A tour route planning model for tourism experience utility maximization

Xiong-bin, WU; Guan, Hongzhi; Han, Yan

doi:10.1177/1687814017732309

Cited by 38 publications

(20 citation statements)

References 16 publications

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“…In [31], a 3-stage framework targets on satisfying user preferences for visiting selected POI by generating an optimal personalized route. A tour route planning model was proposed [32] targeting to maximize the tourism experience within a specific budget and time frame by satisfying real-time requirements of Web or mobile users. The method avoids tourists to pass through dangerous zones or areas.…”

Section: Related Work For Route Planning Modelingmentioning

confidence: 99%

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A route planning framework for smart wearable assistive navigation systems

Ntakolia

Iakovidis

2021

SN Appl. Sci.

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Route planning (RP) enables individuals to navigate in unfamiliar environments. Current RP methodologies generate routes that optimize criteria relevant to the traveling distance or time, whereas most of them do not consider personal preferences or needs. Also, most of the current smart wearable assistive navigation systems offer limited support to individuals with disabilities by providing obstacle avoidance instructions, but often neglecting their special requirements with respect to the route quality. Motivated by the mobility needs of such individuals, this study proposes a novel RP framework for assistive navigation that copes these open issues. The framework is based on a novel mixed 0–1 integer nonlinear programming model for solving the RP problem with constraints originating from the needs of individuals with disabilities; unlike previous models, it minimizes: (1) the collision risk with obstacles within a path by prioritizing the safer paths; (2) the walking time; (3) the number of turns by constructing smooth paths, and (4) the loss of cultural interest by penalizing multiple crossovers of the same paths, while satisfying user preferences, such as points of interest to visit and a desired tour duration. The proposed framework is applied for the development of a system module for safe navigation of visually impaired individuals (VIIs) in outdoor cultural spaces. The module is evaluated in a variety of navigation scenarios with different parameters. The results demonstrate the comparative advantage of our RP model over relevant state-of-the-art models, by generating safer and more convenient routes for the VIIs.

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Section: Related Work For Route Planning Modelingmentioning

confidence: 99%

“…Maximization of the tourism experience utility within the tour time and cost budget [32] Mixed 0-1 mixed integer linear programming model…”

Section: Minimum Cost Hamiltonian Cycle Algorithmmentioning

confidence: 99%

A route planning framework for smart wearable assistive navigation systems

Ntakolia

Iakovidis

2021

SN Appl. Sci.

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“…Therefore, the tourists planning model is used to build high-quality tourist routes. Tour Route Planning Problem (TRPP) is used to maximize tourism experience and build high-quality tourist routes [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

2019

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Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data.

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“…The solved TTDP provided an optimal route for the tourist trip design plan (TTDP) in transport vehicles (Yu et al 2014). Wu et al (2017) presented a tour route planning problem (TRPP) solution using a greedy construction procedure for creating a route planning model that maximized the tourism experience utility, considering tourists' preferences in terms of attraction, traveling time, and traveling cost. Tourism visits were time-constrained from 8:30 a.m. until 4:40 p.m. and limited to 200 yuan per day.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results of the problem solving showed that suitability of attractions, degree of interest, tourism time, and tourism cost considerably influenced the tour route plan. However, traffic significantly impacted traveling time on the route (Wu et al 2017). Similarly, Yan et al (2014) developed a tour route to optimize satisfaction and minimize route distance.…”

Section: Literature Reviewmentioning

confidence: 99%

Marketplace Location Decision Making and Tourism Route Planning

Sirirak

Pitakaso

2018

Administrative Sciences

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This research addresses the problem of planning tourism routes and finding appropriate shopping (market place) locations for agricultural product transportation. Generally, tourists visit popular tourism attractions; and generally, unpopular tourism attractions do not stimulate the economy, trade, or local income. Popular tourism attractions that are located far away from each other require the transportation of local products, and tourists must make decisions as to which locations to visit when planning their vacation. Planning a tourism route while balancing tourism attractions and shopping markets is important for the economic stimulation of tourism. This work presents a problem-solving method for tourism route-planning for a particular case study in Chiang Rai province, Thailand, using the Adaptive Large Neighborhood Search (ALNS) method. Six main destruction and five repair cycles in the ALNS method were applied to solve the tourism route design problem and to find the best solution so that tourists can visit all of the main attractions. We found that 13 tourism routes provide the shortest travel distance for each travel route. The total distance traveled was 2538.02 km for all routes. To balance the tourism on all routes, the popular and less popular tourism attractions were combined. For all routes, the shopping market location is the best place for tourism products to be sold and where tourist relaxation occurs. The results from ALNS were compared with the results from those obtained by the exact Lingo program V11. The ALNS algorithm results were not significantly different from the Lingo results. For the computational results for all examined cases, the ALNS algorithm was shown to be competitive, with short processing times given the sizes of the problems. For the traveling distance, the ALNS result significantly differs from the exact method by approximately 1.12%, and had a better effect than the exact method by approximately 99% in terms of processing time. Therefore, the proposed methodology provides an effective and high-quality solution for tourism route planning.

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A tour route planning model for tourism experience utility maximization

Cited by 38 publications

References 16 publications

A route planning framework for smart wearable assistive navigation systems

A route planning framework for smart wearable assistive navigation systems

Design and Development of a Real-Time Optimal Route Recommendation System Using Big Data for Tourists in Jeju Island

Marketplace Location Decision Making and Tourism Route Planning

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