Interactive Assistance for Tour Planning
Yohei Kurata
Department of Tourism Science, Tokyo Metropolitan University
1-1 Minami-Osawa, Hachioji, Tokyo 192-0397, Japanykurata@tmu.ac.jp

Abstract. It is often difficult for individual tourists to make a sightseeing tour
plan because they do not have prior knowledge about the destination. Althoughseveral systems have been developed for assisting the user’s tour planning,these systems lack interactivity, while demanding a lot of data input from theuser. In this paper, we introduce a new computer-aided tour planning system,
called CT-Planner, which realizes collaborative tour planning. The systemprovides several tour plans with different characters and asks the user to give
feedback. The feedback is utilized by the system for inferring the user’s
preferences and then revising the tour plans. This cycle is repeated until the user
is satisfied with the final plan. Thanks to this cycle the user does not have toregister his profiles in advance. In addition, the system allows the user tospecify his special requests, which leads to a more satisfying experience ofcomputer-aided tour planning.
Keywords: spatial assistance system, tour planning, personalization, user’s
preference, human-computer interaction, selective travelling salesman problem

1

Introduction

Thanks to the popularization of online reservation systems, it has become much easierfor people to arrange their transportation and accommodation by themselves.
However, it is still difficult for individual tourists to make a sightseeing tour plan, as
they often do not have prior knowledge about the destination. Some guidebooks
kindly show typical tour plans, but the problem is that attractive plans differ fromperson to person due to a large variety of tour preferences. Of course, this problem is
not so serious when the destination is a small city or village where possible activities
are limited. However, if people make a short visit to a large city with various POIs
(points of interest), it becomes a very difficult problem for them to find an efficient
tour plan (i.e., the plan that gives high satisfaction to the tourist in a short time) from alarge possibility of tour plans.
To save individual tourists from such difficulty, several computer systems forsupporting the user’s tour planning have been developed. Some systems show a list of
attractive POIs in the target area, taking the user’s tour preference into account (e.g.,[1-3]). However, even if people are informed about attractive POIs, it is still difficult

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Yohei Kurata

for them to make a tour plan without the knowledge about the spatial arrangements ofthese POIs and the transportations between them. Thus, several systems were
equipped with the ability to generate personalized tour plans [4-8]. Unfortunately, at
the moment, these systems lack interactivity (e.g., we cannot request the system toinsert certain POIs into the recommended tour plan), while demanding a lot of data
input from the user (Section 2).
In this paper, we introduce a new computer-aided tour planning system, called CTPlanner, which overcomes the usability issues in the previous systems. CT-Plannerstands for Collaborative Tour Planner (and also City Tour Planner). The key featureof this system is its collaborative planning process, which models the interaction
between a tourist and a human tour advisor. First, the system provides several tour
plans, which have different characters (nature-oriented, art-oriented, and so on).
Shown these example plans, the user gives certain feedback to the system. From thisfeedback the system infers the user’s preference and then revises the tour plansaccordingly. By repeating this process, the recommended tour plan eventually agrees
with the user’s preference. This cyclic process nicely fits the assistance to ordinary
tourists, because they usually do not have concrete requests from the very beginning,
but rather the requirements are gradually formed during the process of planning [9].
This idea was briefly introduced in our previous work [10] among a number of ideas
for future tourist information systems. Among these ideas, this paper focuses on theidea of the cyclic tour planning process. We concretize this idea in comparison withtypical interactions between a tourist and a tour advisor, develops a prototype systemthat implements the idea, and demonstrates that this idea really works.
In the spatial sciences, many researchers studied problems in route planning (e.g.,[11, 12]) and following navigation (e.g., [13, 14]). In contrast, the tour planningproblem has attracted less interest, probably because it looks a domain-specific,
application-oriented problem. Nevertheless, we believe the importance of thisproblem, because tourism is an activity that ordinary people have to handle heavy
amount of spatial information to make decisions and they often need assistance fromsomebody. In addition, as an application of today’s mobile and ubiquitous
technologies, tourist assistance attracts more and more attention. In such a context,
what we should pursue is appropriate assistance for the user’s decision making, rather
than simply providing more and more location-based information. We therefore
consider that tour planning assistance will be one of the key topics for future researchon spatial information systems.
The remainder of this paper is structured as follows: Section 2 gives a review ofprevious tour planning systems. Based on this review Section 3 discusses desirable
designs of the systems. Section 4 introduces our implementation and its technicaldetail. Section 5 demonstrates how a user can make a tour plan in our system. Section
6 discusses the remaining issues toward more intelligent tour planning assistance.
Finally, Section 7 concludes with a discussion of future problems.

Interactive Assistance for Tour Planning2

3

Previous Tour Planning Systems

Most previous tour planning systems take similar three-step approaches for generatinga tour plan. First, the system profiles the user’s tour preference. Second, the systemevaluates all POIs in the target area, considering the user’s preference. And lastly, thesystem generates the ‘best’ tour plan, considering the calculated values of the POIs
and given time/origin/goal constraints.
Profiling the user’s preference is a problematic process. Early decision-support
systems ask the user to set several preference parameters manually, for instance, bysliders (e.g., [4, 15]). This interface often frustrates the user, since he is forced toevaluate his own preference under given artificial criteria. To make the preference
setting interface more user-friendly, Kurata et al. [5] introduced a questionnaire-based
approach. In their system, the user is given fifteen questions, each showing two tour
purposes and asking the user’s preference on them (Fig. 1). Then, from the answers tothese fifteen questions, the system calculates quantitative weights to ten tour purposes(Fig. 2) using AHP [16]. The problem of this approach is the time taken to answer
fifteen questions [5]. To realize quicker user profiling, some systems ask the user to
input his demographic data (age, gender, occupation, etc.) and calculates his
preference parameters, assuming that tourists with demographically similar properties
have similar tour preference [1, 8]. However, this approach also has a risk to annoy
some users, giving an impression that the system offends their privacy and has astereotyped view of their preferences. As an alternative approach, P-Tour [6] asks theuser to evaluate the POIs by himself, from which the system generates the tour plan.
This approach allows the user to specify his request directly and to skip the
problematic profiling process, but it is hard work to estimate and input theattractiveness of all POIs in the target area.

Playing and Exercising

Resting andRegaining Your HealthFig. 1. Questionnaire-based preference profiling in [5]Tour Purposes
Relaxing
Play andExercise

HealthEnhancement

Food and
Shopping

Social Needs
Viewing
Sceneries

Cultivate
Friendship

Visit Famous
PlacesSelf‐Actualization
SocialExperience

Enlarge One’s
Knowledge

Experience
New Things

Regional
Study

Fig. 2. Structure of ten tour purposes that forms the foundation of the user’s preference modelin [5]

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Yohei Kurata

Once the user’s tour preference has been profiled, the system estimates the value ofeach POI for the user. The value may be calculated from the matching between theuser’s preference and the POI’s chacter, or from the evaluation of this POI by othertourists with similar properties. In [5], each POI is scored by ten criteria, whichcorrespond to the ten tour purposes in Fig. 2. Thus, by summing the scores of the ten
criteria while applying the corresponding weights, the value of each POI for the useris calculated.
Finally, the system computes the ‘best’ tour plan. In the previous systems, the best
tour plan is usually considered to be the plan that maximizes the total values of POIs
to be visited during the tour. This problem is represented mathematically as follows(see also the illustration in Fig. 3):
Given a complete graph , , the utility of each node , the visitation time spentat each node
, the travel time between two nodes, origin node
∈ ,goal node∈ , and time constraint , find a series of nodes to be visited,…,∈that maximize the sum of utilities ∑under the following
three constraints:

Here each node represents a POI or a transportation node (e.g., a train station). The
utility of a POI-node is given as the POI’s value for the user, while the utility of atransportation node is set zero. The link and traveling time between each node pair
may be computed beforehand in a GIS using the shortest path algorithm. On the other
hand, visitation time spent at each POI-node has to be specified manually by experts.
From a computational point of view, this problem is essentially the Selective
Travelling Salesman Problem (STSP), which is known to be an NP-hard combinatory
optimization problem [17]. Thus, the previous systems have adopted approximate
methods, such as a genetic algorithm [7], to derive semi-optimal solutions in apractical time.
V3

V1

V4
utility 50
30 min10 min20 minOrigin30 min45 minV2utility 40
20 minutility 30
40 min30 minutility 70
50 min

V6

Goal

V5
T = 120 min

Fig. 3. Illustration of the choice of the ‘best’ tour planInteractive Assistance for Tour Planning

3

5Interactive Tour Planning

Seifert [9] pointed out that the main disadvantage of personalized tour generation isthat it excludes the user’s participation in the process of planning. Indeed, according
to the user test in [5], users complained about the inability to customize therecommended tour plans by adding or removing POIs which they want to visit/avoid.
The tour planning systems in [1, 8] also have the same problem. Exceptionally, PTour [6] allows the user to express where he wants to visit/avoid by assigninghigh/low scores to the POIs. However, P-Tour forces the user to spend a lot of time toevaluate all POIs in the target area.
How can we avoid these problems? We can glean some hints from typicalinteractions between a tourist and a tour advisor. For instance, let us imagine thefollowing conversation:“I’d like to look around Yokohama in three hours or so.”“Three hours. Okay, then, this is the most popular plan.”“Well, hmm, looks like lots of museums in it. Honestly, I don’t have much interest
in museums…”“I see. Then, how about this plan? This visits more scenic sites along the port. Or,
how about this plan visiting Sankei-en Park. You can enjoy lots of cherry trees in fullbloom right now.”“Hmm. For me, visiting portside scenic points sounds better. Well, but, I’d also
like to visit the Port Museum. It’s very famous, isn’t it?”“Yes, I definitely recommend it. So, how about this plan? First, you go …”
Usually a tour advisor does not know the interest of a tourist at the beginning of
their conversation; the advisor learns the tourist’s interest from the conversation. The
tourist’s response to recommendations indicates something about his preference. In
many cases, the tourist is also not well aware of his interest at the beginning. He
develops his interest while viewing the actual plans recommended by the advisor.
Thus, experience advisor introduces several plans in a smart order, so that the tourist
becomes aware of his own interest and able to judge what plan fits him.
Our system imitates this process; that is, we adopt the cycle where the systempresents a set of plans to the user, infers the user’s preference from the user’s
response, and then generates a new set of plans. This cycle for computer-assisted
planning is called the candidate/critique model [18]. Although this cycle takes a bit oftime to reach a final plan, the user probably attains a high level of satisfaction with theplan, as he can experience that his involvement directly results in the improvement of
the plan. Probably the user would not attain such satisfaction, even if the smart system
could derive the truly best solution in a single step. Another merit of this cycle is thatthe user does not have to specify his preference in advance. The user can discover his
preference and requests through the comparison of actual plans.
Seifert [9] discussed a similar cyclic model of computer-aided tour planning. The
difference from her approach is that our system intentionally generates several plans
with different characters in order to seek the user’s tour preference, while her system
simply generates the solutions—possibly many—that satisfy given constraints.

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Yohei Kurata

In the previous conversation, the tourist said that he did not have an interest inmuseums, while he requested to visit the Port Museum. Like this example, a tourist
sometimes wants to visit a POI of the sort that he usually avoids. Conversely, hesometimes wants to skip a POI of the sort that he/she usually visits. For instance, atourist, who usually likes modern art but not historical monuments, may request to
visit the Arc de Triomphe because it is world-famous, or not to visit Musée d'Orsay
because he has been there before. Computer-aided tour planning systems, therefore,should be able to support such case-by-case requests, just like a human tour advisor
can do it.

4

CT-Planner

Based on the design concepts discussed in Section 3, we developed a new computeraided tour planning system, called CT-Planner (Collaborative Tour Planner). As thisname indicates, the system aims at the assistance of user-driven tour planning, rather
than computer-based tour optimization. Currently the system is a stand-alone
application for Windows PC, but we are also planning its server-client version thatcan be accessed from mobile devices.
Fig. 4 shows the main screen of CT-Planner. It shows two tour plans. The plan on
the left is a recommended plan, which is computed based on the current model of theuser’s tour preference. The plan on the right is one of alternative plans. We have six
alternative plans, namely 30min-shorter plan, 30min-longer plan, more-education
plan, more-art plan, more-nature plan, and more-experience plan. The user canbrowse different alternatives by clicking tabs above the map and, if he wants, he canselect the alternative plan that looks more attractive than the recommended plan
shown on the left. Based on this selection, the time constraint or the model of theuser’s preference is updated and accordingly, all plans, including alternative plans, arerevised under the new conditions. From the user’s viewpoint, it looks that thealternative plan, if it is selected, moves to the left and becomes the next recommended
plan. Note that we designed to display only two tour plans each time, following the
key idea of AHP [16] that paired comparison is much easier than selecting one
preferable plan from a number of alternatives.
If the user clicks the name tag of a node, its properties are shown in a new window(Fig. 5). In this window, the user can specify his request with regard to this node; thatis, whether he wants to set this node as the origin/goal of the tour or, if it is a POInode, whether he wants to visit/avoid this POI. The request yields the revision of all
tour plans, including alternative plans. Once the user specifies a request, the system
accommodates this request until he cancels it, no matter if he selects anotheralternative plan or changes the tour conditions afterwards.
Through repeated selection of preferable tour plans and/or specification ofrequests, the recommended plan will eventually become the plan that follows theuser’s preference and requests. Thus, when the user agrees with the recommended
plan, the system prints out the plan and ends the process.

Interactive Assistance for Tour Planning7

Our setting is similar to the setting of multi-criteria decision analysis (MCDA)[19], where the user is asked to select the best plan from multiple alternative. MCDA
provides several techniques, such as AHP [16], to assign quantitative weights ongiven criteria. In our setting, education, art, nature, experience, and popularity
correspond to the criteria in MCDA. Thus, we can alternatively apply MCDA
techniques to decide the weights on five criteria. Actually, our previous system
adopted AHP to calculate such weights [5]. We, however, did not apply AHP in thecurrent CT-Planner, because under AHP the user has to compare the criteria
themselves, which can be a burden for the user. Instead, we proposed a new techniqueto estimate the weights on the criteria indirectly through a cyclic process, in which theuser can concentrate the comparison of actual plans.

Fig. 4. The main screen of CT-Planner, which shows a recommended plan on the left and oneof six alternative plans on the rightFig. 5. A window showing the properties of a POI-node, in which the user can specify his
request about this node

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Yohei Kurata

4.1

User’s Preference Model

In CT-Planner, the user’s tour preference is modeled by a five-dimensional unit vector(wedu, wart, wnat, wexp, wpop), called the preference vector. Items wedu, wart, wnat, and wexp
represent the user’s weights on four types of tourist attractions—education, art,
nature, and experience. This idea is similar to that of a conceptual vector space [20],in which instances of a concept are represented as points in a vector space. Raubal[20], for instance, represents building façades in a 7D space and considers the
distance between instances to see the distinctiveness of each façade from others.
Similarly, it is possible in our framework to evaluate the distinctiveness and similarity
of tourists using our 5D conceptual vector space.
Initially, the weight is assigned only to popularity; i.e., (wedu, wart, wnat, wexp, wpop)= (0, 0, 0, 0, 1). Naturally, the initial plan on the left becomes a ‘standard’ plan, which
visits popular POIs as much as possible. If the user selects more-education plan,more-art plan, more-nature plan, or more-experience plan, the preference vector
rotates toward the corresponding axis by θ degrees (Fig. 6). Such rotation is repeated
if the user selects these plans again. In our current prototype, the parameter θ isinitially assigned 45 degrees. If the user selects the recommended plan shown on theleft, θ is reduced, because this selection means that all alternative plans are too biased
for the user. Finally, if the user selects 30min-shorter plan or 30min-longer plan, thepreference vector does not rotate.
Weight on Popularity

Weight on Popularity“More Education” Plan
is Selected

Weight on
Education
Weight on
Artθ

Weight on Popularity“More Art” Plan
is Selectedθ

Weight on
Education
Weight on
Art

Weight on
Education

Weight on
Art

Fig. 6. Adaptation of user’s preference model (here only three of five criteria are shown)

The reason why we adopted the user’s weights on four types of tourist attractions,wedu, wart, wnat, and wexp, for representing the user’s preference is that people often
express their tour preference in terms of the type of tourist attractions they like. In thispaper, we considered that education, art, nature, and experience are the four
representative types of tourist attractions, which are highly independent from eachother. There are other possible classifications—say, nature, culture, and complex, as
used in the classification of UNESCO’s world heritage sites, or leisure andsightseeing as used in many travel guidebooks. However, we should avoid detailed
classification (say, more than five types), because under such classification we haveto provide many tour plans for the user in each cycle, which would overwhelm theuser (Section 4.3). Note that we further added one item wpop, which represents theweight on ‘popularity’, because people typically prefer to visit popular POIs when
they do not have any specific request.

Interactive Assistance for Tour Planning

4.2

9

Evaluation of POIs

Each POI has five-grade scores in five criteria—satisfaction level of education,satisfaction level of art, satisfaction level of nature, satisfaction level of experience,and popularity—which are determined by experts in advance. In other words, each
POI is represented in another 5D conceptual vector space [20].
The value of the POI for each user is calculated from the total of these scores withthe user’s weight applied on the corresponding element. This value is calculated every
time the user’s preference model is updated.
On the screen (Fig. 4), the value of each POI is shown with one to three stars,
instead of actual scores, such that the user easily figures out whether the POI isinteresting to him or not.
4.3

Generation of Plans

For the generation of the recommended tour plan, we adopted the approximate
algorithm by Kurata et al. [5]. This algorithm works in O
, where is thenumber of POIs in the target area and is the length of tour time. With this algorithm,
an ordinary PC generates a semi-optimal tour plan almost instantly. We believe thatthe optimality of the solution is not a serious problem in our application, since theplan is sufficient enough if it serves a nice draft, which is eventually customized by
the user to satisfy his request in detail.
The six alternative plans are calculated by the same process but under slightly
different conditions; that is, the 30min-longer plan and 30min-shorter plans are thesemi-optimal plans under the 30 minute longer/shorter time constraint, respectively,
while the more-education plan, more-art plan, more-nature plan, and moreexperience plans are the semi-optimal plans under the preference vector tentatively
rotated toward the corresponding axis by θ degrees. As a consequence, if the user
selects the alternative plan shown on the right, then this plan comes to the left side in
the next step.

5

Demonstration

Let us observe how CT-Planner works in actual tour planning. Imagine a user who
wants to plan a tour in Yokohama (a portside city near Tokyo) starting from
Sakuragicho Station at 10:00 and arriving at Kan-nai Station before 13:00. Once theuser inputs these basic conditions, the system generates two plans (Fig. 4). As stated
before, the left plan is a popularity-based ‘standard’ plan, while the right plan is oneof six alterative plans—for instance, more-education plan. The left plan is generated
based on the preference vector (wedu, wart, wnat, wexp, wpop) = (0, 0, 0, 0, 1), while theright one is based on (.707, 0, 0, 0, .707). In order to clarify the difference of thesetwo plans, the POIs that only the alternative plan visits (in this case, Custom Museum
and Port Museum) are given red name tags on the actual screen, while the POIs thatthe alternative plan misses (in this case, Yamashita Park) are given green name tags.

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Yohei Kurata

Imagine that the user selects the more-education plan, because he likes museums.
Now the selected plan is transferred to the left side, while on the right side the systemshows the second generation of more-education plan (Fig. 7a), which is generated
based on the preference vector (1, 0, 0, 0, 0). He feels that this new plan contains too
many museums and thus, he starts browsing other alternative plans. Then, he getsinterested in more-experience plan (Fig. 7b) and selects it. Note that this planemphasizes both education and experience, since the user has selected moreeducation plan in the previous step and accordingly, this plan is generated based on
the preference vector (.5, 0, 0, .707, .5).
Now he notices that the current plan misses Port Museum where he wants to visit.
Thus, he clicks the name tag of the Port Museum to open its property window (Fig. 5)
and clicks the Visit button. Now the system shows a new plan that visits this museum(Fig. 7c). However, due to the shortage of time, the new plan now skips Akarenga
Park, which also looks attractive for him. Thus, he changes the arrival time from13:00 to 13:30. As a consequence, the plan is revised, such that it contains both the
Akarenga Park and the Port Museum (Fig. 7d). Since this plan look satisfactory forhim, he clicks “I take this tour” button to print out it and finish planning.
In this way, CT-planner allows the user to make a tour plan in an interactive
manner. Compared with the previous computer-aided tour planning systems, the user
has much freedom to customize the recommended plans by himself. In addition, theuser does not have to set preference parameters or input his demographic data for user
profiling.

(a)

(b)

(c)

(d)

Fig. 7. Evolution of a tour plan in the scenario of Section 5

Interactive Assistance for Tour Planning6

11

Toward More Intelligent Tour Planning Assistance

Although CT-Planner realizes easy and flexible tour planning, it has several
remaining problems:
1. The attractiveness of routes is not considered.
2. Temporal/seasonal change of POI values is not supported.
3. Travel time between POIs is fixed, even though it may be affected by traffic jams,
infrequent transportation service, or the user’s choice of transportation modes.
4. Similarly, expected visitation time spent at each POI is fixed regardless of users.
5. Tour plans tend to become monotonous.
The followings are potential solutions to these problems, which revise the ideas
discussed in our earlier paper [10].
An intuitive solution for Problem 1 is to assign values not only to nodes, but also tolinks. With this extension, the problem becomes the Enhanced Profitable Tour
Problem (EPTP) [21]. The algorithm for deriving semi-optimal solutions of thisproblem is already proposed in [21].
Problem 2 is critical for actual tour planning. In the conversation in Section 3, forinstance, the tour advisor told that the Sankei-en Park was nice to visit because its
cherry trees were in full bloom. There are some POIs, like parks and botanicalgardens, whose value changes by seasons. Similarly, there are some POIs, like scenic
overlooks, whose value depends on the time of a day. An extreme example ismuseums, whose values are totally lost when they are closed at night. Thus, it isdesirable that the system can support such temporal fluctuation of POI values.
Matsuda et al. [22] already tackled Problem 2 (as well as Problem 3) from acomputational point of view, introducing time-variant POI values and Fuzzified travel
time between nodes.
As for Problem 4, a tourist probably spends more time at the POIs that fit his
interest. Also, some people (perhaps older people) prefer slower tours. Thus, it isdesirable that the system can estimate the time spent at each POI by each user, forinstance, based on a certain statistical model.
Problem 5 arises from the fact that the current system evaluates POIs individually,
but not in combinations. For instance, once the system learns that the user isinterested in education, the system tends to recommend a tour plan that visits
museums for the entire day—which may be boring even for museum lovers. Thus, itwill be nice if the system can evaluate the monotony of each tour plan and utilize it for
generating tour plans.

7

Conclusions and Future WorkFor the people who travel unfamiliar places, tourist information is vital for theirdecision making. However, an overwhelming amount of information makes theirdecision rather difficult. Thus, how to customize the information for individual users
is an important issue for tourist assistance systems, which should serve a wide variety

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Yohei Kurata

of users. As an example of such systems, we introduce CT-Planner, with whichpeople can make a tour plan easily as if they are assisted by a human advisor in a
tourist information office. A nice feature of computer-aided tour planning systems, incomparison with human advisors, is that the user can consult the systems at any time,
as much as he wants, using their own language even in foreign countries.
Our system is considered an example of recommender systems, which have beenstudied extensively in AI communities and is now used in many online shopping sites[23]. A major technique used in recommender systems is collaborative filtering (CF)[24], in which the system records each user’s history of purchase or evaluation of
items, finds similar users who have similar histories, and recommends an item thatsimilar user has bought or given high evaluation. CF is particularly useful for thedomains where it is difficult to provide explicit features of items and accordingly, it isdifficult to model the user’s preference (e.g., music and literature). Another merit isthat each user does not have to report his/her preference to the system in an explicit
manner. Meanwhile, in order to manage a practical CF-based service, we need a hugeamount of past user data [23]. Unfortunately, our tour planning domain looks
different from other domains where CF works well: First, the tourist’s choice of a POI
does not mean that he/she likes this POI. For instance, a tourist who has no interest in
history may visit a historical site, in case this site is the only POI located between two
POIs where he wants to visit. This implies a risk to consider that two tourists who
have visited similar POIs have similar preferences. Second, even the same tourist may
have different preferences at different times, depending on the destination,
motivation, and companions, as found in the user survey of our previous system [5].
For instance, a tourist may prefer art-oriented tourism when she visits Paris by herself,while she may prefer nature-oriented tourism when she visits Switzerland with her
husband. Thus, it is risky to rely on the tourist’s preference model derived from
his/her past behavior. For these reasons, we avoided a CF-based approach andadopted an approach using an explicit model of user’s preference. Moreover,considering the instability of tourist’s preferences, we decided not to store the profile
of each user in the system.
We are planning a systematic user test in near future, which is necessary to
substantiate our claim that CT-Planner gives more satisfying tour planning experience
than the previous tour planning systems or guidebook-based planning. In this test, we
also want to look at psychological aspects of tour planning processes in our user test,
hopefully in collaboration with other researchers. For instance, we want to observe
each user’s interaction with the system—say, how many times he repeats the selectionof alternatives and how much he customizes the recommended plan by himself—andseek the patterns of users’ decision making. In order to conduct such survey, we arenow equipping CT-Planner with the capability of recording the user’s operations.
We are also planning to extend CT-planner, such that it works on mobile devices.
There a key question is how to provide the service tailored to those devices. For
instance, the mobile version of P-Tour [25] monitors the user’s location and warns theuser if he is off the route or behind schedule. Another idea is to modify the tour planin case of sudden rain or delay. Such schedule monitoring and real-time reschedulingfunctions are potential strengths of mobile tour planning systems. In addition, mobile

Interactive Assistance for Tour Planning

13

tour planning systems will become further useful if they are combined with otherintelligent technologies for assisting tourists, such as route-specific route instructions[26] and location-based information querying by pointing [27].
Another potential strength of mobile tour planning systems is availability of
trajectory data. Where a user visits and how much time he spends there should tellsomething about his preference [28]. Of course, a long stay in a certain POI does notnecessarily indicate his interest in that kind of POI, because he may actually takes arest at a café in the POI, for instance [2]. Thus, it is better to use microscopic
trajectory data (hopefully both indoor and outdoor data) to analyze the user’s action in
a POI and seek his interest. The idea of such trajectory-based preference detection is
also applicable to other spatial assistance systems. For instance, bike navigation
systems may learn from the trajectory what kind of routes the user prefers. In
shopping centers, data about where and how long the user spends time may be usefulfor adapting advertisements to the user. We therefore consider that trajectory-based
preference detection is an important topic for the research on spatial assistancesystems.

Acknowledgments
This work was supported by Tokyo Metropolitan University’s research fund. We
appreciate anonymous reviewers, participants of AGILE’09 Workshop on“Adaptation in Spatial Communication”, and participants of GISA-Japan research
conference 2009 for their precious comments and encouragement on our work.

References
1. Ricci, F., Arslan, B., Mirzadeh, N., Venturini, A.: ITR: A Case-Based Travel Advisory
System. In: Craw, S., Preece, A. (eds.): ECCBR 2002, Lecture Notes in Computer Science,
vol. 2416, pp. 613-627. Springer, Berlin/Heidelberg, Germany (2002)
2. Schmidt-Belz, B., Nick, A., Poslad, S., Zipf, A.: Personalized and Location-Based Mobile
Tourism Services. In: Workshop on Mobile Tourism Support Systems, pp. 18-20 (2002)
3. Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: Personalized
Recommendation of Tourist Attractions for Desktop and Handset Devices. Applied
Artificial Intelligence: Special Issue on Artificial Intelligence for Cultural Heritage andDigital Libraries 17, 687-714 (2003)
4. Kishimoto, H., Mizuno, S.: Development of Tour Planning Support System Using MDL and
Genetic Algorithm. In: AI symposium '97, SIG-KBS (in Japanese), pp. 71-76 (1997)
5. Kurata, Y., Okunuki, K., Sadahiro, Y.: Development of a Preference-Based Tour Planning
System. In: Papers and Proceedings of the Geographic Information Systems Association (inJapanese), vol. 9, pp. 199-202 (2000)
6. Maruyama, A., Shibata, N., Murata, Y., Yasumoto, K., Ito, M.: A Personal Tourism
Navigation System to Support Traveling Multiple Destinations with Time Restrictions. In:
IEEE International Conference on Advanced Information Networking and Applications, vol.
2, pp. 18-22 (2004)

14

Yohei Kurata

7. Goy, A., Magro, D.: Star: A Smart Tourist Agenda Recommender. In: Configuration
Workshop on ECAI 2004, pp. 8/1-8/7 (2004)
8. Lee, J., Kang, E., Park, G.: Design and Implementation of a Tour Planning System for
Telematics Users. In: Gervasi, O., Gavrilova, M. (eds.): ICCSA 2007, Lecture Notes in
Computer Science, vol. 4707, pp. 179-189. Springer, Berlin/Heidelberg, Germany (2007)
9. Seifert, I.: Collaborative Assistance with Spatio-Temporal Planning Problems. In:
Barkowsky, T., Knauff, M., Ligozat, G., Montello, D. (eds.): Spatial Cognition 2006,
Lecture Notes in Artificial Intelligence, vol. 4387, pp. 90-106. Springer (2008)
10.Kurata, Y.: Challenges in User-Adaptive Tour Planning Systems. In: Tomko, M., Richter,
K.-F. (eds.): AGILE 2009 Workshop on Adaptation in Spatial Communication, SFB/TR 8
Spatial Cognition report series, pp. 19-26 (2009)
11.Mark, D.: Finding Simple Routes: 'Ease of Description' as an Objective Function in
Automated Route Selection In: Second Symposium on Artificial Intelligence Applications,pp. 577-581. IEEE Press (1985)
12.Duckham, M., Kulik, L.: "Simplest" Paths: Automated Route Selection for Navigation. In:
Kuhn, W., Worboys, M., Timpf, S. (eds.): COSIT '03, Lecture Notes in Computer Science
vol. 2825, pp. 169-185. Springer, Berlin/Heidelberg, Germany (2003)
13.Denis, M.: The Description of Routes: A Cognitive Approach to the Production of Spatial
Discourse. Current Psychology of Cognition 16, 409-458 (1997)
14.Klippel, A., Tappe, H., Kulik, L., Lee, P.: Wayfinding Choremes: A Language for Modeling
Conceptual Route Knowledge. Journal of Visual Languages and Computing 16, 311-329(2005)
15.Hochmair, H., Rinner, C.: Investigating the Need for Eliminatory Constraints in the User
Interface of Bicycle Route Planners. In: Cohn, A., Mark, D. (eds.): COSIT '05, Lecture
Notes in Computer Science vol. 3693, pp. 49-66. Springer, Berlin/Heidelberg, Germany(2005)
16.Saaty, T.: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation.
McGraw-Hill, New York, USA (1980)
17.Laporte, G., Martello, S.: The Selective Travelling Salesman Problem. Discrete Applied
Mathematics 26, 193-207 (1990)
18.Linden, G., Hanks, S., Lesh, N.: Interactive Assessment of User Preference Models: The
Automated Travel Assistant. In: Jameson, A., Paris, C., Tasso, C. (eds.): Sixth International
Conference on User Modeling, pp. 67-78. Springer, Berlin/Heidelberg, Germany (1997)
19.Malczewski, J.: GIS and Multicriteria Decision Analysis. Wiley (1999)
20.Raubal, M.: Formalizing Conceptual Spaces. In: Varzi, A., Vieu, L. (eds.): FOIS 2004, vol.
114, pp. 153-164. IOS Press, Amsterdam, the Netherlands (2004)
21.Joset, M., Stille, W.: A User-Aware Tour Proposal Framework Using a Hybrid Optimization
Approach. In: Voisard, A., Chen, S. (eds.): 10th ACM International Symposium on
Advances in Geographic Information Systems, pp. 81-87. ACM Press (2002)
22.Matsuda, Y., Nakamura, M., Kang, D., Miyagi, H.: An Optimal Routing Problem for
Sightseeing with Fuzzy Time-Varying Weights. In: IEEE International Conference on
Systems, Man and Cybernetics, vol. 4, pp. 3665-3669 (2004)
23.Bachrach, Y., Porat, E., Rosenschein, J.: Sketching Techinique for Collaborative Filtering.
In: International Joint Conference on Artificial Intelligence, pp. 2016-2021. AAAI Press(2009)
24.Rensnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: Grouplens: An Open
Architecture for Collaborative Filtering of Netnews. In: ACM 1994 Conference on
Computer Supported Cooperative Work, pp. 175-186. ACM Press (1994)

Interactive Assistance for Tour Planning15

25.Shiraishi, T., Nagata, M., Shibata, N., Murata, Y., Yasumoto, K., Ito, M.: A Personal
Navigation System with Functions to Compose Tour Schedules Based on Multiple
Conflicting Criteria. IPSJ Digital Courier 1, 528-536 (2005)
26.Richter, K.-F., Klippel, A., Freksa, C.: Shortest, Fastest, - but What Next? A Different
Approach to Route Directions. In: Raubal, M., Sliwinski, A., Kuhn, W. (eds.): Münsteraner
GI-Tagen 2004, pp. 205-217. IFGI prints, Münster, Germany (2004)
27.Frank, C.: An Egocentric Spatial Data Model for Intelligent Mobile Geographic Information
Systems. Department of Spatial Information Science and Engineering, Master's Thesis.
University of Maine, Orono, Maine, USA (2003)
28.Kanda, T., Shiomi, M., Perrin, L., Nomura, T., Ishiguro, H., Hagita, N.: Analysis of People
Trajectories with Ubiquitous Sensors in a Science Museum. In: IEEE International
Conference on Robotics and Automation, pp. 4846-4853 (2007)