Analysis of Mixed Traffic Flow in Urban District Using Multi-Agent Model
Kenichi TAKANE
Graduate Student
Dept. of Info. Engineering and Logistics
Tokyo University of Marine Science andTechnology
2-1-6 Etchujima, Koto-ku, Tokyo,
135-8533 Japan
Fax: +81-3-5620-6492
E-mail: m075013@kaiyodai.ac.jp

Tetsuro HYODO
Professor
Dept. of Info. Engineering and Logistics
Tokyo University of Marine Science andTechnology
2-1-6 Etchujima, Koto-ku, Tokyo,
135-8533 Japan
Fax: +81-3-5245-7386
E-mail: hyodo@kaiyodai.ac.jp

Yuya ARAKI
Graduate Student
Dept. of Info. Engineering and Logistics
Tokyo University of Marine Science andTechnology
2-1-6 Etchujima, Koto-ku, Tokyo,
135-8533 Japan
Fax: +81-3-5620-6492
E-mail: m065012@kaiyodai.ac.jp

Yoji TAKAHASHI
Advanced Research Institute for Sciencesand Humanities(ARISH)
Nihon University
1-2-1 Izimicho, Narashino City
Chiba, 275-8575 Japan
E-mail: y.takahashi@catv29.ne.jp

Abstract: In Japan, several modes use the sidewalk extensively apart from pedestrians. The
existence of this mixed traffic in the sidewalk causes confusion and increase conflicts betweenusers. As a result, the sidewalk is not used effectively and eventually leads to the decreased
amenity of the sidewalk users. It is therefore necessary to introduce measures and tools foranalysis and evaluation in considering equality and enhancing the comfort and safety of all
sidewalk users. The purpose of this study is to develop a tool for analyzing mixed traffic flow
and use this tool to evaluate the measures and the impact of each mode’s behavior on othermodes. Study method is to build multi-agent simulation model and to evaluate measures andthe impact of the mode’s behavior using simulation model. As a result, it became possible toanalyze quantitatively the effectiveness of measures and the impact of the mode’s behavior.
Key Words: traffic simulation, multi-agent model, mixed traffic flow
1.

Introduction

In Japan, there are several modes that use the sidewalk extensively apart from pedestrians. For
example, there are bicycles, wheelchairs, baby carriages and push carts to name a few. The
existence of this mixed mode in the sidewalk causes confusion and increase conflicts between
users. As a result, the sidewalk is not used effectively and eventually leads to the decreased
amenity of the sidewalk users. Another concern is the recent increase in usage of bicycles due
to environmental issues and health consciousness. This could possibly cause muchdeterioration of sidewalk user’s amenity. In addition, it is envisioned in the near future that a
new type of mode will appear as a reflection of an aging society’s pursuit for mobility. This
includes the wide-spread use of motorized wheelchairs and personal transporters such as
Segway HT, a two-wheeled self-balancing transportation device.
With these circumstances, confusion and conflicts will be aggravated and cause the user’s
amenity to deteriorate more. It is therefore necessary to introduce measures and tools foranalyses and evaluation, considering equality and enhancing the comfort and safety of all
sidewalk users.

2.

Purpose of the study

The purpose of this study is to develop a tool for mixed traffic flow analyses and use this tool
to evaluate the measures and the impact that each mode’s behavior has on other modes.
3.

Study methodology

The adopted methodology is to a build multi-agent simulation model for analyses of mixed
traffic flow and to evaluate measures and the impact of the mode’s behavior has on others
using simulation model.
3.1 What is Multi-agent model?
The agent is used in various senses in many fields. In this research, agent is a practice actor
that recognizes neighboring environmental states, judge it depending on the situation
autonomously, act and produce effect on the environment. Multi-agent model is the systemthat allows a lot of autonomous agents to coexist. It is used for dispersion (cooperation)
problem solving or the emergence of various kinds of complicated phenomena. The behaviorof the whole system is built from interaction of the agents. From this feature, each agent itself
has only a simple function but it becomes possible to reproduce complicated phenomenon by
the fact that the interacting agents operate mutually. Judging from the above, this study
adopted multi-agent concept as the basis for building the simulation model.
In this study each mode is considered as the agent. Complex traffic flow is reproduced by thefact that each mode acts from information from its environment and its own behavior.
4.

Simulation model building

4.1 Multi-agent simulator – Artisoc
In this research, multi-agent simulator artisoc is used for construction of the simulation
model. Artisoc is a simulator specialized in multi agent simulation that KOZO KEIKAKUENGINEERING Inc developed. Artisoc has various features including the following:





Various functions for building multi-agent simulation.
Easy of use in agent generation, arrangement and movement.
Rule format is similar to visual basic a simple and widely used programming language.
A GUI allows setting of simulation output.
Readily available online tutorials for beginners.

This features were the deciding factor for choosing Artisoc for this study.
4.2 Mode parameters
This research used results from existing researches that calculated each mode’s parameters byprocessing images in the animation recorded from a walking experiment inside school campus(figure1). The resulting parameters used in this study are shown below.

Figure 1 Sidewalk experiment

Table 1 Mode parameters
pedestrian

1.55(m/s)

bicycle2.23(m/s)

Elderly pedestrian

0.7(m/s)

wheelchair

0.89(m/s)

pedestrian

0.6(m/s)

bicycle0.85(m/s)

Elderly pedestrian

0.3(m/s)

wheelchair

0.3(m/s)for pedestrian of bicycle(x,y)=(0.21[m],6.3[m]）for elderly pedestrian of pedestrian(x,y)=(0.54[m],3.48[m])for wheelchair of pedestrian(x,y)=(0.22[m],6.2[m])for wheelchair of bicycle(x,y)=(0.21[m],6.3[m])other than those above(x,y)=(0.54[m],8.9[m])passing area

all modes(x,y)=(0.6[m],2.0[m]）rate of decrease

all modes

60%

average speed

avoidance speed

cross-purposes area

＊As for x left and right of mode, as for y front distance of mode is displayed.

4.3 Simulation algorithm
Algorithm of simulation which is used in this research is shown below
Setting default position and speed

Start moving
Search theshortest mode

Direction ofshortest mode

Same direction

Takepassingactionnoopposition direction

Take Passing
each otheraction

Search theshortest mode

yes

noyes

Passing action

Passing each other action

Figure 2 Basic mode algorithm

Calculating the position of
oneself and the shortest mode

There is space in
one's width sideyesno

There is space inthe width side
without oneself

noyes

Evading on one's side

Evading on the side
without oneself

take the slowdown
actionFigure 3 Evading algorithm
4.4 Setting evaluation item
This study sets three items to evaluate whether the sidewalk was used safely and comfortably.
Evaluation items are shown below.
 Number of slowdown action
 Number which is penetrated to personal space＊personal space is space to feel unpleasantly when no familiar person approach. Thisresearch defines that circumference of each mode’ personal space is about 40 cm.
 Number of conflict
Using algorithm, parameter and evaluation items mentioned above, Simulation is developedby artisoc. Execution time of simulation is set 30 seconds and Execution step of simulation
was set to a one-tenth second. Evaluation items are counted per one-tenth second anddisplayed by graph. Size of each mode is set circle of a radius of 40 centimeters as alimitation of the software.

Figure 4 Simulation screen shot
5.

Analysis of mixed traffic flow using simulation modelThis section describes the analysis of mixed traffic flow using simulation model. Each result
is the average of 100 trials with varying starting position of each agent. Scenarios where
created such that simulation of different strategies can be examined and that effects of eachmode can be observed.
5.1 Scheme1 One lane per mode
For all modes to comfortably and safely use the sidewalk, it is thought that combining modes
with similar characteristics and considering it as one mode group to be assigned for each lane
is a more effective approach. The characteristic considered are speed, weight, size andbehavior of each mode. It is hypothesized that modes having almost the same characteristics
can coexist in the same lane and that modes with different characteristic would cause conflict
and that coexistence will be difficult.
5.1.1 Grouping modes with similar characteristic
Firstly, the four modes considered are pedestrian, bicycle, old pedestrian and wheelchair. The

method of grouping is to simulate only
only two different modes; isolating one mode andcombining the other modes then analyze the effect of each of the combined mode to theisolated mode. The size of the sidewalk is 5m in width, 30m in length. The number of eachmode is 15. Simulation results arear shown below.

Figure 5 pedestrian

Figure 6 bicycle

Figure 7 old pedestrian

Figure 8 wheelchair

From the simulation results, pedestrian doesn’t
doesn’t have a big effect on other three modes withalmost the same values (figure1). Bicycles are affected strongly by old pedestrian andwheelchair in comparison with the pedestrian (figure2). In the same way old pedestrian andwheelchair are affected strongly
ngly by bicycles. For this reason, old pedestrian and wheelchairare different from bicycle. Their characteristics greatly differ and it is thought that theircoexistence in the same lane is difficult. In addition when the effect the bicycle receives frompedestrian and the effect received by old pedestrian and wheelchair from pedestrian arecompared, the effect received by senior citizen and wheelchair from the pedestrian is small. It
is supposed that pedestrian’s characteristics are similar to old pedestrianpedestrian rather than that of thebicycle. From the above-mentioned
mentioned analysis, two groups were determined - pedestrian, oldpedestrian and wheelchair are grouped into one and the mode bicycle is considered as anothergroup.
grou
5.1.2 Allocating lanes for each group
Two groups are distributed to different lanes and the effect is analyzed. To physically divide
the lane into 2, a block was installed 2 meters from the right side of the sidewalk having awidth of 5m and 30m in length. Pedestrian, old pedestrian and wheelchair
wheelchair are distributed to
the lane 3m wide and bicycle is distributed to the lane 2m wide. The number of each mode is10. As an initial condition, a simulation was performed where the 10 agents of each mode wasdistributed on the sidewalk 3m wide and 30m long without the separating blocks. Simulation
results are shown below.

Figure 9 simulation

Figure 10 slowdown

Figure 11 personal space

Figure 12 conflict

As figure10, 11, and 12 indicates, Numerical value of each evaluation items decreased foreach mode. Looking accordingly at each evaluation item, slowdown is decreased greatly forbicycle. With regards to personal space, all modes decreased in the same way.
way The bicycle
and wheelchair largely decreased conflicts. From the analysis above, this measure is effective.

The grouping of this scheme is similar to creating a bicycle lane and it may be said thathaving of a bicycle lane is effective in increasing amenity.
amen
5.2 Scheme2 Priority is established between modes
Scheme1 is effective but enough width in sidewalk is necessary. In other words, introductionof Scheme1 is difficult on narrow and small sidewalks. Therefore it is necessary to think
about a new measure.
From the analysis in scheme1, old pedestrian and wheelchair are different from bicycle in acharacteristic greatly and it is thought
thought that the coexistence in the same lane is difficult. For
this reason, it is thought that some measures are necessary between these modes. It is thoughtthat it is good to give clear priority between modes as a measure. When different modes takecross-paths
aths and passing action in a narrow sidewalk, if it is predetermined clearly whether
either mode will preferentially evade other, then coexistence in same lane is possible.
In this study, the sidewalk 3m wide and 30m long was retained but the passing area parameterof the bicycle and cross-path
path area for old pedestrian and wheelchair was increased 1.5 times
as listed in Table2. The bicycle was made to preferentially evade old pedestrian andwheelchair. Because old pedestrian and wheelchair are weak compared to bicycle, thisresearch set it so that a bicycle evades with precedence. Under these circumstances, asimulation with 8 agents for each mode was performed. As an initial condition, a simulation
was also executed where the parameter was not changed. Simulation
Simulation results are shown below.

passing areacross-path area forold pedestriancross-path area forwheelchair

Figure 13 slowdown

Table 2 parameter change
Bicycle(initial condition）
condition(x,y）= (0.6[m],2[m])(x,y (0.54[m],8.9[m])(x,y）=→(x,y)= (0.18[m],8.9[m])Figure 14 personal space

Bicycle(expand area)(x,y)= (0.9[m],3[m]）(0.9[m],3[m](x,y) = (0.81[m],13.6[m])(x,y) = (0.27[m],13.6[m])Figure 15 conflictFrom simulation results (figure13, 14, 15) each evaluation item decreased value for thebicycle, old pedestrian and wheelchair. The decrease of evaluation item for bicycle isconspicuous. In addition, old pedestrian and wheelchair decreased by the same level on all ofevaluation item unlike pedestrian when considering slowdown item. It is possible for bicycle
to evade old pedestrian and wheelchair with ease. From analysis above, this measure iseffective.
5.3 analyzing the impact of children and old pedestrian’s behavior on other modes.
5.3.1 the impact children’s behavior on other modes
Analyzing the influence of children to each mode is done by changing the parameter ofpedestrian into young pedestrian. The parameter of young pedestrian is presumed fromparameter of pedestrian. For young pedestrian, walk speed is slow and range of vision isnarrow in comparison with pedestrian. It is thought that young pedestrian acts irregularly orrandom. Each parameter of young pedestrian is less 30% of that of pedestrian (table3). In
addition each mode calculates shortest path in every simulation step, but the child set to moveirregularly with 20% probability every simulation step.
Table 3 young pedestrian parameter
average speed
avoidance speed
cross-purposes
purposes area for bicycle
cross-purposes
purposes area for old pedestriancross-purposes
purposes area for wheelchaircross-purposes
purposes area for other than those above
passing area
rate of decrease

young pedestrian
1.09(m/s)
0.21(m/s)(x,y) = (0.37[m],6.23[m])(x,y) = (0.37[cm],2.4[m])(x,y) = (0.15[m],4.3[m])(x,y) = (0.37[m],6.23[m])(0.42[m],1.4[m](x,y) = (0.42[m],1.4[m]）60%

In order to analyze the influence of young pedestrian on other mode, a simulation with 10
agents for each mode was executed. The size of the sidewalk is 5m in width, 30m in length.
As an initial condition, simulation
simulation before the parameter modification is executed.

Figure 16 the effect of young pedestrianFigure 17 slowdown

Figure 18 personal space

Figure 19 conflict

When pedestrian and young pedestrian are compared from the result of simulation (figure12),young pedestrian have few slowdown number of times. Conflict number of times is bigger
than slowdown and personal space in comparison with pedestrian. It is thought that
deceleration behavior occurs late due to narrow vision and irregular action as personal spaceand conflict number of times increased. Numerical value of each evaluation item increased at
the same level for each mode. From mentioned above, it is said that young pedestrian has
strong effect on other modes in comparison with pedestrian.
5.3.2 Effect of old pedestrian’s deteriorated mobility
Old pedestrian’s perception, reflex, and cognitive skill decreases the physical bodydeteriorates compared with the general adult. But old pedestrian in this simulation model does
not have this characteristic, searching for the shortest mode every step (1/10(1/ seconds). To
reflect decreasing cognitive ability, perception and recognition power is decreased and its
effect is analyzed. Decreasing cognitive ability is expressed as not to search of the shortest
mode every one step with probability of 30%. The size of the sidewalk is 5m in width, 30m inlength. A simulation with 10 agents for each mode is executed. As an initial condition,
simulation before the parameter modification is executed. The results are shown below.

Figure 20 slowdown

Figure 21 personal space

Figure 22 conflict

As for old pedestrian with deteriorated mobility and recognition, the slowdown number oftimes decreased. Personal space approach number of times and the collision number of times
increased.
ed. Especially, increase of personal space number of times was considerable. It isthought that lag of the evasive action with the decrease of judgment occurred. Numericalvalue of each evaluation items increased for each mode. Numerical value of slowdown andpersonal space of bicycle in particular largely increased. From mentioned above, it is thoughtthat old pedestrian has an influence to other modes particularly to bicycles.
5.4 Analysis and discussion point
From the analysis results using simulation
simulation model, the following were observed:
With regards to implementing measures for the comfortable and safety coexistence of eachmode On sidewalks where various modes traverse and if there is enough width, grouping modes
with similar characteristics and providing
providing a separate lane for each group is effective.
 For the small sidewalk without enough width, comfort is secured by determining priority
between modes with greatly different characteristic. In addition, the priority rule that it isdesirable is to consider the vulnerable road user. In this case, bicycles should give way tosenior citizens.
The effect of children and old aged pedestrians on other modes
 The young pedestrian with narrow field of vision, makes irregular moves requiring a big
personal space and conflicts and collides with other modes frequently. It is said that theeffect to other modes is bigger than pedestrian. To decrease this effect, it is necessary todevise of measure for young pedestrian.
 Old pedestrian with deteriorated motor, perception
perception and reaction, conflicts highly to othermode’s personal space and collide frequently because of searching for the shortest

distance mode is slow. Therefore, like a young pedestrian, it is necessary to think aboutmeasures to reduce their effect on other modes.
6.

Conclusions

This research builds multi-agent model simulation. Effectiveness of measure and theinfluence of each mode to one another were analyzed quantitatively. From this result, it isthought that simulation model can be used as a tool for evaluating measures or schemes to beimplemented and to analyze behavior of new mode. However, it is thought that there is aproblem with using the parameter based on an intramural walk experiment for the sidewalk
condition. It is thought that the parameter of each mode changes by width and traffic densityof the sidewalk. Therefore, it is necessary to think about the calculation method of theparameter considered. In addition, there is room for improvement of the evaluation items. In
this study, the weight of each evaluation items is treated equally but it is also a good argument
to differentiate the weight value of the evaluation item depending on the type of mode. Forinstance, speed is much more important to bicycles than to pedestrians. And pedestrians
would value personal space more than speed. Future topic improves each parameter to closely
represent the real movement of modes and the weight of each evaluation items for realisticevaluation.

REFERE4CES
Susumu YAMAKAGE, Syota HATTORI, (2002) artificial society in the computer ~ multiagent Model and complex system. Edited by KYORITSU SHUPPAN CO., LTD. 1-25. (inJapanese)
Megumi OWADA, Tetsuro HYODO, Yoji TAKAHASHI, (2005) recerch for themixedTraffic Simulation method in the sidewalk, master thesis, Tokyo university of marine
science and technology,18. (in Japanese)
Serge P. Hoogendoom, (2003) Pedestrian travel behavior modeling, 10th international
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