Fire Safety Journal 47 (2012) 8–15

Contents lists available at SciVerse ScienceDirect

Fire Safety Journal

0379-71

doi:10.1

n Corr

E-m

journal homepage: www.elsevier.com/locate/firesaf

Experimental study of pedestrian behaviors in a corridor basedon digital image processing

Wei Tian a,b, Weiguo Song a,n, Jian Ma c, Zhiming Fang a, Armin Seyfried d, Jack Liddle d

a State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, PR Chinab China Ship Development and Design Center, Wuhan 430036, PR Chinac School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, PR Chinad Julich Supercomputing Centre, Forschungszentrum Julich GmbH, D-52425 Julich, Germany

a r t i c l e i n f o

Article history:

Received 16 June 2010

Received in revised form

16 September 2011

Accepted 20 September 2011Available online 13 October 2011

Keywords:

Evacuation

Pedestrian

Experiment

Mean-shift algorithm

Behavior

12/$ - see front matter & 2011 Elsevier Ltd. A

016/j.firesaf.2011.09.005

esponding author.

ail address: wgsong@ustc.edu.cn (W. Song).

a b s t r a c t

Evacuation experiments of pedestrians passing through a corridor, which acts as a bottleneck, are

conducted in this study. A video processing method is used to extract the trajectories of pedestrians in

the measurement section. The velocity, density, flow rate and time headway between successive

pedestrians entering the bottleneck are calculated, and the relations between them are obtained from

the trajectories. We report that the mean velocities for each experiment vary greatly, depending on the

lanes contained in the measurement section. The mean velocity of each pedestrian in the corridor

displays a trend that decreases sharply at the beginning and then fluctuates slightly around the mean

value. A fundamental diagram relating density and speed is obtained. We also investigate the relation

between the mean velocities of pedestrians and the density, as a function of time, and report that the

mean velocity decreased while the density increased. These results are consistent with those of other

researchers. The flow rate increases linearly with the bottleneck width, which is consistent with other

results. The time headways are investigated in this study. It is observed that the behavior of pedestrians

in the corridor, such as when they formed lanes, has a significant effect on the time headways and their

distribution. The distribution of time headways in different lanes is also studied, and some interesting

results are obtained. The data and results provided in this paper may be useful for improving

evacuation models and designing public facilities.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Simulating pedestrian movement to obtain accurate macro-scopic and microscopic behaviors and egress rules is critical forboth researchers and decision makers. The simulation resultscould be applied when designing fire escape routes or publicfacilities, such as classrooms, subway stations, stadiums andairports [1–4]. In recent years, increasingly more evacuationmodels have been proposed and used for simulating typicalpedestrian flows [5–13], and the flow through bottlenecks hasreceived the most attention [14–19]. However, the simulationresults deviate from and contradict each other because of thedifferent rules adopted and the different methods employed.In order to resolve these problems, a number of experiments havebeen performed under various conditions [11,14,18–26]. It wasfound that congestion occurs when the pedestrian flow exceeds thecapacity of the bottlenecks [17,19]; this is where a jam will take

ll rights reserved.

place and implies that the density in the corridor will remainsteady after a jam is formed.

The relationship between flow rate and the width of corridorhas been investigated by many researchers [18,19,21,23,27,28].The experiments of Predtechenskii and Milinskii (P–M for short)have shown that flow rate grows linearly with increasing bottle-neck width, which is consistent with the data reported in the SFPEHandbook and Weidmann [19,27]. However, the results haverecently been challenged by several studies. Hoogendoorn andDaamen [18] assumed that the zipper effect causes the bottleneckcapacity to increase in a stepwise fashion rather than a linearfashion with the bottleneck width, but the bottleneck widths intheir experiment are only 1 and 2 m. Gwynne et al. [28] have alsoquestioned the linear relationship between doorway width andachievable flow rate from data obtained during egress from asporting arena. Seyfried et al. [19] performed an experiment toexamine the linear relationship between width and flow rate.However, in their experiment, the bottleneck width ranges from0.8 to 1.2 m, and the number of lanes in the corridor is alwaystwo; thus, their results might lack enough data to sustain thelinearly increasing pattern in a wider range. It has also been found

W. Tian et al. / Fire Safety Journal 47 (2012) 8–15 9

that the measured maximal flow at bottlenecks could signifi-cantly exceed the maximum of the empirical fundamentaldiagram [21]. This finding implies that the knowledge aboutempirical fundamental diagrams is incomplete, and commonassumptions regarding the fundamental diagram and the flowthrough bottlenecks may need to be revised.

Other quantities have also been studied to understand pedes-trian microscopic motion. Kretz [23] has investigated the totaltimes, flow rates, specific fluxes and time gaps. He found that thespecific flux decreases linearly with increasing width when thecorridor allows just one person passing at a time and that it has aconstant value for larger widths. Seyfried has also presented thetime development of quantities, such as individual velocities,density and individual time gaps in bottlenecks of differentwidths. The relations between them are shown in Ref. [19].

In this study, several experiments based on Seyfried’s work arecarried out to verify the relationship between flow rate andbottleneck width. The specific pedestrian behavior, such asdensity, velocity, flow rate, time headway and the relationsbetween them, are studied. Based on data extracted from theexperiments, insights into the behavior of pedestrians movingthrough a bottleneck are obtained.

2. Experimental setup

The experiments were performed in front of a building, andthe participants could leave the corridor without stop after eachexperiment. The ground where we carried out our experiment isnot completely flat, but the slope is minimal. A video camera witha resolution of 1440�1080 was located on the fifth floor,approximately 15 m above the ground.

The experimental scene was set similarly to that in Ref. [19],in order to conveniently compare the results of the two studies.The experimental facility is built up with tables and publicityboards to prevent participants from putting parts of their bodyout of the corridor. The heights of the barriers are all higher thanthe participants, and the scene simulated a real corridor well.The coordinate axes are shown in Fig. 1. The x-axis is the walkingdirection, and the width of the bottleneck ranges from 0.5 to1.4 m. The 62 participants, 13 females and 49 males, were allstudents and were between 17 and 35 years old and between 157and 181 cm tall, averaging 24 years old and 171.6 cm tall. Theparticipants were all told to wear red caps and walk withoutpanic during the evacuation to allow accurate detection of thetrajectories.

There are three holding areas in the experimental area, each ofwhich is1.5 m�3 m. The left holding area is 3 m away from theentrance of the bottleneck. At the beginning of the experiment, allof the people are distributed uniformly in the holding area; thus,the initial participant densities in front of the bottleneck are equal

Fig. 1. Experimental scenario.

for each run. Measurement section 1, where parameters such asvelocity, density and time headway are measured, is approxi-mately 2 m long.

The perspective distortion effect caused by the differentheights of the participants could be ignored because the camerais mounted vertically above the bottleneck, and the distortion isnegligible. However, the horizontal pixels should be calibratedbecause the camera is approximately 11 m away from the rightedge of the scene horizontally, the distortion in both directionswill be large. Consequently, we adopted four known points (A, B,C and D) to adjust the distortion caused by lens distortion usingdirect linear transformation (DLT) [29], as shown in Fig. 1.

3. Tracking algorithm

In this experiment, all of the participants were asked to wearred caps in order to facilitate the detection of the trajectories.From the trajectories, we calculated the specific flux, density,velocity and time headways. Two regions are chosen, as shown inFig. 1. The first is a 2-meter-long corridor, and the second is a1 m�1 m square in front of the bottleneck to investigate therelationship between density and velocity in a high-density area.

The mean-shift algorithm is used to track each pedestrian.The algorithm was first proposed by Fukunaga and Hostetler [30].Conventionally, three kernels are widely used in object tracking,such as the Epanechnikov kernel, the uniform kernel and thenormal kernel [31]. The mean-shift algorithm identifies themaximal density in the feature space of a specific area throughthe calculation of the Bhattacharyya coefficient of the two con-nected regions in the sequential frames n and nþ1. A detailedintroduction of the mean-shift algorithm could be found in Ref.[32,33].

If the Bhattacharyya coefficient is larger than a specific thresh-old, the pedestrian in frame nþ1 can be regarded as the same onein frame n. Then, the pedestrian can continue to be tracked byconsidering the central pixel as the new object region, and thetrajectories of all of the pedestrians can be recorded as ordinatevalues by iteration.

4. Results and analysis

The trajectories of each participant can be obtained using animage processing method that is described in the tracking algo-rithm. The tracking scenarios with different lanes are shown inFig. 2. Meanwhile, the velocity, density, specific flow, time head-way and their relations can be calculated.

4.1. Velocities

Fig. 3 shows the mean velocities of all participants passingthrough measurement section 1 for different bottleneck widths. Itis observed that the mean velocities of the first several partici-pants are significantly higher than those coming later. Afterapproximately the fifteenth person, the velocity fluctuates arounda stable value. This is the result of two factors: the first few peoplepassing through the bottleneck are able to move with their freevelocity because there is empty space in front of them; addition-ally they are motivated to pass through quickly so as to avoidwaiting in a jam. As the effects of front and behind participantstransfer and accumulate, the velocities of behind participantsdecrease until reaching an approximately stable value.

The mean velocity of each group passing through the mea-surement section 1 shows different characteristics along with anincreasing bottleneck width (Table 1). The mean velocity of the

Fig. 2. Tracking scenarios with one, two and three lanes.

0

0.5

1.0

1.5

2.0

2.5

3.0

v [m

/s]

the order of pedestrians passing through the corridor

0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4

10 20 30 40 50 60 70

Fig. 3. Average velocities of all pedestrians for each run. The horizontal axis is the

order of the persons passing through the corridor.

Table 1Mean velocity of all pedestrians for each run.

Width of the

bottleneck b (m)

Mean velocity of all

pedestrians (m/s)

Mean velocity of the last 47

pedestrians (m/s)

0.5 0.99 0.98

0.6 0.91 0.86

0.7 0.88 0.82

0.8 0.95 0.83

0.9 0.93 0.77

1.0 0.96 0.85

1.1 0.98 0.82

1.2 1.01 0.90

1.3 0.93 0.86

1.4 0.93 0.84

00.0

0.5

1.0

1.5

2.0

v [m

/s]

t [s]

group velocity

6 12 18 24

Fig. 4. Group velocities, as a function of time, for the run with b¼1.2 m.

W. Tian et al. / Fire Safety Journal 47 (2012) 8–1510

steady condition that occurs approximately after the 15th personenters the corridor is also shown in Table 1. For both conditions,the velocity decreases when the width is between 0.5 and 0.7 mand then fluctuates around the mean.

The group velocity, which is the mean value of the instanta-neous velocities of all participants inside measurement section 1,is calculated by the following equation:

n¼Xn

i ¼ 1

ni=n ð1Þ

where ni is the instantaneous velocity of the ith person, and n isthe number of participants inside measurement section 1.

Fig. 4 shows the relation between group velocity and time forthe run with b¼1.2 m. It is clear that the velocity at the beginningof the run is significantly larger and fluctuates around the meanvalue after 6 s.

0.00.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8 density and velocity

v [m

/s]

ρ [m-2]

0.5 1.0 1.5 2.0 2.5 3.0 3.5

Fig. 6. Development of the velocity with increasing density.

00.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

this paper P-M

v [m

/s]

ρ [m-2]

1 2 3 4 5 6 7 8 9 10 11

Fig. 7. Fundamental diagram of this paper and a comparison with P–M’s results.

W. Tian et al. / Fire Safety Journal 47 (2012) 8–15 11

4.2. Density–velocity relation

4.2.1. Measurement section 1

In order to obtain the fundamental diagram in measurementsection 1, we calculate the density and velocity. The number ofparticipants involved in measurement section 1 can be calculatedin each frame by the method used in Ref. [34]. The density of eachgroup fluctuates dramatically because the measurement area isnarrow and the number of participants inside changes greatly.To reduce fluctuations, we adopt the method that Seyfried usedto obtain the exact density in the condition of single-file move-ment. The density of measurement section 1 is computed asthe momentary density, as determined by the entrance andthe exit times of person i, tin

i and touti . Thus, we define the

calculation of the number of persons in the measurement regionasDðtÞ ¼

PNi ¼ 1 YiðtÞ, where Yi(t) gives the fraction of space

between person i and person iþ1.

YiðtÞ ¼

t�tini

tintþ 1�tin

i

tA ½tini ,tin

tþ1�

1 tA ½tiniþ1,tout

t �

toutiþ 1�t

touttþ 1�tout

i

tA ½touti ,tout

tþ1�

0 otherwise

8>>>>>>><>>>>>>>:

ð2Þ

When only the first or the last person is in the measurementregion, the number of participant inside the section is consideredto be one. The density can be calculated by the following equationfor each frame,

rðtÞ ¼DðtÞ=S ð3Þ

where S is the area of the measurement region. Fig. 5 shows thetime independence of individual velocity and density as a func-tion of time for the b¼0.7 m run. The upper black line representsthe density, and the lower red points show the velocities of all theparticipants in the experiment. It is clearly shown that theparticipant walks slowly when the pedestrian density is large;that is to say, the velocity of participants may be smaller whenthere are more people in the region. This result is similar to theconclusions reported in Refs. [10–18] about the relation betweendensity and velocity.

In addition, the relationship between the group velocity andthe density in measurement section 1 was also investigated(Fig. 6). We calculated the mean density and group velocity per20 frames using the instantaneous values. The scattering dots arethe velocities according to different densities for all ten

00.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

ρ [m

-2] v

i [m

/s]

t [s]

ρvi

6 12 18 24 30 36 42 48

Fig. 5. Development of the individual average velocity and density, as a function

of time, for the run with b¼0.5 m.

experiments. It is observed that the maximum of the velocity islarger than 1.7 m/s because the first several people pass throughthe corridor quickly without restriction from the front or sides.

4.2.2. Measurement section 2

Measurement section 2, which has an area of 1 m2, is shown inFig. 1. Here, we use a simple equation D¼N/S to calculate theparticipant density D, where N is the number of persons inmeasurement section 2 and S is the area. The mean velocity ofparticipants in this area is calculated with the following equation:

V i ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðxin

i �xouti Þ

2þðyin

i �youti Þ

2q

=ðtouti �tin

i Þ ð4Þ

where xini and xout

i are the ith persons entering and leaving theregion in the x-axis, and yin

i and youti are the same for the y-axis.

touti and tin

i are the exit and entrance time, respectively.The fundamental diagram of measurement section 2 is also

studied in this paper, which probes walking behavior undercrowded conditions. All of the mean velocities of the participantsin measurement section 2 of the ten experiments are obtainedand averaged for the same density. The trend of the group velocityas a function of density is shown in Fig. 7. It is clear that thevelocity decreases with increasing density, which is consistent

Table 2Comparison of flow rates between this study and two others in the

range from 0.8 to 1.2 m.

Bottleneck

width (m)

Flow rate in this

study (persons/s)

Kretz Seyfried

0.8 1.6 �15% �24%

0.9 1.87 �15% �10%

1.0 2.09 �11% �9%

1.1 2.50 �15%

1.2 2.84 �24% �17%

Note: ‘‘� ’’ means decrement compared with the results of

W. Tian et al. / Fire Safety Journal 47 (2012) 8–1512

with P–M’s result obtained from a unidirectional experiment.We also obtain some data points at higher densities, and from thewhole trend, we can see that the velocity first decreases sharplywith the density and then changes slightly. Here, the participantsmay walk in a self-organized manner, and the value of thevelocity depends on the cooperation of the participants.

4.3. Flow rate

The flow rate can be calculated by the following equation:

J¼DN=DT ð5Þ

where DN is the number of participants passing through thecorridor at a given time, and DT is the evacuation time, namelythe time interval between the entrance time of the first personand the last person.

Fig. 8 shows the comparison of flow rate between our experi-ment and others’. The flow rate increases monotonously with theincreasing width of the bottleneck. Another interesting observa-tion from the results is that a flow rate of 0.8 m is markedly largerthan that of 0.7 m. When the width of the bottleneck is 0.7 m, thecorridor could not admit two pedestrians shoulder by shoulder ata time, thus the pedestrians walk in a staggered fashion and thewalking trajectories overlap with each other and look like theshape of a zipper. This ‘‘Zipper’’ effect [18] occurred at times inour experiments. When the width is 0.8 m, there are two lanesthat allow two persons to pass through the corridor at the sametime so that the flow rate increases dramatically. However, thisphenomenon does not occur when the number of lanes changesfrom two to three. Based on the data analysis, we find that thechange of lanes from two to three is not clear. The corridor can bedivided into three lanes because most persons pass through thebottleneck along the sides, while several walk in the middle of thecorridor in the run with b¼1.2 m, so it can be considered as eithertwo or three lanes, but the effective lanes are the two at the sides.In this paper, we assume the number of lanes is two when thewidth is 1.2 m.

It can be seen that the trend of the flow rate is consistent withtwo other results reported in Refs. [19,23]. When the bottleneckwidths range from 0.5 to 0.7 m, the flow rates in this paper aresimilar to Kretz’s. The flow rate displays a linearly increasingtrend with the width, and we can assume that the trend is almostthe same in most experiments when the bottleneck widths range

0.4

1.0

1.5

2.0

2.5

3.0

3.5this paperKretz et al.Seyfried et al.

J [s

-1]

b [m]0.6 0.8 1.0 1.2 1.4

Fig. 8. A comparison of the flow rates between our experiment and other studies

with different bottleneck widths. The empirical data could be downloaded from:

http://www.ped-net.org/index.php?id=37&L=0.

from 0.8 to 1.2 m, but the values may have some variation. Wecan see in Table 2 that the flow rates of this paper are larger thanKretz’s (11%–24%) and Seyfried’s (9%–24%) in this range. Thedifference between this paper and Seyfried’s could potentiallybe attributed to two causes. The first is the difference in body sizebecause more Chinese persons pass the corridor simultaneouslyat the same bottleneck width, making the flow rate a little larger;the second is the cultural differences between Eastern andWestern countries. The difference between our results and Kretz’smay be due to the different cultural norms that exist in the twocountries. More experiments should be carried out to verify thetrue reason for this difference.

Fig. 9 shows the diagram of specific fluxes of all the runs and acomparison with the result reported by Kretz. The specific fluxdecreases until the bottleneck width is 0.7 m, which is consistentwith Ref. [23]. Based on our experiment, it is observed that thewidth of 0.7 m is the critical point for lane changing, i.e., the flowchanged from 1 lane to 2 lanes. When the width increases to0.8 m, it is easy for people to walk in 2 lanes, so the specific fluxincreases. However, in our experiment, the influence of theinteraction among participants is not as obvious when thepedestrian flow changed from 2 lanes to 3 lanes. The overalltrend is that the specific flux did not change significantly after thewidth of the bottleneck became larger than 0.7 m; it variedsmoothly. The mean value and standard deviation are 2.185 per-sons/m/s and 0.114, and we can assume that the specific flux is aconstant when b40.7 m. However, based on Ref. [23], we can seethat the specific fluxes change slightly around 1.8 persons/(ms)from b¼0.7 to b¼1.2 m and then linearly decrease to approxi-mately 1.3 persons/(ms) when b¼1.6 m, which is slightly

this study.

0.40.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

spec

ific

flux

[per

sons

/(m s

)]

b [m]

this paper Kretz

0.6 0.8 1.0 1.2 1.4 1.6

Fig. 9. Diagram of specific fluxes of all the runs and a comparison with the results

of Kretz.

0.0

0.1

0.2

0.3

Pro

babi

lity

Den

sity

Time Interval [s]

0.00.0

0.1

0.2

0.3

0.0

0.1

0.2

0.3

Pro

babi

lity

Den

sity

Time Interval [s]

Pro

babi

lity

Den

sity

Time Interval [s]

entrance time interval exit time interval

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Fig. 10. (a) The entrance and exit time headway distribution probability with

b¼0.5 m; (b) the entrance and exit time headway distribution probability

with b¼0.8 m; (c) the entrance and exit time headway distribution probability

with b¼1.3 m.

W. Tian et al. / Fire Safety Journal 47 (2012) 8–15 13

different from the results of this paper. More work is needed toaccurately define the pattern of the specific flux and bottleneckwidth.

More experiments should be carried out to identify the actualvariation in the specific flux with increasing width, and a moreconstructive conclusion may be obtained from studying pedes-trian behavior, which can be used to direct the planning anddesign of public facilities.

4.4. Time headway

The time headway is defined as the interval betweentwo sequential persons at the entrance or exit of the corridor.We measure the time headways of entrance and exit by recordingthe entrance time tin

i and exit time touti of participant i.

The time headway distribution probability is investigated foreach run. Three situations for different numbers of lanes arestudied. Fig. 10 shows the entrance and exit time headwayprobability for the conditions of one, two, and three lanes.

Fig. 10(a) shows the entrance and exit time headway dis-tribution probability with b¼0.5 m for which there is only onelane in the corridor. We can find one peak in Fig. 10(a), and thismay be because the pedestrians pass the bottleneck one by one,such that the time headways concentrate at a certain value.The time headway distribution probability with b¼0.8 m, forwhich there are two lanes in the corridor, is presented inFig. 10(b). The distribution can be divided into two parts.The first part is from 0 to 0.5 s, and we may assume that thereis one peak here that corresponds to the walking pattern of the‘‘Zipper’’ effect. The second part is from 0.5 to 1.2 s, and the peakhere might correspond to the same walking pattern as thecondition of one lane in the corridor. Fig. 10(c) shows theentrance and exit time headway distribution probability withb¼1.3 m for which there are three lanes in the corridor. Thereare three walking patterns here; the first and second are thesame as two lanes, and the third involves several persons passingthe bottleneck side by side. Thus, the time headway distributionmay be divided into three parts, each of which corresponds toone walking pattern.

In addition, we can obviously see from Fig. 10 that with theincreasing width, the distributions are closer to zero and the valueat which most time headways concentrate decreases. There couldbe more walking patterns when the bottleneck width becomeseven wider, which may be the reason why the time headwaysbecome smaller.

Fig. 11 shows the time headway distribution probability of twolanes with b¼0.8 m and three lanes with b¼1.3 m and 1.4 m. It isclear that the time headway distributions probability of the twolanes with b¼0.8 m is similar to that for which there is just onelane in the corridor. There is only one peak in the distributionprobability, except for b¼1.3 m and b¼1.4 m. We can see that thetime headways in the second lane are larger than the others andthat they decrease with increasing width when the width is largerthan 1.3 m. After reviewing the video, we find that collisions inthe middle seem to be more serious than on the sides, and thus,the pedestrians prefer to walk along the side because they aretrying to avoid colliding with others. Thus, there are fewer peoplewalking in the middle lane, and their time headways are certainlylarger than those walking in side lanes. However, as the width ofthe bottleneck increases, the space in the middle is large enoughand pedestrians feel comfortable enough to enter the corridor inthe middle lane, such that the number in this lane may be closerto the number in the other lanes. Table 3 presents the number ofpeople walking in the ith lane when there are more than twolanes in the corridor. We can see that when there are two lanes inthe corridor, the number of pedestrians walking in each lane is

almost the same; when there are three lanes in the corridor, thenumber of pedestrians passing through in the two side lanes isnearly equal. The number of pedestrians in the middle lane is lessthan those in the side lanes but increases with the width. We canassume that the number of pedestrians in the middle lane mightbecome equal to those of the side lanes when the bottleneckwidth is large enough.

the first lane the second lane the third lane

00.0

0.1

0.2

0.3

0.4

0.5

prob

abili

ty d

ensi

ty

time interval [s]

00.0

0.1

0.2

0.3

0.4

0.5

prob

abili

ty d

ensi

ty

time interval [s]

00.0

0.1

0.2

0.3

0.4

0.5

prob

abili

ty d

ensi

ty

time interval [s]1 2 3 4

1 2 3 4

1 2 3 4

Fig. 11. (a) The time headway distribution probability of both lanes with

b¼0.8 m; (b) the time headway distribution probability of the three lanes with

b¼1.3 m; (c) the time headway distribution probability of the three lanes with

b¼1.4 m.

Table 3Number of pedestrians walking in the ith lane.

Bottleneck

width (m)

First lane Second

lane

Third

lane

0.8 33 29

0.9 28 34

1.0 32 30

1.1 31 31

1.2 28 34

1.3 22 17 23

1.4 22 19 21

W. Tian et al. / Fire Safety Journal 47 (2012) 8–1514

5. Summary

It is widely known that pedestrian behavior is essential inunderstanding evacuation dynamics, but only a few experimentshave been carried out to extract detailed parameters for furtherstudy. In this paper, pedestrian flow experiments at bottlenecks

are conducted to study the velocity, density, flow rate, specificflux and time headway.

The video of the experiment is processed with an imageprocessing method based on the mean-shift algorithm. Usingthe trajectories extracted from the video, the pedestrian move-ment parameters are easily calculated. The average velocitydecreases at the beginning of each run and then fluctuates aroundthe mean value because the first several persons walk with ahigher velocity. The results indicate that the relation betweendensity and velocity has the same trend as that reported inprevious work, but the velocity in our study is larger at the samedensity. The differences could arise from differences in thepedestrians and in the measurement region. The average velo-cities of pedestrians inside the measurement section generallydecreased with increasing density. The increasing flow ratepattern as a function of the bottleneck width is consistent withother results. The specific flux is consistent with the results ofKretz’s when bo0.8 m, but after b¼0.8 m, the results of the twostudies are somewhat different, and further work should becarried out to verify the relation between the specific flux andthe bottleneck width.

The time headway between successive pedestrians is a criticalfactor in studying pedestrian behavior. We investigated theentrance and exit time headways for the bottleneck area. Thetime headways for different lanes are studied and compared witheach other. We found that the pedestrians prefer walking in theside lanes when the bottleneck width b41.2 m, perhaps becausewalking in the side lanes could prevent collision with others andbe more comfortable during the evacuation. We believe this studycould provide useful information for researchers and designerswhen simulating the motion through a corridor or designingbuilding bottlenecks.

The digital image processing method based on the mean-shiftalgorithm is again shown to be an accurate, easy and convenientway to extract pedestrian moving parameters from experimentalvideos. The fundamental data obtained from the bottleneckexperiment is helpful for investigating the walking behaviors ofpedestrians and validating the evacuation models.

Acknowledgments

The study is supported by the China National Natural ScienceFoundation (nos. 51178445 and 91024025), the Program for NewCentury Excellent Talents in the University (NCET-08-0518).

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