Navigating through the complex transport system: A heuristic approach for city tourism recommendation

Abstract The fast development of machine learning and artificial intelligence has led to a great improvement of the smart tourism recommendation system, however many problems associated with the choice of transport modes in city tourism have yet to be solved. This research attempts to address this issue by proposing a model of customized day itineraries with consideration of transport mode choice. With improved particle swarm optimization and differential evolution algorithm, a nondominated sorting heuristic approach was devised. A case study was carried out in Chengdu, China to examine the performance of our approach. The results show that compared with extant methods, our approach achieves better performance. In addition, our approach can create more sensible, multifarious, and customized itineraries than previous methods. Tourism organizations and mobile map app providers could integrate our proposed model into their existing smart service systems, as part of their e-business or digital strategy for enhancing tourist experience.

[1]  Weimin Zheng,et al.  Understanding the tourist mobility using GPS: How similar are the tourists? , 2019, Tourism Management.

[2]  D. Fennell.  A tourist space-time budget in the Shetland Islands , 1996 .

[3]  S. Scagnolari,et al.  The relationship between length of stay and land transportation mode in the tourism sector: A discrete–continuous framework applied to Swiss data , 2019 .

[4]  G. Bel,et al.  Tourism and urban public transport: Holding demand pressure under supply constraints , 2010 .

[5]  L. Masiero,et al.  Tourists intra-destination visits and transport mode: a bivariate probit model. , 2013 .

[6]  Defu Zhang,et al.  Modified differential evolution algorithm using a new diversity maintenance strategy for multi-objective optimization problems , 2014, Applied Intelligence.

[7]  C. Chi,et al.  Examining the structural relationships of destination image, tourist satisfaction and destination loyalty: An integrated approach , 2008 .

[8]  Mahmoud A. Abo-Sinna,et al.  An effective genetic algorithm approach to multiobjective resource allocation problems (MORAPs) , 2005, Appl. Math. Comput..

[9]  Olatz Arbelaitz,et al.  Integrating public transportation in personalised electronic tourist guides , 2013, Comput. Oper. Res..

[10]  Ulrike Gretzel,et al.  Intelligent systems in tourism: a social science perspective , 2011 .

[11]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[12]  Rafael Caballero,et al.  Interactive design of personalised tourism routes , 2012 .

[13]  Bob McKercher,et al.  First and Repeat Visitor Behaviour: GPS Tracking and GIS Analysis in Hong Kong , 2012 .

[14]  Weimin Zheng,et al.  Using a heuristic approach to design personalized urban tourism itineraries with hotel selection , 2020 .

[15]  Christopher Leckie,et al.  Tour recommendation and trip planning using location-based social media: a survey , 2018, Knowledge and Information Systems.

[16]  El Houssaine Aghezzaf,et al.  Solving the stochastic time-dependent orienteering problem with time windows , 2016, Eur. J. Oper. Res..

[17]  Farhad Samadzadegan,et al.  Time-dependent personal tour planning and scheduling in metropolises , 2011, Expert Syst. Appl..

[18]  Sanghamitra Bandyopadhyay,et al.  DECOR: Differential Evolution using Clustering based Objective Reduction for many-objective optimization , 2018, Inf. Sci..

[19]  Dokyun Lee,et al.  How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment , 2019 .

[20]  S. Plog,et al.  The Power of Psychographics and the Concept of Venturesomeness , 2002 .

[21]  Charalampos Konstantopoulos,et al.  The eCOMPASS multimodal tourist tour planner , 2015, Expert Syst. Appl..

[22]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[23]  Weimin Zheng,et al.  Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction , 2017 .

[24]  Ming Yang,et al.  Differential Evolution With Auto-Enhanced Population Diversity , 2015, IEEE Transactions on Cybernetics.

[25]  Zbigniew Michalewicz,et al.  Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review , 2017, Evolutionary Computation.

[26]  Dirk Van Oudheusden,et al.  The Mobile Tourist Guide: An OR Opportunity , 2007, OR Insight.

[27]  Yi Xiang,et al.  A many-objective evolutionary algorithm based on a projection-assisted intra-family election , 2017, Appl. Soft Comput..

[28]  Zhibin Lin,et al.  Britain in bloom? A study into Chinese tourists’ experience , 2015 .

[29]  Clark Hu,et al.  The concept of smart tourism in the context of tourism information services , 2017 .

[30]  Hoong Chuin Lau,et al.  Dynamic Stochastic Orienteering Problems for Risk-Aware Applications , 2012, UAI.

[31]  Vic Ciesielski,et al.  Market segments based on the dominant movement patterns of tourists , 2010 .

[32]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[33]  Santiago Forgas-Coll,et al.  Urban destination loyalty drivers and cross-national moderator effects: The case of Barcelona , 2012 .

[34]  Bob McKercher,et al.  Day tour itineraries: Searching for the balance between commercial needs and experiential desires , 2012 .

[35]  Weimin Zheng,et al.  Using a heuristic approach to design personalized tour routes for heterogeneous tourist groups , 2019, Tourism Management.

[36]  Panlop Zeephongsekul,et al.  Spatial and temporal modelling of tourist movements using Semi-Markov processes , 2011 .

[37]  B. Grün,et al.  The impact of IT-enabled customer service systems on service personalization, customer service perceptions, and hotel performance , 2017 .

[38]  Iis P. Tussyadiah.  A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism , 2020, Annals of Tourism Research.

[39]  Zhibin Lin,et al.  The impact of high-speed railway on tourism spatial structures between two adjoining metropolitan cities in China: Beijing and Tianjin , 2019, Journal of Transport Geography.

[40]  Gregory Ashworth,et al.  Urban tourism research: Recent progress and current paradoxes , 2011 .

[41]  Tony Griffin,et al.  Urban Tourism Research: Developing an Agenda , 2008 .

[42]  Kenneth F. Hyde,et al.  A structural theory of the vacation , 2009 .

[43]  Charalampos Konstantopoulos,et al.  Heuristics for the time dependent team orienteering problem: Application to tourist route planning , 2015, Comput. Oper. Res..

[44]  C. Arrowsmith,et al.  Cultural Influence on Spatial Behaviour , 2011 .

[45]  Keith G. Debbage.  Spatial Behavior in a Bahamian Resort , 1991 .

[46]  Kedar Nath Das,et al.  A robust memory based hybrid differential evolution for continuous optimization problem , 2016, Knowl. Based Syst..

[47]  Theodoros Lappas,et al.  Personalized multi-period tour recommendations , 2017 .

[48]  Regine Gerike,et al.  Factors affecting tourists' public transport use and areas visited at destinations , 2015 .

[49]  Athanasios Migdalas,et al.  A hybrid Particle Swarm Optimization - Variable Neighborhood Search algorithm for Constrained Shortest Path problems , 2017, Eur. J. Oper. Res..

[50]  Zhibin Lin,et al.  Prioritizing destination attributes for optimal resource allocation: a study of Chinese tourists visiting Britain , 2018 .

[51]  Ulrike Gretzel,et al.  Smart tourism: foundations and developments , 2015, Electronic Markets.

[52]  W. Gronau.  On The Move: Emerging Fields of Transport Research in Urban Tourism , 2017 .

[53]  Feng Zhen,et al.  How smart is your tourist attraction?: Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach , 2016 .

[54]  B. Mckercher,et al.  Modeling Tourist Movements: A Local Destination Analysis , 2006 .

[55]  Juan Luis Fernández-Martínez,et al.  A Brief Historical Review of Particle Swarm Optimization (PSO) , 2012 .

[56]  Mahmoud A. Abo-Sinna,et al.  An effective genetic algorithm approach to multiobjective routing problems (MORPs) , 2005, Appl. Math. Comput..

[57]  Riccardo Poli,et al.  Analysis of the publications on the applications of particle swarm optimisation , 2008 .

[58]  B. Prideaux.  The role of the transport system in destination development , 2000 .

[59]  Chieh-Yuan Tsai,et al.  A personalized route recommendation service for theme parks using RFID information and tourist behavior , 2012, Decis. Support Syst..

[60]  Weimin Zheng,et al.  Using a heuristic algorithm to design a personalized day tour route in a time-dependent stochastic environment , 2018, Tourism Management.