ’BIDIREC -bidirectionality of tunnel evacuation.

7.

The method of any of the preceding claims, wherein said number of people to evacuate comprises the number of people to evacuate in the first evacuation area and the number of people to evacuate in the second evacuation area.

8.

The method of claim 7, wherein the number of people to evacuate in the first evacuation area (n 'IT) is calculated by the expression:

where: kVL -is the passenger occupation assumed by light vehicle; kVPP is the passenger occupation assumed by heavy vehicle; kVA is the passenger occupation assumed by coach; nVLDI - number of light vehicles directly involved in the incident; nVPDI - number of heavy vehicles directly involved in the incident; nVADI - number of buses directly involved in the incident.

9.

The method of claim 7, wherein the number of people to evacuate in the first evacuation area (n 'IT) is the sum of people to evacuate with normal mobility (n' IMN), people with reduced mobility (n ' IMR) and people with assisted mobility (n 'IMA).

10.

The method of claim 9, wherein the number of people to be evacuated with normal mobility mobility (n 'IMN), people with reduced mobility (n' IMR) and people with assisted mobility (n 'IMA) is calculated as follows shape:

• In case of serious event: where: n ’IMN - number of people with normal mobility; n ’IMR - number of people with reduced mobility; n ’IMA - number of people with assisted mobility; P (MN | AGSI) -probability that following the occurrence of a Serious Incident, the mobility of a person is normal; P (MR | AGSI) -probability that following the occurrence of a Serious Incident, a person's mobility is reduced cida P (MA | AGSI) -probability that following the occurrence of a Serious Incident, the mobility of a person is assisted; n ’IT - total number of people trapped in Evacuation Area I. • In case the event is not serious: where: P (MN | A¯GSI) -Probability of a person's mobility being normal in case the incident is not serious; P (MR | A¯GSI) -Probability of a person's mobility being reduced in case the incident is not serious; and where where: P (MR | AGSI) -probability that following the occurrence of a Serious Incident, a person's mobility is reduced; P (HL | AGSI) is the probability that those involved in an accident suffer minor injuries if the hypothesis that the event was serious. Y: where P (MA | AGSI) -probability that following the occurrence of a Serious Incident, the mobility of a person is assisted; P (HG | AGSI) - Probability that those involved in an accident suffer serious injuries if the hypothesis that the event was serious, it is true; P (F | AGSI) -probability that those involved in an accident die if the hypothesis that the event was Serious, it's true. 11. The method of any of the preceding claims, wherein said evacuation time of each person (tEIi) is calculated by the following expression: where: tEIi - evacuation time of the first evacuation area of the i-th person; tpmi -function of distribution or probability density function that represents the pre-movement time of the i-th person, corresponding to the time elapsed between the moment the accident occurs until this person begins the movement; dmovi - distance traveled by the i-th person from their original position on stage to leave it; vmovi - distribution function or probability density function that represents the average speed of movement of the ith person during his departure from the first evacuation area. 12. The method of claim 11, wherein said evacuation time of each person is statistically calculated according to the following expression: where: TTEIj - Total Evacuation Time of Scenario I (first evacuation area) for the jth implementation; t (MN)

-

evacuation time of the ith person with normal mobility in the jth embodiment;

EIij t (MR)

-

evacuation time of the ith person with reduced mobility in the jth embodiment;

EIij t (MA)

-

evacuation time of the i-th person with assisted mobility in the j-th embodiment;

EIij max {...} - maximum value of the set. Y: where: t (MN) pmij - pre-movement time of the i-th person with normal mobility in the j-th iteration; t (MR) pmij - pre-movement time of the i-th person with reduced mobility in the j-th iteration; t (MA) pmij - pre-movement time of the i-th person with assisted mobility in the j-th iteration; dmovij - distance traveled by the i-th person in the j-th iteration; v (MN)

-

average speed of movement of the i-th person with normal mobility in the j-th iteration;

movij v (MR)

-

average speed of movement of the i-th person with reduced mobility in the j-th iteration;

movij v (MA) movement-average displacement speed of the i-th person with assisted mobility in the j-th iteration. 13. The method of claim 7, wherein the number of people to evacuate in the second evacuation area is calculated by the expression: where: TTEIIj -Total Evacuation Time of Evacuation Area II (second evacuation area) for the jth implementation; tTEIIij - evacuation time of the i-th person in the j-th embodiment; qj-amount of people to evacuate in the j-th realization; niter - amount of iterations (realizations); max {...} - maximum value of the set. where the number of people to evacuate in each embodiment that is defined by the expression: where: n ’IIVL - quantity of light vehicles in the second evacuation area (Evacuation Area II); n ’IIVA = P (AVPA) · n’ IIVPA is the number of coaches on the Stage (second evacuation area); n ’IIVP = n’ IIVPA -n ’IIVA is the number of heavy vehicles on the Stage (second evacuation area); P (AVPA) -probability that a vehicle, from the set of heavy vehicles and coaches that usually travel through the tunnel, is a coach; k˜VLj - coefficient of occupation of a light vehicle in the j th iteration, which is assumed as a random integer uniformly distributed between 1 and 5; k˜VPj - coefficient of occupation of a heavy vehicle in the jth iteration, which is assumed as a random integer uniformly distributed between 1 and 2; k˜VAj - coefficient of occupation of a coach in the j th iteration, which is assumed as a random integer uniformly distributed between 20 and 40.

14.

The method of claim 13, wherein the evacuation time of each person in each iteration is calculated by the formula:

fifteen.

An intelligent system integrated and automated for the management of an emergency in a road tunnel, characterized by comprising:

• An incident subsystem, configured to perform a predictive analysis of the possible development and consequences of an incident, where said event is any accident, caused by one or more motor vehicles that pass through the tunnel, which causes real or potential damage to the people who are inside and eventually advise their evacuation;

•

An evacuation subsystem; Y

•

A decision making subsystem;

said intelligent system being integrated and automated con fi gured to carry out the steps of the method according to any of claims 1 to 14. SPANISH OFFICE OF THE PATENTS AND BRAND Application no .: 201100724 SPAIN Date of submission of the application: 06.23.2011 Priority Date: REPORT ON THE STATE OF THE TECHNIQUE 51 Int. Cl.: G06Q10 / 00 (2006.01) G08B21 / 00 (2006.01) RELEVANT DOCUMENTS

Category

Documents cited Claims Affected

TO

KR 20070078333 A (SHIN HUNG INDUSTRY ACADEMY COO; BUMCHANG ENGINEERING CO LTD) 31.07.2007, paragraphs [0023-0118]; Figures 1-7. 1-15

TO

EP 1816764 A2 (TOSHIBA KK) 08.08.2007, the whole document. 1-15

TO

JP 10188156 A (KOCHIKI CO) 21.07.1998, summary; figures. Extracted from the WPI database in EPOQUE 1-15

TO

HÄKAN TRANTZICH & DANIEL NILSSON, "Evacuation in Complex Environments. An Analysis of Evacuation Conditions at Tunnel Construction Site", in Fourth International Symposium on Tunnel Safety and Security, March 17-19, 2010, Frankfurt am Main, pages 181-190. 1-15

TO

INGASON, "Design Fires in Tunnels" in Safe & reliable Tunnels, Innovative European Achievements, Second International Symposium, 2006, Lausanne, pages 1-11. one

Category of the documents cited X: of particular relevance Y: of particular relevance combined with other / s of the same category A: reflects the state of the art O: refers to unwritten disclosure P: published between the priority date and the date of priority submission of the application E: previous document, but published after the date of submission of the application

This report has been prepared • for all claims • for claims no:

Date of realization of the report 28.11.2011

Examiner P. Pérez Fernández Page 1/4

REPORT OF THE STATE OF THE TECHNIQUE Application number: 201100724 Minimum documentation searched (classification system followed by classification symbols) G06Q, G08B Electronic databases consulted during the search (name of the database and, if possible, terms of search used) INVENES, EPODOC State of the Art Report Page 2/4  WRITTEN OPINION Application number: 201100724 Date of Written Opinion: 28.11.2011 Statement

Novelty (Art. 6.1 LP 11/1986)

Claims Claims 1-15 IF NOT

Inventive activity (Art. 8.1 LP11 / 1986)

Claims Claims 1-15 IF NOT

The application is considered to comply with the industrial application requirement. This requirement was evaluated during the formal and technical examination phase of the application (Article 31.2 Law 11/1986).  Opinion Base.- This opinion has been made on the basis of the patent application as published. State of the Art Report Page 3/4  WRITTEN OPINION Application number: 201100724 1. Documents considered.- The documents belonging to the state of the art taken into consideration for the realization of this opinion are listed below.

Document

Publication or Identification Number publication date

D01

KR 20070078333 A (SHIN HUNG INDUSTRY ACADEMY COO; BUMCHANG ENGINEERING CO LTD) 07/31/2007

2. Statement motivated according to articles 29.6 and 29.7 of the Regulations for the execution of Law 11/1986, of March 20, on Patents on novelty and inventive activity; quotes and explanations in support of this statement  Lack of Novelty / Inventive Activity Claim 1 Document D01 is established as the closest to the State of the Art. Said document D01 refers to "a method and a system for estimating the danger of fire in a tunnel". He Method comprises the following stages: 1st) scenario review (see paragraphs 85-89). 2nd) traffic congestion analysis (see paragraphs 90-97). 3rd) determination of the moment of protection (see paragraphs 98-106). 4th) measurement of the temperature in the tunnel and the concentration of toxic gases in the tunnel (see paragraphs 107-118). From the foregoing it is clearly seen that the procedure of document D01 is different from that set forth in the claim No. 1. Accordingly, claim 1 has Novelty / Inventive Activity (Art 6.1 LP; Art 8.1 LP). Claims No. 2-14 Claims No. 2-14 depend on one or another form of claim No. 1. Therefore, claims No. 2-14 also possess Novelty / Inventive Activity (Art 6.1 LP; Art 8.1 LP). Claim 15 Document D01 describes a system other than that described in claim 15, since in document D01 neither an incident subsystem, an evacuation subsystem, nor a decision making subsystem appears as if they appear in claim 15 Accordingly, claim 15 has Novelty / Inventive Activity (Art 6.1 LP; Art 8.1 LP). State of the Art Report Page 4/4