A personal tourism navigation system to support traveling multiple destinations with time restrictions

Maruyama, Atsuko; Shibata, Naoki; Murata, Yoshinori; Yasumoto, Keiichi; Ito, M.R.

doi:10.1109/aina.2004.1283747

Cited by 47 publications

(23 citation statements)

References 4 publications

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“…However, this approach also has a risk to annoy some users, giving an impression that the system offends their privacy and has a stereotyped view of their preferences. As an alternative approach, P-Tour [6] asks the user 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 the attractiveness of all POIs in the target area.…”

Section: Previous Tour Planning Systemsmentioning

confidence: 99%

“…However, even if people are informed about attractive POIs, it is still difficult for them to make a tour plan without the knowledge about the spatial arrangements of these POIs and the transportations between them. Thus, several systems were equipped with the ability to generate personalized tour plans [4][5][6][7][8]. Unfortunately, at the moment, these systems lack interactivity (e.g., we cannot request the system to insert certain POIs into the recommended tour plan), while demanding a lot of data input from the user (Section 2).…”

Section: Introductionmentioning

confidence: 99%

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Interactive Assistance for Tour Planning

Kurata

2010

Spatial Cognition VII

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Abstract. It is often difficult for individual tourists to make a sightseeing tour plan because they do not have prior knowledge about the destination. Although several systems have been developed for assisting the user's tour planning, these systems lack interactivity, while demanding a lot of data input from the user. In this paper, we introduce a new computer-aided tour planning system, called CT-Planner, which realizes collaborative tour planning. The system provides 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 to register his profiles in advance. In addition, the system allows the user to specify his special requests, which leads to a more satisfying experience of computer-aided tour planning.

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Section: Previous Tour Planning Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interactive Assistance for Tour Planning

Kurata

2010

Spatial Cognition VII

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“…For this reason, the proposed method is evaluated and compared based on the optimization of the visiting orders in procedure 2. Similar to the application of GA to the tour planning problem [7], GA could be also applied to optimize the visiting orders of the intermediate destinations. Therefore, in this work, the proposed approach of the optimization of the visiting orders using RasID-DP is compared with the optimization using RasID-D and GA.…”

Section: Simulationsmentioning

confidence: 99%

“…The main objective in these tour planning problems is to select a set of destinations for a tourist to travel. References [7], [8] have proposed a genetic algorithm (GA) based method to find the semi-optimal tour plans. The authors also consider the time restriction at destinations and users' preferences in the selection of the tour destinations.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Route Planning with Multiple Intermediate Destinations

Mainali

Mabu

Hirasawa

2011

SICE Journal of Control, Measurement, and System Integration

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: The optimal route recommendation in navigation systems is often considered to be the optimal route recommendation between two locations, i.e., an origin and a destination. However, in practical scenarios, traveling to several intermediate destinations before the final destination needs to be considered. Conventional route search algorithms cannot consider such restrictions in the route search. In this paper, a method to find the optimal route via several intermediate destinations is proposed. The proposed method is divided into three steps. In the first step, the conventional route search algorithm is used to find the optimal traveling time and optimal route among the origin, intermediate destinations and final destination. In the second step, the visiting order of the intermediate destination is optimized using the population based RasID-D (RasID-DP) to minimize the total traveling time. Finally, the optimal route from the origin to destination is determined based on the results of the previous steps. The proposed method is evaluated based on the efficiency of the optimization of the visiting order of intermediate destinations. Simulation results show that RasID-DP based optimization can find better solutions efficiently.

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“…Although Dynamic Tour Guide [6] generated and recalculated routes in real-time (less than five seconds), the routing algorithm was very simple and it had important restrictions: it could only create proper solutions for one day routes and a small number of POIs. A PET called P-Tour [7] applied a genetic algorithm to calculate routes. Nevertheless, tourists had to manually enter the POIs they wanted to visit with their details (visiting time, duration and tourist score).…”

Section: State Of the Artmentioning

confidence: 99%

Personalized Tourist Route Generation

García

Arbelaitz

Linaza

et al. 2010

Current Trends in Web Engineering

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Abstract.When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist agenda. Finally, they fine tune up this route to better fit tourists' needs. We present an intelligent routing system to fulfil the same task. We divide this process in three steps: recommendation, route generation and route customization. We focus on the last two steps and analyze them. We model the tourist planning problem, integrating public transportation, as the Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) and we present an heuristic able to solve it on real-time. Finally, we show the prototype which generates and customizes routes in real-time.

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A personal tourism navigation system to support traveling multiple destinations with time restrictions

Cited by 47 publications

References 4 publications

Interactive Assistance for Tour Planning

Interactive Assistance for Tour Planning

Algorithm for Route Planning with Multiple Intermediate Destinations

Personalized Tourist Route Generation

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