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Mapping fire risk of passenger-carried fire load in metro system via floor field cellular automaton
Automation in Construction 100 (2019) 61–72
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Automation in Construction journal homepage: www.elsevier.com/locate/autcon
Mapping fire risk of passenger-carried fire load in metro system via floor field cellular automaton
T
Danyan Huanga,b, Siuming Lob, Juan Chenc, Zhijian Fud, Yuan Zhenga, Lin Luod, Yifan Zhuange, ⁎ Han Chenga, Lizhong Yanga, a
State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, Hong Kong c Department of Fire Safety Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China d School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China e College of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Fire risk mapping Passenger flow modeling Metro stations Facility management
Rapid urbanization in many large cities around the world fosters the rapid expansion of mass transit rail/metro services. It becomes one of the most important transportation modes in our daily lives and greatly improves the efficiency of travel. Meanwhile, as existing statistics show, fire accidents are one of the serious threats that metro faced with. Of all these accidents, fire risk associated with the fire load carried by the passengers have rarely been studied due to its dynamic nature. To facilitate efficient planning for pedestrian movement and to evaluate the potential fire risk in metro stations, research on the passenger-carried fire load pattern deserves more attention and efforts. In this study, a method for mapping fire risk of passenger-carried fire load is proposed through the combination of pedestrian flow modeling and fire risk assessment. Influencing area of passengercarried fire load, time interval of fire risk map, and desired velocity of passengers constitute the dynamic nature of fire risk, and they are found to have significant effects on the variation of fire risk. A larger influencing area results in a smoother fire risk surface and a wider range of the risk regions, while a shorter time interval may provide a more accurate distribution of fire risk but have a larger variance. Faster desired velocity of passengers makes a larger fluctuation of fire risk distribution as passengers move quicker. The simulation output could represent not only the detailed features of pedestrian traffic but also the dynamic variation of fire risks in the simulation scenarios. This method can serve as a useful tool to find out high-risk regions and provide information for facility management and design of metro facilities.
1. Introduction Public security has drawn extensive attention nowadays with the rapid development of society, and the growing demand for safer environment promote the safety management of public places. Urban rail transit, one of the most common transports that people use to travel, represents an effective tool to relieve the pressures from the city expansion and travel-distance extension due to its low pollution and high efficiency [1]. By now, 35 cities in China including Hong Kong and Macao have already opened 155 subway lines in total, and there are some more still under construction and planning. Metro, as one of the urban rail transit, is built underground in order to liberate the land, and with this feature, a large number of people on the ground begin to move to the underground spaces. However, it would be strikingly
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catastrophic once serious accidents occur in underground spaces, due to the limited space and high population densities. Previous accidents reveal that there exist numerous potential risks during the daily operation of metro, and fire accident is still one of the biggest menaces to metro systems [2,3]. It should also be mentioned that fires would not only lead to serious property losses but also cause a negative influence on society [4–7]. Back to 1987, a fire broke out at King's Cross St. Pancras tube station [8,9], a major interchange of the London Underground. The fire started on a wooden escalator serving the Piccadilly line and erupted in a flashover into the underground ticket hall, ending with 31 people dead and 100 injured. In the Daegu subway fire accident occurred on February 18, 2003, the arsonist brought two milk cartons filled with a flammable liquid and then set fire to the train, leading to a serious consequence of 192 people dead and 151 injured [10–12]. A fire
Corresponding author. E-mail address: [email protected] (L. Yang).
https://doi.org/10.1016/j.autcon.2018.12.021 Received 6 July 2018; Received in revised form 27 November 2018; Accepted 24 December 2018 0926-5805/ © 2019 Elsevier B.V. All rights reserved.
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(a)
(b)
Fig. 1. The illustration of passenger-carried fire load. Note: backpacks, handbags, and bags in the red dotted circle are regarded as passenger-carried fire load. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
based on multi-criteria evaluation [15]. Besides, remote sensing and Geographical Information Systems (GIS) based methodology are extensively used for the fire risk evaluation of larger geographical regions such as forest and urban areas [16–18]. In these methods, the distribution of fire risk would be mapped over space, thus the fire risk regions can be easily recognized. Aforementioned methods enlighten us that spatial distribution of risk is also the important part of risk assessment, which can arouse concern from relevant authorities to take steps. Considerable research efforts are devoted to all of these aforementioned methods, but it should be pointed out that these methods are aimed at a relatively static environment as the facilities or structures are not changed instantly, while one of the difficulties in studying such passenger-carried fire load is its dynamic nature and which is rarely involved. To facilitate efficient planning for pedestrian movement and to evaluate the fire risk pattern in metro systems, researches on the passenger flow and passenger-carried fire load pattern are necessary. Here, a method for mapping fire risk of passenger-carried fire loads in metro systems is put forward through the combination of pedestrian flow modeling and fire risk assessment, namely dynamic risk evaluation model. It can be used to estimate the dynamic potential fire hazard in the whole areas. The rest of this paper is organized as follows. In Section 2, the risk evaluation model is detailed. In Section 3, simulation studies are performed and results are discussed. Section 4 draws our conclusions.
accident caused by a young man igniting his luggage at Huangbeiling station in Shenzhen on July 20, 2013, led to the hurry-scurry evacuation of hundreds of passengers from the platform. At least 18 people got injured in a hurry rush from the Tsim Sha Tsui station, one of the busiest stations on Hong Kong's Mass Transit Railway, due to a maninduced fire occurred on a crowded metro train on February 10, 2017 (Friday evening) [13]. These accidents show that the potential risks of passenger-carried fire load in metro systems are still urgent problems that concerned authorities need to pay more attention to. To better protect human lives in metro systems, some design codes of metro systems have been established since the 1980s, such as the NFPA 130 in USA, Code for Design of Metro (GB 50157–2013) in China, JIS Railway Standards in Japan and Guideline on Formulation of Fire Safety Requirements for New Railway Infrastructures in Hong Kong. When comparing with other civil buildings, high fireproofing requirements such as lower fire resistance level and longer duration of fire resistance are proposed on carriagaes, the interior and materials for designing new trains and metro stations, but nearly no consideration is usually taken into the carried fire load the passengers bring into the stations. As shown in Fig. 1, luggage/baggage, such as backpack, handbags, trolley luggage, is regarded as passenger-carried fire load in our study. First of all, the luggage is carried by passenger and keeps pace with the passenger. Secondly, things in backpack, handbags, and trolley luggage like clothes, papers or others would represent fire load, and these may cause some extra potential fire risks. The fire accidents in the Baku Metro in 1995 and in the funicular railway in the Kaprun tunnel in 2000 show that the carried fire load also has a great impact on the fire occurrence [14]. In addition to adopting relevant laws and legislations to reduce the fire risk of metro stations, fire risk assessment is also implemented by related departments regularly to ensure the safety. Most of the demonstrations about fire risk evaluation in metro stations have so far focused on the spread of smoke and the evacuation strategies under fire or terrorist attacks, however, there are relatively few studies concentrating on fire risk associated with the fire load carried by passengers. Therefore, the evaluation about fire risk induced by passenger-carried fire load in public areas becomes one of the blank fields in risk evaluation. Until now, various methods on fire risk evaluation for building structures have been proposed, including statistical or probabilistic approaches, hazard analyses by using event trees or fault trees, stochastic computer simulation models, and fire risk ranking methods
2. Dynamic risk evaluation model This dynamic risk evaluation (DRE) model is a computational simulation model which represents the dynamic variation of fire risks in the simulation scenarios. The detailed description and the architecture of the DRE model are shown in Fig. 2. The architecture shows that some basic information about the simulated scene, pedestrian properties and security demands are required as inputs of the proposed model. With these inputs, our model can evaluate the dynamic fire risk with the help of two modules: pedestrian movement module and fire risk calculation module. Unlike the decorations, materials and facilities in metro systems, the fire risk that produced by the passenger-carried fire load would be associated with the walking pattern of pedestrians. Therefore, the pedestrian movement module is used to simulate the movement of pedestrians along with the moving of carried fire load. Given that the 62
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Input
Model calculation Pedestrian movement module
Building structure
Properties of pedestrians
Security demands
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Multi-velocity FFCA model
Dynamic risk evaluation model
Fire risk ranking Fire risk calculation factors
Fire risk calculation module
Dynamic distribution of fire risk within the simulated area
Interaction of fire risks Fig. 2. The architecture of simulation model.
model is briefly introduced. In the model, pedestrians can move to their eight nearest cells at each time step, namely Moore neighbor [22]. Also pedestrians make their own decisions by transfer probability, which is determined by both the static field and the dynamic field [23,24]. The transition probability Pij can be calculated by [23–26],
simulation model for microscopic pedestrian movement is widely studied by many researchers, the basic principles are listed in the introduction of multi-velocity floor field cellular automaton (FFCA) model. The second module is the newly proposed method for calculating the fire risk, which is composed of fire risk ranking process, fire risk calculation factors and interaction of fire risks, and it is also the emphasis of our fire risk evaluation model. Combining with these two modules, the dynamic fire risk distribution of concerned areas would be obtained.
Pij = N ⋅exp(KD Dij + K S Sij )(1 − nij ) Mij εij dij
(1)
where N is the normalization factor to make sure ∑Pij = 1. nij is the occupation indicator of cell (i, j). Here, nij = 1 if cell (i, j) is occupied by a pedestrian, otherwise nij = 0. εij reflects the influence from obstacles. εij = 0 if cell (i, j) is occupied by an obstacle, otherwise εij = 1. Mij is the matrix which represents the preference of pedestrian movement. Since the preference of pedestrians is not considered in this research so that Mij is set as a unit matrix, and so is the dynamic field Dij. dij is the inertial factor to increase the transition probability in the previous direction, and it takes a number greater than 1 if the current direction is the same as the direction of the previous time step. Here, the value of dij is set as 1 all the time. KS, KD are the coefficients of the static field (Sij) and the dynamic field (Dij) respectively. The static field is structured by a reported method in Ref. [27]. The dynamic field can be regarded as a virtue trace left by pedestrians at the previous time step with diffusion and decay [28]. In this work, multi-velocity is achieved by the velocity ratio, assuming that vi is the desired velocity of pedestrian i, and vmax is the maximum velocity of the system that includes Num pedestrians, then the velocity ratio of pedestrian i is defined as:
2.1. Main assumptions (1) It is assumed that carrying luggage has no effect on the desired velocity of passengers. The occurrence of roller bags, suitcase and prams is more frequent than before as more and more people choose metro as their major transportation. Passengers who carry handbags or backpacks walk as quickly as those without carrying it, and carrying handbags or backpacks nearly have no impact on pedestrian movement. While the speed of passengers with roller bags or suitcase is apparently slower than those with handbags or backpacks. Field observations on the movement of passengers with luggage/baggage shows the opposite trends of mean velocities between passengers with luggage and without or with small luggage [19,20]. After that, we believe that there are many other reasons may affect the velocities of passengers, including the shoes, gender, age, accompany, travel purpose and so on, and it is difficult to define the speed of each kind of passengers, hence, additional field studies should be put forward. Since our focus is the transition of risks generated by passenger-carried fire load, the desired velocity of each passenger is simplified and referred to the speed of those people without luggage. (2) It is assumed that carrying luggage or not is just an attribute for a passenger, and has no influence on the required moving space between two passengers in the present study. Generally speaking, some passengers would always carry luggage with various sizes, which would occupy some areas around them. However, since the size of baggage is different, it is rather difficult to determine the space occupied by the baggage, and hereby the space of luggage is simply considered as zero to simplify the analysis and to focus on the fire risk caused by passenger-carried luggage.
ηi = vi/ vmax
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A time step is calculated by
Δt = ΔL/ vmax
(3)
where ΔL is the length of a cell, and set as 0.4 m in this model. In each time step, all cells are updated synchronously by the following steps. Step 1. Generate a pseudo-random number series θ = (θ1, θ2, θ3 … θi … θNum) where θi is the pseudo-random number of pedestrian i, ranging from 0 to 1. Step 2. If θi < ηi, the pedestrian i can move according to the transition probability in current time step. Otherwise, he/she stays still. In other words, the probability that pedestrian i moves is ηi. The mathematic proof can be found in Ref. [27].
2.2. Pedestrian movement module Since the distribution of fire risk on the concerned area is affected by pedestrian movement, a multi-velocity FFCA model is used to simulate the crowd motion which involves pedestrian with different walking abilities [21]. In this part, the basics of the multi-velocity FFCA
2.3. Fire risk calculation module 2.3.1. Ranking of fire risk The fire load that passengers bring into the stations can lead to fire 63
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Fig. 3. Pedestrians with different risk levels in the concerned region.
risks in different classes. Combustibles such as portable power source, clothes, etc. may result in a higher level of fire risk, while other uninflammable items like large luggage will cause a lower one. To better recognize the fire risks in the simulation scenarios, fire risks are classified into five levels in our model according to the method in other risk assessments: very low (1), low (2), medium (3), high (4), very high (5), and numbers in parentheses are used for simple expression of fire risk. Since nearly all passengers carry a package (no matter how small the package is) in daily observations and in Ref. [19], hence, the pedestrians shown in our model are assigned a certain rank of fire risk. As shown in Fig. 3, 10 pedestrians with different risk levels are randomly distributed in the concerned regions. The numbers and colors in Fig. 3 indicate the fire risk levels of pedestrians and are for better vision recognition, respectively.
others during walking. Since the scope of fire risk is greater than or equal to the range of the pedestrians themselves, each pedestrian's influencing area will overlap when pedestrians approach. Thus, the interaction of fire risk in these overlapping areas must be taken into consideration. The interaction of risk is divided into the following two situations: 1) pedestrians with the same risk level get close to each other; 2) pedestrians with the different risk levels approach to each other. As can be seen from the part 1 and 2 of Fig. 5, the interaction principle can be simplified into the following three cases: 1) The risk level of each pedestrian remains unchanged, while the risk value of the district where the pedestrian is located in remains variable. 2) The total risk area will decrease when pedestrians get close to each other due to the overlap of the fire risk influencing area. 3) When two adjacent pedestrians are at same risk level, the risk level of the overlapping area will keep pace with the risk level carried by themselves. For two neighboring pedestrians with different risk levels, the risk level of the overlapped area is consistent with the pedestrian with a higher fire risk level, indicating that impact of higher risk level is greater than the lower one.
2.3.2. Fire risk calculation factors In the simulation, we focus on the change of fire risk caused by passenger-carried fire load, and the variation of fire risk is a spatial and temporal process, so it should be addressed both spatially and temporally, and for this reason, the influencing area and time interval have to be taken into consideration. The influencing area A: the proposed model is based on FFCA model, so that the space cannot be divided into circles but into squares. Similar to the density calculation of pedestrians in the room, the distribution of fire risk in the room is also affected by pedestrian movement. In other words, the distribution of fire risk in the area at time t is closely related to the locations of pedestrians in that area at time t. The fire risk influencing area is calculated by
A = (a − k ≤ x ≤ a + k , b − k ≤ y ≤ b + k )
3. Results and discussion In this section, we construct simulation examples on a typical scenario of a simplified metro station hallway with single exit to examine the fire risk evaluation model. In the simulation, we focus on the effects of influencing area, time interval and desired velocity on time-varied fire risk in the model. We also present a dynamic fire risk map to show the whole process of moving pedestrians to the exit and help intuitively investigate the high-risk area.
(4)
where k reflects the size of the influencing area, and (a, b) is the coordinate of pedestrian i at time t. k can be set to different values to meet various demands, the bigger the value of k is, the larger the influencing area A is. As shown in Fig. 4, solid red circle expresses the location of a pedestrian, then influencing area of carried fire load by this pedestrian would be shown as the grey region while k = 1. Time interval Tinterval: time interval is used to structure dynamic fire risk map as risk is not an instantaneous quantity, and fire risk is calculated for every influencing area for an average of the time interval. As mentioned earlier, all pedestrians are updated synchronously in each time step, therefore, time interval must be an integral multiple of a time step, i.e.,
Tint erval = N ⋅Δt
3.1. Simulation setup The simulated structure is a 40 m × 40 m room with a 2.4 m-wide door in the middle of the upper wall. 200 pedestrians are randomly distributed in the system at the beginning of the simulation, and all of them are allocated a specific fire risk level respectively. In all simulated scenarios, we set the following condition into practice: the ratio for pedestrians with each fire risk level (1, 2, 3, 4, 5) is the same, and all pedestrians are walking towards the exit with their desired velocities. Snapshots of the structure, the simulation at the beginning and the initial fire risk distribution are shown in Fig. 6.
(5)
where N denotes a positive integer. 3.2. Effect of the influencing area 2.3.3. Interaction of fire risks Pedestrians are inevitably getting close to and moving away from
Due to the fact that the fire risk of all luggage which may appear in 64
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Fig. 6. (a) The configuration of the simulated room. (b) Snapshot at the beginning of the simulation, pedestrians in different colors denote different fire risk levels. (c) Snapshot of fire risk map at the beginning of the simulation, here, k = 1. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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walking towards the exit, pedestrians are concentrated around the exit, and the corners of the room are rarely visited, which results in the fire risk of periphery area is significantly lower than the other locations during the selected time interval. On the other hand, the influencing area affects the resulting fire risk map. A larger influencing area results in a smoother fire risk surface and a wider range of the risk regions. One of the most important issues is that how these fire risks change over time. Fig. 8 reveals the time-varying spatial distribution of these five fire risk levels quantitatively. Under the conditions of fire risk without diffusion (k = 0) and small diffusion range (k = 1), level 1 covers the largest area during the whole process, followed by level 2. With the expansion of influencing area and increased concentration of pedestrians, high levels gradually occupy an advantageous position when comparing the areas of each fire risk level. That is to say, if passenger-carried fire load is extremely dangerous (large influencing area and high fire risk level), fire risk in the concerned region would be high, and a great number of people would be in jeopardy. This shows that the proposed model can effectively present the above phenomenon.
the metro stations is not fully explored until now, thus, the influencing area of each carried fire load is not doubtless. As an important parameter in our model, A can be set into various values with the change of k, as can be seen from Eq. (4). Moreover, changes in A would bring about the fluctuation in simulation results. Here, Tinterval is set as 1 s (N = 10), and the variable k is set to be 0, 1, 2, 3 and 4 to investigate the impact of the fire risk influencing area during the pedestrian movement process. Therefore, corresponding to each k above, influencing area equals to 0.16 m2, 1.44 m2, 4 m2, 7.84 m2 and 12.96 m2, respectively. In this section, the desired velocity of each pedestrian is set as 1 m/s. Fig. 7 gives the spatial distribution of fire risk with different influencing areas when in the middle of moving process (there remains 97 pedestrians inside the room). The interval between the contours of Fig. 7 is 1 in the aforementioned fire risk level. In the mid-term of the moving process, nearly half of the pedestrians have left the room, and remainders are still walking towards the exit, indicating that the fire risk in Fig. 7 is produced by these remainders. On one hand, the configurations of the fire risk distribution with different influencing areas bear a certain resemblance to each other since the distribution of fire risk mainly varies with pedestrian movement. As all pedestrians are 66
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Fig. 9. Fire risk distributions within the simulated room under different time intervals: (a) N = 1, (b) N = 5, (c) N = 10, (d) N = 15, (e) N = 20, (f) N = 25 and (g) N = 30.
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Fig. 10. Total risk region in the simulated room.
time. Tinterval, by definition, is the length of time step Δt shown in Eq. (2). The maximum desired velocity of pedestrians in this research is 4 m/s, hence, the time step is Δt = ΔL/vmax = 0.1 s. In the following, N is set to be 1, 5, 10, 15, 20, 25 and 30, that means Tinterval is equal to
3.3. Effect of time interval As aforementioned in Section 2, fire risk changes over a period of
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Fig. 13. Fire risk distributions within the simulated room under different desired velocities: (a) v = 1 m/s, (b) v = 2 m/s, (c) v = 3 m/s and (d) v = 4 m/s.
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4m 0.1, 0.5, 1, 1.5, 2, 2.5 and 3 s to explore the influence on the variation of fire risk. Here, other parameters such as influencing area and desired speed remain unchanged, namely, k = 1 and Vd = 1 m/s, respectively. The simulated room was selected as a whole to discover the distribution and variety of fire risk at different time intervals. Fig. 9 gives the distribution of fire risk in the simulated room during the entire pedestrian moving process. Comparing the subfigures in Fig. 9, we found that the distributions of low-grade fire risks (level 1 and level 2) in simulated structure increase with time interval, while the distributions of high-class fire risks just keep unchanged or decrease slightly. As in algorithm of fire risk map mentioned in Section 2.3, fire risk is calculated over a period of time. Once the time interval is chosen, fire risk of a certain region can be obtained through the number of pedestrians and the risk level of fire load they carry. For a fixed space such as P (in Fig. 6(a)), the longer the time interval, the more number of passengers that will passing through the space during this period, but the passenger-carried fire loads are not
Fig. 15. Description of simulated area.
the same, thus fire risk may be averaged accordingly, presenting a relatively lower risk distribution. For a location like Q (in Fig. 6(a)), the number of passenger moving towards the exit through this position is limited, so that even if a pedestrian with fire risk level 5 passes this location, the risk in this region will be reduced to level 2 or level 1 due to the increase in time interval. From Fig. 10 we can clearly observe that the area of risks in simulated room increases as time interval aggrandizes. Furthermore, for the same simulated scenario and the same number of pedestrians, finer discretization of time interval allows us to observe more precise moving patterns and the dynamic distribution of fire risk, but the calculation frequency will increase and the fluctuation of calculation results will be greater. In other words, the shorter time interval may provide a more accurate distribution of fire risk but it requires high demands of computational demands and has a large variance. 69
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3.5 3.0 Flow rate (1/s)
complex, and the competition between them will become more intense, resulting in the remainder vs. time curve of the even distribution quite different from the other four curves. In this section, in order to minimize the interference of pedestrians with different walking speed on simulation results, all pedestrians are assumed to walk to the exit at the identical speed. The unitary distributions of pedestrians' desired velocities of 1, 2, 3 and 4 m/s are picked up to detect the effect of the desired velocity on space-time distribution of fire risk. Fig. 13 presents the fire risk distribution in the entire simulated room under different desired velocities throughout the whole process. It is worthwhile mentioning that when compared with the situations of unitary distributions of desired velocities of 1, 2, 3 and 4 m/s, it can be found that the lowest risk grade covers the largest risk regions, and the area of these risk regions decreases gradually in accordance with fire risk level 1, 2, 3, 4 and 5. The reason for this may be that, within the selected time interval, the walking distances of pedestrians with higher desired velocities would be farther, resulting in more regions at risk. Although pedestrians with higher desired velocities would spend less time than those with lower desired velocities in one cell when there is no stop, fire risk in this cell would be equilibrated. That is, for the same computed time interval, the longer time a pedestrian stays in one cell, the higher fire risk of this cell, but not higher than its original risk level of carried fire load. In addition, the faster desired velocity of a pedestrian, the lower fire risk it will be, and the larger fluctuation of fire risk distribution within the studied area. Fig. 14 shows the total risk in the studying area. Combining with pedestrian movement, some features of fire risk distribution can be obtained. In the early stage of movement, all pedestrians are randomly distributed in the room, and the distance between each other is not too close to prevent the walking of each other, so they move freely. The total risk distribution at this stage keeps unchanged, but the duration of this stage is shorter as the desired velocity increases, so we can only clearly observe this stage at the velocity of 1 m/s. The second stage is the stage when pedestrians gather towards the exit. As the influencing area overlaps after the gather of pedestrians, the total risk region in the room gradually decreases. In the third stage, the total risk region is getting smaller and smaller as pedestrians gradually walk out of the room, but the reduction rate of the total risk region is slightly lower than that in the second stage as the competition between pedestrians near the exit would be fiercer. Back to our daily lives, assuming that a large number of passengers with luggage rush into the metro station, the distribution of fire risk in the whole area can be acquired easily. People walking slowly and
Kretz Armin This study Fitted curve in Ref.[28]
2.5 2.0 1.5 1.0 0.5 0.4
0.6
0.8
1.0
1.2
1.4
1.6
b (m) Fig. 16. Flow rate at the bottlenecks/exits under different bottleneck/exit widths.
In brief, if someone carries some dangerous items into the metro station, our evaluation model will present different risk situations at diverse time intervals, and the shorter the time interval, the higher of corresponding risk levels. When faced with high-security demands, managers should shorten time interval to increase the accuracy of fire risk distribution in subway stations and adopt more stringent measures to deal with them. On the contrary, in daily operations, it is appropriate to increase time interval to regulate the distribution of fire risk inside the stations, focusing on controlling some areas with higher risks. Risk maps of various time intervals in the middle of moving process are demonstrated in Fig. 11. With the increase of time interval, fire risk region is expanded while fire risk level in the same location is lowered. Under the condition of pedestrians with same desired speed, the greater the time interval, the more areas pedestrians pass through. As a result, fire risk does not accumulate continuously in this area during the selected time interval, which is why the regions of fire risk level 4 and 5 in Fig. 11 show no big difference. 3.4. Effect of the desired velocity As can be found in our risk evaluation model, the desired speed is another key parameter to the simulation results since the velocity determines pedestrians' movement process. It can be seen from Fig. 12 when pedestrians with different desired velocities are taken into consideration, the evolution of pedestrian movement will be more
(a)
(b)
Fig. 17. Trajectories at the bottleneck/exit. (a) experiment data from Ref. [29], (b) our simulation result after smoothing. 70
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carrying high-risk fire load will be shown as the red or orange point in the dynamic risk map, revealing that more attention should be paid to these areas and some effective measures should be taken.
safety. The model can be used for studying risk evaluation on passengerscarried fire load at public places such as metro stations, terminal stations and so on. However, as a grid-based discrete model, the movement of pedestrians cannot be reproduced as smooth as in continuous models and reality but its computing efficiency is apparently higher than that of continuous models. For the larger size of structures with more pedestrians, the computational complexity would increase significantly and require more time to map the fire risk. This limitation may be weakened naturally with the fast-growing computing technology. Furthermore, this paper shows the basic modeling of the risk mapping method on pedestrians with fire load, but not the strategic and tactical level management method, the method can be used to analyze/ find out high-risk regions and provide information for facility management and design of metro facilities. In future, substantial research efforts will be devoted to the extension and modification of the model and apply it for more dynamic situations. Moreover, additional surveys and field studies will be conducted in metro stations to more explicitly classify the risk levels of passenger-carried fire load, and the experimental study on fire behaviors of all types of passenger-carried fire load should be implemented to estimate the influencing area of each risk level of passenger-carried fire load.
4. Comparison with empirical data As mentioned in Section 2, fire risk that produced by the passengercarried fire load would be associated with the walking pattern of pedestrians, hence, the accuracy of pedestrian movement plays an important role in the credibility of our DRE model. To enhance the credibility of the model, we compare the simulation results with published empirical data [29–31]. We first simulated the pedestrian movement by using our DRE model on a scenario including similar population size with the practical case. We considered a 7.6 m × 4 m room as the simulated area, in which the exit was located at the shorter wall (Fig. 15). 60 pedestrians are randomly distributed in the holding area which is 2.8 m far away from the exit, with the initial average density of 3.125 person/m2. Given the fact that the initial density has major impact on the flow through bottlenecks/exits [31,32], thus we compare our simulation results among experiment data [30,31] with similar initial densities to see the flow rate difference through the bottlenecks/exits, and the simulated results obtained through our DRE model showed very good consistence with the experimental trends (Fig. 16). In addition, Fig. 17 displays the trajectories of empirical data [29] at the bottleneck and our simulation results smoothed by using Loess method [33]. The comparison in Fig. 15 and Fig. 16 indicate that our simulation results are in good agreement with the empirical data, which in turn reveals that our model is credible.
Acknowledgment This research was supported by the Research Grants Council, University Grants Committee of the Hong Kong Special Administrative Region (Project No. CityU 11300815), the Key Research Program of the Chinese Academy of Sciences (No. QYZDB-SSW-JSC029) the Fundamental Research Funds for the Central Universities (No. WK2320000035) and National Natural Science Foundation of China (Grant No. 11602206). The authors deeply appreciate the supports.
5. Conclusions This paper has presented a new approach of dynamic fire risk evaluation by focusing on the introduction of the algorithm used in risk evaluation model. The model is implemented through the following two modules: pedestrian movement module and fire risk calculation module. Pedestrian movement module is the basis of the risk evaluation model as the focus of our study is the passenger-carried fire loads, which is determined by pedestrians' movement, and multi-velocity FFCA model is used in this module. Fire risk calculation module is the key point of the model and the algorithm was introduced in detail. Since every pedestrian and carried fire load are regarded as a whole in the module, thus the distribution of fire risk can be updated per time step. The influencing area of passenger-carried fire load, time interval of fire risk map and desired velocity of passengers constitute the dynamic nature of fire risk. The effects of these relevant parameters in the risk evaluation model on the variation of fire risk were analyzed by running a series of simulations. For the parameter k, which represents the size of influencing area, the greater the value of parameter k, the more likely passengers will suffer high risk. As different fire loads have various danger, more information about them needs to be obtained to determine their influencing areas. For parameter Tinterval, which indicates the updating time of dynamic fire risk distribution, longer time intervals will result in smaller fluctuations of fire risk distribution, and shorter time interval may provide a more accurate distribution of fire risk but needs higher computing power. Concerned authorities can choose different time intervals according to their security needs. The desired velocity of pedestrians is also one of the influencing factors to the simulation results of the model. Simulation results show that the longer time one person stays at one place, the higher fire risk this place will be, and administrative staffs should pay more attention to those regions that passengers remained unchanged or move a little especially when they carry high-risk luggage. With this insight into the pedestrian traffic in metro stations, effective facility and security planning, design, management and control strategies can be established to assure the
References [1] L. Zhang, M. Liu, X. Wu, S.M. AbouRizk, Simulation-based route planning for pedestrian evacuation in metro stations: a case study, Autom. Constr. 71 (2016) 430–442, https://doi.org/10.1016/j.autcon.2016.08.031. [2] C. Shi, M. Zhong, X. Nong, L. He, J. Shi, G. Feng, Modeling and safety strategy of passenger evacuation in a metro station in China, Saf. Sci. 50 (2012) 1319–1332, https://doi.org/10.1016/j.ssci.2010.07.017. [3] R. Gao, A. Li, Y. Zhang, N. Luo, How domes improve fire safety in subway stations, Saf. Sci. 80 (2015) 94–104, https://doi.org/10.1016/j.ssci.2015.07.015. [4] M. Zhong, W. Fan, T.M. Liu, P.H. Zhang, X. Wei, G.X. Liao, China: some key technologies and the future developments of fire safety science, Saf. Sci. 42 (2004) 627–637, https://doi.org/10.1016/j.ssci.2003.10.003. [5] J. Shi, A. Ren, C. Chen, Agent-based evacuation model of large public buildings under fire conditions, Autom. Constr. 18 (2009) 338–347, https://doi.org/10. 1016/j.autcon.2008.09.009. [6] N. Li, B. Becerik-Gerber, B. Krishnamachari, L. Soibelman, A BIM centered indoor localization algorithm to support building fire emergency response operations, Autom. Constr. 42 (2014) 78–89, https://doi.org/10.1016/j.autcon.2014.02.019. [7] M. Liu, S.M. Lo, The quantitative investigation on people's pre-evacuation behavior under fire, Autom. Constr. 20 (2011) 620–628, https://doi.org/10.1016/j.autcon. 2010.12.004. [8] King's Cross fire, https://en.wikipedia.org/wiki/King's_Cross_fire, (1987). [9] P.A. Langston, R. Masling, B.N. Asmar, Crowd dynamics discrete element multicircle models, Saf. Sci. 44 (2006) 395–417, https://doi.org/10.1016/j.ssci.2005.11. 007. [10] Daegu subway fire, https://en.wikipedia.org/wiki/Daegu_subway_fire, (2003). [11] J.S. Roh, H.S. Ryou, W.H. Park, Y.J. Jang, CFD simulation and assessment of life safety in a subway train fire, Tunn. Undergr. Space Technol. 24 (2009) 447–453, https://doi.org/10.1016/j.tust.2008.12.003. [12] M. Tsukahara, Y. Koshiba, H. Ohtani, Effectiveness of downward evacuation in a large-scale subway fire using Fire Dynamics Simulator, Tunn. Undergr. Space Technol. 26 (2011) 573–581, https://doi.org/10.1016/j.tust.2011.02.002. [13] At Least 18 Injured in Fire on Hong Kong Metro Train, https://edition.cnn.com/ 2017/02/10/asia/hong-kong-train-fire/, (2017). [14] M. Kumm, Carried fire load in mass, Transport systems (2010). [15] S. Lo, A fire safety assessment system for existing buildings, Fire. Technol. 35 (1999) 131–152, https://doi.org/10.1023/A:1015463821818. [16] S.S. Pulugurtha, V.K. Krishnakumar, S.S. Nambisan, New methods to identify and rank high pedestrian crash zones: an illustration, Accid. Anal. Prev. 39 (2007) 800–811, https://doi.org/10.1016/j.aap.2006.12.001. [17] E. Chuvieco, I. Aguado, M. Yebra, H. Nieto, J. Salas, M.P. Martín, L. Vilar,
71
Automation in Construction 100 (2019) 61–72
D. Huang et al.
[18]
[19]
[20]
[21]
[22] [23]
[24]
[25]
J. Martínez, S. Martín, P. Ibarra, J. de la Riva, J. Baeza, F. Rodríguez, J.R. Molina, M.A. Herrera, R. Zamora, Development of a framework for fire risk assessment using remote sensing and geographic information system technologies, Ecol. Model. 221 (2010) 46–58, https://doi.org/10.1016/j.ecolmodel.2008.11.017. R.E. Keane, S.A. Drury, E.C. Karau, P.F. Hessburg, K.M. Reynolds, A method for mapping fire hazard and risk across multiple scales and its application in fire management, Ecol. Model. 221 (2010) 2–18, https://doi.org/10.1016/j.ecolmodel. 2008.10.022. Z. Zhao, D. Liang, Pedestrian flow characteristic of Metro Station along with the mall, Procedia Engineering 135 (2016) 601–605, https://doi.org/10.1016/j. proeng.2016.01.118. Z. Fang, W. Lv, L. Jiang, Q. Xu, W. Song, Observation, simulation and optimization of the movement of passengers with baggage in railway station, Int. J. Mod. Phys. C 26 (2015) 1550124, , https://doi.org/10.1142/S0129183115501247. Z. Fu, Q. Jia, J. Chen, J. Ma, K. Han, L. Luo, A fine discrete field cellular automaton for pedestrian dynamics integrating pedestrian heterogeneity, anisotropy, and timedependent characteristics, Transportation Research Part C: Emerging Technologies 91 (2018) 37–61, https://doi.org/10.1016/j.trc.2018.03.022. N. Margolus, T. Toffoli, Cellular automata machines, Complex Systems 1 (1987) 967–993. A. Kirchner, A. Schadschneider, Simulation of evacuation processes using a bionicsinspired cellular automaton model for pedestrian dynamics, Physica A: Statistical Mechanics and Its Applications 312 (2002) 260–276, https://doi.org/10.1016/ S0378-4371(02)00857-9. Z. Fu, L. Luo, Y. Yang, Y. Zhuang, P. Zhang, L. Yang, H. Yang, J. Ma, K. Zhu, Y. Li, Effect of speed matching on fundamental diagram of pedestrian flow, Physica A: Statistical Mechanics and Its Applications 458 (2016) 31–42, https://doi.org/10. 1016/j.physa.2016.03.060. A. Schadschneider, A. Kirchner, K. Nishinari, CA approach to collective phenomena
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
72
in pedestrian dynamics, Cellular Automata. ACRI 2002. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2002, pp. 239–248, , https://doi.org/10. 1007/3-540-45830-1_23. L. Luo, Z. Fu, X. Zhou, K. Zhu, H. Yang, L. Yang, Fatigue effect on phase transition of pedestrian movement: experiment and simulation study, J. Stat. Mech: Theory Exp. 2016 (2016), https://doi.org/10.1088/1742-5468/2016/10/103401. Z. Fu, L. Yang, Y. Chen, K. Zhu, S. Zhu, The effect of individual tendency on crowd evacuation efficiency under inhomogeneous exit attraction using a static field modified FFCA model, Physica A: Statistical Mechanics and Its Applications 392 (2013) 6090–6099, https://doi.org/10.1016/j.physa.2013.07.062. W. Yuan, K. Tan, A novel algorithm of simulating multi-velocity evacuation based on cellular automata modeling and tenability condition, Physica A: Statistical Mechanics and Its Applications 379 (2007) 250–262, https://doi.org/10.1016/j. physa.2006.12.044. M. Boltes, A. Tordeux, J. Zhang, Experimentation, data collection, modeling and simulation of pedestrian dynamics, Statistics, Probability and Numerical Analysis 2014, Tirana, 2014. T. Kretz, A. Grünebohm, M. Schreckenberg, Experimental study of pedestrian flow through a bottleneck, J. Stat. Mech: Theory Exp. 2006 (2006) P10014, https://doi. org/10.1088/1742-5468/2006/10/P10014. A. Seyfried, T. Rupprecht, O. Passon, B. Steffen, W. Klingsch, M. Boltes, New insights into pedestrian flow through bottlenecks, Transp. Sci. 43 (2009) 395–406, https://doi.org/10.1287/trsc.1090.0263. R. Nagai, M. Fukamachi, T. Nagatani, Evacuation of crawlers and walkers from corridor through an exit, Physica A: Statistical Mechanics and Its Applications 367 (2006) 449–460, https://doi.org/10.1016/j.physa.2005.11.031. W.S. Cleveland, S.J. Devlin, Locally weighted regression: an approach to regression analysis by local fitting, J. Am. Stat. Assoc. 83 (1988) 596–610, https://doi.org/10. 1080/01621459.1988.10478639.
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Automation in Construction journal homepage: www.elsevier.com/locate/autcon
Mapping fire risk of passenger-carried fire load in metro system via floor field cellular automaton
T
Danyan Huanga,b, Siuming Lob, Juan Chenc, Zhijian Fud, Yuan Zhenga, Lin Luod, Yifan Zhuange, ⁎ Han Chenga, Lizhong Yanga, a
State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, Hong Kong c Department of Fire Safety Engineering, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China d School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China e College of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Fire risk mapping Passenger flow modeling Metro stations Facility management
Rapid urbanization in many large cities around the world fosters the rapid expansion of mass transit rail/metro services. It becomes one of the most important transportation modes in our daily lives and greatly improves the efficiency of travel. Meanwhile, as existing statistics show, fire accidents are one of the serious threats that metro faced with. Of all these accidents, fire risk associated with the fire load carried by the passengers have rarely been studied due to its dynamic nature. To facilitate efficient planning for pedestrian movement and to evaluate the potential fire risk in metro stations, research on the passenger-carried fire load pattern deserves more attention and efforts. In this study, a method for mapping fire risk of passenger-carried fire load is proposed through the combination of pedestrian flow modeling and fire risk assessment. Influencing area of passengercarried fire load, time interval of fire risk map, and desired velocity of passengers constitute the dynamic nature of fire risk, and they are found to have significant effects on the variation of fire risk. A larger influencing area results in a smoother fire risk surface and a wider range of the risk regions, while a shorter time interval may provide a more accurate distribution of fire risk but have a larger variance. Faster desired velocity of passengers makes a larger fluctuation of fire risk distribution as passengers move quicker. The simulation output could represent not only the detailed features of pedestrian traffic but also the dynamic variation of fire risks in the simulation scenarios. This method can serve as a useful tool to find out high-risk regions and provide information for facility management and design of metro facilities.
1. Introduction Public security has drawn extensive attention nowadays with the rapid development of society, and the growing demand for safer environment promote the safety management of public places. Urban rail transit, one of the most common transports that people use to travel, represents an effective tool to relieve the pressures from the city expansion and travel-distance extension due to its low pollution and high efficiency [1]. By now, 35 cities in China including Hong Kong and Macao have already opened 155 subway lines in total, and there are some more still under construction and planning. Metro, as one of the urban rail transit, is built underground in order to liberate the land, and with this feature, a large number of people on the ground begin to move to the underground spaces. However, it would be strikingly
⁎
catastrophic once serious accidents occur in underground spaces, due to the limited space and high population densities. Previous accidents reveal that there exist numerous potential risks during the daily operation of metro, and fire accident is still one of the biggest menaces to metro systems [2,3]. It should also be mentioned that fires would not only lead to serious property losses but also cause a negative influence on society [4–7]. Back to 1987, a fire broke out at King's Cross St. Pancras tube station [8,9], a major interchange of the London Underground. The fire started on a wooden escalator serving the Piccadilly line and erupted in a flashover into the underground ticket hall, ending with 31 people dead and 100 injured. In the Daegu subway fire accident occurred on February 18, 2003, the arsonist brought two milk cartons filled with a flammable liquid and then set fire to the train, leading to a serious consequence of 192 people dead and 151 injured [10–12]. A fire
Corresponding author. E-mail address: [email protected] (L. Yang).
https://doi.org/10.1016/j.autcon.2018.12.021 Received 6 July 2018; Received in revised form 27 November 2018; Accepted 24 December 2018 0926-5805/ © 2019 Elsevier B.V. All rights reserved.
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(a)
(b)
Fig. 1. The illustration of passenger-carried fire load. Note: backpacks, handbags, and bags in the red dotted circle are regarded as passenger-carried fire load. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
based on multi-criteria evaluation [15]. Besides, remote sensing and Geographical Information Systems (GIS) based methodology are extensively used for the fire risk evaluation of larger geographical regions such as forest and urban areas [16–18]. In these methods, the distribution of fire risk would be mapped over space, thus the fire risk regions can be easily recognized. Aforementioned methods enlighten us that spatial distribution of risk is also the important part of risk assessment, which can arouse concern from relevant authorities to take steps. Considerable research efforts are devoted to all of these aforementioned methods, but it should be pointed out that these methods are aimed at a relatively static environment as the facilities or structures are not changed instantly, while one of the difficulties in studying such passenger-carried fire load is its dynamic nature and which is rarely involved. To facilitate efficient planning for pedestrian movement and to evaluate the fire risk pattern in metro systems, researches on the passenger flow and passenger-carried fire load pattern are necessary. Here, a method for mapping fire risk of passenger-carried fire loads in metro systems is put forward through the combination of pedestrian flow modeling and fire risk assessment, namely dynamic risk evaluation model. It can be used to estimate the dynamic potential fire hazard in the whole areas. The rest of this paper is organized as follows. In Section 2, the risk evaluation model is detailed. In Section 3, simulation studies are performed and results are discussed. Section 4 draws our conclusions.
accident caused by a young man igniting his luggage at Huangbeiling station in Shenzhen on July 20, 2013, led to the hurry-scurry evacuation of hundreds of passengers from the platform. At least 18 people got injured in a hurry rush from the Tsim Sha Tsui station, one of the busiest stations on Hong Kong's Mass Transit Railway, due to a maninduced fire occurred on a crowded metro train on February 10, 2017 (Friday evening) [13]. These accidents show that the potential risks of passenger-carried fire load in metro systems are still urgent problems that concerned authorities need to pay more attention to. To better protect human lives in metro systems, some design codes of metro systems have been established since the 1980s, such as the NFPA 130 in USA, Code for Design of Metro (GB 50157–2013) in China, JIS Railway Standards in Japan and Guideline on Formulation of Fire Safety Requirements for New Railway Infrastructures in Hong Kong. When comparing with other civil buildings, high fireproofing requirements such as lower fire resistance level and longer duration of fire resistance are proposed on carriagaes, the interior and materials for designing new trains and metro stations, but nearly no consideration is usually taken into the carried fire load the passengers bring into the stations. As shown in Fig. 1, luggage/baggage, such as backpack, handbags, trolley luggage, is regarded as passenger-carried fire load in our study. First of all, the luggage is carried by passenger and keeps pace with the passenger. Secondly, things in backpack, handbags, and trolley luggage like clothes, papers or others would represent fire load, and these may cause some extra potential fire risks. The fire accidents in the Baku Metro in 1995 and in the funicular railway in the Kaprun tunnel in 2000 show that the carried fire load also has a great impact on the fire occurrence [14]. In addition to adopting relevant laws and legislations to reduce the fire risk of metro stations, fire risk assessment is also implemented by related departments regularly to ensure the safety. Most of the demonstrations about fire risk evaluation in metro stations have so far focused on the spread of smoke and the evacuation strategies under fire or terrorist attacks, however, there are relatively few studies concentrating on fire risk associated with the fire load carried by passengers. Therefore, the evaluation about fire risk induced by passenger-carried fire load in public areas becomes one of the blank fields in risk evaluation. Until now, various methods on fire risk evaluation for building structures have been proposed, including statistical or probabilistic approaches, hazard analyses by using event trees or fault trees, stochastic computer simulation models, and fire risk ranking methods
2. Dynamic risk evaluation model This dynamic risk evaluation (DRE) model is a computational simulation model which represents the dynamic variation of fire risks in the simulation scenarios. The detailed description and the architecture of the DRE model are shown in Fig. 2. The architecture shows that some basic information about the simulated scene, pedestrian properties and security demands are required as inputs of the proposed model. With these inputs, our model can evaluate the dynamic fire risk with the help of two modules: pedestrian movement module and fire risk calculation module. Unlike the decorations, materials and facilities in metro systems, the fire risk that produced by the passenger-carried fire load would be associated with the walking pattern of pedestrians. Therefore, the pedestrian movement module is used to simulate the movement of pedestrians along with the moving of carried fire load. Given that the 62
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Input
Model calculation Pedestrian movement module
Building structure
Properties of pedestrians
Security demands
Output
Multi-velocity FFCA model
Dynamic risk evaluation model
Fire risk ranking Fire risk calculation factors
Fire risk calculation module
Dynamic distribution of fire risk within the simulated area
Interaction of fire risks Fig. 2. The architecture of simulation model.
model is briefly introduced. In the model, pedestrians can move to their eight nearest cells at each time step, namely Moore neighbor [22]. Also pedestrians make their own decisions by transfer probability, which is determined by both the static field and the dynamic field [23,24]. The transition probability Pij can be calculated by [23–26],
simulation model for microscopic pedestrian movement is widely studied by many researchers, the basic principles are listed in the introduction of multi-velocity floor field cellular automaton (FFCA) model. The second module is the newly proposed method for calculating the fire risk, which is composed of fire risk ranking process, fire risk calculation factors and interaction of fire risks, and it is also the emphasis of our fire risk evaluation model. Combining with these two modules, the dynamic fire risk distribution of concerned areas would be obtained.
Pij = N ⋅exp(KD Dij + K S Sij )(1 − nij ) Mij εij dij
(1)
where N is the normalization factor to make sure ∑Pij = 1. nij is the occupation indicator of cell (i, j). Here, nij = 1 if cell (i, j) is occupied by a pedestrian, otherwise nij = 0. εij reflects the influence from obstacles. εij = 0 if cell (i, j) is occupied by an obstacle, otherwise εij = 1. Mij is the matrix which represents the preference of pedestrian movement. Since the preference of pedestrians is not considered in this research so that Mij is set as a unit matrix, and so is the dynamic field Dij. dij is the inertial factor to increase the transition probability in the previous direction, and it takes a number greater than 1 if the current direction is the same as the direction of the previous time step. Here, the value of dij is set as 1 all the time. KS, KD are the coefficients of the static field (Sij) and the dynamic field (Dij) respectively. The static field is structured by a reported method in Ref. [27]. The dynamic field can be regarded as a virtue trace left by pedestrians at the previous time step with diffusion and decay [28]. In this work, multi-velocity is achieved by the velocity ratio, assuming that vi is the desired velocity of pedestrian i, and vmax is the maximum velocity of the system that includes Num pedestrians, then the velocity ratio of pedestrian i is defined as:
2.1. Main assumptions (1) It is assumed that carrying luggage has no effect on the desired velocity of passengers. The occurrence of roller bags, suitcase and prams is more frequent than before as more and more people choose metro as their major transportation. Passengers who carry handbags or backpacks walk as quickly as those without carrying it, and carrying handbags or backpacks nearly have no impact on pedestrian movement. While the speed of passengers with roller bags or suitcase is apparently slower than those with handbags or backpacks. Field observations on the movement of passengers with luggage/baggage shows the opposite trends of mean velocities between passengers with luggage and without or with small luggage [19,20]. After that, we believe that there are many other reasons may affect the velocities of passengers, including the shoes, gender, age, accompany, travel purpose and so on, and it is difficult to define the speed of each kind of passengers, hence, additional field studies should be put forward. Since our focus is the transition of risks generated by passenger-carried fire load, the desired velocity of each passenger is simplified and referred to the speed of those people without luggage. (2) It is assumed that carrying luggage or not is just an attribute for a passenger, and has no influence on the required moving space between two passengers in the present study. Generally speaking, some passengers would always carry luggage with various sizes, which would occupy some areas around them. However, since the size of baggage is different, it is rather difficult to determine the space occupied by the baggage, and hereby the space of luggage is simply considered as zero to simplify the analysis and to focus on the fire risk caused by passenger-carried luggage.
ηi = vi/ vmax
(2)
A time step is calculated by
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where ΔL is the length of a cell, and set as 0.4 m in this model. In each time step, all cells are updated synchronously by the following steps. Step 1. Generate a pseudo-random number series θ = (θ1, θ2, θ3 … θi … θNum) where θi is the pseudo-random number of pedestrian i, ranging from 0 to 1. Step 2. If θi < ηi, the pedestrian i can move according to the transition probability in current time step. Otherwise, he/she stays still. In other words, the probability that pedestrian i moves is ηi. The mathematic proof can be found in Ref. [27].
2.2. Pedestrian movement module Since the distribution of fire risk on the concerned area is affected by pedestrian movement, a multi-velocity FFCA model is used to simulate the crowd motion which involves pedestrian with different walking abilities [21]. In this part, the basics of the multi-velocity FFCA
2.3. Fire risk calculation module 2.3.1. Ranking of fire risk The fire load that passengers bring into the stations can lead to fire 63
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risks in different classes. Combustibles such as portable power source, clothes, etc. may result in a higher level of fire risk, while other uninflammable items like large luggage will cause a lower one. To better recognize the fire risks in the simulation scenarios, fire risks are classified into five levels in our model according to the method in other risk assessments: very low (1), low (2), medium (3), high (4), very high (5), and numbers in parentheses are used for simple expression of fire risk. Since nearly all passengers carry a package (no matter how small the package is) in daily observations and in Ref. [19], hence, the pedestrians shown in our model are assigned a certain rank of fire risk. As shown in Fig. 3, 10 pedestrians with different risk levels are randomly distributed in the concerned regions. The numbers and colors in Fig. 3 indicate the fire risk levels of pedestrians and are for better vision recognition, respectively.
others during walking. Since the scope of fire risk is greater than or equal to the range of the pedestrians themselves, each pedestrian's influencing area will overlap when pedestrians approach. Thus, the interaction of fire risk in these overlapping areas must be taken into consideration. The interaction of risk is divided into the following two situations: 1) pedestrians with the same risk level get close to each other; 2) pedestrians with the different risk levels approach to each other. As can be seen from the part 1 and 2 of Fig. 5, the interaction principle can be simplified into the following three cases: 1) The risk level of each pedestrian remains unchanged, while the risk value of the district where the pedestrian is located in remains variable. 2) The total risk area will decrease when pedestrians get close to each other due to the overlap of the fire risk influencing area. 3) When two adjacent pedestrians are at same risk level, the risk level of the overlapping area will keep pace with the risk level carried by themselves. For two neighboring pedestrians with different risk levels, the risk level of the overlapped area is consistent with the pedestrian with a higher fire risk level, indicating that impact of higher risk level is greater than the lower one.
2.3.2. Fire risk calculation factors In the simulation, we focus on the change of fire risk caused by passenger-carried fire load, and the variation of fire risk is a spatial and temporal process, so it should be addressed both spatially and temporally, and for this reason, the influencing area and time interval have to be taken into consideration. The influencing area A: the proposed model is based on FFCA model, so that the space cannot be divided into circles but into squares. Similar to the density calculation of pedestrians in the room, the distribution of fire risk in the room is also affected by pedestrian movement. In other words, the distribution of fire risk in the area at time t is closely related to the locations of pedestrians in that area at time t. The fire risk influencing area is calculated by
A = (a − k ≤ x ≤ a + k , b − k ≤ y ≤ b + k )
3. Results and discussion In this section, we construct simulation examples on a typical scenario of a simplified metro station hallway with single exit to examine the fire risk evaluation model. In the simulation, we focus on the effects of influencing area, time interval and desired velocity on time-varied fire risk in the model. We also present a dynamic fire risk map to show the whole process of moving pedestrians to the exit and help intuitively investigate the high-risk area.
(4)
where k reflects the size of the influencing area, and (a, b) is the coordinate of pedestrian i at time t. k can be set to different values to meet various demands, the bigger the value of k is, the larger the influencing area A is. As shown in Fig. 4, solid red circle expresses the location of a pedestrian, then influencing area of carried fire load by this pedestrian would be shown as the grey region while k = 1. Time interval Tinterval: time interval is used to structure dynamic fire risk map as risk is not an instantaneous quantity, and fire risk is calculated for every influencing area for an average of the time interval. As mentioned earlier, all pedestrians are updated synchronously in each time step, therefore, time interval must be an integral multiple of a time step, i.e.,
Tint erval = N ⋅Δt
3.1. Simulation setup The simulated structure is a 40 m × 40 m room with a 2.4 m-wide door in the middle of the upper wall. 200 pedestrians are randomly distributed in the system at the beginning of the simulation, and all of them are allocated a specific fire risk level respectively. In all simulated scenarios, we set the following condition into practice: the ratio for pedestrians with each fire risk level (1, 2, 3, 4, 5) is the same, and all pedestrians are walking towards the exit with their desired velocities. Snapshots of the structure, the simulation at the beginning and the initial fire risk distribution are shown in Fig. 6.
(5)
where N denotes a positive integer. 3.2. Effect of the influencing area 2.3.3. Interaction of fire risks Pedestrians are inevitably getting close to and moving away from
Due to the fact that the fire risk of all luggage which may appear in 64
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walking towards the exit, pedestrians are concentrated around the exit, and the corners of the room are rarely visited, which results in the fire risk of periphery area is significantly lower than the other locations during the selected time interval. On the other hand, the influencing area affects the resulting fire risk map. A larger influencing area results in a smoother fire risk surface and a wider range of the risk regions. One of the most important issues is that how these fire risks change over time. Fig. 8 reveals the time-varying spatial distribution of these five fire risk levels quantitatively. Under the conditions of fire risk without diffusion (k = 0) and small diffusion range (k = 1), level 1 covers the largest area during the whole process, followed by level 2. With the expansion of influencing area and increased concentration of pedestrians, high levels gradually occupy an advantageous position when comparing the areas of each fire risk level. That is to say, if passenger-carried fire load is extremely dangerous (large influencing area and high fire risk level), fire risk in the concerned region would be high, and a great number of people would be in jeopardy. This shows that the proposed model can effectively present the above phenomenon.
the metro stations is not fully explored until now, thus, the influencing area of each carried fire load is not doubtless. As an important parameter in our model, A can be set into various values with the change of k, as can be seen from Eq. (4). Moreover, changes in A would bring about the fluctuation in simulation results. Here, Tinterval is set as 1 s (N = 10), and the variable k is set to be 0, 1, 2, 3 and 4 to investigate the impact of the fire risk influencing area during the pedestrian movement process. Therefore, corresponding to each k above, influencing area equals to 0.16 m2, 1.44 m2, 4 m2, 7.84 m2 and 12.96 m2, respectively. In this section, the desired velocity of each pedestrian is set as 1 m/s. Fig. 7 gives the spatial distribution of fire risk with different influencing areas when in the middle of moving process (there remains 97 pedestrians inside the room). The interval between the contours of Fig. 7 is 1 in the aforementioned fire risk level. In the mid-term of the moving process, nearly half of the pedestrians have left the room, and remainders are still walking towards the exit, indicating that the fire risk in Fig. 7 is produced by these remainders. On one hand, the configurations of the fire risk distribution with different influencing areas bear a certain resemblance to each other since the distribution of fire risk mainly varies with pedestrian movement. As all pedestrians are 66
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time. Tinterval, by definition, is the length of time step Δt shown in Eq. (2). The maximum desired velocity of pedestrians in this research is 4 m/s, hence, the time step is Δt = ΔL/vmax = 0.1 s. In the following, N is set to be 1, 5, 10, 15, 20, 25 and 30, that means Tinterval is equal to
3.3. Effect of time interval As aforementioned in Section 2, fire risk changes over a period of
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4m 0.1, 0.5, 1, 1.5, 2, 2.5 and 3 s to explore the influence on the variation of fire risk. Here, other parameters such as influencing area and desired speed remain unchanged, namely, k = 1 and Vd = 1 m/s, respectively. The simulated room was selected as a whole to discover the distribution and variety of fire risk at different time intervals. Fig. 9 gives the distribution of fire risk in the simulated room during the entire pedestrian moving process. Comparing the subfigures in Fig. 9, we found that the distributions of low-grade fire risks (level 1 and level 2) in simulated structure increase with time interval, while the distributions of high-class fire risks just keep unchanged or decrease slightly. As in algorithm of fire risk map mentioned in Section 2.3, fire risk is calculated over a period of time. Once the time interval is chosen, fire risk of a certain region can be obtained through the number of pedestrians and the risk level of fire load they carry. For a fixed space such as P (in Fig. 6(a)), the longer the time interval, the more number of passengers that will passing through the space during this period, but the passenger-carried fire loads are not
Fig. 15. Description of simulated area.
the same, thus fire risk may be averaged accordingly, presenting a relatively lower risk distribution. For a location like Q (in Fig. 6(a)), the number of passenger moving towards the exit through this position is limited, so that even if a pedestrian with fire risk level 5 passes this location, the risk in this region will be reduced to level 2 or level 1 due to the increase in time interval. From Fig. 10 we can clearly observe that the area of risks in simulated room increases as time interval aggrandizes. Furthermore, for the same simulated scenario and the same number of pedestrians, finer discretization of time interval allows us to observe more precise moving patterns and the dynamic distribution of fire risk, but the calculation frequency will increase and the fluctuation of calculation results will be greater. In other words, the shorter time interval may provide a more accurate distribution of fire risk but it requires high demands of computational demands and has a large variance. 69
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3.5 3.0 Flow rate (1/s)
complex, and the competition between them will become more intense, resulting in the remainder vs. time curve of the even distribution quite different from the other four curves. In this section, in order to minimize the interference of pedestrians with different walking speed on simulation results, all pedestrians are assumed to walk to the exit at the identical speed. The unitary distributions of pedestrians' desired velocities of 1, 2, 3 and 4 m/s are picked up to detect the effect of the desired velocity on space-time distribution of fire risk. Fig. 13 presents the fire risk distribution in the entire simulated room under different desired velocities throughout the whole process. It is worthwhile mentioning that when compared with the situations of unitary distributions of desired velocities of 1, 2, 3 and 4 m/s, it can be found that the lowest risk grade covers the largest risk regions, and the area of these risk regions decreases gradually in accordance with fire risk level 1, 2, 3, 4 and 5. The reason for this may be that, within the selected time interval, the walking distances of pedestrians with higher desired velocities would be farther, resulting in more regions at risk. Although pedestrians with higher desired velocities would spend less time than those with lower desired velocities in one cell when there is no stop, fire risk in this cell would be equilibrated. That is, for the same computed time interval, the longer time a pedestrian stays in one cell, the higher fire risk of this cell, but not higher than its original risk level of carried fire load. In addition, the faster desired velocity of a pedestrian, the lower fire risk it will be, and the larger fluctuation of fire risk distribution within the studied area. Fig. 14 shows the total risk in the studying area. Combining with pedestrian movement, some features of fire risk distribution can be obtained. In the early stage of movement, all pedestrians are randomly distributed in the room, and the distance between each other is not too close to prevent the walking of each other, so they move freely. The total risk distribution at this stage keeps unchanged, but the duration of this stage is shorter as the desired velocity increases, so we can only clearly observe this stage at the velocity of 1 m/s. The second stage is the stage when pedestrians gather towards the exit. As the influencing area overlaps after the gather of pedestrians, the total risk region in the room gradually decreases. In the third stage, the total risk region is getting smaller and smaller as pedestrians gradually walk out of the room, but the reduction rate of the total risk region is slightly lower than that in the second stage as the competition between pedestrians near the exit would be fiercer. Back to our daily lives, assuming that a large number of passengers with luggage rush into the metro station, the distribution of fire risk in the whole area can be acquired easily. People walking slowly and
Kretz Armin This study Fitted curve in Ref.[28]
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In brief, if someone carries some dangerous items into the metro station, our evaluation model will present different risk situations at diverse time intervals, and the shorter the time interval, the higher of corresponding risk levels. When faced with high-security demands, managers should shorten time interval to increase the accuracy of fire risk distribution in subway stations and adopt more stringent measures to deal with them. On the contrary, in daily operations, it is appropriate to increase time interval to regulate the distribution of fire risk inside the stations, focusing on controlling some areas with higher risks. Risk maps of various time intervals in the middle of moving process are demonstrated in Fig. 11. With the increase of time interval, fire risk region is expanded while fire risk level in the same location is lowered. Under the condition of pedestrians with same desired speed, the greater the time interval, the more areas pedestrians pass through. As a result, fire risk does not accumulate continuously in this area during the selected time interval, which is why the regions of fire risk level 4 and 5 in Fig. 11 show no big difference. 3.4. Effect of the desired velocity As can be found in our risk evaluation model, the desired speed is another key parameter to the simulation results since the velocity determines pedestrians' movement process. It can be seen from Fig. 12 when pedestrians with different desired velocities are taken into consideration, the evolution of pedestrian movement will be more
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Fig. 17. Trajectories at the bottleneck/exit. (a) experiment data from Ref. [29], (b) our simulation result after smoothing. 70
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carrying high-risk fire load will be shown as the red or orange point in the dynamic risk map, revealing that more attention should be paid to these areas and some effective measures should be taken.
safety. The model can be used for studying risk evaluation on passengerscarried fire load at public places such as metro stations, terminal stations and so on. However, as a grid-based discrete model, the movement of pedestrians cannot be reproduced as smooth as in continuous models and reality but its computing efficiency is apparently higher than that of continuous models. For the larger size of structures with more pedestrians, the computational complexity would increase significantly and require more time to map the fire risk. This limitation may be weakened naturally with the fast-growing computing technology. Furthermore, this paper shows the basic modeling of the risk mapping method on pedestrians with fire load, but not the strategic and tactical level management method, the method can be used to analyze/ find out high-risk regions and provide information for facility management and design of metro facilities. In future, substantial research efforts will be devoted to the extension and modification of the model and apply it for more dynamic situations. Moreover, additional surveys and field studies will be conducted in metro stations to more explicitly classify the risk levels of passenger-carried fire load, and the experimental study on fire behaviors of all types of passenger-carried fire load should be implemented to estimate the influencing area of each risk level of passenger-carried fire load.
4. Comparison with empirical data As mentioned in Section 2, fire risk that produced by the passengercarried fire load would be associated with the walking pattern of pedestrians, hence, the accuracy of pedestrian movement plays an important role in the credibility of our DRE model. To enhance the credibility of the model, we compare the simulation results with published empirical data [29–31]. We first simulated the pedestrian movement by using our DRE model on a scenario including similar population size with the practical case. We considered a 7.6 m × 4 m room as the simulated area, in which the exit was located at the shorter wall (Fig. 15). 60 pedestrians are randomly distributed in the holding area which is 2.8 m far away from the exit, with the initial average density of 3.125 person/m2. Given the fact that the initial density has major impact on the flow through bottlenecks/exits [31,32], thus we compare our simulation results among experiment data [30,31] with similar initial densities to see the flow rate difference through the bottlenecks/exits, and the simulated results obtained through our DRE model showed very good consistence with the experimental trends (Fig. 16). In addition, Fig. 17 displays the trajectories of empirical data [29] at the bottleneck and our simulation results smoothed by using Loess method [33]. The comparison in Fig. 15 and Fig. 16 indicate that our simulation results are in good agreement with the empirical data, which in turn reveals that our model is credible.
Acknowledgment This research was supported by the Research Grants Council, University Grants Committee of the Hong Kong Special Administrative Region (Project No. CityU 11300815), the Key Research Program of the Chinese Academy of Sciences (No. QYZDB-SSW-JSC029) the Fundamental Research Funds for the Central Universities (No. WK2320000035) and National Natural Science Foundation of China (Grant No. 11602206). The authors deeply appreciate the supports.
5. Conclusions This paper has presented a new approach of dynamic fire risk evaluation by focusing on the introduction of the algorithm used in risk evaluation model. The model is implemented through the following two modules: pedestrian movement module and fire risk calculation module. Pedestrian movement module is the basis of the risk evaluation model as the focus of our study is the passenger-carried fire loads, which is determined by pedestrians' movement, and multi-velocity FFCA model is used in this module. Fire risk calculation module is the key point of the model and the algorithm was introduced in detail. Since every pedestrian and carried fire load are regarded as a whole in the module, thus the distribution of fire risk can be updated per time step. The influencing area of passenger-carried fire load, time interval of fire risk map and desired velocity of passengers constitute the dynamic nature of fire risk. The effects of these relevant parameters in the risk evaluation model on the variation of fire risk were analyzed by running a series of simulations. For the parameter k, which represents the size of influencing area, the greater the value of parameter k, the more likely passengers will suffer high risk. As different fire loads have various danger, more information about them needs to be obtained to determine their influencing areas. For parameter Tinterval, which indicates the updating time of dynamic fire risk distribution, longer time intervals will result in smaller fluctuations of fire risk distribution, and shorter time interval may provide a more accurate distribution of fire risk but needs higher computing power. Concerned authorities can choose different time intervals according to their security needs. The desired velocity of pedestrians is also one of the influencing factors to the simulation results of the model. Simulation results show that the longer time one person stays at one place, the higher fire risk this place will be, and administrative staffs should pay more attention to those regions that passengers remained unchanged or move a little especially when they carry high-risk luggage. With this insight into the pedestrian traffic in metro stations, effective facility and security planning, design, management and control strategies can be established to assure the
References [1] L. Zhang, M. Liu, X. Wu, S.M. AbouRizk, Simulation-based route planning for pedestrian evacuation in metro stations: a case study, Autom. Constr. 71 (2016) 430–442, https://doi.org/10.1016/j.autcon.2016.08.031. [2] C. Shi, M. Zhong, X. Nong, L. He, J. Shi, G. Feng, Modeling and safety strategy of passenger evacuation in a metro station in China, Saf. Sci. 50 (2012) 1319–1332, https://doi.org/10.1016/j.ssci.2010.07.017. [3] R. Gao, A. Li, Y. Zhang, N. Luo, How domes improve fire safety in subway stations, Saf. Sci. 80 (2015) 94–104, https://doi.org/10.1016/j.ssci.2015.07.015. [4] M. Zhong, W. Fan, T.M. Liu, P.H. Zhang, X. Wei, G.X. Liao, China: some key technologies and the future developments of fire safety science, Saf. Sci. 42 (2004) 627–637, https://doi.org/10.1016/j.ssci.2003.10.003. [5] J. Shi, A. Ren, C. Chen, Agent-based evacuation model of large public buildings under fire conditions, Autom. Constr. 18 (2009) 338–347, https://doi.org/10. 1016/j.autcon.2008.09.009. [6] N. Li, B. Becerik-Gerber, B. Krishnamachari, L. Soibelman, A BIM centered indoor localization algorithm to support building fire emergency response operations, Autom. Constr. 42 (2014) 78–89, https://doi.org/10.1016/j.autcon.2014.02.019. [7] M. Liu, S.M. Lo, The quantitative investigation on people's pre-evacuation behavior under fire, Autom. Constr. 20 (2011) 620–628, https://doi.org/10.1016/j.autcon. 2010.12.004. [8] King's Cross fire, https://en.wikipedia.org/wiki/King's_Cross_fire, (1987). [9] P.A. Langston, R. Masling, B.N. Asmar, Crowd dynamics discrete element multicircle models, Saf. Sci. 44 (2006) 395–417, https://doi.org/10.1016/j.ssci.2005.11. 007. [10] Daegu subway fire, https://en.wikipedia.org/wiki/Daegu_subway_fire, (2003). [11] J.S. Roh, H.S. Ryou, W.H. Park, Y.J. Jang, CFD simulation and assessment of life safety in a subway train fire, Tunn. Undergr. Space Technol. 24 (2009) 447–453, https://doi.org/10.1016/j.tust.2008.12.003. [12] M. Tsukahara, Y. Koshiba, H. Ohtani, Effectiveness of downward evacuation in a large-scale subway fire using Fire Dynamics Simulator, Tunn. Undergr. Space Technol. 26 (2011) 573–581, https://doi.org/10.1016/j.tust.2011.02.002. [13] At Least 18 Injured in Fire on Hong Kong Metro Train, https://edition.cnn.com/ 2017/02/10/asia/hong-kong-train-fire/, (2017). [14] M. Kumm, Carried fire load in mass, Transport systems (2010). [15] S. Lo, A fire safety assessment system for existing buildings, Fire. Technol. 35 (1999) 131–152, https://doi.org/10.1023/A:1015463821818. [16] S.S. Pulugurtha, V.K. Krishnakumar, S.S. Nambisan, New methods to identify and rank high pedestrian crash zones: an illustration, Accid. Anal. Prev. 39 (2007) 800–811, https://doi.org/10.1016/j.aap.2006.12.001. [17] E. Chuvieco, I. Aguado, M. Yebra, H. Nieto, J. Salas, M.P. Martín, L. Vilar,
71
Automation in Construction 100 (2019) 61–72
D. Huang et al.
[18]
[19]
[20]
[21]
[22] [23]
[24]
[25]
J. Martínez, S. Martín, P. Ibarra, J. de la Riva, J. Baeza, F. Rodríguez, J.R. Molina, M.A. Herrera, R. Zamora, Development of a framework for fire risk assessment using remote sensing and geographic information system technologies, Ecol. Model. 221 (2010) 46–58, https://doi.org/10.1016/j.ecolmodel.2008.11.017. R.E. Keane, S.A. Drury, E.C. Karau, P.F. Hessburg, K.M. Reynolds, A method for mapping fire hazard and risk across multiple scales and its application in fire management, Ecol. Model. 221 (2010) 2–18, https://doi.org/10.1016/j.ecolmodel. 2008.10.022. Z. Zhao, D. Liang, Pedestrian flow characteristic of Metro Station along with the mall, Procedia Engineering 135 (2016) 601–605, https://doi.org/10.1016/j. proeng.2016.01.118. Z. Fang, W. Lv, L. Jiang, Q. Xu, W. Song, Observation, simulation and optimization of the movement of passengers with baggage in railway station, Int. J. Mod. Phys. C 26 (2015) 1550124, , https://doi.org/10.1142/S0129183115501247. Z. Fu, Q. Jia, J. Chen, J. Ma, K. Han, L. Luo, A fine discrete field cellular automaton for pedestrian dynamics integrating pedestrian heterogeneity, anisotropy, and timedependent characteristics, Transportation Research Part C: Emerging Technologies 91 (2018) 37–61, https://doi.org/10.1016/j.trc.2018.03.022. N. Margolus, T. Toffoli, Cellular automata machines, Complex Systems 1 (1987) 967–993. A. Kirchner, A. Schadschneider, Simulation of evacuation processes using a bionicsinspired cellular automaton model for pedestrian dynamics, Physica A: Statistical Mechanics and Its Applications 312 (2002) 260–276, https://doi.org/10.1016/ S0378-4371(02)00857-9. Z. Fu, L. Luo, Y. Yang, Y. Zhuang, P. Zhang, L. Yang, H. Yang, J. Ma, K. Zhu, Y. Li, Effect of speed matching on fundamental diagram of pedestrian flow, Physica A: Statistical Mechanics and Its Applications 458 (2016) 31–42, https://doi.org/10. 1016/j.physa.2016.03.060. A. Schadschneider, A. Kirchner, K. Nishinari, CA approach to collective phenomena
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
72
in pedestrian dynamics, Cellular Automata. ACRI 2002. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2002, pp. 239–248, , https://doi.org/10. 1007/3-540-45830-1_23. L. Luo, Z. Fu, X. Zhou, K. Zhu, H. Yang, L. Yang, Fatigue effect on phase transition of pedestrian movement: experiment and simulation study, J. Stat. Mech: Theory Exp. 2016 (2016), https://doi.org/10.1088/1742-5468/2016/10/103401. Z. Fu, L. Yang, Y. Chen, K. Zhu, S. Zhu, The effect of individual tendency on crowd evacuation efficiency under inhomogeneous exit attraction using a static field modified FFCA model, Physica A: Statistical Mechanics and Its Applications 392 (2013) 6090–6099, https://doi.org/10.1016/j.physa.2013.07.062. W. Yuan, K. Tan, A novel algorithm of simulating multi-velocity evacuation based on cellular automata modeling and tenability condition, Physica A: Statistical Mechanics and Its Applications 379 (2007) 250–262, https://doi.org/10.1016/j. physa.2006.12.044. M. Boltes, A. Tordeux, J. Zhang, Experimentation, data collection, modeling and simulation of pedestrian dynamics, Statistics, Probability and Numerical Analysis 2014, Tirana, 2014. T. Kretz, A. Grünebohm, M. Schreckenberg, Experimental study of pedestrian flow through a bottleneck, J. Stat. Mech: Theory Exp. 2006 (2006) P10014, https://doi. org/10.1088/1742-5468/2006/10/P10014. A. Seyfried, T. Rupprecht, O. Passon, B. Steffen, W. Klingsch, M. Boltes, New insights into pedestrian flow through bottlenecks, Transp. Sci. 43 (2009) 395–406, https://doi.org/10.1287/trsc.1090.0263. R. Nagai, M. Fukamachi, T. Nagatani, Evacuation of crawlers and walkers from corridor through an exit, Physica A: Statistical Mechanics and Its Applications 367 (2006) 449–460, https://doi.org/10.1016/j.physa.2005.11.031. W.S. Cleveland, S.J. Devlin, Locally weighted regression: an approach to regression analysis by local fitting, J. Am. Stat. Assoc. 83 (1988) 596–610, https://doi.org/10. 1080/01621459.1988.10478639.