CT-Planner2: More Flexible and Interactive Assistancefor Day Tour Planning
Yohei Kurata
Department of Tourism Science, Tokyo Metropolitan University, Japanykurata@tmu.ac.jp

Abstract
Tourists often have a hard time making their tour plans, especially when they are visiting alarge city on a tight schedule. To relieve tourists from such difficulty, a computer-aided
interactive tour planning system, called CT-Planner2, was developed. This system models atourist advisor who makes tour plans customized for individual tourists. Guided by aninteractive agent, each user of this system can request when and where he starts/ends his tour,
which tour criteria he wants to emphasize, and which attractions he especially wants tovisit/avoid, as well as compare several plans that the system automatically generates. By
repeating the revision of tour plans in an interactive manner, the user can eventually build acustom-made tour plan that fits his requests and preference. In our user test, the usability andpotential of the system is evaluated positively by the participants.
Keywords: computer-aided tour planning; candidate/critique model; selective travelling
salesman problem; user’s preference; custom-made tour plan.

1 Introduction
Imagine that you are visiting Tokyo for a conference, after which you have a day off.
Probably you want to visit several places for sightseeing. If so, which places do you
visit? You may browse a guidebook or web site, or ask somebody, to find out whatplaces in Tokyo will be interesting for you. However, even if you have found several
interesting places, it is difficult to determine how many of them you can visit only in aday, since you do not know the time necessary for visiting each place, as well as thetime necessary for travelling from one place to another. Your guidebook may kindly
show some model plans, but it is not guaranteed that these plans include one that fits
your interest and schedule. Alternatively, you can consult an expert, such as a staff in
a tourist information office or a concierge in your hotel. However, in a foreigncountry you may experience difficulty in communicating with such experts.
To relieve tourists from such difficulty in tour planning, several researchers havedeveloped the systems that support the user’s tour planning. Some systems
recommend attractive points-of-interest (POIs) in the target area, taking the user’s
tour preference into account (e.g., Ricci, et al., 2002; Schmidt-Belz, et al., 2002;
Ardissono, et al., 2003). Some systems optimize the tour schedule complying with theuser’s request (Maruyama, et al., 2004; Seifert, 2008). Some systems score the POIs
based on the user’s preference and then generate a customized tour plan for each user
(Kishimoto & Mizuno, 1997; Kurata, et al., 2000; Goy & Magro, 2004; Lee, et al.,2007). Typically, these systems are aimed at rather fully-automated generation of tour
plans. CT-Planner (Kurata, 2010), on the other hand, emphasizes the concept ofinteractive assistance—it models the interaction between a tourist and a tour advisor,where the advisor shows several sample plans to the tourist, learns the tourist’s
preference and requests from the tourist’s feedback, customizes the plans accordingly,
and asks for the tourist’s feedback again to refine the plans. Kurata (2010) asserted
the potential of such interactive assistance, because the user can see the actual plansfrom the beginning, without thinking about his own tour interests which he is often
not well aware of. However, he did not conduct an official user test to substantiate his
claim. In addition, CT-Planner still had room for improvement of user-friendlinessand interactivity. Thus, in this work, the revised version of CT-Planner, namely CTPlanner2, was developed and evaluated through a user test. This paper introduces CTPlanner2 and its mechanism, as well as reports the result of the user test. Note that
CT-Planner stands for Collaborative Tour Planner, and also City Tour Planner as it
mainly targets city-scale day tours. Currently, CT-Planner2 works on a Windows PC
as a stand-alone application.
The major contribution of this work is, in an academic perspective, to introduce analternative approach to computer-aided tour planning that emphasizes collaborative
design of tour plans by the system and the user, and in an industry perspective, todemonstrate that this approach is highly potential as the users get high satisfaction
through their active involvement in revising their tour plans.
The remainder of this paper is structured as follows: Section 2 reviews some key
ideas of computer-aided tour planning. Section 3 describes the design concepts of CTPlanner2, while Section 4 explains its mechanism. Section 5 reports the result of ouruser test and identifies remaining problems. Finally, Section 6 concludes with a
discussion of future work.

2 Computer-Aided Tour Planning
People have a large variety of tour interests. In addition, there are a large variety of
tourist attractions, especially in huge cities which attract many tourists. Hence, useradaptation techniques for filtering, sorting, and refabricating tourist information for
individual tourists have been long discussed and implemented in many tourist
information systems. Such techniques are especially important for tourist information
services on mobile devices, because the users have to process the tourist information
in a short time during their tour on a tiny screen.
A key question in such user-adaptation techniques is how to estimate the value ofeach POI for each user. In early systems, the user was asked to set several preference
parameters manually, for instance, by sliders (e.g., Kishimoto & Mizuno, 1997;
Maruyama, et al., 2004; Hochmair & Rinner, 2005). Then, the value of each POI in
the target area is estimated, considering the matching between the POI’s character andthe user’s preference. To achieve more natural interactions, the system by Kurata et al.
(2000) adopted an on-lime questionnaire, in which the user is asked to compare
several pairs of tour purposes. Then, from the user’s answers the system calculates theuser’s preference parameters. Alternatively, CT-Planner (Kurata, 2010) asks the user
to compare tour plans; the system provides several plans of different characters,
calculates the user’s preference from the selection of his favourite plan, and again thesystem shows a set of adapted tour plans to the user to ask his feedback. With thisinterface, the user no longer has to specify his tour interests explicitly before seeingactual plans. This technique, called candidate/critique model, was proposed by
Linden et al. (1997) who applied it to on-line airplane ticket sales.
The controversial issue of such preference-based approaches is the validity ofmeasurement of users’ tour preference. To skip this problem, some systems adopted
collaborative filtering (Rensnick, et al., 1994), which is now used practically in many
recommender systems (Bachrach, et al., 2009). In this approach, the evaluation ofeach POI by previous visitors is used for the estimation of its value for each user,
assuming that tourists with similar profiles (gender, age, travel history, etc.) give
similar evaluations to the same POI (Ricci, et al., 2002; Lee, et al., 2007). Even
though this approach imposes fewer burdens on the users, the validity of the above
assumption needs to be carefully examined. In addition, this approach requires a large
amount of evaluation data by tourists.
Seifert (2008) pointed out that the main disadvantage of computer-aided tour planningsystems is that they exclude the user’s participation in the process of planning. Indeed,according to the user test by Kurata et al. (2000), users complained about the inability
to customize the recommended tour plans by adding or removing POIs that they wantto visit/avoid. The same problem is also seen in other tour planning systems.
Exceptionally, P-Tour (Maruyama, et al., 2004) allows the user to indicate where hewants to visit/avoid, although indirectly, by assigning high/low scores to the POIs.
However, P-Tour forces the user to evaluate all POIs in the target area, which requires
a lot of time and effort. From this lesson, CT-Planner (Kurata, 2010) allows the users
both to specify their requests on some POIs and to leave the evaluation of theremaining POIs to the system.

3 System Design
Following the former version, CT-Planner2 models a tour advisor who makes tour
plans customized for individual tourists. It emulates the following roles of the tour
advisor: To propose sample tour plans to the tourist, based on the advisor’s knowledge
about the tourist’s profile To handle the tourist’s request about the overall character of his tour plan, as wellas his special request to include certain POIs in the plan or to remove them fromthe plan To judge the tourist’s preference from his feedback
The idea of modelling a tour advisor’s role to realize user-friendly interactions is also
seen in Garcia et al. (2010), but they do not adopt a repetitive process like ours where
the tour plans are refined gradually as the user’s preference and requests become
clearer.
Currently CT-Planner2 targets Yokohama, a portside city near Tokyo, which attracts
more than forty million tourists in a year. The number of POIs we consider is 28,
which looks relatively fewer than the studies that target European cities where historic
attractions are distributed densely (for instance, the system by Garcia et al. (2010)
targets 50 POIs in San Sebastian, Spain).

Fig. 1 shows the main screen of CT-Planner2. A tour plan, recommended by thesystem, is shown on the map of central Yokohama. This plan visits eight POIs in five
hours (from 10:00 to 15:00). On the right side the itinerary of this plan is shown,
together with a photo, a short description, and a rating (one to three stars) of each POI
visited during the tour. The user can see more detailed information about each POI by
clicking it in the map or itinerary (Figs. 2a-b).

Fig. 1. The main window of CT-Planner2

(a)

(b)

Fig. 2. Two small windows that show the property of (a) a POI and(b) a transportation facility, respectively
In the upper right of the main screen, there are two frames: the left one, namely
Start/Goal Conditions frame, shows when and where the tour starts/ends, and theright one, named Weights frame, shows a radar chart that visualizes the weights onfive tour criteria—popularity, learning, art, nature, and amusement—under which the
POIs are evaluated and the recommended plan is generated. The radar chartessentially represents the user’s preference.
In the upper left of the main screen is a cartoon character who guides the user. The
user can interact with this character simply by selecting one of the options shown to
the right of the character’s message. Basically, the user has five options:
1. To change where and when the tour starts/ends;
2. To specify which POIs the user wants to visit/avoid;
3. To change the character of the tour;
4. To see other plans for comparison; and5. To finish tour planning
If option 1 is selected, the user is guided to manipulate the four components in theStart/Goal Conditions frame (Fig. 1). The start/goal locations can be changed also byclicking a node in the map and then clicking the Start/Goal buttons in its property
window (Figs. 2a-b). Similarly, if option 2 is selected, the user is guided to click a
POI in the map or itinerary and then to click the Visit/Avoid button in its property
window (Fig. 2a), and if option 3 is selected, the user is led to manipulate the radar
chart in the Weights frame. As soon as the user manipulates any element, the tour plan
shown on the screen is revised accordingly. For instance, when the user clicks the +button to the left of Learning in the Weights frame repeatedly, the tour plan is revised
to visit more and more museums.
If the user selects option 4, the system shows two tour plans side-by-side, so that theuser can compare the plans (Fig. 3). Both plans follow the same tour conditions (i.e.,
when and where to the tour should start/end and which POIs the tour should
visit/avoid). The difference is that the left one follows the current weights shown on
the screen, while the right one puts more weight on either learning, art, nature, oramusement more than now. If the user adopts the alternative plan, the window
switches to the previous state (Fig. 1) but the plan is now replaced with the adopted
one, and the weights are updated accordingly.
By repeating the above four types of operations, the user can make his own tour plan
supported by the system. When the user gets satisfied with the plan, he is supposed to
select option 5. Then, the map and itinerary of the current plan are printed out for theuser, while the system is restarted for the next user.
CT-Planner2 implemented several new ideas that are not in the former version. First,
CT-Planner2 adopted a cartoon character as a navigator, in order to enrich the senseof user-friendliness and interactivity. Second, CT-Planner2 shows the tour plan not
only in a map, but also in an itinerary enriched with photos and short texts. Finally,
CT-Planner2 shows the weights on the five tour criteria and allows its manualadjustment. The old version hides such weights and determines the weights from theuser’s repeated selection of alternative plans. However, some users complained thatthey could not directly decide the character of the tour plan. Hence, CT-Planner2 isdesigned to allow the user’s direct manipulation on the weights on the five tour
criteria. The evaluation of these new ideas is reported in Section 5.

Fig. 3. CT-Planner2 shows two plans of different characters to seek the user’s
preference from his feedback

4 Underlying Mechanism
This section explains how CT-Planner2 generates custom-made tour plans forindividual users. The underlying mechanism is almost the same as the former CTPlanner, although we redesigned the user interface.
CT-Planner2 targets a city-size tourist area which contains multiple POIs. The tourist
area is modelled as a graph. Each node represents either POI or transportation facility(TF), while each link represents a route between two POI/TFs. The routes arecomputed beforehand using Dijkstra’s shortest path algorithm (Dijkstra, 1959). POInodes are assigned two parameters: visitation time that a tourist normally spends at
the POI and expected utility (i.e., the amount of satisfaction) that the user will obtainby visiting it. TF-nodes are assigned zero visitation time and zero utility in order todeal with both POI-nodes and TF-nodes simultaneously in computation. Links are
assigned a single parameter travel time that a tourist normally spends to travel thecorresponding route.
4.1 Plan Generation
The system recommends the best tour plan that visits highly-evaluated POIs as muchas possible under a given time constraint and current evaluations of POIs. The
problem to find out the best plan is formalized as follows (Kurata, 2009):
Given a complete graph V , E  , the expected utility of each node ui , the visitation
time t visit i , the travel time t travel ij , origin node vori V , goal node v goal V , and time

constraint T , find a series of nodes to be visited va ,, va va V that maximizes
1ki
the sum of utilities  ua under the following three constraints:
i
i 1
k

 t
k

i 1

visit

 i 0 t travel ai a j  T
k 1

ai

va0  vorivak 1  vdes
Currently, the maximum tour time is restricted to less than or equal to 7 hours (i.e.,T  420 min ), since we target day-tour planning. Another reason of this restriction isthat the above problem setting presumes that all POIs are always open and accessible(usually 10am to 5pm).
The above problem is essentially the Selective Travelling Salesman Problem (STSP),
which is known to be an NP-hard combinatory optimization problem (Laporte &Martello, 1990). CT-Planner2 uses a heuristic high-speed algorithm for deriving semioptimal solution, which was proposed by Kurata et al. (2000). This algorithm works
in O nt 2 , where n is the number of POIs in the target area and t is the length of tour
time. Alternatively, a genetic algorithm (GA) used in STAR (Goy & Magro, 2004) or
P-Tour (Maruyama, et al., 2004) may also be applied to generate more optimal
solutions. We, however, did not adopt a GA-based approach, because it may take a bittime to find a solution (Garcia et al. 2010) while quick feedback from the system isvital in our system to achieve smooth interactions.

 

4.2 Evaluation of POIs
To conduct the plan generation as explained in Section 4.1, we have to estimate theexpected utilities of POIs in the target area beforehand. Each POI is given five-gradescores in five criteria—popularity, satisfaction level of learning, satisfaction level ofart, satisfaction level of nature, and satisfaction level of amusement—determined byexperts in advance. On the other hand, the weights on the corresponding five tour
criteria—popularity, learning, art, nature, and amusement—are determined by every
user (Section 4.4). The expected utility of each POI is calculated from the weighted
total of its five scores.
We adopted the above four criteria for representing the user’s tour preference,
because people often express their preference by the categories of tourist attractions
they like. We considered that education, art, nature, and amusement are four
representative categories of tourist attractions. Of course there are other possible
categorizations, but we should avoid detailed ones in order not to overwhelm the user.
Note that we further added one item, popularity, because people typically prefer tovisit popular POIs when they do not have any specific request.
The expected utility is transformed into star-based rating (one to three stars) anddisplayed on the screen (Figs. 1 and 2a), such that the user can easily judge whether
the POI will be interesting to him or not.

4.3 Processing Visit/Avoid Requests
If the user requests the system to visit certain POIs, the system tentatively assigns
very high utility to these POIs. Accordingly, the generated plans contain the requested
POIs as many as possible under the given time constraint. This function is useful, forinstance, if the user assigns a high weight on learning as she likes museum, but she
also wants to visit an amusement park as it is famous.
Similarly, if the user requests the system to avoid certain POIs, the system tentatively
assigns zero utility to these POIs, such that the generated tour plans no longer containthese POIs. Again, this function is useful, for instance, if the user has been to thetarget area before and she does not want to visit the same POIs again.
4.4 Changing Weights
To estimate the utility of POIs for each user (Section 4.2), the user has to specify theweights on the five tour criteria. Initially, the weight is assigned only to popularity.
Accordingly, the initial recommended plan emphasizes the POIs that are popular to
everyone. The user can adjust the weights on the five tour criteria by clicking therespective + and buttons in the Weights frame (Fig. 1). Let the current weights on
popularity, learning, art, nature, and amusement be w p , wl , wa , wn , we  and supposethat the user clicks the + button to the left of Amusement. Then, the weights are
modified to aw p , awl , awa , awn , we    , where a  1  w e   / 1  w e  in order tokeep the sum of weights 1. In the current version, the parameter α is assigned 0.1, or1  w e  if it is smaller than 0.1, such that the weight on each criterion is kept less orequal to one. Note that the + button to the left of Amusement is enabled only when
we  1 .
The weights can be modified also by the user’s selection of alternative plans (Fig. 3).
The alternative plans are generated using tentatively modified weights. For instance,
when the current weights are w p , wl , wa , wn , we  , the nature-oriented plan isgenerated under the weight bw p , bwl , bwa , wn   , bwe  where b  1  w n    / 1  w n  .
is min0.1, 1  wn  . If the user adopts this plan, the weights are modified tobw , bw , bw , w   , bw .
p

l

a

n

e

Once the weights on the five tour criteria are changed, the system promptly revises
the expected utility of each POI, as well as the tour plan shown on the screen.
Accordingly, the user can see the result of weight adjustment dynamically.

5 Evaluation
In order to evaluate CT-Planner2, a user test was conducted from August to
September 2010. Twenty people participated in the test as volunteers. The participants
were seventeen students and three faculty members in the department of Tourism
Science, Tokyo Metropolitan University. Sixteen were males and four were females.
Their average age was 25.4. All subjects had been to Yokohama before, as it is a very
popular tourist destination near Tokyo.

In this test, the participants were asked to play with the system freely for about five toten minutes and then to make his favourite day-trip plan eventually. They were notgiven any instruction about how to operate the system, so that we can examine theintuitiveness of the system for naïve users. The participants were left alone and givenno advice during their trial, but their actions were recorded by the system. After thetrial, the participants were asked to fill in a questionnaire.
On average, the participants spent 9.7 minutes ( =3.4) for the trial. Three major types
of users were found from the user log:
 Goal-oriented users, who made their final plan in fewer steps without trying
various tour conditions; Experimenting users, who tried various tour conditions and eventually found thebest plan; and DIY-type users, who specified their requests as many as possible and did not leave
the system to propose which POIs to visit.
Naturally, the first group finished tour planning relatively quickly, while the latter twogroups spent longer time.
In the first part of the questionnaire, the participants were asked six questions about
their satisfaction with and impression of the system. The results, as summarized inTable 1, show that the system is evaluated positively by the users. Especially, mostusers evaluated the system as easy to understand (Q2). However, some participants
did not feel that they had successfully made their favourite plan (Q4). Two complaints
were heard from them: one is that CT-Planner2 does not allow them to specify when
they visit a POI and how long they spend there, and another is that the current sample
data misses the information about restaurants and shops. Many participants answeredthat they would like to use the system on the Web (Q5), while some participants were
not willing to use it in a tourist information office (Q6). This indicates that the sales
point of CT-Planner2 is that it enables the users to consult the system about theirplans at any time at any place.
Table 1. User evaluation of CT-Planner2 (Five-grade scale where yes=5 and no=1)
Question

AverageRatingStandard
Deviation

Q1 Are you satisfied with your tour planning experience
supported by CT-Planner2?

3.95

0.51

Q2 Was CT-Planner2 easy to understand?

4.40

0.68

Q3 Was CT-Planner2 user-friendly?

3.95

0.76

Q4 Did you successfully make your favourite plan with
CT-Planner2?

3.65

0.99

Q5 Will you use CT-Planner2 if you can access it on the Web?

4.20

0.83

Q6 Will you use CT-Planner2 if you can access it at a tourist
information office in your destination?

3.80

1.20

In the second part of the questionnaire, the participants were asked to evaluate fivefeatures of CT-Planner2. The results, as summarized in Table 2, show thatvisualization of a tour plan and support of user’s visit/avoid requests were highly
evaluated, while presence of a guiding character was not well. Some participants toldthat the character was not lively, as his image was fixed and his messages were very
limited. Display of multiple plans for comparison, which was the key feature of theformer CT-Planner, did not attract many participants. According to the user log, theparticipants rather preferred to adjust the weights on the five tour criteria bythemselves, in order to see the tour plans of different characters.
Table 2. User evaluation of CT-Planner2’s five features (Five-grade scale where
good=5 and bad=1)
Function

Average
Score

Standard
Deviation

Visualization of a tour plan by the combination of a map
and an itinerary

4.55

0.60

Presence of a guiding character

3.65

1.09

Display of multiple plans of different characters for user’s
comparison

3.68

0.95

Support of user’s request about where to visit/avoid

4.45

0.69

Support of user’s manual setting of weights on tour criteria

3.9

1.04

The last part of the questionnaire asked the participants to write their comments andideas freely. There some participants mentioned the system’s restriction on tour time
(10:00 to 17:00). This restriction arises from the problem of the current algorithm
which presumes that all POIs are open and accessible. This problem can be avoided
by the use of an approximate algorithm by Matsuda et al. (2004) for FORPS (Fuzzy
Optimal Routing Problem for Sightseeing), which supports temporal fluctuation of
POI values—we should simply assign zero utility to POIs when they are in closed
hours.
Some participants wanted the system to support not only point-like tourist attractions,but also attractive paths. If the value of paths is taken into account, the problem
becomes EPTP (Enhanced Profitable Tour Problem). A heuristic algorithm forderiving semi-optimal solutions of this problem is already available (Joset & Stille,
2002). Thus, the remaining question is how we should evaluate the attractiveness ofpaths in a reasonable way. Further possible extensions of the tour planning problem
are reviewed in Souriau and Vansteenwegen (2010).
Some participants wished if they could edit multiple plans simultaneously, such thatthey could keep candidates and compare them afterwards. Some participants
requested an undo function as well. The implementation of these ideas is left for
future work.

6 Conclusions and Future Work
Tourist information is essential for tourists who visit unfamiliar places, but theirdecision making becomes rather difficult if they are given an overwhelming amountof information. Thus, the question of how to refine and customize the information for
individual tourists is important for intelligent tourist information systems. This paper
introduced CT-Planner2, with which people can create custom-made tour plansinteractively, as if they are assisted by a human advisor. Compared with the former
version, CT-Planner2 increased the user-friendliness and interactivity thanks to theemployment of the guiding character, the visually-enriched itinerary, and the radar
chart that shows the weights on different tour criteria and allows their adjustment. The
system was evaluated positively by the participants of our user test. At the same time,
the user test revealed several remaining issues, such as limitation of tour hours andinability to request the time and duration for visiting POIs. How to overcome theseissues is left for future work.
A great advantage of computer-aided tour planning systems is that a user can consult
a system at any time, as much as he wants, using his own language even in foreigncountries. To make the most of this advantage, an online multi-language version of
our system for serving tourists visiting Japanese cities is now under preparation. Of
course, the system itself is applicable to non-Japanese cities if their data are provided.
In addition, the development of a mobile version of our system is also on our road
map. A key question is how to provide a service tailored to mobile devices. Real-timemonitoring of the user’s location and schedule (Shiraishi, et al., 2005) and subsequentreal-time rescheduling are potential services of mobile-oriented tour planning systems.

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Acknowledgements
This work is supported by JST RISTEX’s research program on Needs-oriented
Service Science, Management and Engineering (project title: “Architecting service
with customer participation based on the analysis of customer experience and design
processes: sophisticating tour design processes as a case study”).