A Tourism Route-Planning Approach Based on Comprehensive Attractiveness

In recent years, “free travel” has been increasingly popular. How to plan personalized travel routes based on the perspective of tourists, rather than that of tourism intermediaries, is in great need. However, some factors reflecting tourists’ preferences are ignored in the related work. What’s more, the evaluation about scenic spots is incomplete. Besides, real data sets are seldom used in existing works. We propose a novel route-planning method that considerate multiple factors (that is, the distance between sites, initial travel position, initial departure time, time duration of tour, total cost, scores and popularities of sites) comprehensively, and routes were rated by what we call a comprehensive attractiveness index. We conducted comprehensive case studies based on the real-world data of sites from the Baidu and Xiecheng websites and found that our proposed method is feasible. It is also found that the genetic algorithm outperformed two baseline ones in terms of run time.

[1]  Kalyanmoy Deb,et al.  Comparing Classical Generating Methods with an Evolutionary Multi-objective Optimization Method , 2005, EMO.

[2]  Halife Kodaz,et al.  A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem , 2015, Appl. Soft Comput..

[3]  Yueshen Xu,et al.  QoS Prediction for Service Recommendation with Deep Feature Learning in Edge Computing Environment , 2019, Mob. Networks Appl..

[4]  Honghao Gao,et al.  Applying Probabilistic Model Checking to Path Planning in an Intelligent Transportation System Using Mobility Trajectories and Their Statistical Data , 2019, Intelligent Automation and Soft Computing.

[5]  Yucong Duan,et al.  Transformation-based processing of typed resources for multimedia sources in the IoT environment , 2019, Wireless Networks.

[6]  Jean-Yves Potvin,et al.  A parallel implementation of the Tabu search heuristic for vehicle routing problems with time window constraints , 1994, Comput. Oper. Res..

[7]  Qingsheng Zhu,et al.  Fluctuation-Aware and Predictive Workflow Scheduling in Cost-Effective Infrastructure-as-a-Service Clouds , 2018, IEEE Access.

[8]  Yu Li,et al.  Group-Wise Itinerary Planning in Temporary Mobile Social Network , 2019, IEEE Access.

[9]  Yueshen Xu,et al.  Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems , 2017, Sensors.

[10]  Georgios Dounias,et al.  Honey bees mating optimization algorithm for the Euclidean traveling salesman problem , 2011, Inf. Sci..

[11]  Tarek M. Hamdani,et al.  Distributed MOPSO with dynamic pareto front driven population analysis for TSP problem , 2014, 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[12]  Jianwei Yin,et al.  Deploying Data-intensive Applications with Multiple Services Components on Edge , 2020, Mob. Networks Appl..

[13]  Cheng Zhang,et al.  A Density-Based Offloading Strategy for IoT Devices in Edge Computing Systems , 2018, IEEE Access.

[14]  Bin Li,et al.  Optimization algorithm behavior modeling: A study on the traveling salesman problem , 2018, 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI).

[15]  Qiming Zou,et al.  Research on Cost-Driven Services Composition in an Uncertain Environment , 2019 .

[16]  Jiajie Xu,et al.  An Efficient Trust-Oriented Trip Planning Method in Road Networks , 2014, 2014 IEEE 11th Intl Conf on Ubiquitous Intelligence and Computing and 2014 IEEE 11th Intl Conf on Autonomic and Trusted Computing and 2014 IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops.

[17]  Raymond Chiong,et al.  Local search for the Traveling Salesman Problem: A comparative study , 2015, 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).

[18]  Yueshen Xu,et al.  QoS Prediction for Mobile Edge Service Recommendation With Auto-Encoder , 2019, IEEE Access.

[19]  Jian Wan,et al.  Location-Aware Service Recommendation With Enhanced Probabilistic Matrix Factorization , 2018, IEEE Access.

[20]  Ellis Horowitz,et al.  Fundamentals of Computer Algorithms , 1978 .

[21]  Yucong Duan,et al.  Toward service selection for workflow reconfiguration: An interface-based computing solution , 2018, Future Gener. Comput. Syst..

[22]  Rui Li,et al.  Context-Aware QoS Prediction With Neural Collaborative Filtering for Internet-of-Things Services , 2020, IEEE Internet of Things Journal.

[23]  Mihaela Breaban,et al.  Tackling the Bi-criteria Facet of Multiple Traveling Salesman Problem with Ant Colony Systems , 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

[24]  Zhaohui Wu,et al.  TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints , 2015, IEEE Transactions on Intelligent Transportation Systems.

[25]  Weichen Liu,et al.  Combining two local searches with crossover: an efficient hybrid algorithm for the traveling salesman problem , 2017, GECCO.

[26]  Kang Zhang,et al.  Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks , 2018, Int. J. Distributed Sens. Networks.