A System for the Analysis of Passenger Flow on the Station Concourse

K. Sakamoto1, R. Shibasaki2, H. Zhao2, K. Nakamura2, N. Suzukawa3

1East Japan Railway Company, Japan; 2The University of Tokyo, Japan; 3JR East Consultants Company, Japan

Abstract

We have developed a unique and unprecedented system to analyze passenger flow on the busy

concourse in railway stations by using networked laser scanner sensors. Applying the system at stations in

Tokyo area, we could have succeeded to extract a series of novel data, such as passengers’ trajectories,

velocity, density, and collision avoidance behavior among passengers, necessary information to

comprehend how the concourse is used by passengers. This was possible by developing some algorithms

to extract passengers’ position from the data taken by laser sensors, and to relate each position between

one frame and another.

Background of the research and its objective

The passenger flow, or passenger ’s traffic line, is the basis in planning the railway station. East Japan

Railway Company (JR East) has positioned the station as one of the most important resources for the

company’s business development, and its function has been diversified to correspond passengers’

convenience. The most prominent change in stations is the introduction of a number of commercial

facilities such as retail shops, book stores, and restaurants. In some busy area, especially in Tokyo area,

the structure of the station has been extended even on railway tracks to supply the space for these.

Naturally, that results in the complexity of the plan and simplification of the passenger flow is eminent

necessity.

But the passenger flow cannot be determined easily in the area like the concourse of the station. Usually a

system consists of video cameras has been examined for this purpose, but it is not ideal for the analysis in

railway stations, because 1) the data taken by one video camera is limited spatially, so considerable

number of cameras should be set to cover the whole concourse, and 2) the video data is too heavy to

handle in long hours, so it is difficult to grasp difference in passenger flow among different hours. Thus, no

information exists how the concourse is used by passengers and this is not changed until a new approach

will be developed.

Based on these backgrounds, the Frontier Service Development Laboratory of JR East has been

developing a system , which is made of laser scanner sensors and computer network, to analyze

passenger flow. We have focused especially on the analysis of passengers’ trajectory, density, velocity,

and some phenomenon experienced in busy stations such as the collision avoidance between passengers.

In this paper the followings will be described:

1) the outline of the system

2) the experiment in railway stations

3) the analysis on the passenger flow

System to determine the passenger flow and the process to analyze it

The outline of the system to determine the passenger flow is shown on Fig. 1 and 2. The system consists of

1) laser scanner sensors, 2) client computers, and 3) server computer.

Multiple laser scanner sensors (Sick LMS 200) are used to determine the position of passengers. They are

set on the floor of the concourse to scan the horizontal plane at the height of 16 centimeters. The

concourse is crowded during rush hours and determining the thinnest part of the human body, or ankles, is

necessary to lessen the occlusion. The passengers’ range data , a sequence of x, y positions for each

pedestrian, is stored in the client computer and temporal information sent from the server computer is

given to the data.

Fig. 1 Concept of the network of laser scanner sensors and computers (Source: Huijing Zhao and Ryosuke Shibasaki, “A Novel System for Tracking Pedestrians Using Multiple Single-Row Laser-Range Scanners”)

Fig. 2 Image of the determination of passengers flow in the railway station

Laser scanner sensor Client computer

Server computer

Concourse

Network

The range data taken from different sensors are transformed into a common coordinate system by using

the Hermart Transformation that deals with a shift and a rotation. Then tracking algorithm, consists of the

following process, is applied to draw passengers’ trajectory.

1) Foot detection by clustering range data.

2) Passengers detection by grouping feet data.

3) Motion detection by tracing passengers’ data.

4) Trajectory tracing by utilizing the Extended Kalman Filter.

Clustering the range data, which is the points that hit around feet is necessary process to identify a foot of

passenger. The range data that gathers around within 15 centimeters in radius will be accumulated and its

central gravity is regarded as the sectional image of a foot. The extracted points are to be connected

according to time sequence and extended by utilizing the Extended Kalman Filter. This filter is to connect

likelihood candidates of clustered points by assuming pedestrians’ motional model based on the principal

of dynamics. When people walk, one of two legs must work as a pivot. This means the pedestrian has a

simple pattern in motion that is defined by legs’ swing and the change in speed and acceleration. The

clustered points on the extended trajectory will be grouped if two points are positioned within 30

centimeters. Points overlapped for three frames in time sequence are regarded to belong to the same legs,

a process called “seeding.” The whole process is shown on Fig. 3.

Fig. 3 Flow of the tracking system (Source: Huijing Zhao and Ryosuke Shibasaki, “A Novel System for Tracking Pedestrians Using Multiple Single-Row Laser-Range Scanners”)

Experiment

In order to verify the capability of the system, a

field test in gaining passenger flow data was

taken in a busy station in Tokyo area (hereafter

called “E station”). E station has two platforms at

which three different lines stop by. Some 250,000

passengers use the station every weekday. Six

laser scanner sensors were placed on the

concourse of 20 meters X 30 meters (Fig. 4).

Each sensor was networked by cable, client

computers, and a server computer. A video

camera was also attached on the ceiling

around the central area of the concourse

where congestion had been experienced

especially during the rush hour. The camera

was set to certify the result in analyzing the

laser data, which would track passengers. To

do this, the range data taken from different

sensors and video data were synchronized and both images were overlapped (Fig. 5). The verification was

executed by counting the foot image drawn by the laser data upon that of the video for a set of fifty images

extracted at random during ten seconds around 8:34 AM, or the most crowded hour at the station (see the

circles on the photograph of Fig. 5-right).

The Table 1 is the result. The “pattern 1” in the table means the result of the verification that uses the range

data taken by three sensors on one side (Fig. 6). As well, “pattern 2” means the verification of the image

obtained by the data of four sensors on two sides, and “pattern 3” is the verification by the data of six

sensors on three sides. As the table shows, there exists obvious relation between the accuracy on the

laser image and the number of sensors. Thus, to gain more accurate laser image for passengers,

increasing the number of sensors and their positioning sides are effective.

Fig. 4 The concourse of E station and laser scanner sensors

Fig. 5 Passenger data taken by video camera and laser scanner

99%92%100%Pattern 3

95%83%100%Pattern 2

91%78%100%Pattern 1

Mean valueMinimum determination

Maximum determination

Sensors’ position

99%92%100%Pattern 3

95%83%100%Pattern 2

91%78%100%Pattern 1

Mean valueMinimum determination

Maximum determination

Sensors’ position

Table 1 Sensors’ position pattern and determination value

Laser Scanner Sensor

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Kiosk

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Analysis

Fig. 7 is a series of images on passenger flow obtained through the trajectory tracing process. Trajectories

on each image are drawn with the range data for fifty seconds. These images show the tendency of

passengers’ traffic line on the concourse that changes according to hours. One can also comprehend the

direction of passengers flow, by the contrast of the color on trajectory that can be given according to the

direction. In actual usage, some useful information, such as the specification of densely walked areas, is

visualized by this method, for brighter regions mean the areas where many passengers passed through.

By utilizing this analysis data, the distribution of passengers’ velocity and density, or mean value of each

analysis in given grids, can be also expressed. Fig. 8 and 9 are the result.

Fig. 10 is the analysis on the collision avoidance among passengers. This is executed by setting two

conditions, that is, distance between each passenger on the same frame, and crossed axes angle of

trajectories (Fig. 11). Certain thresholds are set for each condition and white dots are plotted if both values

are lower than the threshold. It should be noted that the plots on the figure show nothing but “candidates”

of the collision avoidance and actual phenomenon will be detected by giving exact parameter that will be

obtained through the careful observation on passengers’ behaviors.

Fig. 6 Sensors’ position pattern

Fig. 7 Passengers flow on the concourse of E station

Laser Scanner Sensor

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Pattern 1 Pattern 2 Pattern 3

Laser Scanner Sensor

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Area covered by video camera

Ticket gates

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Pattern 1 Pattern 2 Pattern 3

Some applied analyses on passengers’ behavior are also possible by giving some conditions and the

application of statistic methods such as Karnel density estimation. Recently, there was a thorough change,

or the betterment, of train information monitors at “S” station, which is one of the busiest stations in Japan.

At S station the train information had been displayed with the light emitting diode (hereafter called LED) of

three colors (red, green, and orange) on the monitor. The monitor was renewed to the one with new device

and design, such as the introduction of the “full-color LED,” highly distinguishable fonts, lines’ symbolic

colors, and the new zoning and layout of information (Fig. 12). In addition, the information of trains which

reach some major terminal stations first was also to be displayed.

crossed axes angle

distance

collision

pedestrian Apedestrian B

Fig. 8 Distribution of passengers’ velocity Fig. 9 Distribution of passengers’ density

Fig. 10 Collision avoidance among passengers (white dots) Fig. 11 Model of collision avoidance among passengers

Fig. 12 Train information monitors at S station (before and after the betterment)

Before After

We focused on the change in passengers’ stationary behavior (Fig. 13) before and after the betterment, for

we were interested in the fact whether the change contributed to the increase of passengers’ convenience

and efficiency in the mobility on the concourse. Fig. 14 shows the estimated result of the statistical density

on stationary passengers during daytime before and after the betterment. The brighter the circle is, the

higher the stationary behavior was recorded. On the Fig. 14-left, two brightest circles are drawn in front of

two different train information, that is, “S” line and “SS” line, each had been displayed on separate zone.

On the Fig. 14-right, the brightest circles are converged to one. It is assumable that this convergence

occurred because train information on “S” and “SS” line also were put together in the same zone. Fig. 15

also shows the estimated result of the statistical density on stationary passengers during daytime at a

different exit in S station. It is remarkable that the brightest circle is shifted to rightward after the betterment.

This shift means passengers’ stationary behavior was set back from the ticket gate after the betterment. It

is assumable that this happened because 1) train

information on “S” line that had been positioned further

away from the gate was brought near the gate; 2) visibility

of the train information on the monitor was improved by the

new design , such as coloring and fonts. These changes,

the convergence and the shift of the peaks of density circles

on stationary behavior, can be interpreted as passengers’

mobility came to smoother after the betterment.

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Fig. 14 Change in passengers stationary behavior (before and after the betterment)

Fig. 13 Passenger’s stationary behavior

Conclusion

In this paper, we have introduced the outline of the system, the experiment in the railway station, and the

analysis on the passengers flow. The application of the system in busy stations in Tokyo area, will enable

us to extract a series of novel data, such as passengers’ trajectories, velocity, density, and the collision

avoidance among passengers. These are necessary information to comprehend how the concourse is

used by passengers.

Although we could demonstrate the feasibility of the system, there are some problems to solve for further

development. For example, the tracking algorithm is not robust enough to extract continuous thorough

trajectory on the whole concourse, especially under the congested condition. The guide line of optimum

number of the sensors that eliminate the occlusion should be also considered for the effective

determination.

Considering above problems, we are developing the system as a practical tool to estimate the quality of

architectural space in railway stations, through the analysis on passenger flow.

Reference

Huijing Zhao and Ryosuke Shibasaki, “A Novel System for Tracking Pedestrians Using Multiple

Single-Row Laser-Range Scanners,” IEEE Transcriptions on Systems, Vol. 35, No. 2, March 2005.

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“S”-line “SS”-lineConcourse outside

Ticket gates Ticket gates

Concourse outside

“S”+”SS”-line

Fig. 15 Change in passengers stationary behavior (before and after the betterment)