CT-Planner5: a Computer-Aided Tour Planning Service
Which Profits Both Tourists and Destinations
Yohei Kurata

Yasutaka Shinagawa

Tatsunori Hara

Tokyo Metropolitan University
1-1 Minami-osawa, Hahioji
192-0397, Japan+81-42-877-2866

The University of Tokyo
5-1-5 Kashiwanoha, Kashiwa
277-8568, Japan+81-4-7136-4250

The University of Tokyo
5-1-5 Kashiwanoha, Kashiwa
277-8568, Japan+81-4-7136-4250

ykurata@tmu.ac.jp

shinagawa@race.u-tokyo.ac.jp hara_tatsu@race.u-tokyo.ac.jp

ABSTRACT
CT-Planner is a web-based tour planning service, which promotes
the collaborative design of tour plans by a user and the system. Its
remarkable feature is its interactive and cyclic process of tour
planning, in which the user provides his/her requests in a stepwisemanner and the system keeps revising the plan until he/she getssatisfied. Such interface design fits service domains where users
are not well-aware of their interests/requests at the beginning,such as tour planning. This paper reports its latest version, CTPlanner5, which supports 25 destinations in Japan at this moment.
CT-Planner5 makes it possible to generate tour plans which use
public transportation when it is efficient. We explain themechanism of plan generation, as well as the process of authoring
destination data. We also introduce our future plan to use the user
log of CT-Planner5 for destination marketing in collaboration
with travel agencies, DMOs, and communities, as the collecteduser log allows us to know the demand and emerging trend of
tourists for each destination without cost.

Categories and Subject Descriptors
H.3.5 [Information Storage and retrieval]: online Information –web-based services.

General Terms
Design, Human Factors.

Keywords
computer-aided tour planning, personalization, critiquing-based
recommender system, user log, marketing analysis

1. INTRODUCTION
Making a tour plan is an exciting process of travel, but sometimes
people feel hard to do it, especially when they are visiting
unfamiliar destinations on a tight schedule. In order to relieve

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people from such difficulty, researchers have developed tour
recommenders that generate personalized tour plans (e.g., [7][17][19]; See [26] for a review). Early systems typically aim at
generating an optimal plan in a single step, asking users to give all
tour conditions/requests in advance. This interface makes theirusers feel lack of participation [24]. In order to realize more usercentered planning, some systems introduced customization phase,in which the users are allowed to modify recommended plans[6][23]. Furthermore, we developed CT-Planner in pursuit forrealizing system-user collaboration of tour planning [12]. In CTPlanner, the users can provide their requests in a stepwise mannerand the system keeps revising the plan until they get satisfied. We
confirmed that this interactive interface helps the users to clarify
their requests and eventually leads to higher user satisfaction [15].
CT-Planner’s interaction model is similar to that of critiquingbased recommender systems [5][18] in the sense that the users arerequested to give feedbacks to the system about its
recommendations. However, the requested feedback in CTPlanner is not the evaluation of each recommended plan, butpieces of requests which come up to the users’ mind while
examining the recommended plans.
This paper introduces the latest version of CT-Planner, called CTPlanner5. CT-Planner5 is available online at http://ctplanner.jp.
CT-Planner5 succeeds the following features from its previous
versions: collaborative planning approach, online accessibility,
and use of a genetic algorithm for generating plans. In addition,
CT-Planner5 now supports the generation of tour plans which use
public transportation, and equips with two family tools, namelyplan viewer for smartphones and log analyzer.
Our CT-Planner5 has two goals. The apparent goal is to provide a
useful planning service to tourists on the Web. With CT-Planner5,
people can consult on their plan from anywhere at any time, asmuch as they want. Another implicit goal is to collect log datafrom a large number of users, which can be eventually utilized asa resource for marketing analysis.
The remainder of this paper is structured as follows: Section 2
consider general requirements of computer-aided tour planning
services. Section 3 looks back the history of CT-Planner. Section
4 describes the outline of CT-Planner5’s service, including planviewer for smartphones. Section 5 briefly explains how CTPlanner5 generates tour plans. Section 6 discusses theapplicability of CT-Planner5’s user log for marketing analysis,
together with the introduction of our log analyzer’s prototype.
Section 7 explains the process of data authoring in CT-Planner5.
Finally, Section 8 concludes with a discussion of future work.

2. GENERAL REQUIREMENTS OF TOURPLANNING SERVICES

processes (e.g., [29]). In the use phase, users use and adapt a
product/service to the environment in the use phase.

Figure 1 shows such a general engineering process that does not
involve users. The design process includes concept design anddetailed design, and the production process includes production,
manufacture, and quality control. In this entire process, users
attend only to the use phase. In other words, users simply
consume the products/services designed and produced byproviders. In the design process, users only appear as the Voice ofCustomer (VoC), and their characteristics and behaviors are notconsidered.
Production
by provider
Production
designConcept
designDetailed
designFull designby provider

Manufactureof parts

Used by user
Assembly

Use

RepairTake-backQualitycontrol

Figure 1. General engineering processes of a product/service(modified from [8])On the other hand, Figure 2 shows an engineering process
involving high user participation [8]. User participation is used as
an approach to respond to each customer [22]. The main featureof this process is the processes of configuration and adaptation by
the user. The process of full design by the provider is changed tothat of preparatory design. In this process, providers design thebasis of products/services and prepare for configuration by a user.
In this situation, users not only use products/services but
configure and adapt the parts prepared by the provider. Thus, they
become co-designers of products/services. By providing users
with more freedom for developing products/services, they canmake these products/services more fit to their requirements andthe conditions in the use phase. Preparatory design by theprovider includes formulating the PFA (Product Family
Architecture) [27] and preparing the configurator. Next, theprocess of configuration by the user is carried out as assembly andconfiguration in the configuration design of use. Interactive
design methods have been proposed to support these user’s

Attention

Check

SearchPerceive

Production
designCompareActionManufactureof parts

Assembly&
configureUse &adaptation

Qualitycontrol

Evaluate

Evaluate

Share

Share

Production
by provider

2.1 Design Perspective: Configuration Design
by UserThanks to advancement of ICT, the design and the use phases
have become strongly and inseparably related. In some forms ofdesign, users attend to the development process [3][22][28] andsimultaneously design and use products/services. Many
approaches have been used for achieving it, such as user-centered
design [1], participatory design [21], lead-user innovation [10],and reinvention [4]. Hara et al. have introduced a model for
combining design activities by the product/service provider,individual user, and user community [8]. They also explainedengineering processes that involve and do not involve users. The
importance of design by users is explained from the difference
between these two processes. The discussion here does notdistinguish between physical products and non-physical services.

Adaptationby user

Configurationby user

Before introducing CT-Planner5, we discuss general requirementsof tour planning service from a viewpoint of design engineering.

Concept
designDetailed
designPreparatory designby provider

Take-backFeedback data from user side

Figure 2. Engineering processes of a product/service involving
high user participation (modified from [8])2.2 Tour Planning AssistanceAccording to the above design perspective, tour planning isviewed as a configuration design activity of a service, in whichtour components, mainly point of interests (POIs), are selectedfrom numerous candidates and combined as into series, such thatthe plan maximizes the expected satisfaction of its users (i.e.,
tourists) under such constraints as total time, budget, start/goalpoint, mode of transportation.
Mathematically speaking, it is modeled as a combinatory
optimization problem (Section 5). A unique feature of this design
activity is that, not like product design, the design is conducted
not only by experts, but often by ordinary users (in other words,
people often make their tour plans by themselves). However, eachuser usually has insufficient knowledge about the destination and,accordingly, he/she is forced to estimate the value of eachcomponent and time necessary for enjoying it, as well as thetravel time between these components, based on the limited
amount of knowledge and experience. This task may be
acceptable if the destination has only a small number of POIs
within a walkable distance, but rapidly becomes difficult as thedestination has larger number of POIs in a broader area. For
instance, making the best one-day tour plan in Tokyo is anextremely difficult problem with countless number of possibilities.
This justifies the significance of assistance service of tour
planning by experts.
Of course, human experts, such as staffs at tourist information
offices and hotel concierges, can assist individual tourists for tour
planning. However, human resources of these experts are limited,
especially during such busy hours as morning, and open hours ofsuch services are also limited. In addition, people may feel
reluctance to communicate with these human experts in foreign
languages. Such situations motivate the development ofcomputer-aided tour planning service, as it is expected to support
tourists at any time at anywhere in the world without human cost.
Another unique feature of tour planning as a design activity is thatusers are often not well-aware of their own interests/requests atthe beginning. This is simply because tourism is an activity thatusually takes place at unfamiliar environments and people do notknow what they can do at the destination. In this sense, tour
planning should be distinguished from such online services as
shortest-path finders and transit search tools where the users

usually recognize their own interests/requests before use. Thus,
we consider that tour planning service should have a certain
educational aspect, providing appropriate amount of knowledge
about the destination and promoting the users to find their own
requests. Such knowledge enlightenment may also help the users
at the destination if they want to rearrange their tour plan.

3. HISTORY OF CT-PLANNER
CT-Planner has a long history of improvement. It has its root on
Kurata’s preference-based tour planner [11], which asks its user toanswer fifteen pairwise comparison questions to deduce his tour
preference, and derives the best-scored personalized plan using a
greedy optimization algorithm. In this system, however, the user
is not allowed to modify the recommended plan. Thus, in order toachieve interactive tour planning, the first CT-Planner wasdeveloped [12]. The first CT-Planner allows such user requests as
POI addition/removal. In addition, it has a plan-comparing
function—it simultaneously shows two tour plans with different
focuses, asks the user to select a preferable plan, and deduces
his/her preference in a stepwise manner.
CT-Planner2 [13] has both a plan-comparing mode and a singleplan mode. In addition, it provides a manipulatable radar chart
which represents preference model. In its user test, most users
actually preferred the single-plan mode and manipulated his
preference model by themselves. Thus, the plan-comparing
function is no longer succeeded to the later versions.
CT-Planner became a web-based application since CT-Planner3[14]. CT-Planner3 adopted a genetic algorithm for plan generation.
Since we expected that this algorithm requires heavier
computation than our previous greedy algorithm, we made two
versions for experiment: a server-client version in which the plan
generation is conducted on the server side, and a client-alone
version. In our experiment, the response speed of the client-alone
version was unexpectedly better than that of server-client version,even on mobile devices with low computation power. Thus, theclient-alone model is succeeded to the later versions.
CT-Planner4 [15] supported multiple destinations and multiple
languages. In order to increase the number of our destinations, wedeveloped a macro-enhanced Excel template for authoring
destination data.

Figure 4. Proposal of an alternative route in the last part of
the trip [20]4. CT-PLANNER5This section introduces the latest version of CT-Planner (CTplanner5) from a viewpoint of its user interface.
Figure 3 shows the initial screen of CT-Planner5. Here you are
asked to select your destination and your favorite travel style.
Currently CT-Planner supports 23 destinations in Japan, among
which six has English contents as well. These destinations includei) large cities, which presumes the use of public transportations
for sightseeing (e.g, Sapporo, Yokohama, Nagoya, Kobe, andFukuoka), ii) subparts of Tokyo megalopolis, such as Shinjuku
and Shibuya, which are typical units of a day trip in Tokyo, iii)
walkable-size rural destinations such as Shikine-jima Island andIkaho Hot Springs, and iv) even facility-scale destinations such as
Hongo Campus, University of Tokyo. The favorite travel styles
you can select are the following five: anything welcome, city
stroller, relaxed traveler, locality-oriented, and with children. We
adopted these five styles based on the result of our previous GPSassisted survey on foreign tourists visiting Tokyo [2].

In addition to the above versions, there were also several extended
experimental versions of CT-Planner. For instance, Shimada et al.
[25] revised CT-Planner4, such that it suggests the modificationof user preference parameters based on his/her POI requests(Figure 3). Nakamura et al. [20] developed another extension of
CT-Planner4, in which the user is proposed an alternative route in
the last one-third part of his trip (Figure 4). This idea is motivatedby our finding that people often deviate from their original plan
made with CT-Planner, especially at the last part of their tour.

Figure 3. Proposal of modification of a user preference model
based on his POI requests [25]Figure 5. Initial screen (English version)

Figure 6. Main screen
Imagine that you select Yokohama and With Children as yourdestination and travel style, respectively. When you click theStart Planning button, you will see the main screen like Figure 6
in a few seconds. The main screen shows the route of an initialplan on the map, together with its itinerary on the right end. The
initial plan is generated based on the travel style you haveselected and typical tour conditions among Yokohama visitors(four-hour stay, starting from a west-side station, and ending at aneast-side station).
The left end of the main screen shows your tour conditions andtour characteristics (see Figure 7 for detail). When you update any
element on them, the displayed tour plan is revised immediately.
The tour conditions consist of five items: duration, start time, dayof the week, walking speed, and degree of difficulty to walk. The
tour characteristics represent a model of user preference andconsists of two parts: focus and taste. Focus refers to the tour’sfunctional features that the user wants. It is represented by amanipulatable radar chart with five axes: entertainment, culture,
shopping, art, and nature. If you put more weight on culture, forinstance, your tour plan will visit more museums. Taste refers to
the tour’s emotional characteristics that the user wants. We
considered four types of taste, each represented by two poles;
less-known or well-known, calm or energetic, local or universal,
and for adult or with kids. If you move the top slider to the rightend (labeled well-known), for instance, your plan will visit
popular places more likely. Note that the initial value of eachparameter is determined based on your initial selection of favorite
travel styles, such that the user do not worry about the settings ofthese parameters at the beginning. These initial values arepredetermined by us.

Figure 7. Tour conditions and characteristics
The map shown on the center (Figure 6) is depicted with the aidof Google Maps JavaScript API. Accordingly, you canzoom/scroll the map and even see the isometric view of satellite
images, which is nice to understand the tour route (Figure 8). If
you click the name tag of a POI on the map or the satellite image,an information window opens (Figure 9). This window shows thePOI’s name, estimated value for the user, description, hyperlinks
to the related websites (if available), open hours/days, staying
time, photo, scores on the five items, and several buttons. If you
click Visit button, the system generates tour plans which visit this
POI as long as possible. Conversely, once you click Avoid button,
the system no longer shows the plans that visit this POI.
Start/Goal button allows you to set this POI as the tour’s
start/goal location. Finally, +10/-10 button allows you to adjust
the staying time of this POI. Note that CT-Planner5 does not force
the users to select all POIs they want to visit—instead, they canentrust the selection of POIs to the system other than those with
their visit requests.

When you get satisfied with your plan, you can print it or send it
to your smartphone via a two-dimensional barcode (Figure 10).
Our plan viewer for smartphones shows your plan and yourcurrent location on the screen. In addition, this viewer can show
you the latest transportation schedule with the aid of Yahoo
Transit.

Figure 10. Linkage to our plan viewer for smartphones

5. UNDERLYING MECHANISM
Figure 8. The route of a recommended tour plan displayedover an isometric satellite image

CT-Planner5’s mechanism for generating tour plans is similar tothat of CT-Planner4 [15]. Here we briefly describe its essence.
Basically, CT-Planner5 estimates the value of individual POIs foreach user, and calculates the most efficient plan under given
constraints that maximizes the sum of the estimated values of the
POIs to be visited in the tour.
The value of each POI is estimated from the matching between
the POI’s characteristics and the current setting of the tour
characteristics that the user wants. The previous versions of CTPlanner considers simple weighted sum of the scores in five focus
categories (i.e., culture, entertainment, nature, art, and shopping),
but this method is advantageous to the POIs with balanced scores.
Thus, CT-Planner5 modifies this method, such that the POI with ahigh score in a specific category is highly evaluated if it matcheswith the user’s interest. Note that the estimated value of a POI is
replaced to zero if the user wants to avoid this POI, and to a very
high value if he wants to visit it.

Figure 9. An example of information window
The design of CT-Planner5 presumes the following interaction
cycle between the user and the system:1.

the user examines the displayed tour plan, as well as the
POIs on/off the route,2.

the user gives certain feedback to the system (modificationof tour conditions, modification of tour characteristics, orvisit/avoid request), and3.

the system revises the plan accordingly and displays it.

This cycle is repeated until the user gets satisfied with thedisplayed plan. This cyclic interface relieves its user from theburden of specifying their requests at the beginning of planning[12].

The calculation of the most efficient plan under given time andstart/goal constraints is formalized as a selective travelingsalesman problem [16]. Since this is a NP-hard combinatory
optimization problem, we adopt a genetic algorithm (GA) for
deriving semi-optimal solutions in a short time. In this algorithm,
each tour plan is regarded as a gene (i.e., a series of symbols, each
representing a POI). We randomly generate thousands of initial
genes and then simulate evolution (crossover and mutation) andsurvival competition over a large number of generations. In eachcompetition, the plans with higher evaluation survive more likely(but not always). This process eventually leads to the generationof superior plans, although the optimality of the final plan is notguaranteed. A nice point of this algorithm is that we can easily
support open/close hours of POIs, and even temporally-changing
values of POIs, only by adjusting the evaluation function of eachplan.
Of course, the above method still has a room for improvement. In
reality, the plan’s value may be affected by the combination andorder of POIs to be visited. We should also consider reciprocal
effects—if people visit similar POIs repeatedly, they may getbored, or conversely, they may get more satisfaction. How toincorporate such effects into the model is our future question.

6. USE OF LOG DATA
CT-Planner5 records all operations and plans by each user. Thislog will be useful for navigating the same user at the destination.
Our previous experiment shows that our users often deviate fromthe plan they have made, because the interaction with CT-Plannerhelps them to identify the interesting POIs near their route. Thisimplies that not only the final plan they have made, but also thelist of the POIs they have checked are useful for a smart on-site
navigation.
The user log of CT-Planner5 is also useful from a viewpoint ofdestination marketing, if it is analyzed statistically. We aredeveloping CT-Planner5 log analyzer powered by Google
Analytics (Figure 11) to examine the following statistics:-

the number of accesses,-

average tour durations,-viewing rate of each POI (i.e., how much percentage of users
have opened its info-window),-

adopting rate of each POI (i.e., how much percentage ofusers have made the tour plans that visit it), and-appearance of POI pairs (i.e., which POIs are often listed
together in user-generated tour plans),

as well as conducting data-mining analyses for discovering
unique tour plans. A nice point is that we can examine suchstatistics for each user group (genders, ages, countries, languages,
and interests) and compare them with the aid of Google Analytics.
Such information will help DMOs to examine their promotion
strategies, as well as travel agencies to design their package tours[9]. Another nice point is that, not like ordinary questionnairebased tourists surveys, we do not have to pay any extra cost tocollect data from customers.

Figure 12. CT-Planner5’s template for destination data
Once the table is completed, its macro program computes theroute between every pair of the POIs listed in the table, in orderfor CT-Planner5’s main program to reduce the time for generating
tour plans. Our template has several modes for the routecalculation. In walking-only mode and driving mode, the user canselect either Google Directions API or Map Quest API (using
OpenStreetMap data) as a route calculating engine, considering
the coverage area of these two services. In public transportationmode, the program uses both Google Directions API and Yahoo
Transit, because Google Directions API does not provide theroutes that use public transportations in Japan, while Japaneseversion of Yahoo Transit provides such routes together with theiraverage travel time, but not with their geometrical shapes. In
public transportation mode, the walking route between a pair of
POIs is calculated only if the straight distance between them isless than 1.5 km, while the possibility of public transportation use
is sought if the distance is more than 1km. By this mechanism, thenumber of API requests is considerably reduced. Normally thisroute calculation process takes about ten minute to one hour.
We have tested the above macro-enhanced template with several
groups, including students, professionals, and local people.
Through the collaboration with them, we learnt that the
involvement of various people in data creation is effective not
only for expanding CT-Planner’s coverage, but also for enriching
the diversity of our service. We are, therefore, seeking further
possibility of user participation, by introducing a web-based CTPlanner’s data editor, with which people can create destination
data in a collaborative manner.

8. CONCLUSION AND FUTURE WORK

Figure 11. Prototype of CT-Planner5 Log Analyzer

7. AUTHORING DESTINATION DATA
We made a macro-enhanced Excel template with which people
can easily make a destination data for CT-Planner (Figure 12). On
this template the user input for each POI the name, description,
type, URLs of related websites, URL of photo data, open
days/hours, and scores for the nine criteria (Section 4). It
normally takes a few hours to complete the table for a typicaldestination with thirty to sixty POIs.

This paper reports the latest version of our web-based tour
planning service, CT-Planner5. It promotes the collaborative
design of tour plans by the user and the system through its cyclic
interaction. In addition, this system now equips with sub-systems
for viewing the plans for smartphones, and analyzing user logs.
In Japan, the number of tourists from abroad increases rapidly due
to the nation’s policy toward a tourism-oriented country, as wellas rapid economic growth in East Asia. The interests of visitors toJapan are much more diversified than those of domestic tourists
who have dominated our market for long. As a result, Japanese
tourism industry is facing two critical problems: how to know theinterest of inbound visitors and how to serve them appropriately
and efficiently based on such knowledge. We expect that CTPlanner will contribute to the tourism industry under this situation.
Firstly, CT-Planner serves as a virtual consultant of tour planning.

Since CT-Planner is an online service, it allows inbound tourists
to consult their tour plans at any time at anywhere, without paying
human cost. Secondly, CT-Planner will serve as a survey tool,with which we can know the interest of inbound tourists. In orderto achieve these two goals, we are now working on the translationof contents into English, Chinese, and Korean languages, as wellas the enrichment of destinations supported by CT-Planner5.
Figure 13 shows our future vision. We expect that CT-Plannerbasically serves individual tourists who are relatively advanced in
the sense that they actively collect tourist information bythemselves. In exchange for providing planning service to them,
we will obtain their planning log, from which we can analyze
their demand and emerging trend. We will provide the result of
our analyses to travel agencies and DMOs. This information will
be useful for providing more attractive tour merchandise and/or
information service to tourists in general. This will motivate thoseorganizations to cooperate with us and provide their destination
data. To realize this virtuous cycle is our future challenge.
CT‐Planner 5Marketing Data

Tour Planning Service
User Log

Relatively‐Advanced
Tourists

Up-to-date Destination Data

Travel Agencies
DMOs

More Attractive Tourism
Merchandise/Information(More Profit)

General Tourists

Figure 13. Future vision of CT-Planner

10. REFERENCES[1] Abras, C., Maloney-Krichmar, D., Preece, J., 2004. UserCentered Design, Encyclopedia of Human-Computer
Interaction, Thousand Oaks: Sage Publications.
[2] Aratani, K., Shimada, S., Ota, J. and Hara, T. 2012.
Classification of inbound tourist activities using GPS log
data toward service innovation. 12th Japan-Korea Design
Engineering Workshop, 28-31[3] Barki, H., Hartwick, J. 1994. User Participation, Conflict,and Conflict Resolution: The Mediating Roles of Influence.
Information System Research, 5, 4, 422-4384.
[4] Carroll, J. 2004. Completing Design in Use: Closing theAppropriation Cycle. European Conference on Information
Systems (Turku, Finland, June 14-16, 2004). CD-ROM.
[5] Chen, L., and Pu, P. 2009. Interaction Design Guidelines on
Critiquing-Based Recommender Systems. User Modelingand User-Adapted Interaction, 19, 3, 167-206.
[6] Garcia, A., Arbelaitz, O., Linaza, M., Vansteenwegen, P.,
and Souffriau, W. 2010. Personalized Tourist RouteGeneration. Current Trends in Web Engineering (Vienna,
Austria, July 5-6, 2010), CWE 2010 Workshops, Lecture
Notes in Computer Science 6385, 486-497.
[7] Goy, A., and Magro, D. 2004. STAR: a Smart Tourist
Agenda Recommender. Workshop on Configuration(Valencia, Spain, Aug. 2004). ECAI 2004, 8/1-8/7

Furthermore, we envision the application of CT-Planner totourism-oriented community design. Tourism is a key to achieve asustainable community development in both economic andcultural aspects. Meanwhile, it has been difficult for local people
to know how visitors evaluate their region and what thecommunity should improve it from the viewpoint of tourism. Now
the user log of CT-Planner, as well as user-posted contents in suchSNSs as Trip Advisor, Twitter, and Flickr, are available for thevisualization of visitors’ activities and evaluations. Suchvisualization will serve as a useful material for the local people todiscuss the future of their community by themselves. In order todemonstrate its potential, we are currently working with several
local communities in Japan, where we have created CT-Planner’s
destination data with local people, started planning service online(and eventually offline at information kiosks), and kept collecting
the log data from various users for future analysis.

[8] Hara, T., and Arai, T. 2013. Design-of-use and design-in-use
by customers in differentiating value creation. CIRP Annals Manufacturing Technology, 62/1 (ISSN 1660-2773), 103106.

Another future challenge for us is to develop a smart on-site
navigation tool, which works in combination with CT-Planner.
We consider not a virtual tour conductor, as seen in [19], which
controls the user to follow his tour plan, but the one that supports
his flexible trip, taking his original plan and planning activity into
account. Then, by analyzing the difference between the originalplan and the actual behaviors recorded by this tool, we want topursue deeper understanding of tourists.

[12] Kurata, Y. 2010. Interactive Assistance for Tour Planning.
Spatial Cognition VII (Mt. Hood, OR, Aug. 15-19, 2010).
Lecture Notes in Artificial Intelligence 6222, 289-302.

9. ACKNOWLEDGMENTS
This work is supported by JST RISTEX’s Implementation
Support Program (Project Title: Platform Foundation and Social
Implementation of Computer-aided Tour Planning Technologies
That Contribute the Harmony of Tourists and Communities).

[9] Hara, T., Shimada, S., Yabe, N., Kurata, Y., Aoyama, K. and
Hompo, Y. 2012. Value Co-Creation in Tourism:
Incorporating Non-Expert's Design into Expert's Design
Activities. 1st International Conference on Human Side ofService Engineering (San Francisco, CA, July 21-15, 2012).
[10] Hippel, V. E. 2005. Democratizing Innovation, London: MIT
Press.
[11] Kurata, Y., Sadahiro, Y., and Okunuki, K. 2000.
Development of a Preference-Based Tour Planning System.
9th Conf. of GISA Japan (Tokyo, Japan, Oct 2000). 199-202(in Japanese).

[13] Kurata, Y. 2011. CT-Planner2: More Flexible and Interactive
Assistance for Day Tour Planning. ENTER 2011 (Innsbruck,
Austria, Jan. 26-28,2011). 25-37.
[14] Kurata, Y. 2012. CT-Planer 3: An Interactive Support Tool
for Tour Planning on the Web. International Journal of
Tourism Science 5, 159-165 (in Japanese).
[15] Kurata, Y. and Hara, T. 2014. CT-Planner4: Toward a More
User-Friendly Interactive Day-Tour Planner. ENTER 2014
(Dublin, Ireland, Jan. 21-24, 2014), 73-86, Dublin, Ireland.
[16] Laporte, G., and Martello, S. (1990). The Selective
Travelling Salesman Problem. Discrete Applied Mathematics,
26, 2, 193-207.

[17] Lee, J., Kang, E., and Park, G. 2007. Design and
Implementation of a Tour Planning System for Telematics
Users. ICCSA 2007 (Kuala Lumpur, Malaysia, Aug. 26-29,
2007). Lecture Notes in Computer Science 4707, 179-189.
[18] Linden, G., Hanks, S., and Lesh, N. 1997. Interactive
Assessment of User Preference Models: The AutomatedTravel Assistant. 6th Intl. Conf. on User Modelling (Chia
Laguna, Sardinia, Italy, June 2-5, 1997), 67-78.
[19] Maruyama, A., Shibata, N., Murata, Y., Yasumoto, K., andIto, M. 2004. A Personal Tourism Navigation System toSupport Traveling Multiple Destinations with Time
Restrictions. Advanced Information Networking andApplications 2004 (Fukuoka, Japan, Mar. 29-31, 2004). 2,
18-21.
[20] Nakamura, Y., Shimada, S., Ogata, T., Kurata, Y., Ota, J.
and Hara, T. 2014. An Interactive Tool for Tour Planning
Considering Adaptive Behaviors. JSPE Spring Conference
2014 (Tokyo, Japan, Mar. 18-20, 2014). CD-ROM (inJapanese).
[21] Pilemalm, S., Timpka, T. 2008. Third generation
participatory design in health informatics--making user
participation applicable to large-scale information systemprojects, Journal of Biomedical Informatics,
doi:10.1016/j.jbi.2007.09.004, April 2008.
[22] Robey, D., Farrow, D., 1982. User Involvement inInformation System Development: A Conflict Model andEmpirical Test. Management Science, 28, 1, 73-85.

[23] Schaller, R. 2011. Planning and Navigational Assistance forDistributed Events. 2nd Workshop on Context Aware
Intelligent Assistance (Berlin, Germany, Oct. 2011) 44-56.
[24] Seifert, I. 2008. Collaborative Assistance with Spatiotemporal Planning Problems. Spatial Cognition V (Freiburg,
Germany, Sep. 15-19, 2008). Lecture Notes in Computer
Science 4387, 90-106.
[25] Shimada, S., Ogata, T., Ota, J., Hara, T., and Kurata, Y.
2014. A Support for Design of Use in Consideration of Use
Phase. 13th International Design Conference (Dubrovnik,
Croatia, May 19-22, 2014). 995-1004.
[26] Souffriau, W. and Vansteenwegen, P. (2010) Tourist Trip
Planning Functionalities: State-of-the-Art and Fitire. Current
Trends in Web Engineering (Vienna, Austria, July 5-6, 2010).
ICWE 2010 Workshops, Lecture Notes in Computer Science
6385, 474-485.
[27] Tseng, M. M., J. Jiao and M. E. Merchant 1996. Design for
Mass Customization. CIRP Annals - Manufacturing
Technology, 45, 1, 153-156.
[28] Yang, B., and Burns, N. 2003. Implications of postponement
for the supply chain. International Journal of Production
Research, 41, 9, 2075-2090.
[29] Yanagisawa, H., Fukuda, S., 2003, Interactive Design
Support System by Customer Evaluation and Genetic
Evolution: Application to Eye Glass Frame. 7thInternational Conference on Knowledge-Based Intelligent
Information & Engineering Systems (Oxford, UK, Sep. 3-5,
2003), Lecture Notes in Computer Science 2774, 481-487.