research-article

A personalized multimodal tourist tour planner

Authors:

Damianos Gavalas,

Vlasios Kasapakis,

Charalampos Konstantopoulos,

Grammati Pantziou,

Nikolaos Vathis,

Christos ZaroliagisAuthors Info & Claims

MUM '14: Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia

Pages 73 - 80

https://doi.org/10.1145/2677972.2677977

Published: 25 November 2014 Publication History

Get Access

Abstract

Tourists become increasingly dependent on mobile city guides to locate tourist services and retrieve information about nearby points of interest (POIs) when visiting unknown destinations. Although several city guides support the provision of personalized tour recommendations to assist tourists visiting the most interesting attractions, existing tour planners only consider walking tours. Herein, we introduce eCOMPASS, a context-aware mobile application which also considers the option of using public transit for moving around. Far beyond than just providing navigational aid, eCOMPASS incorporates multimodality (i.e. time dependency) within its routing logic aiming at deriving near-optimal sequencing of POIs along recommended tours so as to best utilize time available for sightseeing and minimize waiting time at transit stops. Further advancing the state of the art, eCOMPASS allows users to define arbitrary start/end locations (e.g. the current location of a mobile user) rather than choosing among a fixed set of locations. This paper describes the routing algorithm which comprises the core functionality of eCOMPASS and discusses the implementation details of the mobile application using the metropolitan area of Berlin (Germany) as case study.

References

[1]

Brown B., & Chalmers, M. 2003. Tourism and mobile technology. Proceedings of the 8th European Conference on Computer Supported Cooperative Work (ECSCW'03), 335--354.

Digital Library

Google Scholar

[2]

Dibbelt, J., Pajor, T. and Wagner, D. 2012. User-constrained multi-modal route planning. Proceedings of the 14th Meeting on Algorithm Engineering and Experiments (ALENEX'12), 118--129.

Google Scholar

[3]

Garcia, A., Vansteenwegen, P., Arbelaitz, O., Souffriau, W. and Linaza, M. T. 2013. Integrating public transportation in personalised electronic tourist guides. Computers & Operations Research, 40(3), 758--774.

Digital Library

Google Scholar

[4]

Gavalas, D., Konstantopoulos, C., Mastakas, K. and Pantziou, G. 2014. A Survey on Algorithmic Approaches for Solving Tourist Trip Design Problems. Journal of Heuristics, 20(3), 291--328.

Digital Library

Google Scholar

[5]

Gavalas, D., Konstantopoulos, C., Mastakas, K., Pantziou, G., Vathis, N. 2014. Efficient Heuristics for the Time Dependent Team Orienteering Problem with Time Windows. Proceedings of the International Conference on Applied Algorithms (ICAA'2014), 151--162.

Digital Library

Google Scholar

[6]

Kurata, Y. and Hara, T. 2014. CT-Planner4: Toward a More User-Friendly Interactive Day-Tour Planner. Proceedings of the International Conference on Information and Communication Technologies in Tourism 2014 (ENTER'2014), 73--86.

Google Scholar

[7]

Lew, A. and McKercher, B. 2006. Modeling tourist movements: A local destination analysis. Annals of Tourism Research, 33(2), 403--423.

Crossref

Google Scholar

[8]

Likas, A., Vlassis, N., & Verbeek, J. 2003. The global k-means clustering algorithm. Pattern Recognition, 36(2), 451--461.

Crossref

Google Scholar

[9]

McKean, J., Johnson, D. and Walsh, R. 1995. Valuing time in travel cost demand analysis: An empirical investigation. Land Economics, 71: 96--105.

Crossref

Google Scholar

[10]

Vansteenwegen, P., Souffriau, W., Vanden Berghe, G. and Van Oudheusden, D. V. 2009. Iterated local search for the team orienteering problem with time windows. Computers & Operations Research, 36, 3281--3290.

Digital Library

Google Scholar

[11]

Vansteenwegen, P., Souffriau, W. and Van Oudheusden, D. 2011. The orienteering problem: a survey. European Journal of Operational Research, 209(1), 1--10.

Crossref

Google Scholar

[12]

Vansteenwegen, P., Souffriau, W., Vanden Berghe, G. and Van Oudheusden, D. 2011. The city trip planner: an expert system for tourists. Expert Systems with Applications, 38(6), 6540--6546.

Digital Library

Google Scholar

Cited By

View all

Fan LZhang W(2022)Personalized Travel Recommendation Based on the Fusion of TGI and POI AlgorithmsWireless Communications & Mobile Computing10.1155/2022/40587292022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4058729
Chen BHan JChen SYin JChen Z(2022)Automatic Itinerary Planning Using Triple-Agent Deep Reinforcement LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316900223:10(18864-18875)Online publication date: Oct-2022
https://doi.org/10.1109/TITS.2022.3169002
KINOSHITA Y(2018)Effects of Streetscape Atmosphere Visualization on Strolling Support Applications街歩き旅行支援アプリケーションにおける街並みの雰囲気可視化の効果Journal of Japan Society of Kansei Engineering10.5057/kansei.16.3_16116:3(161-166)Online publication date: 30-Sep-2018
https://doi.org/10.5057/kansei.16.3_161
Show More Cited By

Index Terms

A personalized multimodal tourist tour planner
1. Information systems
  1. Information systems applications
2. Mathematics of computing
  1. Discrete mathematics
    1. Combinatorics
      1. Combinatorial algorithms
      2. Permutations and combinations

Recommendations

Tourist Support System Using User Context Obtained from a Personal Information Device
UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

Tour planning is a difficult task for those who visit unfamiliar city destinations. Furthermore, building an itinerary becomes more difficult as the number of options, which can be incorporated into travel, increases. The authors aim to propose place of ...
The eCOMPASS multimodal tourist tour planner
Highlights
- Personalized near-optimal multiple-day tours (via several POIs) for tourists.
- ...
Abstract
Tour planning represents a challenging task for individuals visiting unfamiliar tourist destinations, mainly due to the availability of numerous attractions (points of interest, POIs) and the complexity of metropolitan public transit ...
Scenic route planning for tourists

Tourists visiting unknown destinations become increasingly dependent on mobile city guides to locate tourist services and retrieve informative content about nearby points of interest (POIs). Several mobile guides already support the provision of ...

Comments

Information & Contributors

Information

Published In

MUM '14: Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia

November 2014

275 pages

ISBN:9781450333047

DOI:10.1145/2677972

General Chair:
Arkady Zaslavsky,
Program Chairs:
Seng W. Loke,
Lars Kulik,
Evaggelia Pitoura

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 November 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Seventh Framework Programme

Conference

MUM '14

MUM '14: International Conference on Mobile and Ubiquitous Multimedia

November 25 - 28, 2014

Victoria, Melbourne, Australia

Acceptance Rates

Overall Acceptance Rate 190 of 465 submissions, 41%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
375
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Fan LZhang W(2022)Personalized Travel Recommendation Based on the Fusion of TGI and POI AlgorithmsWireless Communications & Mobile Computing10.1155/2022/40587292022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4058729
Chen BHan JChen SYin JChen Z(2022)Automatic Itinerary Planning Using Triple-Agent Deep Reinforcement LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.316900223:10(18864-18875)Online publication date: Oct-2022
https://doi.org/10.1109/TITS.2022.3169002
KINOSHITA Y(2018)Effects of Streetscape Atmosphere Visualization on Strolling Support Applications街歩き旅行支援アプリケーションにおける街並みの雰囲気可視化の効果Journal of Japan Society of Kansei Engineering10.5057/kansei.16.3_16116:3(161-166)Online publication date: 30-Sep-2018
https://doi.org/10.5057/kansei.16.3_161
Huang CDuy-Tuan Dao Chia-Ming Mai (2017)Location-Based Service (LBS) data sharing using the k-member-limited clustering mechanism over the 4G and Wi Fi hybrid wireless mobile networks2017 International Conference on Information Networking (ICOIN)10.1109/ICOIN.2017.7899550(526-531)Online publication date: 2017
https://doi.org/10.1109/ICOIN.2017.7899550
Gao SNiu JSun D(2016)Supporting Adaptive Tour with High Level Petri NetsProcedia Computer Science10.1016/j.procs.2016.08.09896:C(81-89)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1016/j.procs.2016.08.098

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Tourist Support System Using User Context Obtained from a Personal Information Device

The eCOMPASS multimodal tourist tour planner

Scenic route planning for tourists