an intelligence-based route choice model for pedestrian ﬂow in a transportation station

8/11/2019 An intelligence-based route choice model for pedestrian flow in a transportation station

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Applied Soft Computing 24 (2014) 3139

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Applied Soft Computing

j ournal homepage : www.elsevier .com/ locate /asoc

An intelligence-based route choice model for pedestrian flow

in a transportation station

J.K.K. Yuen a,, E.W.M. Lee a, W.W.H. Lam a,b

a Department of Civil and Architectural Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong (SAR),PR Chinab MTR CorporationLimited, MTRHeadquarter Building, Telford Plaza, 33WaiYip Street, Kowloon Bay, Hong Kong (SAR), PR China

a r t i c l e i n f o

Article history:

Received 13 December 2012

Received in revised form 10 June 2013

Accepted 29 May 2014

Available online 15 July 2014

Keywords:

Artificial neural networks

Intelligent system

Crowd movement

Evacuation

Human behaviour

Transportation

a b s t r a c t

This study proposes a method that uses an artificial neural network (ANN) to mimic human decision-

making about route choice in a crowded transportation station. Although ANN models have been

developed rapidly and widely adopted in various fields in the last three decades, their application to

predict human decision-making in pedestrian flows is limited, because the video clip technology used to

collect pedestrian movement data in crowded conditions is still primitive. Data collection must be carried

out manually or semi-manually, which requires extensive resources and is time consuming. This study

adopts a semi-manual approach to extract data from video clips to capture the route choice behaviour of

travellers, and then applies an ANN to mimic such decision-making. A prediction accuracy of86% (ANN

model with ensemble approach) is achieved, which demonstrates the feasibility of applying the ANN

approach to decision-making in pedestrian flows.

2014 Published by Elsevier B.V.

Introduction

To cope with rapid population growth, increasing numbers of railway systems

have been constructed acrossthe world, ranging from high-speed railways such as

the Eurostar in Europe to the more recent magnetic levitation trains such as the

Shanghai airport train in China. The projected trend is that railway systems will

continue to increase in size, and thus crowd movement and evacuation planning

will play a more importantrolein railway systems than ever before. TheHongKong

transportation system is characterised by high passengerflows,a short train head-

way andlimitedcapacityin thetransportation stations.The failure offacilitiesinside

a station may cause accidentsand threatenpassengerslives. A study of route choice

between escalators andstairways in subwaystationsin Hong Kong wascarried out

[1] to explore the optimisation of station facilities, which is critical for both safety

and operational efficiency.

In the past three decades, several dynamic evacuation and pedestrian mod-

els have been developed for modelling complex crowd movements. These models

include social force (SF) models [2], cellular automata (CA) models [3], lattice gas

(LG) models [4], fluid-dynamic model [5], agent-based (AB) models [6] and SGEM

model [7,8]. They provide important information about the spatial design of com-plexbuildings,undergroundstationsand other publicamenities.In additionto these

microscopic [24,68] and macroscopic models [5], network models [9,10] have

proved useful in the design of emergency evacuation plans, because they allow

the detailed modelling of human cognitive processes. However, most of the exist-

ing pedestrian flow models that simulate the dynamic movement of pedestrians

are based on mathematical models, which may not be able to sufficiently mimic

actual human behaviour. Indeed,the movementdecisionsthat areobtainedby these

Corresponding author. Tel.: +852 3442 2307; fax: +852 2788 7612.E-mail addresses:[email protected], [email protected]

(J.K.K. Yuen).

models are determined by either empirical equations (e.g., Bradley [5] employed

NavierStokes equations that govern fluid motion to describe crowd movementat high densities) or by assumptions (e.g., the SF models [2], CA models [3], and

LG models [4] treat individuals or groups as homogeneous, and do not consider

heterogeneous human behaviour such as herding). Even with AB models such as

Simulex [11] and buildingEXODUS [12], occupant behaviour is assignedby theoper-

ator accordingto hisor herpreference,and theresultant simulatedpedestrian flow

patterns may not reflect real-life situations.

One of the critical behavioural reactions of humans during evacuation and in

moving crowds is route choice.Route choice is influencedby manyfactors, including

personal experience, building geometry, interactions among occupants and envi-

ronmental factors [8,12,13]. In general, passengers will choose the path with the

shortest travel time, traveldistanceor a combination of both [14]. However, Proulx

[15] pointed outthat evacueestend to prefer familiar routes rather thanthe shortest

path to theexit, because they feel that unknown paths increase thethreat. Proulxs

pioneering work depicted the complexity of route choice in human movement.

Gwynne et al. [12] proposed an exit selection behaviour model that is based on

Queuing and Familiarity Behaviour. Lo et al. [8] introduced a game theory based

exit selection model for evacuation. Hoogendoorn and Bovy [16] proposed a newtheory of pedestrianroutechoice behaviourunderuncertainty basedon theconcept

ofutility maximisation.

These route choice models all usemathematics to simulatethe humandecision-

making.In contrast,thispaper proposesthe alternative approachof applyingan ANN

model to capture human decision-making behaviour by data collected from actual

passengers.

Route choice behaviour on escalators

In transportation stations, escalators, stairways and elevators serve as vertical

transport between theconcourse andthe platforms. Passengers tend to useescala-

tors rather than stairs or lifts. They avoid climbing stairs to save energy and avoid

spending time waiting for lifts. Escalators play an important role in transportation

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32 J.K.K. Yuen et al. / Applied Soft Computing 24 (2014) 3139

Fig. 1. Example of routes choice decision-making.

around a station. Optimising the use of escalators is thus the first priority in the

internal transportation system of stations.

This study presents an example of route choice in a transportation station. Pas-

sengers approach two escalators and choose one of them to travel to the upper

floor. The decision-making involves the consideration of various factors, which are

explored in the following sections.

Routes choice behaviour

Initial research in thisarea [17,18] focused on the correlation between through-

put capacity and thewidth of exits. Fig. 1 shows the layout of a compartment with

two exits. In an emergency (e.g., the outbreak of fire), each occupant inside the

compartment will choose one of theexits to leave thecompartment. This is a typi-

cal example ofroute choice decision-making. In predictingthe evacuationtime from

the origin of an evacuee to the exit of the compartment, Togawa [18] established

empirically that the evacuation time Te can be expressed as a sum of the flow time

(To) and the travel time (Tf) of theevacuee as follows.

Te= To + Tf (1)

In the simplest form,To can be further expressed in Eq. (2), where ks is the dis-

tance from thepointof theevacueeto thedestination, andvrepresents the walking

speed of thecrowd.

To=ksv

(2)

The travel time Tf isestimated by Eq. (3), where Na is the total numberof evac-

uees, fp represents theflow rate perunit width of theexit andB isthe width of the

exit.

Tf=NafpB

(3)

Usually, pedestrians choose the route with the least travelling time. According

to Togawa [18], the factors that contribute to thetravelling time are as follows.

1. Thewalking velocity of thepedestrian.

2. Thedistance from the originof thepedestrian to the exit.

3. The maximum flow capacitiesof the exits.

Thesefactors formthe basisof thisstudyto investigatethe parametersin human

route choice decision-making for ANN model training.

The remainder of this paper is organised as follows. Section Artificial neural

network introduces the ANN for route choice in moving crowds. Section Data

collection discusses how the data was collected from the transportation station.

SectionDevelopmentof theMLP modeloutlinesthedevelopmentand architecture

ofthe ANN model. Sections Model training and Results and discussion, respec-

tively, present the model training process and evaluatethe performance of the ANN

model. Section Conclusion concludes the paper.

Artificial neural network

ANN models have developed rapidly in the last few decades,

to the extent that they are now able to mimic the correlation of

system parameters that are unknown or complex [19] and capture

the nonlinear behaviour of a system via a learning process (also

known as the training of the network function) from historical

system data. ANN models have also become a popular approach to

the prediction of non-linear functions in the past decade. Among

the various ANN models, the multi-layered perceptron (MLP) [20]

is one of the most widely used for forecasting due to its simple

and flexible nature. MLP has been successfully used to predict the

weather, flank wear in drills and thermal load predictions [2126].

However, researchershave seldom applied ANNmodels to simulate

pedestrian decision-making in moving crowds or evacuation plan-

ning due to the extensive resources required for the data collection

and pre-processing.

One of the merits of ANN models is that they do not require

highly specialised human expertise nor any assumptions. The

learning feature of ANNmodels is especially usefulfor human deci-

sion models, as the relationships between the input parameters

are less well known than in highly structured expert systems or

equation-base approaches [19]. In this study, the MLP is adopted to

predict route choice in pedestrian flows.

Data collection

The data was collected from a transportation station in HongKong. A bank of escalators inside the station, as shown in Fig. 2,

was offered by the transportation company for the study.There are

three escalators, one of which had been stopped to save energy.

The other twoescalators were moving upwardat an equal andcon-

stant speed. The passenger flow was unidirectional. All passengers

are required to ascend to the upper floor via escalator to leave the

station. Passengers approaching the escalators have to choose one

of the two escalators to ride to the upper floor. This is the decision

making that is investigated and mimicked by the MLP model.

It is normal practice in Hong Kong, as in some other countries,

for passengers not in a hurry to stand on one side of the escala-

tor and leave the other side for passengers in a hurry to walk up

to shorten the time spent riding the escalator. Both of the escala-

tors shown in Fig. 2 are 60m long. This long length prevents most


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J.K.K. Yuen et al. / Applied Soft Computing 24 (2014) 3139 33

Fig. 2. Route selection by passengers in the transportation station.

passengers from walking all the way up the escalators and

simplifiesthe decision-makingprocess. Accordingto our siteobser-

vations, the general behaviour of passengers is in line with our

assumption, with most passengers in the video footage standing

on the escalators and just a few walking up.

The video technology used to collect pedestrian movement

data in crowded conditions is still primitive, and existing image

processing techniques are unable to fully overcome the occlusion

problem [36]. Therefore, data collection was carried out manually

or semi-manually, which requires extensive resources and is time

consuming.In thispaper, the locationsof the passengersat different

time steps were extracted from the video clips by using a semi-

manual approach with a Passenger Trap, which was presented

by Teknomo [36]. The Passenger Trap is an imaginary rectan-

gle marked as a boundary for counting passengers on the video

clips. Only the passengers who pass the trap are considered. The

trap width is perpendicular to the path of the passenger, and the

trap length is parallel to the walkway. Therefore, the area and the

coordinates of this rectangle can be obtained more easily.

In this study, the data were collected from video clips captured

by a closed-circuit television (CCTV), as shown in Fig. 3a. Fig. 3b

shows a typical snapshot (front view) of the train station. The loca-

tions of the passengers at different time steps were extracted from

the video clips using a semi-manual approach. A computer pro-

gram was developed to play the video on the computer screen. At

each time step, the computer mouse was moved over the screen

to click on the feet of passengers shown on the screen. The data on

Input1

Input 2

Input 3

Input Layer Hidden Layer(s) Output Layer

Output 1

Output 2

Fig. 4. Typical architecture of the multilayer perception (MLP) model.

the passengers locations were then converted to geometrical data

by the computer program. These data were further processed to

obtain the input and output data for the training of the ANN model.

Details of the input and output parameters of the ANN model are

discussed in Section Development of the MLP model.

Development of the MLP model

TheMLP architectural model consists of several layers: the input

layer, hidden layer(s), and output layer. Fig. 4 shows the archi-

tecture of a typical MLP model. The neurons in each layer are

interconnected with the neurons in the adjacent layers. There may

be more than one hidden layer in a model, but it has been proven

that an MLP with a single hidden layer is a universal function

approximator [27], subject to the provision of a sufficient number

of hidden neurons. A three-layered (input layer, hidden layer and

output layer) MLP model was thus adopted in this study.

Input layer

The number of neurons in the input layer corresponds to the

number of input parameters in the model. As mentioned in Section

Routes choice behaviour, the main factors affecting the evacua-

tion time according to Togawa [18] are (1) the walking velocity of

the evacuee, (2) the distance from the origin of the evacuee to the

exit and (3) the flow rate of the exits. However, non-linear human

behaviour such as queuing and familiarity behaviour is obviously

not included in Togawas mathematical model, and further mod-

ification based on empirical equations is necessary. We used the

following six parameters as inputs for the ANN model to decide

which escalator would be chosen by each passenger.

Fig. 3. (a) Snapshot from the CCTV footage and (b)Snapshotof theescalators (frontview).


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Fig. 5. Initial position of the passengers.

Input 1 (walking velocity of passengers)

Normally,the walking speedof a passengerreflectshow much of

a hurry he or sheis in.Passengers in a hurry will walk faster. Passen-

gersin a hurry may also be impatientto board thenearest escalator,

andmay overtake otherpassengers [11,28]. Alternatively,they may

choose the escalator that is not the nearest one but has less peo-

ple using it. Passengers walking slowly are less likely to be in a

hurry,and may choose a less dense escalator to avoid collisionwith

hurrying passengers.

Input 2 (initial position of passengers)

Pedestrians dislike taking detours or moving contrary to their

desired walking direction, even in crowded conditions [29]. How-

ever, due to the high population density in Hong Kong, passengers

usually move as a crowd due to the limited capacity of stations. The

initial position of passengers entering the crowd will affect their

route choice, in that such decisions a prevented when the densityreaches a certain level. In this situation, a passenger is most likely

to follow the crowd or follow the path of the passenger in front

to avoid collisions with nearby passengers [29]. Sudden changes of

direction are seldom found in normal situations, as this action may

cause collisions and block the routes of passengers nearby. The ini-

tial position of the passenger was thus also selected as one of the

inputs for the ANN model training.

According to our onsite observations, the initial position of the

passengers entering the crowd resulted in different probabilities of

passengers choosing the escalators. In our data capturing process,

we evenly divided the initial position of the passengers into six

locations, as shown in Fig. 5.

Input 36 (initial and final densities of the escalators)Humans are required to make decisions when they face two or

more options. Similarly, the passengers in this study were required

to make a decision as they walked to a position where they could

see the two escalators (i.e., where the two escalators fell into their

line-of-sight). We denote this location as the initial position, which

is chosen as one of the parameters influencing the decision of pas-

sengers and an input in the ANN training model. Fig. 6 illustrates

the initial decision point of passengers according to the concept of

the minimum line-of-sight.

The densities at the platform and on the steps of escalators 1

and 2 are captured when the passengers approach the initial deci-

sion point as input parameters 3 and 4, respectively. The idea of

level of service (LOS) proposed by Fruin [30] reveals that the walk-

ing velocity of a pedestrian and the density of the surrounding area

Fig. 6. Initial decision point of passengers with a minimum line-of-sight.

are highly correlated. Passengers approaching the two escalators

and deciding which escalator to use may also consider the densi-

ties at the entrances of the escalators to avoid being blocked in by

otherpassengers. Passengers typicallymake their initial decision at

the initial decision point based on their walking velocity, position

and the densities at the escalator entrance. If an alternative route

of the same length is available, then according to [29] passengers

will make the route choice decision as late as possible, meaning

that a change of mind is still possible at the last minute. Our model

accounts for this observation and considers the scenario at a final

decision point, as shown in Fig. 7. The densities at the landing plat-

forms of the escalators 1 and 2 when the passengers approach the

final decision point are respectively recruited as input variables 5

and 6.

Hidden layer

The number of neurons in the input and output layers is equal

to the number of input parameters and output parameters. This

is defined by the system itself. The required number of hidden

neurons is crucial to the performance of the model. Currently,

there is no analytical approach to determine the number of hidden

Fig. 7. Final decision point of passengers.


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Table 1

Input and outputparameters of theMLP model.

Parameter Unit

Input Walking velocity of passengers [m/s]

Initial position of passengers

(i.e., range from 1 to 6)

Initial density of escalator 1 [person/m2]

Initial density of escalator 2 [person/m2]

Final density of escalator 1 [person/m2]

Final density of escalator 2 [person/m2

]

Output Passengers choosing escalator

1 o r 2 (i .e. , 0 o r 1)

neurons, but different rules of thumb are available to provide

heuristic hints to the user. The rule of thumb adopted in this study

is therule developedby Ward [31] as describedin Eq. (4), whereNh,

Ninand Noutare respectively the number of neurons in the hidden

layer, the input layer and the output layer, and Ns is the number of

training samples.

Nh= (Nin +Nout)/2 +Ns (4)Before the adoption of the rule-of-thumb, the sensitivity of the

performance of the model to the number of hidden neurons was

studied. However, it was found that a change in the number of hid-

den neurons did not significantly affect the model performance in

terms of themean of theaccuracyof theactual outputs andthe out-

puts predicted by the model obtained from the training (described

in Section Model training). The number of hidden neurons exam-

ined was set at Nh5. For each number of hidden neurons, 5000trials were performed to determine the model performance with a

95% confidence level. If the 95% confidence intervals of the models

with differentnumbers of hidden neurons overlap, then from a sta-

tisticalpointof view itcan be concludedthatthe performanceof the

models with different numbers of hidden neurons is comparable.

It was thus concluded that the model performance is insensitive

to the number of hidden neurons, and the rule of thumb could beadopted.

Output layer

The output of the MLP model is whether passengers choose

escalator 1 or 2, which is represented by 0 or 1, respectively.

The inputs and output of the MLP model are listed in Table 1. The

architecture of the MLP model is illustrated in Fig. 8.

Passengerschoosingescalator 1 or 2

Input layer

Walking velocity of passengers

Initial position of passengers

Initial density of escalator 1

Initial density of escalator 2

Final density of escalator 1

Final density of escalator 2

layerHidden Output layer

Fig. 8. Architecture of theMLP model.

Prediction

Error

Minimumvalidation error

Network training stopshere, as there is nofurther improvement inthe validation error

TrainingEpoch

Trainingerror

Validationerror

Pre-defined number of epochs(1,000 epochs in this study)

Fig. 9. Early-stop validation approach.

Model training

Back-propagation (BP) [32] is the traditional training algorithm

usedfor the MLP model.It feeds back theprediction errorsfrom the

output layer to the input layer and adjusts the weights of the links

between the neurons. Upon completion of the weight adjustment,

a new prediction is carried out to evaluate a new prediction error

for the next epoch of weight adjustments. These procedures are

repeatednumeroustimes untila satisfactory predictionis achieved.A total of 621 samples were collected in August 2010 for the

network training and testing. Fifty per cent of the samples were

used for network training, 25% were hidden during the network

training phase and the remaining 25% served as a testing set to

evaluate the performance of the trained network. The training set

was used to train the model with the BP algorithm, whereas the

validation set was used to monitor and stop the BP training using

the early-stop validation approach. The testing set did not play a

role in thetraining ofthe MLPmodel. Uponcompletion of the model

training,the testing setwas used toevaluatethe performanceof the

trained model.

To prevent overfitted training, the intermediate-state trained

model in every training epoch was applied to the validation set to

evaluate the prediction error (i.e., the validation error). The net-work training was stopped when the validation error reached the

minimum value. As we had no prior knowledge of the trend of the

validation error, early-stop training was adopted.This records the

statusof themodel continuouslyin thecourse of thetraining.When

there is no reductionin thevalidation error over a predefinednum-

ber of epochs (the number of epochs selected here was 150), the

model state with the minimum validation error is taken to be the

trained model. Fig. 9 illustrates the early-stop training process.

Upon completion of the network training, the trained MLP was

applied to the testing set to evaluate the performance indices by

comparing the target values of the testing set and the values pre-

dicted by the trained model. The performance index used here was

the fraction of correct predictions (a), as defined in Eq. (5), whereN

is the total number of samples and {ti=pi}Ni=1are the target values

and the predicted values, respectively. A fraction of correct pre-

dictions of 1 indicates that 100% of the test samples are correctly

predicted.

a =Ni=1

(ai)/N where ai=

1 if ti=pi0 if ti /=pi

(5)

It shouldbe noted that a randomprocess is normally involved in

the network training of an MLP model, especially when the avail-

able samples are divided into training and validation sets. It is thus

possible for the random process to result in fortuitous samples

that show favourable evaluated performance indices. Instead of

reporting only the best simulation result, a less-prejudice statisti-

cal approach was adopted to minimise the effect of randomisation.


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Beta distribution

95% of totalarea under

x

95% Confidencex

p

(x)

10

Fig. 10. Example of thestatistical performance evaluation of theANN model.

We carried out the network training and performance evaluation

process 5000 times. The 5000 results were statistically analysed by

evaluating the mean and limit of the 95% confidence intervals of

the results.

In the model training, the training, validation and testing sam-

ple sets were obtained by random sampling. The performance of

the trained model might not always be favourable, as it very muchdepends on the quality of the training sample. It is possible that

the sample batch favours the network training, and thus produces

a better model. To verify the performance of the trained model, it

is thus necessary to evaluate it statistically.

As thesample extraction process is random, we carried out5000

trials of model training and testing (i.e., the results converged at

5000 trials). In each trial, the training, validation and testing sam-

ples were randomly grouped. The model was trained and tested

with the samples and the performance index of that trial was

obtained. The 5000 trials gave 5000 performance indices. As the

performance indices (i.e., the fractions of correct predictions) were

well bounded between 0 and1, a beta distribution was used to rep-

resent the probability distribution of the accuracy of the model, as

shown in the example in Fig. 10. The limit of the one-sided 95%confidence level from the right (i.e.,x95) represents the fraction of

correct predictions of the model. This statistical approach alleviates

the effect of randomness in evaluating the model performance.

Results and discussion

MLPmodel

The number of hidden neurons was determined by the rule-

of-thumb in Eq. (6), in which the number of training samples

(Ns), is taken to be 50% of the available samples initially (i.e.,

0.5621= 311). In this case, the number of input (Nin) and out-put parameters (Nout) is 6 and 1, respectively. According to Eq. (6),

25 hidden neurons are needed.

Nh= (6 + 1)/2 +

311 = 21.14 21 (6)

A study of the sensitivity of the number of hidden neurons to

the performance of the model was carried out. The number of hid-

den neurons to be investigated ranged from 17 to 26 (i.e., 215).For each number of hidden neurons, 5000 trials of model training

andperformance evaluation were conductedto obtainthe 95% con-

fidence intervals of the models with different numbers of hidden

neurons. The results are shown in Fig. 11.

The 95% confidence intervals overlap, which indicates that the

performance of the models with different numbers of hidden

neurons is comparable, and thus the number of hidden neuron

is insensitive to the model performance. Thus, the number of

NO. OF HIDDEN NEU RONS

CORRELATION

COEFFICIENT

17

18

19

20

21

22

23

24

25

260.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Fig. 11. Performance of models with different numbers of hidden neurons.

hidden neurons determined by the rule of thumb (25) was justi-

fiably adopted.

Back-propagation was applied to train the MLP model with the

training sample. As explainedin SectionModel training, thesame

process was repeated 5000 times to obtain the total accuracy of

the model. Upon the completion of model training, the model was

applied to the testing dataset to evaluate the performance. The

mean of accuracy of the prediction results is 0.7782.The results are

presented in histogram form in Fig. 12. As the accuracy was well

bounded between 0 and 1, the beta distribution shown in Eq. (7)

was used to describe the accuracy distribution.

f(x|,) = x1(1 x)1

1

0u1(1 u)1du

(7)

By applying the beta distribution to describe the distributionof the 5000 samples, the parameters of the distribution were esti-

mated to be = 116.4770 and =33.2015. The profile of the beta

distribution reasonably matches the profile of the histogram. The

minimum fraction of correct predictions obtained by the trained

MLP model of 0.7204with a 95% confidence level indicates that the

MLP model performs reasonably well.

Fig.12. Histogramshowingthe distribution of the fraction of correct predictions of

the model approximated by a beta distribution.


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A sample is randomlydrawn as test sample

For Training and Validation For Testing

Original data set

The order of thesamples is shuffled

Step 2

Step 1

Fig. 13. Iterative process of the leave-one-out approach.

Leave-one-out validation approach

As there were only 621 samples available for the MLP model

training and testing, we adopted the leave-one-out approach

[33,34] to evaluate theperformance of theMLP model. In theleave-

one-out approach, in every trial, only one sample is reserved as

the test sample to evaluate the performance of the model beingtrained by the other 620samples. Fig. 13 illustrates the mechanism

of the leave-one-out approach. It is, in general, a two-step process.

In the first step, the order of the samples is randomly shuffled. This

process ensures that the samples used for training, validation and

testing are different foreach trial.In thesecondstep,assuming that

there areNnumbers of available samples, one sample is randomly

taken out from the pool and kept back to evaluate the performance

of the trained model while the other samples (i.e., N1 samples)

are used as training and validation samples in the model training.

Upon completion of the training, the trained MLP model is applied

to the omitted sample to evaluate its performance. This process is

repeated 5000 times to obtain 5000 performance indices.

As mentioned in Section MLP model, the dataset sample size

ratio for training, validation and testing in the MLP model com-

prised 50%, 25% and 25%, respectively, of the total number of

samples. To evaluate the model performance of the MLP model by

the leave-one-out approach, the sample size ratio for training and

validation with the leave-one-out approach is 70% and 30% of the

total number of samples. A further 5000 trials were run to ensure

that all of the samples were tested thoroughly at each iteration.

In each trial, the predicted output was compared with the target

output of the test sample to determine whether the prediction was

correct. Among the 5000trials,3974 trials were correctlypredicted.

The fraction of correct predictions was thus0.7948,which is slightly

higher than the MLP model (i.e. 0.7782) obtained in Section MLP

model.

Ensemble approach

The performance of an ANN model can be further enhanced

using the ensemble approach [35]. The general concept of theensemble approach is illustrated in Fig. 14. Assuming that we have

a total ofNsamples to evaluate the performance of the ensemble

approach,a total ofNtests need tobe carried out. Inthe first test,the

first sample is drawn out as the test sample. In the second test, the

secondsample is drawn as thetest sample, andso on.The ensemble

approach is a two-step process. In the first step, the jth sample is

drawn outas a test samplefromthe totalNnumbers of samples, and

the output of this test sample is denoted asT(j)o . In the first trial, the

Predicted Output

For testingShuffling the order of the trainingand validation samples

T1

T2

TM

T1

T2

TM

Predicted Outputs EnsembleOutput

TE

Final Result (byvoting)

Original data set

Trial M

Step 2

Trial 2

Trial 1

ModelTraining and

prediction

To

TargetOutput

Compare

P=1 if TE=To

P=0 if TETo

Step 1

To

Fig. 14. Iteration process of the ensemble approach.


8/9


S pecific Ac curacy

ProbabilityDensity

0.6 0.65 0.7 0.75 0.8 0.85 0.90

50

100

150

2

0

0

250

300

350

400

450

Mean of Fraction of CorrectPredictions of the MLP =

0.7782

Mean of Fraction of CorrectPredictions of the MLP withthe Leave-one-out approach

= 0.7948

Mean of Fraction of CorrectPredictions of the MLP withthe Ensemble approach =

0.8647

Beta distribution of the specific accuracy

Fig.15. Comparison ofthe accuracyof theMLP, leave-one-outapproachand ensem-

ble approach.

order of the remainingN1 samples isshuffled andthesamplesaregrouped into training samples and validation samples to train theANN model. Upon completion of training, thetrained ANN model is

applied to predict the output of the test sample. The predicted out-

put is then denoted as T(j)1 , where the superscript (j) refers to the

test where the jth sample is taken as the test sample and the sub-

script 1 refers to the first trial of this test. In the second trial, the

N1 samples areshuffled andgrouped again.The grouped trainingand validation samples are used to train another ANN model and

areapplied to thetest sampleto generate thenext predictedoutput

(i.e., T(j)2

). The process is repeatedMtimes (30 times). Eventually,

Mnumbers of predictedoutputs (i.e., T(j)1 , T

(j)2 , . . ., T

(j)M

) are created

on which a plurality vote is applied to obtain the final prediction

result of the jthT(j), as shown in Eq. (8).

T(j) =Mi=1

T(j)iM

(8)

The performance of theprediction using theensembleapproach

was evaluated by Eq. (9).

P(j) =

1 if T(j) = T(j)o0 if T(j) /= T(j)o

(9)

The process was repeated N times. The overall performance

of the ensemble approach was evaluated by

Ni=1

P(i)

N

100%,

which represents the percentage of correct predictions obtained

with this approach. For the 621 samples used in this study, thefraction of correct predictions using the ensemble approach was

0.8647.

Fig. 15 shows a comparison of the means of the fraction of

correct predictions of the MLP,leave-one-out approach and ensem-

ble approach, which are 0.7782, 0.7948 and 0.8647, respectively.

This result indicates that applying the ensemble approach effec-

tively improves the performance of the ANNprediction, and further

demonstrates the feasibility of applying ANN model prediction in

our route choice study.

The performance of theleave-one-out approach andthe ensem-

ble approach was further investigated by presenting the results in

confusion matrices, as shown in Tables 2 and 3. Each is a two-

by-two matrix. The rows of the matrix represent the predicted

escalators chosen and the columns the actual escalators chosen.

Table 2

Prediction results with the leave-one-out approach.

Actual output

(escalator 1)

Actual output

(escalator 2)

Predicted output (escalator 1) 40.14% 10.64%


Table 3

Prediction results with the ensemble approach.

Actual output

(escalator 1)

Actual output

(escalator 2)



The diagonal elements are the percentages of correct predictions

in all scenarios (i.e., the escalator used by a passenger is correctly

predicted)and the off-diagonal elements representthe percentages

of incorrect predictions in all scenarios. The tables show that the

percentage of correct predictions for escalator 1 is almost the same

as that for escalator 2, with a sample ratio of escalator 1 to esca-

lator 2 of 308 to 313. The prediction accuracy and the predictionerror of MLP model with leave-one-out approach is respectively,

79.48% (40.14% + 39.34%) and 20.52% (10.64% + 9.88%). By contrast,

theprediction accuracy andthe prediction error of MLP model with

ensemble approach is respectively, 86.48% (43.00% + 43.48%) and

13.52% (6.60% +6.92%). It can also be observed that every entry

of the confusion matrix is improved by applying the ensemble

approach.

Prediction by the shortest distance

Ortuzar and Willumsen [14] concluded that passengers will

choose the path with the shortest travel time, travel distance or a

combination of both. The simplest way to model route choice is by

stipulating minimum pathchoice and this approach was adopted in

Eindhoven [37]. Therefore, we further compared our trained MLP

models with the prediction by the shortest path model to deter-

mine their effectiveness. For prediction by the shortest path, we

simply assumed that all passengers will select the shortest esca-

lator (escalator 1) as their desired path. The means of the fraction

of correct predictions was 49.60% which is significantly lower than

the trained MLP models as shown in Table 4.

Classical route choicemodel

The multinomial Logit (MNL) model was proposed as general-

isation to the deterministic model. Borgers and Timmermans [38]

presenteda MNLmodel forpedestrian route choicewithin city cen-tres in the Netherlands. In 1998, Cheung and Lam [1] adopted the

MNL model to predict the passengers choice between escalators

Table 4

Comparison of different modelsadopted in this study.

Model Means of the

fraction of correct

predictions

MLP model 77.82%

MLP model with leave-one-out approach 79.48%

MLP model with ensemble approach 86.47%

Prediction by shortest distance 49.60%

MNLmodel 71.34%


9/9


and stairs in a station. Therefore, a MNLmodel also developed to

evaluate the MLP models developed in this study as follows:

P1=1

(1 + exp X) (10)

where P1is the probability of passengers choosing escalator 1 and

X is the different of the perceived total travel time between using

escalator 1 and escalator 2, i.e.

X= a+ b(t1 t2) (11)where a and b are the parameters to be estimated, t1 and t2 is

respectively the perceived total travel time from the origin (i.e.,

position 1 to position 6) to the escalator 1 and escalator 2.

The result of passenger route choice by using the MNLmodel is

given as follow:

P1=1

1 + exp (5.7931 3.5096)t) (12)

The means of thefractionof correct predictions predictedby the

MNLmodel was 71.34%.

Table 4 shows a comparison of the performances of MLP model,

MLP model with leave-one-out approach, MLP model with ensem-

ble approach, prediction by the shortest distance, and MNLmodel.

The performances of the MLP models were better than those of theMNL model and the prediction by the shortest path in this case.

The mean specific accuracy of the MLP model with the ensemble

approach was 86.47%, which indicates that the performance of the

developed ANN model mimics route selection behaviour reason-

ably well in this study. It should be noted that even our trained

ANN models provided a better performance than the MNLmodel

and the prediction by the shortest distance in our case. This study

only provide an alternative-tool to predict route choiceby using the

ANN models, it cannot be concluded that our trained ANN model is

superior to the other models.

Conclusion

This study develops an intelligent approach to mimic the gen-eral behaviour of passengers making a route choice between two

escalators in a transportation station, based on the passengers

walking velocities and positions and the passenger densities at the

entrances of the escalators. This model is useful for both station

design and daily operation, as escalators are a critical transporta-

tion facility in transportation stations. The approach provides a

rapid method for engineers to estimate the loadings of escalators,

even for new stations, so that they can optimise their utilisation

to achieve maximum efficiency. This study successfully demon-

strates the feasibility of this approach. In future studies, we will

explore other facilities to improve overall crowd movement inside

transportation stations.

Acknowledgements

The authors wouldlike to acknowledge the MassTransitRailway

Corporation (MTRC) for their support for this research. The work

that is described in this paper was fully supported by a grant from

CityU (Project No. 7008028).

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an intelligence-based route choice model for pedestrian ﬂow in a transportation station

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